autism (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1006,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "data = pd.read_csv('data sets//data_autism.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1007,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
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       "      <th>A9_Score</th>\n",
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       "      <th>ethnicity</th>\n",
       "      <th>jundice</th>\n",
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       "      <th>used_app_before</th>\n",
       "      <th>age_desc</th>\n",
       "      <th>relation</th>\n",
       "      <th>Class/ASD</th>\n",
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       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>United States</td>\n",
       "      <td>no</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Self</td>\n",
       "      <td>NO</td>\n",
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       "      <td>Latino</td>\n",
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       "      <td>yes</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>no</td>\n",
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       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spain</td>\n",
       "      <td>no</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Parent</td>\n",
       "      <td>YES</td>\n",
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       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>no</td>\n",
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       "      <td>NO</td>\n",
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       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>no</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>?</td>\n",
       "      <td>NO</td>\n",
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       "      <td>7</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Russia</td>\n",
       "      <td>no</td>\n",
       "      <td>18 and more</td>\n",
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       "      <td>YES</td>\n",
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       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>m</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>no</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Parent</td>\n",
       "      <td>NO</td>\n",
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       "      <th>701</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>f</td>\n",
       "      <td>?</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Russia</td>\n",
       "      <td>no</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>?</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>703</td>\n",
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       "      <td>0</td>\n",
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       "      <td>m</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>no</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Self</td>\n",
       "      <td>NO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>704</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>f</td>\n",
       "      <td>White-European</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>no</td>\n",
       "      <td>18 and more</td>\n",
       "      <td>Self</td>\n",
       "      <td>YES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>704 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
       "0      1         1         1         1         1         0         0   \n",
       "1      2         1         1         0         1         0         0   \n",
       "2      3         1         1         0         1         1         0   \n",
       "3      4         1         1         0         1         0         0   \n",
       "4      5         1         0         0         0         0         0   \n",
       "..   ...       ...       ...       ...       ...       ...       ...   \n",
       "699  700         0         1         0         1         1         0   \n",
       "700  701         1         0         0         0         0         0   \n",
       "701  702         1         0         1         1         1         0   \n",
       "702  703         1         0         0         1         1         0   \n",
       "703  704         1         0         1         1         1         0   \n",
       "\n",
       "     A7_Score  A8_Score  A9_Score  ...  result gender       ethnicity jundice  \\\n",
       "0           1         1         0  ...       6      f  White-European      no   \n",
       "1           0         1         0  ...       5      m          Latino      no   \n",
       "2           1         1         1  ...       8      m          Latino     yes   \n",
       "3           1         1         0  ...       6      f  White-European      no   \n",
       "4           0         1         0  ...       2      f               ?      no   \n",
       "..        ...       ...       ...  ...     ...    ...             ...     ...   \n",
       "699         1         1         1  ...       7      f  White-European      no   \n",
       "700         0         1         0  ...       3      m        Hispanic      no   \n",
       "701         1         1         0  ...       7      f               ?      no   \n",
       "702         1         0         1  ...       6      m     South Asian      no   \n",
       "703         1         1         1  ...       8      f  White-European      no   \n",
       "\n",
       "    austim  contry_of_res used_app_before     age_desc relation Class/ASD  \n",
       "0       no  United States              no  18 and more     Self        NO  \n",
       "1      yes         Brazil              no  18 and more     Self        NO  \n",
       "2      yes          Spain              no  18 and more   Parent       YES  \n",
       "3      yes  United States              no  18 and more     Self        NO  \n",
       "4       no          Egypt              no  18 and more        ?        NO  \n",
       "..     ...            ...             ...          ...      ...       ...  \n",
       "699     no         Russia              no  18 and more     Self       YES  \n",
       "700     no         Mexico              no  18 and more   Parent        NO  \n",
       "701     no         Russia              no  18 and more        ?       YES  \n",
       "702     no       Pakistan              no  18 and more     Self        NO  \n",
       "703     no         Cyprus              no  18 and more     Self       YES  \n",
       "\n",
       "[704 rows x 22 columns]"
      ]
     },
     "execution_count": 1007,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1008,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['id','used_app_before','age_desc'],axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1009,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>A10_Score</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>704.000000</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>704.000000</td>\n",
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       "      <td>704.000000</td>\n",
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       "      <td>704.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>0.721591</td>\n",
       "      <td>0.453125</td>\n",
       "      <td>0.457386</td>\n",
       "      <td>0.495739</td>\n",
       "      <td>0.498580</td>\n",
       "      <td>0.284091</td>\n",
       "      <td>0.417614</td>\n",
       "      <td>0.649148</td>\n",
       "      <td>0.323864</td>\n",
       "      <td>0.573864</td>\n",
       "      <td>4.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.448535</td>\n",
       "      <td>0.498152</td>\n",
       "      <td>0.498535</td>\n",
       "      <td>0.500337</td>\n",
       "      <td>0.500353</td>\n",
       "      <td>0.451301</td>\n",
       "      <td>0.493516</td>\n",
       "      <td>0.477576</td>\n",
       "      <td>0.468281</td>\n",
       "      <td>0.494866</td>\n",
       "      <td>2.501493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
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      ],
      "text/plain": [
       "         A1_Score    A2_Score    A3_Score    A4_Score    A5_Score    A6_Score  \\\n",
       "count  704.000000  704.000000  704.000000  704.000000  704.000000  704.000000   \n",
       "mean     0.721591    0.453125    0.457386    0.495739    0.498580    0.284091   \n",
       "std      0.448535    0.498152    0.498535    0.500337    0.500353    0.451301   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "50%      1.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "75%      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "\n",
       "         A7_Score    A8_Score    A9_Score   A10_Score      result  \n",
       "count  704.000000  704.000000  704.000000  704.000000  704.000000  \n",
       "mean     0.417614    0.649148    0.323864    0.573864    4.875000  \n",
       "std      0.493516    0.477576    0.468281    0.494866    2.501493  \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000    0.000000    3.000000  \n",
       "50%      0.000000    1.000000    0.000000    1.000000    4.000000  \n",
       "75%      1.000000    1.000000    1.000000    1.000000    7.000000  \n",
       "max      1.000000    1.000000    1.000000    1.000000   10.000000  "
      ]
     },
     "execution_count": 1009,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1010,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(704, 19)"
      ]
     },
     "execution_count": 1010,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1011,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1012,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1_Score         0\n",
       "A2_Score         0\n",
       "A3_Score         0\n",
       "A4_Score         0\n",
       "A5_Score         0\n",
       "A6_Score         0\n",
       "A7_Score         0\n",
       "A8_Score         0\n",
       "A9_Score         0\n",
       "A10_Score        0\n",
       "age              0\n",
       "result           0\n",
       "gender           0\n",
       "ethnicity        0\n",
       "jundice          0\n",
       "austim           0\n",
       "contry_of_res    0\n",
       "relation         0\n",
       "Class/ASD        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1012,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1013,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95, 19)"
      ]
     },
     "execution_count": 1013,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['ethnicity'] == '?'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1014,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(95, 19)"
      ]
     },
     "execution_count": 1014,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['relation'] == '?'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1015,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer\n",
    "imp = SimpleImputer(missing_values='?', strategy='most_frequent')\n",
    "data = pd.DataFrame(imp.fit_transform(data),columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1016,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 19)"
      ]
     },
     "execution_count": 1016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['ethnicity'] == '?'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1017,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 19)"
      ]
     },
     "execution_count": 1017,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['relation'] == '?'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1018,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NO', 'YES'], dtype=object)"
      ]
     },
     "execution_count": 1018,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Class/ASD'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1019,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Class/ASD'].replace(['NO', 'YES'],[0,1],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1020,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 1020,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Class/ASD'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1021,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gender'].replace(['f', 'm'],[0,1],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1022,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['jundice'].replace(['no', 'yes'],[0,1],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1023,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['austim'].replace(['no', 'yes'],[0,1],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1024,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['relation'].replace(['self', 'Parent'],[0,1],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1025,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>A10_Score</th>\n",
       "      <th>age</th>\n",
       "      <th>result</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>jundice</th>\n",
       "      <th>austim</th>\n",
       "      <th>contry_of_res</th>\n",
       "      <th>relation</th>\n",
       "      <th>Class/ASD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>Self</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Self</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Latino</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Parent</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>Self</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Self</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Self</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Parent</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>Self</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>South Asian</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Self</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>White-European</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Cyprus</td>\n",
       "      <td>Self</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>704 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    A1_Score A2_Score A3_Score A4_Score A5_Score A6_Score A7_Score A8_Score  \\\n",
       "0          1        1        1        1        0        0        1        1   \n",
       "1          1        1        0        1        0        0        0        1   \n",
       "2          1        1        0        1        1        0        1        1   \n",
       "3          1        1        0        1        0        0        1        1   \n",
       "4          1        0        0        0        0        0        0        1   \n",
       "..       ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "699        0        1        0        1        1        0        1        1   \n",
       "700        1        0        0        0        0        0        0        1   \n",
       "701        1        0        1        1        1        0        1        1   \n",
       "702        1        0        0        1        1        0        1        0   \n",
       "703        1        0        1        1        1        0        1        1   \n",
       "\n",
       "    A9_Score A10_Score age result  gender       ethnicity  jundice  austim  \\\n",
       "0          0         0  26      6       0  White-European        0       0   \n",
       "1          0         1  24      5       1          Latino        0       1   \n",
       "2          1         1  27      8       1          Latino        1       1   \n",
       "3          0         1  35      6       0  White-European        0       1   \n",
       "4          0         0  40      2       0  White-European        0       0   \n",
       "..       ...       ...  ..    ...     ...             ...      ...     ...   \n",
       "699        1         1  25      7       0  White-European        0       0   \n",
       "700        0         1  34      3       1        Hispanic        0       0   \n",
       "701        0         1  24      7       0  White-European        0       0   \n",
       "702        1         1  35      6       1     South Asian        0       0   \n",
       "703        1         1  26      8       0  White-European        0       0   \n",
       "\n",
       "     contry_of_res relation  Class/ASD  \n",
       "0    United States     Self          0  \n",
       "1           Brazil     Self          0  \n",
       "2            Spain   Parent          1  \n",
       "3    United States     Self          0  \n",
       "4            Egypt     Self          0  \n",
       "..             ...      ...        ...  \n",
       "699         Russia     Self          1  \n",
       "700         Mexico   Parent          0  \n",
       "701         Russia     Self          1  \n",
       "702       Pakistan     Self          0  \n",
       "703         Cyprus     Self          1  \n",
       "\n",
       "[704 rows x 19 columns]"
      ]
     },
     "execution_count": 1025,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1026,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plot_e(e):\n",
    "    plt.figure(figsize=(15,6))\n",
    "    sns.countplot(x='contry_of_res',data=data[data['ethnicity']==e],order=data[data['ethnicity']==e]['contry_of_res'].value_counts().index[:10],palette='viridis')\n",
    "    plt.title(f'Positive ASD of {e} Ethnicities country wise distribution')\n",
    "    plt.xlabel('Countries')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1027,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAAC2UlV9ZV5/l5AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJ23mtK7CWHniHp611FZjj3Se9fK2rAAAAwMRogQEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8lYswKiqPavqP6vqc1V1RlU9bex//ao6vqq+OP693hKfP2Qs88WqOmSl6gkAAABM30q2wLgoyR929y8luVOSp1TVPkmemeQD3b13kg+M7y+jqq6f5LlJfi3Jfkmeu1TQAQAAAFz5rViA0d1f7+6Tx+7vJ/lckpskeWiS147FXpvkYXM+fv8kx3f3Bd397STHJzlgpeoKAAAATNuq3AOjqvZK8qtJPpHkht399WQIOZL83JyP3CTJ2TPvzxn7zRv3oVW1oao2bNy4cVtWGwAAAJiIFQ8wqmrXJG9L8vTu/t5yPzanX88r2N1Hdvf67l6/bt26ra0mAAAAMGErGmBU1S4Zwos3dvfbx97frKobj8NvnOS8OR89J8meM+9vmuTclawrAAAAMF0r+RSSSvKaJJ/r7pfODDo2ycJTRQ5J8m9zPv6+JPerquuNN++839gPAAAA2AGtZAuMuyR5XJJ7VdUp4+sBSV6U5L5V9cUk9x3fp6rWV9Wrk6S7L0jygiSfGl/PH/sBAAAAO6CdV2rE3f3RzL+XRZLce075DUmeNPP+qCRHrUztAAAAgO3JqjyFBAAAAOCKEGAAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOTtvFIjrqqjkjwoyXndfZux31uS3Hosct0k3+nufed89qwk309ycZKLunv9StUTAAAAmL4VCzCSHJ3k8CSvW+jR3Y9e6K6qlyT57iY+f8/uPn/FagcAAABsN1YswOjuD1fVXvOGVVUleVSSe63U9AEAAIArj7W6B8bdknyzu7+4xPBO8v6qOqmqDt3UiKrq0KraUFUbNm7cuM0rCgAAAKy9tQowDk5yzCaG36W7b5/kwCRPqaq7L1Wwu4/s7vXdvX7dunXbup4AAADABKx6gFFVOyd5RJK3LFWmu88d/56X5B1J9lud2gEAAABTtBYtMO6T5PPdfc68gVV1rarabaE7yf2SnL6K9QMAAAAmZsUCjKo6JsmJSW5dVedU1RPHQQdl0eUjVbVHVR03vr1hko9W1alJPpnk3d393pWqJwAAADB9K/kUkoOX6P+EOf3OTfKAsfvMJLdbqXoBAAAA25+1uoknAAAAwLIJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8lYswKiqo6rqvKo6fabfYVX1tao6ZXw9YInPHlBVX6iqL1XVM1eqjgAAAMD2YSVbYByd5IA5/V/W3fuOr+MWD6yqnZK8MsmBSfZJcnBV7bOC9QQAAAAmbsUCjO7+cJILtuKj+yX5Unef2d3/L8mbkzx0m1YOAAAA2K6sxT0wnlpVp42XmFxvzvCbJDl75v05Yz8AAABgB7XaAcY/JLllkn2TfD3JS+aUqTn9eqkRVtWhVbWhqjZs3Lhx29QSAAAAmJRVDTC6+5vdfXF3/yzJP2W4XGSxc5LsOfP+pknO3cQ4j+zu9d29ft26ddu2wgAAAMAkrGqAUVU3nnn78CSnzyn2qSR7V9UtquqqSQ5Kcuxq1A8AAACYpp1XasRVdUyS/ZPsXlXnJHlukv2rat8Ml4ScleS3x7J7JHl1dz+guy+qqqcmeV+SnZIc1d1nrFQ9AQAAgOlbsQCjuw+e0/s1S5Q9N8kDZt4fl+Ryj1gFAAAAdkxr8RQSAAAAgC0iwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJO3YgFGVR1VVedV1ekz/f6mqj5fVadV1Tuq6rpLfPasqvpMVZ1SVRtWqo4AAADA9mElW2AcneSARf2OT3Kb7r5tkv9O8qeb+Pw9u3vf7l6/QvUDAAAAthMrFmB094eTXLCo3/u7+6Lx7ceT3HSlpg8AAABceazlPTB+M8l7lhjWSd5fVSdV1aGbGklVHVpVG6pqw8aNG7d5JQEAAIC1tyYBRlU9K8lFSd64RJG7dPftkxyY5ClVdfelxtXdR3b3+u5ev27duhWoLQAAALDWVj3AqKpDkjwoyWO6u+eV6e5zx7/nJXlHkv1Wr4YAAADA1KxqgFFVByT5kyQP6e4fLlHmWlW120J3kvslOX1eWQAAAGDHsJKPUT0myYlJbl1V51TVE5McnmS3JMePj0g9Yiy7R1UdN370hkk+WlWnJvlkknd393tXqp4AAADA9O28UiPu7oPn9H7NEmXPTfKAsfvMJLdbqXoBAAAA25+1fAoJAAAAwLIIMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5ywowquoDy+kHAAAAsBJ23tTAqrp6kmsm2b2qrpekxkHXTrLHCtcNAAAAIMlmAowkv53k6RnCipNyaYDxvSSvXMF6AQAAAFxikwFGd788ycur6ve6++9XqU4AAAAAl7G5FhhJku7++6r69SR7zX6mu1+3QvUCAAAAuMSyAoyqen2SWyY5JcnFY+9OIsAAAAAAVtyyAowk65Ps0929kpUBAAAAmGdZj1FNcnqSG61kRQAAAACWstwWGLsn+WxVfTLJTxZ6dvdDVqRWAAAAADOWG2ActpKVAAAAANiU5T6F5EMrXREAAACApSz3KSTfz/DUkSS5apJdkvygu6+9UhUDAAAAWLDcFhi7zb6vqocl2W9FagQAAACwyHKfQnIZ3f3OJPfaxnUBAAAAmGu5l5A8YubtVZKsz6WXlAAAAACsqOU+heTBM90XJTkryUO3eW0AAAAA5ljuPTD+z0pXBAAAAGApy7oHRlXdtKreUVXnVdU3q+ptVXXTla4cAAAAQLL8m3j+c5Jjk+yR5CZJ3jX2AwAAAFhxyw0w1nX3P3f3RePr6CTrVrBeAAAAAJdYboBxflU9tqp2Gl+PTfKtlawYAAAAwILlBhi/meRRSb6R5OtJHpnEjT0BAACAVbHcx6i+IMkh3f3tJKmq6yf52wzBBgAAAMCKWm4LjNsuhBdJ0t0XJPnVlakSAAAAwGUtN8C4SlVdb+HN2AJjua03AAAAAK6Q5QYYL0nysap6QVU9P8nHkrx4cx+qqqOq6ryqOn2m3/Wr6viq+uL493pLfPaQscwXq+qQZdYTAAAAuBJaVoDR3a9L8htJvplkY5JHdPfrl/HRo5McsKjfM5N8oLv3TvKB8f1ljC08npvk15Lsl+S5SwUdAAAAwJXfsi8D6e7PJvnsloy8uz9cVXst6v3QJPuP3a9NckKSP1lU5v5Jjh/vtZGqOj5DEHLMlkwfAAAAuHJY7iUk29INu/vrSTL+/bk5ZW6S5OyZ9+eM/S6nqg6tqg1VtWHjxo3bvLIAAADA2luLAGM5ak6/nlewu4/s7vXdvX7dunUrXC0AAABgLaxFgPHNqrpxkox/z5tT5pwke868v2mSc1ehbgAAAMAErUWAcWyShaeKHJLk3+aUeV+S+1XV9cabd95v7AcAAADsgFY0wKiqY5KcmOTWVXVOVT0xyYuS3LeqvpjkvuP7VNX6qnp1kow373xBkk+Nr+cv3NATAAAA2PEs+ykkW6O7D15i0L3nlN2Q5Ekz749KctQKVQ0AAADYjkz1Jp4AAAAAlxBgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJO381pXANbCgQ9/3lpXgTne847nrnUVAACAidICAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyVv1AKOqbl1Vp8y8vldVT19UZv+q+u5Mmeesdj0BAACA6dh5tSfY3V9Ism+SVNVOSb6W5B1zin6kux+0mnUDAAAApmmtLyG5d5Ivd/dX1rgeAAAAwIStdYBxUJJjlhh256o6tareU1W/vNQIqurQqtpQVRs2bty4MrUEAAAA1tSaBRhVddUkD0nyL3MGn5zk5t19uyR/n+SdS42nu4/s7vXdvX7dunUrU1kAAABgTa1lC4wDk5zc3d9cPKC7v9fdF47dxyXZpap2X+0KAgAAANOwlgHGwVni8pGqulFV1di9X4Z6fmsV6wYAAABMyKo/hSRJquqaSe6b5Ldn+j05Sbr7iCSPTPI7VXVRkh8lOai7ey3qCgAAAKy9NQkwuvuHSW6wqN8RM92HJzl8tesFAAAATNNaP4UEAAAAYLMEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweWsWYFTVWVX1mao6pao2zBleVfWKqvpSVZ1WVbdfi3oCAAAAa2/nNZ7+Pbv7/CWGHZhk7/H1a0n+YfwLAAAA7GCmfAnJQ5O8rgcfT3LdqrrxWlcKAAAAWH1rGWB0kvdX1UlVdeic4TdJcvbM+3PGfpdRVYdW1Yaq2rBx48YVqioAAACwltYywLhLd98+w6UiT6mquy8aXnM+05fr0X1kd6/v7vXr1q1biXoCAAAAa2zNAozuPnf8e16SdyTZb1GRc5LsOfP+pknOXZ3aAQAAAFOyJgFGVV2rqnZb6E5yvySnLyp2bJLHj08juVOS73b311e5qgAAAMAErNVTSG6Y5B1VtVCHN3X3e6vqyUnS3UckOS7JA5J8KckPk/yfNaorAAAAsMbWJMDo7jOT3G5O/yNmujvJU1azXgAAAMA0TfkxqgAAAABJBBgAAADAdkCAAQAAAEyeAAMAAACYPAEGAAAAMAR0faQAABavSURBVHkCDAAAAGDyBBgAAADA5AkwAAAAgMkTYAAAAACTJ8AAAAAAJk+AAQAAAEyeAAMAAACYPAEGAAAAMHkCDAAAAGDyBBgAAADA5AkwAAAAgMkTYAAAAACTJ8AAAAAAJk+AAQAAAEyeAAMAAACYPAEGAAAAMHkCDAAAAGDyBBgAAADA5AkwAAAAgMkTYAAAAACTJ8AAAAAAJk+AAQAAAEyeAAMAAACYPAEGAAAAMHkCDAAAAGDyBBgAAADA5AkwAAAAgMlb9QCjqvasqv+sqs9V1RlV9bQ5Zfavqu9W1Snj6zmrXU8AAABgOnZeg2lelOQPu/vkqtotyUlVdXx3f3ZRuY9094PWoH4AAADAxKx6C4zu/np3nzx2fz/J55LcZLXrAQAAAGw/1vQeGFW1V5JfTfKJOYPvXFWnVtV7quqXV7ViAAAAwKSsxSUkSZKq2jXJ25I8vbu/t2jwyUlu3t0XVtUDkrwzyd5LjOfQJIcmyc1udrMVrDEAAACwVtakBUZV7ZIhvHhjd7998fDu/l53Xzh2H5dkl6rafd64uvvI7l7f3evXrVu3ovUGAAAA1sZaPIWkkrwmyee6+6VLlLnRWC5VtV+Gen5r9WoJAAAATMlaXEJylySPS/KZqjpl7PdnSW6WJN19RJJHJvmdqrooyY+SHNTdvQZ1BQAAACZg1QOM7v5oktpMmcOTHL46NQIAAACmbk2fQgIAAACwHAIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDkCTAAAACAyRNgAAAAAJMnwAAAAAAmT4ABAAAATJ4AAwAAAJg8AQYAAAAweQIMAAAAYPIEGAAAAMDk7bzWFQBYTXf77ResdRWY4yP/+OcrPo31f/r8FZ8GW2bDC5+zKtO53cueuyrTYflO/YPnrcp07vfmP12V6bB87z/ohasynWee8ORVmQ7L96L9j1jxabz14/da8Wmw5R51pw9us3FpgQEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOQJMAAAAIDJE2AAAAAAkyfAAAAAACZPgAEAAABMngADAAAAmDwBBgAAADB5AgwAAABg8gQYAAAAwOStSYBRVQdU1Req6ktV9cw5w69WVW8Zh3+iqvZa/VoCAAAAU7HqAUZV7ZTklUkOTLJPkoOrap9FxZ6Y5NvdfaskL0vy16tbSwAAAGBK1qIFxn5JvtTdZ3b3/0vy5iQPXVTmoUleO3b/a5J7V1WtYh0BAACACanuXt0JVj0yyQHd/aTx/eOS/Fp3P3WmzOljmXPG918ey5w/Z3yHJjl0fHvrJF9Y4a8wVbsnudz8YYdg2e+4LPsdl2W/47Lsd0yW+47Lst9x7ejL/ubdvW5xz53XoCLzWlIsTlGWU2bo2X1kkiOvaKW2d1W1obvXr3U9WH2W/Y7Lst9xWfY7Lst+x2S577gs+x2XZT/fWlxCck6SPWfe3zTJuUuVqaqdk1wnyQWrUjsAAABgctYiwPhUkr2r6hZVddUkByU5dlGZY5McMnY/MskHe7WvdQEAAAAmY9UvIenui6rqqUnel2SnJEd19xlV9fwkG7r72CSvSfL6qvpShpYXB612PbdDO/xlNDswy37HZdnvuCz7HZdlv2Oy3Hdclv2Oy7KfY9Vv4gkAAACwpdbiEhIAAACALSLAAAAAACZPgDFhVXXhFpbfv6r+fex+SFU9c2Vqxqyq6qp6ycz7Z1TVYdtw/E+pqlNmXqeP0/ylrRzfFq1XmxjPXlV1+rYY1/Zm3nevqsOq6hmb+dz6qnrF2L1/Vf36Vkz7rKrafU7/36yqz1TVaeM68tCx/xOqao9ljHdZ5dgyVfWsqjpjXC6nVNWvLVHu+VV1nzn9j66qR26DepxQVR7Ftsqq6uKZ/fa/VNU1N1P+cvvnqtqjqv51E5+5blX97rao75XFFPfR47BfHf9/339Lx7u5cc+UeUJVbVx03LDPFkzjyVX1+C2s11bNqx1BVd2oqt5cVV+uqs9W1XFV9QsrOL1tcozHfDP79FOr6uSVWO9nz+e24DNb9T9+Wx1jrLZVv4knq2O8Gerip7uwMn6S5BFV9cLuPn9bj7y7X5nklQvvq+qvkpzS3Z/b1tNiZXX3hiQbxrf7J7kwyceu6Hir6qZJnpXk9t393araNcm6cfATkpyeyz+uerHllmOZqurOSR6UYbn8ZDzxuOqccjt193NWvYKshh91975JUlVvTPLkJC/dkhF097kZnsi2lOsm+d0kr9raSjJYqX30jIOTfHT8+77FA6uqMtyf7mdXcDpv6e6nbs0Hu/uIef2raufuvmiJj+2fbT+vtnvj8nxHktd290Fjv32T3DDJf69l3dhqs/v0+yd5YZJ7rG2VdjxaYGwHxiTuhKr616r6fFW9cdwppqoOGPt9NMkjZj7zhKo6fOx+cFV9oqo+XVX/UVU3XKOvcmV1UYa7BP/B4gFVta6q3lZVnxpfdxn7f2b81ayq6lsLv3ZU1evn/Qo7M767J3lUhoPVVNVOVfU347hPq6rfHvvvWlUfGNPhzyz8Gr9oXHPLjL9cfa6q/qmGX47fX1XXGIfdYUydT0zylCs43660xu31r6vqk1X131V1t7H//lX171W1V4YTmT8Yk/y7bWJducG4DD5dVf+YpOZM8ueSfD/DAWS6+8Lu/p8xVV+f5I3jdK5RVc8Zx396VR05roPzyt2hqj5UVSdV1fuq6sZjfX5//BXptKp688rOye3ejZOc390/SZLuPn88GV34JfU54777f23JryBbs+3OfPYqVfXaqvqL8f0/VNWGsfzztuWX53I+kuRWSVJV7xy3rTOq6tDFBatq96o6saoeWDOtCarql8f9yinjNrh3khclueXY72+uyPqxo1iDffTCyewjM4TF96uqq4/9F5bLq5KcnGTPzWyXfzTW+5NVdast+M77j/v0t47f+UVV9ZhxPJ+pqluO5S5pqTLOp7+qqg8leVrNOZ7cwnl1j7q0Vcinq2q35dZ/O3XPJD+dDYW6+5Qkn97SbbSqfmucl6eO8/aaY/9bjPuKT1XVCxams9R+gG3q2km+nWz1MfUdx/34ieO++3Ktmqtqv6r62Li9fKyqbj32v0YNLXtOq6q3JLnGzGfuN47z5Bpa/u069n9RXXr89rczk7n7OO4za3tpjdHdXhN9Jblw/Lt/ku8muWmG0OnEJHdNcvUkZyfZO8M/zLcm+ffxM09IcvjYfb1c+sSZJyV5yVp/tyvTK8NJ47WTnJXkOkmekeSwcdibktx17L5Zks+N3UckeWCS2yT5VJJ/Gvt/McmuS0znukm+nOQuM/0OTfLssftqGX45ukWG1lXXHvvvnuRLM+vAwno1t0ySvTKEMvuOw96a5LFj92lJ7jF2/02S09d6/q/RMt9r8XdPcliSZ4zdJyxsZ0kekOQ/xu79Z7bRS8pvZl15RZLnjN0PTNJJdl807Z0y/Jr31ST/nOTBM8NOSLJ+5v31Z7pfv1B2tlySXTL8krZufP/oDI+8ToYWGldbWCfXellM+ZVk1ySnZPil7VUL28447Kwkfzzz/ugkj5wzjsv138pt94Qkd0pyTJJnLV4fxnXohCS3Xev5dmV65bL7239L8juL5vs1MrR8usFC+Qy/zn4iyX3Hfntl3N8k+fskjxm7rzp+/pLhW7t+XNlei+fJ2O+wrNE+ehx21yQfmBnXI2bq+rMkd5opO3e7zLDfeNbY/fiFui6azhOSbMyw71l4XWP8bt/JEKxeLcnXkjxv/MzTkvzdEvPpVTPjnns8uQXz6l0Zj2Ey7B93Xut1ZYXXw99P8rI5/bdmH36Dmc//RZLfG7uPTfL4sfsp2cwx3lrPk+39leTicZv6fIZzsztcgWV6epJfH7tflEv38/vn0v3QtRe2kyT3SfK2sfv/5tLjstuO01g/TvvDSa41DvuTJM9Jcv0kX5jZfq87/j06yb9kOL/cJ8mX1noeL+flEpLtxye7+5wkqapTMmwQFyb5n+7+4tj/DRlOaBe7aZK31PAL6lWT/M+q1HgH0t3fq6rXZfhn9aOZQfdJss/ww0uS5NrjLw4fSXL3JF9J8g9JDq2qmyS5oLuXun7xH5K8obv/a6bf/ZLcdiYxvU6GQOucJH9VQ4uNnyW5SYaD4m/MfLaWKJMM69UpY/dJSfaqqutk2OF9aOz/+iQHbmbWXFkt9fzp2f5vH/+elGF73Zyl1pW7Z2xd1d3vrqpvX26i3RdX1QFJ7pjk3kleVlV36O7D5kznnlX1x0mumeEf2hkZDipn3TpDuHb8WJ+dknx9HHZahpYa70zyzmV8rx1Wd19YVXdIcrcMv8S9paqe2d1Hj0XespWj3qJtd+Zz/5jkrd39lzP9HlVDC4CdM5zY7JNhGbNtXGP8n50M+/3XjN2/X1UPH7v3zLDf/laG8PADSZ4ys6+ddWKSZ9Vw2djbu/uLM/uMBVu7flyZTGofPTo4yUKrtTcnedxMHb7S3R+fKbup7fKYmb8vW2Jal7uEZKz3p7r76+P7Lyd5/zj4Mxn2UXPHNdO93OPJpebVfyV5aQ2XU7194bh2B7Q12+htamg5d90M4c/CJUh3SfIbY/frk/z1ZqYxexzIlpu9hOTOSV5XVbfJlh9TXzfJbt29cNnVmzJccrrYdZK8tobWdp3hf0Qy7HdekSTdfVpVLewf7pRhf/Ff4/Z31Qz/N76X5MdJXl1V704ye4+Nd/Zw2dpnaztppS/A2H78ZKb74ly67Jb6Jz3r75O8tLuPrar9MyTlbHt/l6H55z/P9LtKkjt392yokar6cIak/GYZ7l3w8AxNSz8yb8RVdUiGf2KPWzwoQwr/vkXln5DhHgh36O6fVtVZGVrszHrMJsosXt+uMU5rOevbjuBbGX6JmnX9XPZgbmEezm6vm7LUupIsY773EKV/Msknq+r4DOvhYYvGdfUMLQHWd/fZNdxsdvF6kQzL+ozuvvOcYQ/M8I/zIUn+vKp+uZe+LnqH190XZ/gV84Sq+kySQzL84pEkP9jK0W7ptrvgYxkCrJd094+r6hYZWozdsbu/XVVHZ/76wNa75GB3wfh/+D4ZtvcfVtUJuXS+X5ThAPf+SS4XYHT3m6rqExm2w/dV1ZOSnLmo2NauH1cmk9pHV9VOGU4yH1JVz8qwj71BXXoJxQ9mym5uu+wlupdjdvn/bOb9z7L0PJjdTy33eHLuvEryovHk6QFJPl5V9+nuz29B/bc3Z2T+/Wu2Zhs9OsnDuvvU8Rhv/5ly89aDTU2DbaC7T6zh3lbrMqzTW3pMvRwvSPKf3f3wGi7XOmG2CnPKV5Lju/vgyw2o2i/Dj1wHJXlqknvNqd9y67Wm3ANj+/b5JLeo8brFDOn+PNfJ0FQwGQ6eWQHdfUGGpmFPnOn9/gw7iSSX3Lwp3X12hmZee3f3mRlu6vWMzAkwqurnk/xlhmbDi08U35fkd6pql7HsL1TVtTIs8/PGneg9k9x8TpWXU2b2+30nyXer6q5jr8dsqvyV2dhK5utVde8kqarrJzkgw3Jcru8nmb3+d+66kqEp4GPGfgfm8gflqeEpBbef6bVvhtY9i6ez8M/0/PGayNkDq9lyX0iybvx1IVW1Sw3X3l8lyZ7d/Z9J/jiX/hLEHFV16/FXkwWzy+WK2KJtd8ZrkhyX5F+qaucMTVN/kGG7vmF23BZVq+06Sb49hhe/mOEXswWd5DeT/GLNeZLY+P/gzO5+RYam47fN5fclW7t+XGlMbR+dIbA6tbv37O69uvvmSd6W5GFzym5uu3z0zN8Tt+D7bAtLHU8ua15V1S27+zPd/dcZLnn9xZWt7pr7YJKrVdVvLfSoqjtm2Ca3dBvdLcM6vUsue/z1XxlOSLOo/w6/H1hp4/57pwyB6ZYeU387yferamH/f9ASRWe3uSfM9J/d79wmw/+CJPl4krvUeH+cqrrmeG6wa5LrdPdxSZ6e4XhkuyXA2I51948zXDLy7hpuBLfUgfFhGQ5YP5Jkmz8lg8t4SYZgYsHvJ1lfww1zPpvhRlcLPpFL70L9kQzNzeYdXP1JkmsleXtd9rFod0vy6iSfTXJyDTf/+ccMv6K8cZzuhgw7uHm/cCynzGL/J8kra7iJ5+JfVnY0j0/y7Bqah38ww7XEX96Cz78rycNnluVS68rzMtxg6eQMlwx9dc64dknytzXc0PeUDAe2TxuHHZ3kiLH/T5L8U4bmwu/McP+VzCm3U4Zw46+r6tQM13v++tj/DWNLgk9nuLb3O1vwnXc0u2Zo+vnZsXnnPtm6FnD/WFXnjK8Ts3XbbpKku1+aoaXY6zOsB5/O8CvhURkOhFl5702y87hOvCDDAeclxlY7B2VoLbP48aiPTnL6uJ3+YpLXdfe3MjQXPr2q/iZXYP24kpnSPvrgDE+jmPW2JP97ccHuPjWb3i6vNrbCeVrm3Dx89OhFxwvb6lGPh2X+8eRy59XTx/X01AzHEO/ZRvWapLFl5MOT3LeGx6iekWEeHpct30b/PMNx4/GLyj8tyVOq6lMZTnYX2A+sjGssbFcZLq86ZNxnb838fmKSI8f/65XhnhqLvTjJC6vqvzIcgy34hyS7jv9H/jhDC9x098YMQccx47CPZ/hfsVuSfx/7fShL7zu2Cws38gAAAABWWFXtunDfu7HF3Y27+2mb+RhxDwwAAABYTQ+sqj/NcD7+lVz2EhE2QQsMAAAAYPLcAwMAAACYPAEGAAAAMHkCDAAAAGDyBBgAwDZXVTeqqjePjw/8bFUdV1W/sA3Hv/+mHg9ZVQ8Z7+wOAFxJuIknALBNVVUl+ViS13b3EWO/fZPs1t0f2UbTOCzJhd39t3OG7dzdF22L6QAA0yHAAAC2qaq6V5LDuvvui/pXkhcnOTBJJ/mL7n5LVe2f5Bnd/aCx3OFJNnT30VV1VpLXJnlwkl2S/K8kP07y8SQXJ9mY5PeSPDHJBUl+NcnJST6TZH13P7Wq1iU5IsnNxqo8vbv/q6rukfz/9u7gxYcwjuP4+4NC7XJxUy5KSNnIQWlL9i4ursoqIe1B/gYu2IPyD0ihvTiIg7JRwiYrLi6UA6WUTVG2r8OMWttaP/Vrd9L7VdNT32meeWZO02eeeYbxtlbAcFXN9Pt+SJKk/li13AOQJEn/nR3A1AL1w8AQsBPYADxNMtlDf5+qaleSkzRBx2iSq8yZgZHkGLAFGKmq2SRH5xw/DlyqqodJNgF3gW3AWeBUG2YM0AQjkiSpowwwJEnSUtkHXK+qWeBjkgfAHuDLX46baNspmhDkT262fc83AmxvJoAAsC7JIPAIuJjkGjBRVe97vA5JkrQMXMRTkiT12ytg9wL1LFAD+MHvzyRr5u3/3razLP7y5esf6iuAvVU11G4bq2qmqs4Do8Ba4HGSrYv0LUmSlpkBhiRJ6rf7wOokx38VkuwBPgNHkqxs16UYBp4A72hmSKxOsh440MM5ZoDBHsdzDzg9ZyxDbbu5ql5W1QXgGWCAIUlSh/kJiSRJ6quqqiSHgMvtr0y/AW+BMWAAeEGzaOa5qvoAkOQGMA28AZ73cJrbwK0kB2kW8VzMGeBKkmmaZ59J4AQwlmQ/zcyO18Cdf7lOSZK0tPwLiSRJkiRJ6jw/IZEkSZIkSZ1ngCFJkiRJkjrPAEOSJEmSJHWeAYYkSZIkSeo8AwxJkiRJktR5BhiSJEmSJKnzDDAkSZIkSVLn/QTeMdceHsJ0SQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in data['ethnicity'].unique():\n",
    "    plot_e(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1028,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YTuaGf6ekzDq567uNzLrJn0VEWTf6Gk1mZuqElNLi9eo+Tuar+8vIJIM3SCxExNob2N2aUvpPiZ9pZG4MNYBMEvyHwMxszP/InlPJG5ednD22JNvvJ0DHlLmJ3fp9ds2OzZ3r9fk4meUGTs0mQM8nk2xdSOYmhQtKNPNnMmvEFpJJ3jxcxvjA/xLvn0REudYGzSb0jiWTFH2PzAzSe8nMUF7fn8jMJP8AmM2Giff7yKxPuiQiHi2jy8fJ3NxsUXb927VxjCMzy31MZJbfeJ3MjHXIzA6+h8zYzCMz5jeVcT4ryNx8cFZK6cvs7inAvOyasKXZ7HFbq5zXFcDZZGZdf0JmFnbJGaMTgFnAfyKi5LW91v5kbmC2PHsuf0gpTdycOEuTUvo7cGu2/7fZ+A3LNhbDdcAV2ee9zBshluLPwAgySyDUI3szwpTSUuA8MtfhB2Q+VCj5mtjU83V/tu1JZK7pVcCFmxHXxmxR25s55uXRn8wNKGeTeX2MJbNWdln9b+x1timbuk43FU956ldIdpmm75P5t+w/wFwySxOtX+5zMjcRfDl7vXYtpZ2eZNaN/jDb1v/jfx9w9AMKs2M3CPi/yjwPSZIkSaoOkTZ5jyVJkiRJkiRJkjbkDGZJkiRJkiRJUoWYYJYkSZIqUUTcHxEfRcTrZRyPiBgWEW9HxIyIOGhrxyhJkiRVFhPMkiRJUuUaQeY+EmX5AZm16fcHzgHu2goxSZIkSVXCBLMkSZJUiVJKk4BPN1LkOOBPKeMVYNeIKPMGm5IkSVJNVqu6A9gSe+65Z2rWrFl1hyFJkqTt2LRp0xanlBpVYpNNgPklthdk9y0sWSgiziEzw5mddtqpY8uWLSsxBEmSJGldFX3fu00nmJs1a0Z+fn51hyFJkqTtWETMq+wmS9mXNtiR0t3A3QB5eXnJ972SJEmqShV93+sSGZIkSdLWtQDYt8T2PsCH1RSLJEmStEVMMEuSJElb1+NA/8joCixNKS3cVCVJkiSpJtqml8iQJEmSapqIGA30APaMiAXAb4DaACml4cDTQC/gbeBz4PTqiVSSJEnaciaYJQn46quvWLBgAatWraruUFQJ6tWrxz777EPt2rWrOxRJX0MppVM3cTwB52+lcCRJkqQqZYJZkoAFCxbQsGFDmjVrRkRp917StiKlxCeffMKCBQto3rx5dYcjSZIkSdJ2zTWYJQlYtWoVe+yxh8nl7UBEsMceezgbXZIkSZKkrcAEsyRlmVzefvhcSpIkSZK0dZhgliRJkiRJkiRViAlmSdrGXHvttcWPCwsLadu27WbVz8/P56KLLtpomV69erFkyRKWLFnCH/7whwrFKUmSJEmStn8mmCVpG1MywVwReXl5DBs2bKNlnn76aXbddVcTzJIkSZIkaaNMMEtSDfbQQw/RuXNncnNzOffccxk8eDArV64kNzeXvn37ArB69WrOPvts2rRpQ8+ePVm5ciUAPXr04Je//CWdO3emRYsWvPjiiwBMnDiR3r17A7B8+XJOP/102rVrR/v27XnkkUcAaNasGYsXL2bIkCG888475ObmMnjwYPr168djjz1WHF/fvn15/PHHt+aQSJIkSZKkGsQEsyTVUHPmzOHhhx/m5ZdfpqCggJycHNq1a8eOO+5IQUEBI0eOBGDu3Lmcf/75zJo1i1133bU4SQxQVFTE1KlTufXWW7nqqqs26OO3v/0tu+yyCzNnzmTGjBl873vfW+f49ddfz7e//W0KCgq48cYbOeuss3jggQcAWLp0KZMnT6ZXr15VOAqSJEmSJKkmq1XdAUiSSvfcc88xbdo0OnXqBMDKlStp3LjxBuWaN29Obm4uAB07dqSwsLD42AknnFDq/rXGjx/PmDFjird32223jcZ0+OGHc/755/PRRx/xt7/9jRNPPJFatfxTIkmSJEnS15VZAUmqoVJKDBgwgOuuu26d/TfddNM623Xr1i1+nJOTU7xERsljOTk5FBUVldpHRGxWXP369WPkyJGMGTOG+++/f7PqSpIkSZKk7YtLZEhSDXXEEUcwduxYPvroIwA+/fRT5s2bR+3atfnqq68qpY+ePXtyxx13FG9/9tln6xxv2LAhy5YtW2ffwIEDufXWWwFo06ZNpcQhSZIkSZK2TSaYJamGat26Nddccw09e/akffv2fP/732fhwoWcc845tG/fvvgmf1viiiuu4LPPPqNt27YceOCBPP/88+sc32OPPTjkkENo27YtgwcPBmCvvfaiVatWnH766VvcvyRJkiRJ2rZFSqm6Y6iwvLy8lJ+fX91h8JPTPqzS9u8atXeVti8pc0O9Vq1aVXcY24TPP/+cdu3aMX36dHbZZZfqDqdMPqeSKktETEsp5VVnDDXlfa8kSZK2XxV93+sMZklSuY0fP56WLVty4YUX1ujksiRJkiRJ2jq8yZ8kqdyOPPJI3n///eoOQ5IkSZIk1RDOYJYkSZIkSZIkVYgJZkmSJEmSJElShZhgliRJkiRJkiRViAlmSZIkSZIkSVKFeJM/SSrFT077sFLbu2vU3pss87Of/YymTZvy05/+FICjjjqKfffdl3vvvReASy65hCZNmjBhwgSefPLJDeqfddZZ/PznP6d169Zce+21XHbZZZsV48SJEznuuONo3rx58b6bbrqJI488crPakSRJkiRJXx/OYJakGqJbt25MnjwZgDVr1rB48WJmzZpVfHzy5Ml89dVXZda/9957ad26NQDXXntthWI47LDDKCgoKP7ZnORyUVFRhfqUJEmSJEnbLhPMklRDHHLIIcUJ5lmzZtG2bVsaNmzIZ599xhdffMGcOXPo0KEDy5cvp0+fPrRs2ZK+ffuSUgKgR48e5OfnM2TIEFauXElubi59+/YF4KGHHqJz587k5uZy7rnnsnr16nLHVVhYSNu2bYu3b7rpJoYOHVrc52WXXcbhhx/Obbfdxrx58zjiiCNo3749RxxxBO+//z4AAwcOZNCgQRx22GG0aNGieAb26tWrGTx4MJ06daJ9+/b88Y9/BGD58uUcccQRHHTQQbRr147HHnusOJZWrVpx9tln06ZNG3r27MnKlSu3YNQlSZIkSdKWMMEsSTXE3nvvTa1atXj//feZPHkyBx98MF26dGHKlCnk5+fTvn176tSpw7///W9uvfVWZs+ezbvvvsvLL7+8TjvXX389O+64IwUFBYwcOZI5c+bw8MMP8/LLL1NQUEBOTg4jR44sNYYXX3yR3Nzc4p933nlnk3EvWbKEF154gUsuuYQLLriA/v37M2PGDPr27ctFF11UXK6wsJAXXniBp556ikGDBrFq1Sruu+8+dtllF1599VVeffVV7rnnHt577z3q1avHuHHjmD59Os8//zyXXHJJcSJ97ty5nH/++cyaNYtdd92VRx55ZAtGXZIkSZIkbQnXYJakGmTtLObJkyfz85//nA8++IDJkyezyy670K1bNwA6d+7MPvvsA0Bubi6FhYUceuihZbb53HPPMW3aNDp16gTAypUrady4callDzvssA3Wdy4sLNxozCeffHLx4ylTpvC3v/0NgH79+vGLX/yi+NiPf/xjdthhB/bff3/2228/3njjDZ555hlmzJjB2LFjAVi6dClz585ln3324bLLLmPSpEnssMMOfPDBByxatAiA5s2bk5ubC0DHjh03GZ8kSZIkSao6JpglqQZZuw7zzJkzadu2Lfvuuy+///3v2XnnnTnjjDMAqFu3bnH5nJycTa59nFJiwIABXHfddevsHzduHFdddRVA8Y0ES1OrVi3WrFlTvL1q1ap1ju+0005l1o2IUh+v3U4pcfvtt3PUUUetc2zEiBF8/PHHTJs2jdq1a9OsWbPiftc/f5fIkCRJkiSp+rhEhiTVIIcccghPPvkku+++Ozk5Oey+++4sWbKEKVOmcPDBB5e7ndq1axffEPCII45g7NixfPTRRwB8+umnzJs3jx/96EfFN/PLy8srs6299tqLjz76iE8++YQvvvhigxnOJXXr1o0xY8YAMHLkyHVmVv/1r39lzZo1vPPOO7z77rsccMABHHXUUdx1113Fsb711lusWLGCpUuX0rhxY2rXrs3zzz/PvHnzyn3ukiRJkiRp63EGsySV4q5Re1dLv+3atWPx4sWcdtpp6+xbvnw5e+65Z7nbOeecc2jfvj0HHXQQI0eO5JprrqFnz56sWbOG2rVrc+edd9K0adMN6q1dg3mtK664gj59+nDllVfSpUsXmjdvTsuWLcvsd9iwYZxxxhnceOONNGrUiAceeKD42AEHHMDhhx/OokWLGD58OPXq1eOss86isLCQgw46iJQSjRo14tFHH6Vv374ce+yx5OXlkZubu9E+JUmSJElS9Ym1N03aFuXl5aX8/PzqDoOfnPZhlbZfXYku6etkzpw5tGrVqrrD2G4NHDiQ3r1706dPn63Wp8+ppMoSEdNSSmV/1WMrqCnveyVJkrT9quj7XpfIkCRJkiRJkiRViEtkSJKq3IgRI6o7BEmSJEmSVAWcwSxJkiRJkiRJqhATzJIkSZIkSZKkCjHBLEmSJEmSJEmqEBPMkiRJkiRJkqQK8SZ/klSKnte8W6ntPXPFfpss06BBA5YvX16u9iZOnEidOnXo1q0bAMOHD6d+/fr0799/i+KUJEmSJEnaHCaYJWkbNHHiRBo0aFCcYB40aFA1RyRJkiRJkr6OqmyJjIjYNyKej4g5ETErIi7O7h8aER9EREH2p1eJOr+KiLcj4s2IOKqqYpOkbcUTTzxBly5d6NChA0ceeSSLFi2isLCQ4cOHc8stt5Cbm8uLL77I0KFDuemmmwDo0aMHv/zlL+ncuTMtWrTgxRdfBGDVqlWcfvrptGvXjg4dOvD8889X56lJkiRJkqTtQFXOYC4CLkkpTY+IhsC0iHg2e+yWlNJNJQtHRGvgFKANsDcwPiJapJRWV2GMklSjHXroobzyyitEBPfeey833HADv//97xk0aBANGjTg0ksvBeC5555bp15RURFTp07l6aef5qqrrmL8+PHceeedAMycOZM33niDnj178tZbb1GvXr2tfl6SJEmSJGn7UGUJ5pTSQmBh9vGyiJgDNNlIleOAMSmlL4D3IuJtoDMwpapilKSabsGCBZx88sksXLiQL7/8kubNm5er3gknnABAx44dKSwsBOCll17iwgsvBKBly5Y0bdqUt956i/bt21dJ7JIkSZIkaftXZUtklBQRzYAOwL+yuy6IiBkRcX9E7Jbd1wSYX6LaAjaekJak7d6FF17IBRdcwMyZM/njH//IqlWrylWvbt26AOTk5FBUVARASqnK4pQkSZIkSV9PVZ5gjogGwCPAT1NK/wXuAr4N5JKZ4fz7tUVLqb5BNiQizomI/IjI//jjj6soakmqGZYuXUqTJpnP2h588MHi/Q0bNmTZsmWb1Vb37t0ZOXIkAG+99Rbvv/8+BxxwQOUFK0mSJEmSvnaqcg1mIqI2meTyyJTS3wBSSotKHL8HeDK7uQDYt0T1fYAP128zpXQ3cDdAXl6e0/EkVYlnrthvq/f5+eefs88++xRv//znP2fo0KGcdNJJNGnShK5du/Lee+8BcOyxx9KnTx8ee+wxbr/99nK1f9555zFo0CDatWtHrVq1GDFiRPFMZ0mSJEmSpIqosgRzRARwHzAnpXRzif3fzK7PDPAj4PXs48eBURFxM5mb/O0PTK2q+CSpplmzZk2p+4877rgN9rVo0YIZM2YUbx922GHFjydOnFj8eM899yxeg7levXqMGDGiUmKVJEmSJEmCqp3BfAjQD5gZEQXZfZcBp0ZELpnlLwqBcwFSSrMi4i/AbKAIOD+ltLoK45MkSZIkSZIkbYEqSzCnlF6i9HWVn95Ind8Bv6uqmCRJkiRJkiRJlafKb/InSZIkSZIkSdo+mWCWJEmSJEmSJFWICWZJkiRJkiRJUoWYYJYkSZIkSZIkVUiV3eRPkrZlTUf9u1Lbm3dah3KVW7BgAeeffz6zZ89mzZo19O7dmxtvvJHZs2fz4Ycf0qtXLwCGDh1KgwYNuPTSSys1TkmSJEmSpM3hDGZJqiFSSpxwwgkcf/zxzJ07l7feeovly5dz+eWXU1BQwNNPP11pfa1evVlvOYMAACAASURBVLrS2pIkSZIkSV9fJpglqYaYMGEC9erV4/TTTwcgJyeHW265hXvvvZdf/OIXPPzww+Tm5vLwww8DMHv2bHr06MF+++3HsGHDitt56KGH6Ny5M7m5uZx77rnFyeQGDRpw5ZVX0qVLF6ZMmcKQIUNo3bo17du3dya0JEmSJEmqEBPMklRDzJo1i44dO66zb+edd6ZZs2ZcccUVnHzyyRQUFHDyyScD8MYbb/DPf/6TqVOnctVVV/HVV18xZ84cHn74YV5++WUKCgrIyclh5MiRAKxYsYK2bdvyr3/9i9atWzNu3DhmzZrFjBkzuOKKK7b6+UqSJEmSpG2fazBLUg2RUiIiyr3/mGOOoW7dutStW5fGjRuzaNEinnvuOaZNm0anTp0AWLlyJY0bNwYyM6JPPPFEIJO4rlevHmeddRbHHHMMvXv3rsIzkyRJkiRJ2ysTzJJUQ7Rp04ZHHnlknX3//e9/mT9/Pjk5ORuUr1u3bvHjnJwcioqKSCkxYMAArrvuug3K16tXr7idWrVqMXXqVJ577jnGjBnDHXfcwYQJEyr5jCRJkiRJ0vbOJTIkqYY44ogj+Pzzz/nTn/4EZG7Ed8kllzBw4ED22msvli1bVq42xo4dy0cffQTAp59+yrx58zYot3z5cpYuXUqvXr249dZbKSgoqNyTkSRJkiRJXwvOYJakUsw7rcNW7zMiGDduHOeddx6//e1vWbNmDb169eLaa69lxYoVXH/99eTm5vKrX/2qzDZat27NNddcQ8+ePVmzZg21a9fmzjvvpGnTpuuUW7ZsGccddxyrVq0ipcQtt9xS1acnSZIkSZK2QyaYJakG2XfffXniiSc22F+3bl1effXVMuu9/vrrxY9PPvnk4hsBlrR8+fLix9/85jeZOnXqFkYrSZIkSZK+7lwiQ5IkSZIkSZJUISaYJUmSJEmSJEkVYoJZkiRJkiRJklQhJpglSZIkSZIkSRViglmSJEmSJEmSVCEmmCVJkiRJkiRJFVKrugOQpJroe6+8UqntTejadZNlcnJyaNeuHSklcnJyuOOOO+jWrRuFhYX07t2b119/fbP77dGjBzfddBN5eXkVCVuSVAERcTRwG5AD3JtSun69498CHgR2zZYZklJ6eqsHKkmSJFUCE8ySVEPsuOOOFBQUAPDPf/6TX/3qV7zwwgvVHJUkaXNERA5wJ/B9YAHwakQ8nlKaXaLYFcBfUkp3RURr4Gmg2VYPVpIkSaoELpEhSTXQf//7X3bbbbcN9hcWFnLYYYdx0EEHcdBBBzF58uTiYzfccAPt2rXjwAMPZMiQIevUW7NmDQMGDOCKK66o8tgl6WuuM/B2SundlNKXwBjguPXKJGDn7ONdgA+3YnySJElSpXIGsyTVECtXriQ3N5dVq1axcOFCJkyYsEGZxo0b8+yzz1KvXj3mzp3LqaeeSn5+Pn//+9959NFH+de//kX9+vX59NNPi+sUFRXRt29f2rZty+WXX741T0mSvo6aAPNLbC8AuqxXZijwTERcCOwEHFlaQxFxDnAOwLe+9a1KD1SSJEmqDM5glqQaYu0SGW+88Qb/+Mc/6N+/Pymldcp89dVXnH322bRr146TTjqJ2bMz37geP348p59+OvXr1wdg9913L65z7rnnmlyWpK0nStmX1ts+FRiRUtoH6AX8OSI2eF+eUro7pZSXUspr1KhRFYQqSZIkbTkTzJJUAx188MEsXryYjz/+eJ39t9xyC3vttRevvfYa+fn5fPnllwCklIgoLacB3bp14/nnn2fVqlVVHrckiQXAviW292HDJTDOBP4CkFKaAtQD9twq0UmSJEmVzASzJNVAb7zxBqtXr2aPPfZYZ//SpUv55je/yQ477MCf//xnVq9eDUDPnj25//77+fzzzwHWWSLjzDPPpFevXpx00kkUFRVtvZOQpK+nV4H9I6J5RNQBTgEeX6/M+8ARABHRikyC+WMkSZKkbZBrMEtSKSZ07brV+1y7BjNkZiQ/+OCD5OTkrFPmvPPO48QTT+Svf/0r3/3ud9lpp50AOProoykoKCAvL486derQq1cvrr322uJ6P//5z1m6dCn9+vVj5MiR7LCDny9KUlVIKRVFxAXAP4Ec4P6U0qyIuBrITyk9DlwC3BMRPyOzfMbAtP6aSJIkSdI2Irbl97J5eXkpPz+/usPgJ6dV7Y2/7xq1d5W2LwnmzJlDq1atqjsMVSKfU0mVJSKmpZTyqjOGmvK+V5IkSduvir7vdQqbJEmSJEmSJKlCTDBLkiRJkiRJkirEBLMkSZIkSZIkqUJMMEuSJEmSJEmSKsQEsyRJkiRJkiSpQkwwS5IkSZIkSZIqpFZ1ByBJNdF58ydVant/2Ld7ucqNGzeOE044gTlz5tCyZcsyy/Xq1YtRo0ax6667VlaIkiRJkiRJm80ZzJJUg4wePZpDDz2UMWPGbLTc008/bXJZkiRJkiRVOxPMklRDLF++nJdffpn77ruvOMG8cOFCunfvTm5uLm3btuXFF18EoFmzZixevBiA448/no4dO9KmTRvuvvvu4vYaNGjA5ZdfzoEHHkjXrl1ZtGjR1j8pSZIkSZK0XTPBLEk1xKOPPsrRRx9NixYt2H333Zk+fTqjRo3iqKOOoqCggNdee43c3NwN6t1///1MmzaN/Px8hg0bxieffALAihUr6Nq1K6+99hrdu3fnnnvu2dqnJEmSJEmStnMmmCWphhg9ejSnnHIKAKeccgqjR4+mU6dOPPDAAwwdOpSZM2fSsGHDDeoNGzaseJby/PnzmTt3LgB16tShd+/eAHTs2JHCwsKtdi6SJEmSJOnrwZv8SVIN8MknnzBhwgRef/11IoLVq1cTEdxwww1MmjSJp556in79+jF48GD69+9fXG/ixImMHz+eKVOmUL9+fXr06MGqVasAqF27NhEBQE5ODkVFRdVybpIkSZIkafvlDGZJqgHGjh1L//79mTdvHoWFhcyfP5/mzZszadIkGjduzNlnn82ZZ57J9OnT16m3dOlSdtttN+rXr88bb7zBK6+8Uk1nIEmSJEmSvo6cwSxJpfjDvt23an+jR49myJAh6+w78cQTGThwIDvttBO1a9emQYMG/OlPf1qnzNFHH83w4cNp3749BxxwAF27dt2aYUuSJEmSpK85E8ySVANMnDhxg30XXXQRF110UanlS66n/Pe//73UMsuXLy9+3KdPH/r06bNFMUqSJEmSJK3PJTIkSZIkSZIkSRViglmSJEmSJEmSVCEmmCVJkiRJkiRJFWKCWZIkSZIkSZJUISaYJUmSJEmSJEkVYoJZkiRJkiRJklQhtao7AEmqiW5b8o9Kbe/iXY/eZJmI4P/+7//485//DEBRURHf/OY36dKlC08++SSPP/44s2fPZsiQIRvUbdCgAcuXL99g/8CBA+nduzd9+vShR48e3HTTTeTl5ZUr5oEDB/LCCy+wyy67AFC/fn0mT55crrprLVmyhFGjRnHeeedtVj1JkiRJkrRtcAazJNUQO+20E6+//jorV64E4Nlnn6VJkybFx3/4wx+WmlyuSjfeeCMFBQUUFBRsdnIZMgnmP/zhD5tVJ6XEmjVrNrsvSZIkSZK09ZlglqQa5Ac/+AFPPfUUAKNHj+bUU08tPjZixAguuOACAN577z0OPvhgOnXqxK9//eviMiklLrjgAlq3bs0xxxzDRx99VGo/zzzzDAcffDAHHXQQJ510Uqmzn8sydepUunXrRocOHejWrRtvvvkmALNmzaJz587k5ubSvn175s6dy5AhQ3jnnXfIzc1l8ODBQCZp3alTJ9q3b89vfvMbAAoLC2nVqhXnnXceBx10EPPnz6dBgwZcfvnlHHjggXTt2pVFixZtxkhKkiRJkqStwQSzJNUgp5xyCmPGjGHVqlXMmDGDLl26lFru4osv5ic/+Qmvvvoq3/jGN4r3jxs3jjfffJOZM2dyzz33lDrrePHixVxzzTWMHz+e6dOnk5eXx80331xqP4MHDyY3N5fc3Fz69u0LQMuWLZk0aRL//ve/ufrqq7nssssAGD58OBdffDEFBQXk5+ezzz77cP311/Ptb3+bgoICbrzxRp555hnmzp3L1KlTKSgoYNq0aUyaNAmAN998k/79+/Pvf/+bpk2bsmLFCrp27cprr71G9+7dueeee7ZobCVJkiRJUuVzDWZJqkHat29PYWEho0ePplevXmWWe/nll3nkkUcA6NevH7/85S8BmDRpEqeeeio5OTnsvffefO9739ug7iuvvMLs2bM55JBDAPjyyy85+OCDS+3nxhtvpE+fPuvsW7p0KQMGDGDu3LlEBF999RUABx98ML/73e9YsGABJ5xwAvvvv/8G7T3zzDM888wzdOjQAYDly5czd+5cvvWtb9G0aVO6du1aXLZOnTr07t0bgI4dO/Lss8+WOR6SJEmSJKl6mGCWpBrmhz/8IZdeeikTJ07kk08+KbNcRGzW/rVSSnz/+99n9OjRFYrv17/+Nd/97ncZN24chYWF9OjRA4DTTjuNLl268NRTT3HUUUdx7733st9++23Q969+9SvOPffcdfYXFhay0047rbOvdu3axeeSk5NDUVFRheKVJEmSJElVp8qWyIiIfSPi+YiYExGzIuLi7P7dI+LZiJib/b1bdn9ExLCIeDsiZkTEQVUVmyTVZGeccQZXXnkl7dq1K7PMIYccwpgxYwAYOXJk8f7u3bszZswYVq9ezcKFC3n++ec3qNu1a1defvll3n77bQA+//xz3nrrrXLHt3Tp0uKbD44YMaJ4/7vvvst+++3HRRddxA9/+ENmzJhBw4YNWbZsWXGZo446ivvvv794zecPPvigzHWiJUmSJElSzVeVM5iLgEtSStMjoiEwLSKeBQYCz6WUro+IIcAQ4JfAD4D9sz9dgLuyvyVpq7t416Orre999tmHiy++eKNlbrvtNk477TRuu+02TjzxxOL9P/rRj5gwYQLt2rWjRYsWHH744RvUbdSoESNGjODUU0/liy++AOCaa66hRYsWG5QdPHgw11xzTfH21KlT+cUvfsGAAQO4+eab11mC4+GHH+ahhx6idu3afOMb3+DKK69k991355BDDqFt27b84Ac/4MYbb2TOnDnFS3I0aNCAhx56iJycnM0bJEmSJEmSVCNESmnrdBTxGHBH9qdHSmlhRHwTmJhSOiAi/ph9PDpb/s215cpqMy8vL+Xn52+N8DfqJ6d9WKXt3zVq7yptXxLMmTOHVq1aVXcYqkQ+p5IqS0RMSynlVWcMNeV9ryRJkrZfFX3fW2VLZJQUEc2ADsC/gL3WJo2zvxtnizUB5peotiC7b/22zomI/IjI//jjj6sybEmSJEmSJEnSRlR5gjkiGgCPAD9NKf13Y0VL2bfB9OqU0t0ppbyUUl6jRo0qK0xJkiRJkiRJ0maq0gRzRNQmk1wemVL6W3b3ouzSGGR/r7270wJg3xLV9wGqdu0JSZIkSZIkSVKFVVmCOSICuA+Yk1K6ucShx4EB2ccDgMdK7O8fGV2BpRtbf1mSJEmSJEmSVL1qVWHbhwD9gJkRUZDddxlwPfCXiDgTeB84KXvsaaAX8DbwOXB6FcYmSZIkSZIkSdpCVZZgTim9ROnrKgMcUUr5BJxfVfFIkiRJkiRJkipXVc5glqRt1hOLR1dqe8fueeomy+Tk5NCuXTuKiopo1aoVDz74IPXr19+sfq688kq6d+/OkUceyYsvvsigQYOoXbs2Tz31FBdffDFjx45lxIgR5Ofnc8cdd1T0dCRJkiRJkoAqvsmfJKn8dtxxRwoKCnj99depU6cOw4cP3+w2rr76ao488kgARo4cyaWXXkpBQQFNmjRh7NixlR2yJEmSJEn6mjPBLEk10GGHHcbbb78NwPHHH0/Hjh1p06YNd999NwCrV69m4MCBtG3blnbt2nHLLbcAMHDgQMaOHcu9997LX/7yF66++mr69u1LYWEhbdu23aCfp556ioMPPpjFixfzxBNP0KVLFzp06MCRRx7JokWLtt4JS5IkSZKkbZJLZEhSDVNUVMTf//53jj76aADuv/9+dt99d1auXEmnTp048cQTKSws5IMPPuD1118HYMmSJeu0cdZZZ/HSSy/Ru3dv+vTpQ2Fh4Qb9jBs3jptvvpmnn36a3XbbjUMPPZRXXnmFiODee+/lhhtu4Pe//32Vn68kSZIkSdp2mWCWpBpi5cqV5ObmApkZzGeeeSYAw4YNY9y4cQDMnz+fuXPncsABB/Duu+9y4YUXcswxx9CzZ8/N6uv5558nPz+fZ555hp133hmABQsWcPLJJ7Nw4UK+/PJLmjdvXolnJ0mSJEmStkcukSFJNcTaNZgLCgq4/fbbqVOnDhMnTmT8+PFMmTKF1157jQ4dOrBq1Sp22203XnvtNXr06MGdd97JWWedtVl97bfffixbtoy33nqreN+FF17IBRdcwMyZM/njH//IqlWrKvsUJUmSJEnSdsYEsyTVYEuXLmW33Xajfv36vPHGG7zyyisALF68mDVr1nDiiSfy29/+lunTp29Wu02bNuVvf/sb/fv3Z9asWcV9NWnSBIAHH3ywck9EkiRJkiRtl1wiQ5JKceyep1Z3CAAcffTRDB8+nPbt23PAAQfQtWtXAD744ANOP/101qxZA8B111232W0fcMABjBw5kpNOOoknnniCoUOHctJJJ9GkSRO6du3Ke++9V6nnIkmSJEmStj+RUqruGCosLy8v5efnV3cY/OS0D6u0/btG7V2l7UuCOXPm0KpVq+oOQ5XI51RSZYmIaSmlvOqMoaa875UkSdL2q6Lve10iQ5IkSZIkSZJUISaYJUmSJEmSJEkVYoJZkiRJkiRJklQhJpglSZIkSZIkSRViglmSJEmSJEmSVCEmmCVJkiRJkiRJFVKrugOQpJoof8ZNldpeXvtLN1nmd7/7HaNGjSInJ4cddtiBP/7xj3Tp0mWz+5o4cSJ16tShW7duAAwcOJDevXvTp0+fTdYdN24cJ5xwAnPmzKFly5YbLdurVy9GjRrFrrvuutkxSpIkSZKk7YMzmCWpBpgyZQpPPvkk06dPZ8aMGYwfP5599923Qm1NnDiRyZMnV6ju6NGjOfTQQxkzZswmyz799NMmlyVJkiRJ+pozwSxJNcDChQvZc889qVu3LgB77rkne++9NwDPPfccHTp0oF27dpxxxhl88cUXADRr1ozFixcDkJ+fT48ePSgsLGT48OHccsst5Obm8uKLLwIwadIkunXrxn777cfYsWNLjWH58uW8/PLL3HfffeskmBcuXEj37t3Jzc2lbdu2xW2W7P/444+nY8eOtGnThrvvvru4boMGDbj88ss58MAD6dq1K4sWLarMYZMkSZIkSdXMBLMk1QA9e/Zk/vz5tGjRgvPOO48XXngBgFWrVjFw4EAefvhhZs6cSVFREXfddVeZ7TRr1oxBgwbxs5/9jIKCAg477DAgkyR+6aWXePLJJxkyZEipdR999FGOPvpoWrRowe6778706dMBGDVqFEcddRQFBQW89tpr5ObmblD3/vvvZ9q0aeTn5zNs2DA++eQTAFasWEHXrl157bXX6N69O/fcc88WjZMkSZIkSapZTDBLUg3QoEEDpk2bxt13302jRo04+eSTGTFiBG+++SbNmzenRYsWAAwYMIBJkyZtdvvHH388O+ywA61bty5zFvHo0aM55ZRTADjllFMYPXo0AJ06deKBBx5g6NChzJw5k4YNG25Qd9iwYcWzlOfPn8/cuXMBqFOnDr179wagY8eOFBYWbnbskiRJkiSp5vImf5JUQ+Tk5NCjRw969OhBu3btePDBB0udLbxWrVq1WLNmDZCZ6bwxa5feAEgpbXD8k08+YcKECbz++utEBKtXryYiuOGGG+jevTuTJk3iqaeeol+/fgwePJj+/fsX1504cSLjx49nypQp1K9fnx49ehTHU7t2bSKi+PyKiorKPyCSJEmSJKnGcwazJNUAb775ZvGsX4CCggKaNm1Ky5YtKSws5O233wbgz3/+M4cffjiQWQ5j2rRpADzyyCPFdRs2bMiyZcs2q/+xY8fSv39/5s2bR2FhIfPnz6d58+a89NJLzJs3j8aNG3P22Wdz5plnFi+dsdbSpUvZbbfdqF+/Pm+88QavvPJKhcZAkiRJkiRte5zBLEmlyGt/6Vbtb/ny5Vx44YUsWbKEWrVq8Z3vfIe7776bevXq8cADD3DSSSdRVFREp06dGDRoEAC/+c1vOPPMM7n22mvp0qVLcVvHHnssffr04bHHHuP2228vV/+jR4/eYG3mE088kVGjRtG1a1duvPFGateuTYMGDfjTn/60Trmjjz6a4cOH0759ew444AC6du26haMhSZIkSZK2FVHaV6W3FXl5eSk/P7+6w+Anp31Ype3fNWrvKm1fEsyZM4dWrVpVdxiqRD6nkipLRExLKeVVZww15X2vJEmStl8Vfd/rEhmSJEmSJEmSpAoxwSxJkiRJkiRJqhATzJIkSZIkSZKkCjHBLEmSJEmSJEmqEBPMkiRJUiWKiKMj4s2IeDsihpRR5scRMTsiZkXEqK0doyRJklRZalV3AJIkSdL2IiJygDuB7wMLgFcj4vGU0uwSZfYHfgUcklL6LCIaV0+0kiRJ0pYzwSxJpVj8xOBKbW/PY2/cZJkGDRqwfPny4u0RI0aQn5/PHXfcwfDhw6lfvz79+/ev1LhKc+WVV9K9e3eOPPLIKu9LkrZDnYG3U0rvAkTEGOA4YHaJMmcDd6aUPgNIKX201aOUJEmSKokJZknaBgwaNGir9XX11Vdvtb4kaTvUBJhfYnsB0GW9Mi0AIuJlIAcYmlL6x/oNRcQ5wDkA3/rWt6okWEmSJGlLuQazJG0Dhg4dyk033QTAsGHDaN26Ne3bt+eUU04pPt6vXz++973vsf/++3PPPfcAsHz5co444ggOOugg2rVrx2OPPQZAYWEhrVq14uyzz6ZNmzb07NmTlStXAjBw4EDGjh0LwKuvvkq3bt048MAD6dy5M8uWLdvapy5J25ooZV9ab7sWsD/QAzgVuDcidt2gUkp3p5TyUkp5jRo1qvRAJUmSpMrgDGZJqiFWrlxJbm5u8fann37KD3/4ww3KXX/99bz33nvUrVuXJUuWFO+fMWMGr7zyCitWrKBDhw4cc8wxNG7cmHHjxrHzzjuzePFiunbtWtzm3LlzGT16NPf8//buPdyuur4T//tjIpcRFSVRgaCgUu4Y5RAc4ceAaEdKJcggF6UVhxan9Y44D2pbkdrn8db6yOCl1Cpai0B1lAhWxlIQb1ySmiKXYUSaSoyXGAlCFTTw/f2xN/EQTpLNcu+zT5LX63n2c9Za+7u+63P2XjlnnXe++7v+5m9y/PHH57Of/WxOPvnktf398pe/zAknnJCLLrooBx54YH72s59l2223HeErALBZWJ5kl0nr85KsmKLNNa21XyX5t6q6Nb3A+frpKREAAIbHCGaAGWLbbbfN0qVL1z7WN1XF/vvvn5e//OX51Kc+ldmzf/3/hAsXLsy2226bOXPm5PDDD891112X1lre+ta3Zv/9988LXvCCfP/738+PfvSjJMluu+22NtA+4IADsmzZsocc59Zbb82OO+6YAw88MEnyuMc97iHHA2BK1yfZvap2q6qtkpyYZNE6bT6f5PAkqao56U2Zcfu0VgkAAEMiYAbYxFx22WV59atfnSVLluSAAw7ImjVrkiRVD/1UdlXl7//+77Ny5cosWbIkS5cuzZOf/OTce++9SZKtt956bdtZs2at7edBrbWH9QnAhrXW1iR5TZLLk9yS5OLW2k1VdXZVPfixlMuTrKqqm5NcmeTNrbVV46kYAAB+MwJmgE3IAw88kDvuuCOHH3543vOe92T16tW55557kiSXXHJJ7r333qxatSpXXXVVDjzwwNx111150pOelEc/+tG58sor8+///u8DH2vPPffMihUrcv31vU9s33333Q8LoQF4uNbaF1trv9Vae0Zr7S/62/6stbaov9xaa6e31vZure3XWrtwvBUDAEB3PusMMIU5L37vuEuY0v3335+TTz45d911V1preeMb35jtt+/dF2rBggU56qij8r3vfS9/+qd/mp122ikvf/nL8+IXvzgTExOZP39+9txzz4GPtdVWW+Wiiy7Ka1/72vziF7/Itttum3/6p3/KdtttN6pvDwAAANjEVGvr3tR60zExMdEWL1487jLyRy9b974tw/XhC3Yaaf9Acsstt2SvvfYadxmdnXXWWdluu+1yxhlnjLuUGWNTf0+BmaOqlrTWJsZZw0y57gUAYPPV9brXFBkAAAAAAHRiigyAzcBZZ5017hIAAACALZARzAAAAAAAdCJgBgAAAACgEwEzAAAAAACdCJgBAAAAAOjETf4ApvDDd71sqP095cwLNvj8qlWrcsQRR/SO/cMfZtasWZk7d26S5LrrrstWW2210WOcfPLJOe6443LMMcc8ZPsrX/nKnHnmmdljjz2m3O+QQw7Jueeem/nz5w/yrQAAAACsJWAGmAF22GGHLF26NEly1llnZbvttssZZ5wx8P5r1qxZ73Mf//jHf+P6AAAAAKZiigyAGey22257yMjid73rXXnnO9+ZpDfy+G1ve1sOPfTQnHvuuQ/Z7y1veUtOPfXUPPDAAznkkEOydOnSrFmzJr/3e7+X/fbbL/vuu2/OOeecte0vvPDCLFiwIHvssUe+8Y1vTM83BwAAAGzyjGAG2IT97Gc/y9VXX52kN0VGkpx++um577778tGPfjRVtbbtkiVL8pOf/CTf/va3kySrV69e+1xrLdddd10WLVqUs88+O1/60pem8bsAAAAANlVGMANswk488cSHrL/97W/PL37xi3zwgx98SLicJM985jNz66235vWvf30uv/zyPP7xj1/73LHHHpskOeCAC+HCEAAAHmlJREFUA7Js2bKR1w0AAABsHgTMADPY7Nmz88ADD6xdv/feex/y/GMe85iHrC9YsCCLFy/OnXfe+bC+dthhh9xwww055JBDcs455+RVr3rV2ue23nrrJMmsWbM2OJ8zAAAAwGQCZoAZ7ClPeUpWrFiRO++8M/fee28uu+yyDbY/6qij8qY3vSm/+7u/m3vuuechz61cuTKttbz0pS/NO97xjvzLv/zLKEsHAAAAtgDmYAaYwlPOvGDcJSRJttlmm7z1rW/NgQcemKc//enZe++9N7rPiSeemLvvvjsLFy58SCB9xx135NRTT01rLVWVd7/73aMsHQAAANgCVGtt3DV0NjEx0RYvXjzuMvJHL1sx0v4/fMFOI+0fSG655Zbstdde4y6DIfKeAsNSVUtaaxPjrGGmXPcCALD56nrda4oMAAAAAAA6GVnAXFUfq6ofV9WNk7adVVXfr6ql/cfvTHruLVV1W1XdWlX/dVR1AQAAAAAwHAMFzFV1xSDb1nF+khdNsf39rbX5/ccX+33tneTEJPv09/lQVc0apDYAAAAAAMZjgwFzVW1TVU9MMqeqnlBVT+w/dk2ywYmBW2tXJ/npgHUsTHJha+2+1tq/JbktyYIB9wUAAAAAYAw2NoL5VUmWJNmz//XBxyVJPtjxmK+pqhv6U2g8ob9t5yR3TGqzvL/tYarqtKpaXFWLV65c2bEEAAAAAAB+UxsMmFtrH2it7ZbkjNba01tru/Ufz2qtndvheB9O8owk85P8IMlf9rfXVIdfT03ntdYmWmsTc+fO7VACAAAAAADDMHuQRq21/1VVz0uy6+R9WmuffCQHa6396MHlqvqbJJf2V5cn2WVS03lJVjySvgGGafnxhw61v3kXX91pv9WrV+eCCy7IH//xHydJrrrqqrzvfe/LpZdeupE9AQAAAEZv0Jv8/V2S9yU5JMmB/cfEIz1YVe04afUlSW7sLy9KcmJVbV1VuyXZPcl1j7R/gM3N6tWr86EPfWho/a1Zs2ZofQEAAAAMNII5vTB579balNNWTKWqPp3ksPRuELg8yduTHFZV89Ob/mJZenM8p7V2U1VdnOTmJGuSvLq1dv+gxwLYXPzVX/1VPvaxjyVJ/uAP/iDXXHNNvvvd72b+/Pl54QtfmKOOOir33HNPjjvuuNx444054IAD8qlPfSpVlSVLluT000/PPffckzlz5uT888/PjjvumMMOOyzPe97z8vWvfz1HH310nvrUp+Yd73hHZs2alcc//vG5+upuo6sBAAAABg2Yb0zylPTmTR5Ia+2kKTb/7Qba/0WSvxi0f4DNzZIlS/Lxj3881157bVprOeigg/KpT30qN954Y5YuXZqkN0XGt771rdx0003ZaaedcvDBB+frX/96DjrooLz2ta/NJZdckrlz5+aiiy7K2972trVh9erVq/OVr3wlSbLffvvl8ssvz84775zVq1eP7fsFAAAANn2DBsxzktxcVdclue/Bja21o0dSFcAW6Gtf+1pe8pKX5DGPeUyS5Nhjj81Xv/rVh7VbsGBB5s2blySZP39+li1blu233z433nhjXvjCFyZJ7r///uy4469nJTrhhBPWLh988ME55ZRTcvzxx+fYY48d5bcEAAAAbOYGDZjPGmURACSDzkK09dZbr12eNWtW1qxZk9Za9tlnn3zzm9+ccp8HQ+sk+chHPpJrr702l112WebPn5+lS5dmhx12+M2KBwAAALZIA93kr7X2lakeoy4OYEty6KGH5vOf/3x+/vOf5z/+4z/yuc99LgcffHDuvvvuje67xx57ZOXKlWsD5l/96le56aabpmz73e9+NwcddFDOPvvszJkzJ3fcccdQvw8AAABgyzHQCOaquju9G/MlyVZJHp3kP1prjxtVYQDjNO/i6b/x3XOe85yccsopWbBgQZLeTf4OOOCAHHzwwdl3331z5JFH5qijjppy36222iqf+cxn8rrXvS533XVX1qxZkze84Q3ZZ599Htb2zW9+c77zne+ktZYjjjgiz3rWs0b6fQEAAACbrxr0I9kP2anqmCQLWmtvHX5Jg5uYmGiLFy8eZwlJkj962YqR9v/hC3Yaaf9Acsstt2SvvfYadxkMkfcUGJaqWtJamxhnDTPluhcAgM1X1+vegabIWFdr7fNJnt9lXwAAAAAANg+DTpFx7KTVRyWZyK+nzAAAAAAAYAs0UMCc5MWTltckWZZk4dCrARij1lqqatxlMARdpn8CAAAAHrmBAubW2itHXQjAOG2zzTZZtWpVdthhByHzJq61llWrVmWbbbYZdykAAACw2Rt0iox5Sf5XkoPTmxrja0le31pbPsLaAKbNvHnzsnz58qxcuXLcpTAE22yzTebNmzfuMgAAAGCzN+gUGR9PckGSl/bXT+5ve+EoigKYbo9+9KOz2267jbsMAAAAgE3KowZsN7e19vHW2pr+4/wkc0dYFwAAAAAAM9ygAfNPqurkqprVf5ycZNUoCwMAAAAAYGYbNGD+70mOT/LDJD9IclwSN/4DAAAAANiCDToH858neUVr7c4kqaonJnlfesEzAAAAAABboEFHMO//YLicJK21nyZ59mhKAgAAAABgUzBowPyoqnrCgyv9EcyDjn4GAAAAAGAzNGhI/JdJvlFVn0nS0puP+S9GVhUAAAAAADPeQAFza+2TVbU4yfOTVJJjW2s3j7QyAAAAAABmtIGnuegHykJlAAAAAACSDD4HMwAAAAAAPISAGQAAAACATgTMAAAAAAB0ImAGAAAAAKATATMAAAAAAJ0ImAEAAAAA6ETADAAAAABAJwJmAAAAAAA6ETADAAAAANCJgBkAAAAAgE4EzAAAAAAAdCJgBgAAAACgEwEzAAAAAACdCJgBAAAAAOhEwAwAAAAAQCezx10ADMMf33H1SPv/0C6HjrR/AAAAANgUGcEMAABDVFUvqqpbq+q2qjpzA+2Oq6pWVRPTWR8AAAyTgBkAAIakqmYl+WCSI5PsneSkqtp7inaPTfK6JNdOb4UAADBcAmYAABieBUlua63d3lr7ZZILkyycot2fJ3lPknunszgAABg2ATMAAAzPzknumLS+vL9trap6dpJdWmuXbqijqjqtqhZX1eKVK1cOv1IAABgCATMAAAxPTbGtrX2y6lFJ3p/kTRvrqLV2XmttorU2MXfu3CGWCAAAwyNgBgCA4VmeZJdJ6/OSrJi0/tgk+ya5qqqWJXlukkVu9AcAwKZKwAwAAMNzfZLdq2q3qtoqyYlJFj34ZGvtrtbanNbarq21XZNck+To1tri8ZQLAAC/GQEzAAAMSWttTZLXJLk8yS1JLm6t3VRVZ1fV0eOtDgAAhm/2uAsAAIDNSWvti0m+uM62P1tP28OmoyYAABgVI5gBAAAAAOhEwAwAAAAAQCcCZgAAAAAAOhEwAwAAAADQiYAZAAAAAIBOBMwAAAAAAHQiYAYAAAAAoBMBMwAAAAAAnQiYAQAAAADoZGQBc1V9rKp+XFU3Ttr2xKr6clV9p//1Cf3tVVXnVNVtVXVDVT1nVHUBAAAAADAcoxzBfH6SF62z7cwkV7TWdk9yRX89SY5Msnv/cVqSD4+wLgAAAAAAhmBkAXNr7eokP11n88Ikn+gvfyLJMZO2f7L1XJNk+6racVS1AQAAAADwm5vuOZif3Fr7QZL0vz6pv33nJHdMare8vw0AAAAAgBlqptzkr6bY1qZsWHVaVS2uqsUrV64ccVkAAAAAAKzPdAfMP3pw6ov+1x/3ty9PssukdvOSrJiqg9baea21idbaxNy5c0daLAAAAAAA6zfdAfOiJK/oL78iySWTtv9+9Tw3yV0PTqUBAAAAAMDMNHtUHVfVp5MclmROVS1P8vYk70pycVWdmuR7SV7ab/7FJL+T5LYkP0/yylHVBQAAAADAcIwsYG6tnbSep46Yom1L8upR1QIAAAAAwPDNlJv8AQAAAACwiREwAwAAAADQiYAZAAAAAIBOBMwAAAAAAHQiYAYAAAAAoBMBMwAAAAAAnQiYAQAAAADoRMAMAAAAAEAnAmYAAAAAADoRMAMAAAAA0ImAGQAAAACATgTMAAAAAAB0ImAGAAAAAKATATMAAAAAAJ0ImAEAAAAA6ETADAAAAABAJ7PHXQBsCj6w+ksj7f/1279opP0DAAAAwCgYwQwAAAAAQCcCZgAAAAAAOhEwAwAAAADQiYAZAAAAAIBOBMwAAAAAAHQiYAYAAAAAoBMBMwAAAAAAnQiYAQAAAADoRMAMAAAAAEAnAmYAAAAAADoRMAMAAAAA0ImAGQAAAACATgTMAAAAAAB0ImAGAAAAAKATATMAAAAAAJ0ImAEAAAAA6ETADAAAAABAJwJmAAAAAAA6ETADAAAAANCJgBkAAAAAgE4EzAAAAAAAdCJgBgAAAACgEwEzAAAAAACdCJgBAAAAAOhEwAwAAAAAQCcCZgAAAAAAOhEwAwAAAADQiYAZAAAAAIBOBMwAAAAAAHQiYAYAgCGqqhdV1a1VdVtVnTnF86dX1c1VdUNVXVFVTxtHnQAAMAwCZgAAGJKqmpXkg0mOTLJ3kpOqau91mn0ryURrbf8kn0nynumtEgAAhkfADAAAw7MgyW2ttdtba79McmGShZMbtNaubK39vL96TZJ501wjAAAMjYAZAACGZ+ckd0xaX97ftj6nJvnHqZ6oqtOqanFVLV65cuUQSwQAgOERMAMAwPDUFNvalA2rTk4ykeS9Uz3fWjuvtTbRWpuYO3fuEEsEAIDhmT3uAgDY8vzRy1aMtP8PX7DTSPsH2IDlSXaZtD4vycN+6FXVC5K8Lcl/aa3dN021AQDA0BnBDAAAw3N9kt2rareq2irJiUkWTW5QVc9O8tdJjm6t/XgMNQIAwNAImAEAYEhaa2uSvCbJ5UluSXJxa+2mqjq7qo7uN3tvku2S/ENVLa2qRevpDgAAZjxTZAAAwBC11r6Y5IvrbPuzScsvmPaiAABgRMYSMFfVsiR3J7k/yZrW2kRVPTHJRUl2TbIsyfGttTvHUR8AAAAAABs3zikyDm+tzW+tTfTXz0xyRWtt9yRX9NcBAAAAAJihZtIczAuTfKK//Ikkx4yxFgAAAAAANmJcAXNL8n+qaklVndbf9uTW2g+SpP/1SVPtWFWnVdXiqlq8cuXKaSoXAAAAAIB1jesmfwe31lZU1ZOSfLmq/u+gO7bWzktyXpJMTEy0URUIAAAAAMCGjWUEc2ttRf/rj5N8LsmCJD+qqh2TpP/1x+OoDQAAAACAwUx7wFxVj6mqxz64nOS3k9yYZFGSV/SbvSLJJdNdGwAAAAAAgxvHFBlPTvK5qnrw+Be01r5UVdcnubiqTk3yvSQvHUNtAAAAAAAMaNoD5tba7UmeNcX2VUmOmO56AAAAAADoZixzMAMAAAAAsOkTMAMAAAAA0ImAGQAAAACATgTMAAAAAAB0ImAGAAAAAKATATMAAAAAAJ0ImAEAAAAA6ETADAAAAABAJwJmAAAAAAA6mT3uAtgyPP+aa0ba/547j7R7AAAAAGAKRjADAAAAANCJgBkAAAAAgE4EzAAAAAAAdCJgBgAAAACgEwEzAAAAAACdzB53AcD0WHzD+0ba/8T+Z4y0fwAAAABmHiOYAQAAAADoRMAMAAAAAEAnAmYAAAAAADoRMAMAAAAA0ImAGQAAAACATgTMAAAAAAB0MnvcBTB+T7vgWyM/xjOePvJDAAAAAADTzAhmAAAAAAA6ETADAAAAANCJgBkAAAAAgE4EzAAAAAAAdCJgBgAAAACgEwEzAAAAAACdCJgBAAAAAOhEwAwAAAAAQCcCZgAAAAAAOhEwAwAAAADQiYAZAAAAAIBOBMwAAAAAAHQiYAYAAAAAoBMBMwAAAAAAnQiYAQAAAADoRMAMAAAAAEAnAmYAAAAAADoRMAMAAAAA0ImAGQAAAACATgTMAAAAAAB0MnvcBbBxv/3O20d7gKePtnu2DD/5wptH2v+cF793pP0DAAAA8MgZwQwAAAAAQCdGMMMM8IWffHrkx9hx5EcAAAAAYEsjYAYAhu4Dq7808mO8fvsXjfwYAAAAbJgpMgAAAAAA6MQIZgA2O6O+Oer/+RN3RwUAAIDECGYAAAAAADoSMAMAAAAA0IkpMoBNwg/f9bKRH+MpZ14w8mPAoJ5/zTUj7X/PnX850v73eOxIuwcAAGCGMIIZAAAAAIBOBMwAAAAAAHQiYAYAAAAAoJMZNwdzVb0oyQeSzEry0dbau8ZcEgA8xNMu+NbIj/GMp4/8EJu8L/zk0yPtf8cV3x9p/xP7nzHS/hmfjV3PVtXWST6Z5IAkq5Kc0FpbNt11AgDAMMyoEcxVNSvJB5McmWTvJCdV1d7jrQoAAAYz4PXsqUnubK09M8n7k7x7eqsEAIDhmWkjmBckua21dnuSVNWFSRYmuXmsVQFbhOXHHzrS/uddfPVI+wc2LT/5wptHfow1N412FPZTzrxgpP1voga5nl2Y5Kz+8meSnFtV1Vpr01koAAAMw0wLmHdOcsek9eVJDprcoKpOS3Jaf/Weqrp1mmrjN/C9Efd/5Yj772BOkp+MuwhmmKpxV8AmxM9NNglvGe00JTPI0x5B241ez05u01pbU1V3Jdkh6/wbWOe6976quvGRFM0Wwc9OpuK8YF3OCabivGAqe3TZaaYFzFOlLw8ZydFaOy/JedNTDnRTVYtbaxPjrgNgU+HnJpuRjV7PDtjmIde9/o0wFecFU3FesC7nBFNxXjCVqlrcZb8ZNQdzeiM8dpm0Pi/JijHVAgAAj9Qg17Nr21TV7CSPT/LTaakOAACGbKYFzNcn2b2qdquqrZKcmGTRmGsCAIBBDXI9uyjJK/rLxyX5Z/MvAwCwqZpRU2T056B7TZLLk8xK8rHW2k1jLgu6MI0LwCPj5yabhfVdz1bV2UkWt9YWJfnbJH9XVbelN3L5xAG69m+EqTgvmIrzgnU5J5iK84KpdDovymAJAAAAAAC6mGlTZAAAAAAAsIkQMAMAAAAA0ImAGYasql5UVbdW1W1Vdea46wGYyarqY1X146q6cdy1wEywseuIqtq6qi7qP39tVe06/VUy3QY4L06vqpur6oaquqKqnjaOOpk+g/7NUVXHVVWrqonprI/xGOS8qKrj+z8vbqqqC6a7RqbfAL9DnlpVV1bVt/q/R35nHHUyfTb2N1j1nNM/Z26oqudsrE8BMwxRVc1K8sEkRybZO8lJVbX3eKsCmNHOT/KicRcBM8GA1xGnJrmztfbMJO9P8u7prZLpNuB58a0kE621/ZN8Jsl7prdKptOgf3NU1WOTvC7JtdNbIeMwyHlRVbsneUuSg1tr+yR5w7QXyrQa8OfFnyS5uLX27PRuPPyh6a2SMTg/G/4b7Mgku/cfpyX58MY6FDDDcC1Icltr7fbW2i+TXJhk4ZhrApixWmtXJ/npuOuAGWKQ64iFST7RX/5MkiOqqqaxRqbfRs+L1tqVrbWf91evSTJvmmtkeg36N8efp/efDfdOZ3GMzSDnxR8m+WBr7c4kaa39eJprZPoNcl60JI/rLz8+yYpprI8xGOBvsIVJPtl6rkmyfVXtuKE+BcwwXDsnuWPS+vL+NgCAjRnkOmJtm9bamiR3JdlhWqpjXB7p9eWpSf5xpBUxbhs9J6rq2Ul2aa1dOp2FMVaD/Kz4rSS/VVVfr6prqsqnyDZ/g5wXZyU5uaqWJ/liktdOT2nMYI8425o90nJgyzPVCKI27VUAAJuiQa4jXGtseQZ+z6vq5CQTSf7LSCti3DZ4TlTVo9KbQueU6SqIGWGQnxWz0/vI+2HpfdLhq1W1b2tt9YhrY3wGOS9OSnJ+a+0vq+o/J/m7/nnxwOjLY4Z6xNebRjDDcC1Pssuk9Xnx8RIAYDCDXEesbVNVs9P7KKtpZjZvA11fVtULkrwtydGttfumqTbGY2PnxGOT7JvkqqpaluS5SRa50d9mb9DfIZe01n7VWvu3JLemFziz+RrkvDg1ycVJ0lr7ZpJtksyZluqYqR5xtiVghuG6PsnuVbVbVW2V3gT5i8ZcEwCwaRjkOmJRklf0l49L8s+tNSOYN28bPS/60yH8dXrhsjlVN38bPCdaa3e11ua01nZtre2a3rzcR7fWFo+nXKbJIL9DPp/k8CSpqjnpTZlx+7RWyXQb5Lz4XpIjkqSq9kovYF45rVUy0yxK8vvV89wkd7XWfrChHUyRAUPUWltTVa9JcnmSWUk+1lq7acxlAcxYVfXp9D6mOac/79vbW2t/O96qYDzWdx1RVWcnWdxaW5Tkb9P76Opt6Y1cPnF8FTMdBjwv3ptkuyT/0L/n4/daa0ePrWhGasBzgi3MgOfF5Ul+u6puTnJ/kje31laNr2pGbcDz4k1J/qaq3pjeNAin+M/rzdtUf4MleXSStNY+kt5c3L+T5LYkP0/yyo326ZwBAAAAAKALU2QAAAAAANCJgBkAAAAAgE4EzAAAAAAAdCJgBgAAAACgEwEzAAAAAACdCJiBLVpVvaSqWlXtuYE251fVcRvp55SqOre/fExV7b2edmdV1feramlV3VxVJw1Q41lVdcZG2jzkmFV1dlW9YGN9j0JVTVTVOf3lw6rqeZOe2+hrOantw96bqnpUVZ1TVTdW1ber6vqq2q3/3LL+tm/3X9t3VtXWw/7+AAAAgF8TMANbupOSfC3JiUPs85gkUwbMfe9vrc1PsjDJX1fVo4d9zNban7XW/mkI/T5irbXFrbXX9VcPS/K8DTTfkKnemxOS7JRk/9bafklekmT1pOcP729fkOTpSc7reGwAAABgAAJmYItVVdslOTjJqZkUYlbPuf1RsJcledKk55ZV1Zz+8kRVXbVOn89LcnSS9/ZHKT9jfcdvrX0nyc+TPKG/7zOq6ktVtaSqvjrVqOqq+sP+qN1/rarPVtV/muqYD44Urqojq+riSfsfVlVf6C//dlV9s6r+par+of96pKre1f/eb6iq901Rw7eravv+67Sqqn6/v/3vquoF/WNcWlW7JvkfSd7Yr+v/63dxaFV9o6puX99o5vW9N0l2TPKD1toD/ddweWvtzile23v6xz6mqp64vvcAAAAA+M0ImIEt2TFJvtRa+39JflpVz+lvf0mSPZLsl+QP8whG4LbWvpFkUZI3t9bmt9a+u762/eN9p7X24/6m85K8trV2QJIzknxoit3+d2vtwNbas5LckuTUjRzzy0meW1WP6a+fkOSifkj+J0le0Fp7TpLFSU7vh7EvSbJPa23/JO+cooavpxf+7pPk9iQPBsfPTXLNpNdiWZKPpD9iu7X21f5TOyY5JMnvJnnXel6e9b03Fyd5cT+w/suqevZ69k9r7WdJ/i3J7utrAwAAAPxmBMzAluykJBf2ly/sryfJoUk+3Vq7v7W2Isk/D/m4b6yqW5Ncm+SsZO2I3ecl+YeqWprkr9MLYte1b39087eTvDy9kHe9WmtrknwpvVB2dpKjklySXhi8d5Kv94/3iiRPS/KzJPcm+WhVHZveCOt1fTW91+jQJB9Osl9V7Zzkp/2Rwxvz+dbaA621m5M8eT1tpnxvWmvL0wv/35LkgSRXVNURGzhWDVAPAAAA0NHscRcAMA5VtUOS56cX2LYks5K0qvqf/SZtPbuuya//c26bjod/f2vtff0A95P9aTQelWR1f27mDTk/yTGttX+tqlPSm+N4Yy5K8uokP01yfWvt7qqqJF9urT3sJoNVtSDJEelNTfGa9F6nya7u9/fUJG9Lb8TzcekFz4O4b/Lhpjj+et+b1nNfkn9M8o9V9aP0RjtfMUU/j02ya5L/N2BdAAAAwCNkBDOwpTouySdba09rre3aWtslvekUDkkvQD2xqmZV1Y5JDp+037IkB/SX/9t6+r47yWM3VkBr7X+nNzXFKx6czqGqXpqsnQf6WVPs9tgkP+jfGPDlAx7zqiTPSW+6j4v6265JcnBVPbN/vP9UVb/VH0n9+NbaF5O8IcnDAu/W2h1J5iTZvbV2e3o34jsjUwfMA70W61jve1NVz6mqnfo1PyrJ/kn+fd0O+t/Hh9IbLf2wOZoBAACA4RAwA1uqk5J8bp1tn03ysv727yT5dnpTQHxlUpt3JPlAVX01yf3r6fvCJG+uqm9t6CZ/fWenN/fxo9ILjE+tqn9NclOShVO0/9P0ptb4cpL/O8gxW2v3J7k0yZH9r2mtrUxySpJPV9UN6QXOe6YXBl/a3/aVJG9cT93X5tcjg7+aZOf0guZ1fSHJS9a5yd/GbOi9eVKSL1TVjUluSG9E+bmT2l3Zf+66JN9L8qoBjwkAAAB0UK2t71PgAAAAAACwfkYwAwAAAADQiYAZAAAAAIBOBMwAAAAAAHQiYAYAAAAAoBMBMwAAAAAAnQiYAQAAAADoRMAMAAAAAEAn/z8tYnOmk7NqNQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(20,6))\n",
    "sns.countplot(x='austim',data=data,hue='ethnicity',palette='rainbow',ax=ax[0])\n",
    "ax[0].set_title('Positive ASD Adult relatives with Autism distribution for different ethnicities')\n",
    "ax[0].set_xlabel('Adult Relatives with ASD')\n",
    "#sns.countplot(x='Family_mem_with_ASD',data=data2,hue='Ethnicity',palette='rainbow',ax=ax[1])\n",
    "#ax[1].set_title('Positive ASD Toddler relatives with Autism distribution for different ethnicities')\n",
    "#ax[1].set_xlabel('Toddler Relatives with ASD')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1029,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['United States', 'Brazil', 'Spain', 'Egypt', 'New Zealand',\n",
       "       'Bahamas', 'Burundi', 'Austria', 'Argentina', 'Jordan', 'Ireland',\n",
       "       'United Arab Emirates', 'Afghanistan', 'Lebanon', 'United Kingdom',\n",
       "       'South Africa', 'Italy', 'Pakistan', 'Bangladesh', 'Chile',\n",
       "       'France', 'China', 'Australia', 'Canada', 'Saudi Arabia',\n",
       "       'Netherlands', 'Romania', 'Sweden', 'Tonga', 'Oman', 'India',\n",
       "       'Philippines', 'Sri Lanka', 'Sierra Leone', 'Ethiopia', 'Viet Nam',\n",
       "       'Iran', 'Costa Rica', 'Germany', 'Mexico', 'Russia', 'Armenia',\n",
       "       'Iceland', 'Nicaragua', 'Hong Kong', 'Japan', 'Ukraine',\n",
       "       'Kazakhstan', 'AmericanSamoa', 'Uruguay', 'Serbia', 'Portugal',\n",
       "       'Malaysia', 'Ecuador', 'Niger', 'Belgium', 'Bolivia', 'Aruba',\n",
       "       'Finland', 'Turkey', 'Nepal', 'Indonesia', 'Angola', 'Azerbaijan',\n",
       "       'Iraq', 'Czech Republic', 'Cyprus'], dtype=object)"
      ]
     },
     "execution_count": 1029,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['contry_of_res'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1030,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['White-European', 'Latino', 'Others', 'Black', 'Asian',\n",
       "       'Middle Eastern ', 'Pasifika', 'South Asian', 'Hispanic',\n",
       "       'Turkish', 'others'], dtype=object)"
      ]
     },
     "execution_count": 1030,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ethnicity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1031,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Self', 'Parent', 'Health care professional', 'Relative', 'Others'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 1031,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['relation'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1032,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['26', '24', '27', '35', '40', '36', '17', '64', '29', '33', '18',\n",
       "       '31', '30', '34', '38', '42', '43', '48', '37', '55', '50', '53',\n",
       "       '20', '28', '21', '383', '47', '32', '44', '19', '58', '45', '22',\n",
       "       '39', '25', '23', '54', '60', '41', '46', '56', '61', '59', '52',\n",
       "       '49', '51'], dtype=object)"
      ]
     },
     "execution_count": 1032,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1033,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1034,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_csv('data sets//datasets_38367_58429_Toddler Autism dataset July 2018.csv',na_values = '?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1035,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Case_No</th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>Age_Mons</th>\n",
       "      <th>Qchat-10-Score</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Jaundice</th>\n",
       "      <th>Family_mem_with_ASD</th>\n",
       "      <th>Who completed the test</th>\n",
       "      <th>Class/ASD Traits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>f</td>\n",
       "      <td>middle eastern</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>m</td>\n",
       "      <td>White European</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>m</td>\n",
       "      <td>middle eastern</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>m</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>f</td>\n",
       "      <td>White European</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Case_No  A1  A2  A3  A4  A5  A6  A7  A8  A9  A10  Age_Mons  Qchat-10-Score  \\\n",
       "0        1   0   0   0   0   0   0   1   1   0    1        28               3   \n",
       "1        2   1   1   0   0   0   1   1   0   0    0        36               4   \n",
       "2        3   1   0   0   0   0   0   1   1   0    1        36               4   \n",
       "3        4   1   1   1   1   1   1   1   1   1    1        24              10   \n",
       "4        5   1   1   0   1   1   1   1   1   1    1        20               9   \n",
       "\n",
       "  Sex       Ethnicity Jaundice Family_mem_with_ASD Who completed the test  \\\n",
       "0   f  middle eastern      yes                  no          family member   \n",
       "1   m  White European      yes                  no          family member   \n",
       "2   m  middle eastern      yes                  no          family member   \n",
       "3   m        Hispanic       no                  no          family member   \n",
       "4   f  White European       no                 yes          family member   \n",
       "\n",
       "  Class/ASD Traits   \n",
       "0                No  \n",
       "1               Yes  \n",
       "2               Yes  \n",
       "3               Yes  \n",
       "4               Yes  "
      ]
     },
     "execution_count": 1035,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1036,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.drop('Case_No',inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1037,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
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       "      <th>A6</th>\n",
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       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>Age_Mons</th>\n",
       "      <th>Qchat-10-Score</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Jaundice</th>\n",
       "      <th>Family_mem_with_ASD</th>\n",
       "      <th>Who completed the test</th>\n",
       "      <th>Class/ASD Traits</th>\n",
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       "      <td>No</td>\n",
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       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>middle eastern</td>\n",
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       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>m</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>f</td>\n",
       "      <td>White European</td>\n",
       "      <td>no</td>\n",
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       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A1  A2  A3  A4  A5  A6  A7  A8  A9  A10  Age_Mons  Qchat-10-Score Sex  \\\n",
       "0   0   0   0   0   0   0   1   1   0    1        28               3   f   \n",
       "1   1   1   0   0   0   1   1   0   0    0        36               4   m   \n",
       "2   1   0   0   0   0   0   1   1   0    1        36               4   m   \n",
       "3   1   1   1   1   1   1   1   1   1    1        24              10   m   \n",
       "4   1   1   0   1   1   1   1   1   1    1        20               9   f   \n",
       "\n",
       "        Ethnicity Jaundice Family_mem_with_ASD Who completed the test  \\\n",
       "0  middle eastern      yes                  no          family member   \n",
       "1  White European      yes                  no          family member   \n",
       "2  middle eastern      yes                  no          family member   \n",
       "3        Hispanic       no                  no          family member   \n",
       "4  White European       no                 yes          family member   \n",
       "\n",
       "  Class/ASD Traits   \n",
       "0                No  \n",
       "1               Yes  \n",
       "2               Yes  \n",
       "3               Yes  \n",
       "4               Yes  "
      ]
     },
     "execution_count": 1037,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1038,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['yes', 'no'], dtype=object)"
      ]
     },
     "execution_count": 1038,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Jaundice'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1039,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['family member', 'Health Care Professional',\n",
       "       'Health care professional', 'Self', 'Others'], dtype=object)"
      ]
     },
     "execution_count": 1039,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Who completed the test'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1040,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no', 'yes'], dtype=object)"
      ]
     },
     "execution_count": 1040,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Family_mem_with_ASD'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1041,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1054.000000\n",
       "mean       27.867173\n",
       "std         7.980354\n",
       "min        12.000000\n",
       "25%        23.000000\n",
       "50%        30.000000\n",
       "75%        36.000000\n",
       "max        36.000000\n",
       "Name: Age_Mons, dtype: float64"
      ]
     },
     "execution_count": 1041,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Age_Mons'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1042,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Age_Mons'] = data1['Age_Mons']//12 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1043,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 1], dtype=int64)"
      ]
     },
     "execution_count": 1043,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Age_Mons'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1044,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>m</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>f</td>\n",
       "      <td>White European</td>\n",
       "      <td>no</td>\n",
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      ],
      "text/plain": [
       "   A1  A2  A3  A4  A5  A6  A7  A8  A9  A10  Age_Mons  Qchat-10-Score Sex  \\\n",
       "0   0   0   0   0   0   0   1   1   0    1         2               3   f   \n",
       "1   1   1   0   0   0   1   1   0   0    0         3               4   m   \n",
       "2   1   0   0   0   0   0   1   1   0    1         3               4   m   \n",
       "3   1   1   1   1   1   1   1   1   1    1         2              10   m   \n",
       "4   1   1   0   1   1   1   1   1   1    1         1               9   f   \n",
       "\n",
       "        Ethnicity Jaundice Family_mem_with_ASD Who completed the test  \\\n",
       "0  middle eastern      yes                  no          family member   \n",
       "1  White European      yes                  no          family member   \n",
       "2  middle eastern      yes                  no          family member   \n",
       "3        Hispanic       no                  no          family member   \n",
       "4  White European       no                 yes          family member   \n",
       "\n",
       "  Class/ASD Traits   \n",
       "0                No  \n",
       "1               Yes  \n",
       "2               Yes  \n",
       "3               Yes  \n",
       "4               Yes  "
      ]
     },
     "execution_count": 1044,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1045,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = data1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1046,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.impute import SimpleImputer\n",
    "imp = SimpleImputer(missing_values='?', strategy='most_frequent')\n",
    "data1 = pd.DataFrame(imp.fit_transform(data1),columns = columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1047,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>Age_Mons</th>\n",
       "      <th>Qchat-10-Score</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Jaundice</th>\n",
       "      <th>Family_mem_with_ASD</th>\n",
       "      <th>Who completed the test</th>\n",
       "      <th>Class/ASD Traits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>f</td>\n",
       "      <td>middle eastern</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>m</td>\n",
       "      <td>White European</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>m</td>\n",
       "      <td>middle eastern</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>m</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>f</td>\n",
       "      <td>White European</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>family member</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 Age_Mons Qchat-10-Score Sex       Ethnicity  \\\n",
       "0  0  0  0  0  0  0  1  1  0   1        2              3   f  middle eastern   \n",
       "1  1  1  0  0  0  1  1  0  0   0        3              4   m  White European   \n",
       "2  1  0  0  0  0  0  1  1  0   1        3              4   m  middle eastern   \n",
       "3  1  1  1  1  1  1  1  1  1   1        2             10   m        Hispanic   \n",
       "4  1  1  0  1  1  1  1  1  1   1        1              9   f  White European   \n",
       "\n",
       "  Jaundice Family_mem_with_ASD Who completed the test Class/ASD Traits   \n",
       "0      yes                  no          family member                No  \n",
       "1      yes                  no          family member               Yes  \n",
       "2      yes                  no          family member               Yes  \n",
       "3       no                  no          family member               Yes  \n",
       "4       no                 yes          family member               Yes  "
      ]
     },
     "execution_count": 1047,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1048,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Class/ASD Traits '].replace(['No', 'Yes'],[0,1],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1049,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 1049,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Class/ASD Traits '].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1050,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Sex'].replace(['f', 'm'],[0,1],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1051,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Jaundice'].replace(['yes', 'no'],[0,1],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1052,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Family_mem_with_ASD'].replace(['yes', 'no'],[0,1],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1053,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['family member', 'Health Care Professional',\n",
       "       'Health care professional', 'Self', 'Others'], dtype=object)"
      ]
     },
     "execution_count": 1053,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Who completed the test'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1054,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 0, 1, 3, 2])"
      ]
     },
     "execution_count": 1054,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder = preprocessing.LabelEncoder() \n",
    "data1['Who completed the test']= label_encoder.fit_transform(data1['Who completed the test']) \n",
    "data1['Who completed the test'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1055,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1                        object\n",
       "A2                        object\n",
       "A3                        object\n",
       "A4                        object\n",
       "A5                        object\n",
       "A6                        object\n",
       "A7                        object\n",
       "A8                        object\n",
       "A9                        object\n",
       "A10                       object\n",
       "Age_Mons                  object\n",
       "Qchat-10-Score            object\n",
       "Sex                        int64\n",
       "Ethnicity                 object\n",
       "Jaundice                   int64\n",
       "Family_mem_with_ASD        int64\n",
       "Who completed the test     int32\n",
       "Class/ASD Traits           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 1055,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1056,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10', 'Age_Mons',\n",
       "       'Qchat-10-Score', 'Sex', 'Ethnicity', 'Jaundice', 'Family_mem_with_ASD',\n",
       "       'Who completed the test', 'Class/ASD Traits '],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 1056,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1057,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['middle eastern', 'White European', 'Hispanic', 'black', 'asian',\n",
       "       'south asian', 'Native Indian', 'Others', 'Latino', 'mixed',\n",
       "       'Pacifica'], dtype=object)"
      ]
     },
     "execution_count": 1057,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Ethnicity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1058,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8,  5,  0,  7,  6, 10,  2,  3,  1,  9,  4])"
      ]
     },
     "execution_count": 1058,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Ethnicity']= label_encoder.fit_transform(data1['Ethnicity']) \n",
    "data1['Ethnicity'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1059,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1                        object\n",
       "A2                        object\n",
       "A3                        object\n",
       "A4                        object\n",
       "A5                        object\n",
       "A6                        object\n",
       "A7                        object\n",
       "A8                        object\n",
       "A9                        object\n",
       "A10                       object\n",
       "Age_Mons                  object\n",
       "Qchat-10-Score            object\n",
       "Sex                        int64\n",
       "Ethnicity                  int32\n",
       "Jaundice                   int64\n",
       "Family_mem_with_ASD        int64\n",
       "Who completed the test     int32\n",
       "Class/ASD Traits           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 1059,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1060,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,11):\n",
    "    data1.iloc[:,i] = data1.iloc[:,i].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1061,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1                         int32\n",
       "A2                         int32\n",
       "A3                         int32\n",
       "A4                         int32\n",
       "A5                         int32\n",
       "A6                         int32\n",
       "A7                         int32\n",
       "A8                         int32\n",
       "A9                         int32\n",
       "A10                        int32\n",
       "Age_Mons                   int32\n",
       "Qchat-10-Score            object\n",
       "Sex                        int64\n",
       "Ethnicity                  int32\n",
       "Jaundice                   int64\n",
       "Family_mem_with_ASD        int64\n",
       "Who completed the test     int32\n",
       "Class/ASD Traits           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 1061,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1062,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1['Qchat-10-Score'] = data1['Qchat-10-Score'].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1063,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  4, 10,  9,  8,  5,  6,  2,  0,  7,  1])"
      ]
     },
     "execution_count": 1063,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['Qchat-10-Score'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1064,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1                        int32\n",
       "A2                        int32\n",
       "A3                        int32\n",
       "A4                        int32\n",
       "A5                        int32\n",
       "A6                        int32\n",
       "A7                        int32\n",
       "A8                        int32\n",
       "A9                        int32\n",
       "A10                       int32\n",
       "Age_Mons                  int32\n",
       "Qchat-10-Score            int32\n",
       "Sex                       int64\n",
       "Ethnicity                 int32\n",
       "Jaundice                  int64\n",
       "Family_mem_with_ASD       int64\n",
       "Who completed the test    int32\n",
       "Class/ASD Traits          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 1064,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1065,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = data1.iloc[:,0:17]\n",
    "y1 = data1.iloc[:,17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1066,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>Age_Mons</th>\n",
       "      <th>Qchat-10-Score</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Jaundice</th>\n",
       "      <th>Family_mem_with_ASD</th>\n",
       "      <th>Who completed the test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1052</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1054 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A1  A2  A3  A4  A5  A6  A7  A8  A9  A10  Age_Mons  Qchat-10-Score  Sex  \\\n",
       "0      0   0   0   0   0   0   1   1   0    1         2               3    0   \n",
       "1      1   1   0   0   0   1   1   0   0    0         3               4    1   \n",
       "2      1   0   0   0   0   0   1   1   0    1         3               4    1   \n",
       "3      1   1   1   1   1   1   1   1   1    1         2              10    1   \n",
       "4      1   1   0   1   1   1   1   1   1    1         1               9    0   \n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ...       ...             ...  ...   \n",
       "1049   0   0   0   0   0   0   0   0   0    1         2               1    0   \n",
       "1050   0   0   1   1   1   0   1   0   1    0         1               5    1   \n",
       "1051   1   0   1   1   1   1   1   1   1    1         1               9    1   \n",
       "1052   1   0   0   0   0   0   0   1   0    1         1               3    1   \n",
       "1053   1   1   0   0   1   1   0   1   1    0         2               6    1   \n",
       "\n",
       "      Ethnicity  Jaundice  Family_mem_with_ASD  Who completed the test  \n",
       "0             8         0                    1                       4  \n",
       "1             5         0                    1                       4  \n",
       "2             8         0                    1                       4  \n",
       "3             0         1                    1                       4  \n",
       "4             5         1                    0                       4  \n",
       "...         ...       ...                  ...                     ...  \n",
       "1049          5         1                    0                       4  \n",
       "1050          7         0                    1                       4  \n",
       "1051          8         0                    1                       4  \n",
       "1052          5         1                    0                       4  \n",
       "1053          6         0                    0                       4  \n",
       "\n",
       "[1054 rows x 17 columns]"
      ]
     },
     "execution_count": 1066,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "1049    0\n",
       "1050    1\n",
       "1051    1\n",
       "1052    0\n",
       "1053    1\n",
       "Name: Class/ASD Traits , Length: 1054, dtype: int64"
      ]
     },
     "execution_count": 1251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1068,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([64, 13, 56, 22, 43,  9, 14,  7,  3, 36, 33, 62,  0, 38, 63, 55, 34,\n",
       "       47, 10, 16, 25, 17,  6, 15, 52, 42, 50, 58, 59, 46, 29, 48, 57, 54,\n",
       "       23, 66, 31, 18, 26, 40, 51,  4, 28, 44, 27, 35, 61, 37,  1, 65, 53,\n",
       "       49, 39, 21, 45, 11, 12,  5, 24, 60, 41, 30,  2,  8, 32, 20, 19])"
      ]
     },
     "execution_count": 1068,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing \n",
    "label_encoder = preprocessing.LabelEncoder() \n",
    "data['contry_of_res']= label_encoder.fit_transform(data['contry_of_res']) \n",
    "data['contry_of_res'].unique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1069,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['ethnicity']  = label_encoder.fit_transform(data['ethnicity']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1070,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['relation'] = label_encoder.fit_transform(data['relation'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1071,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9,  3,  5,  1,  0,  4,  6,  7,  2,  8, 10])"
      ]
     },
     "execution_count": 1071,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['ethnicity'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1072,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 0, 3, 1])"
      ]
     },
     "execution_count": 1072,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['relation'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1073,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('contry_of_res',inplace = True,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1074,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(704, 18)"
      ]
     },
     "execution_count": 1074,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1075,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1_Score     object\n",
       "A2_Score     object\n",
       "A3_Score     object\n",
       "A4_Score     object\n",
       "A5_Score     object\n",
       "A6_Score     object\n",
       "A7_Score     object\n",
       "A8_Score     object\n",
       "A9_Score     object\n",
       "A10_Score    object\n",
       "age          object\n",
       "result       object\n",
       "gender        int64\n",
       "ethnicity     int32\n",
       "jundice       int64\n",
       "austim        int64\n",
       "relation      int32\n",
       "Class/ASD     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 1075,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1076,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,11):\n",
    "    data.iloc[:,i] = data.iloc[:,i].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1077,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1_Score      int32\n",
       "A2_Score      int32\n",
       "A3_Score      int32\n",
       "A4_Score      int32\n",
       "A5_Score      int32\n",
       "A6_Score      int32\n",
       "A7_Score      int32\n",
       "A8_Score      int32\n",
       "A9_Score      int32\n",
       "A10_Score     int32\n",
       "age           int32\n",
       "result       object\n",
       "gender        int64\n",
       "ethnicity     int32\n",
       "jundice       int64\n",
       "austim        int64\n",
       "relation      int32\n",
       "Class/ASD     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 1077,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1078,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['result'] = data['result'].astype('int') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1079,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1_Score     int32\n",
       "A2_Score     int32\n",
       "A3_Score     int32\n",
       "A4_Score     int32\n",
       "A5_Score     int32\n",
       "A6_Score     int32\n",
       "A7_Score     int32\n",
       "A8_Score     int32\n",
       "A9_Score     int32\n",
       "A10_Score    int32\n",
       "age          int32\n",
       "result       int32\n",
       "gender       int64\n",
       "ethnicity    int32\n",
       "jundice      int64\n",
       "austim       int64\n",
       "relation     int32\n",
       "Class/ASD    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 1079,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1080,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>A10_Score</th>\n",
       "      <th>age</th>\n",
       "      <th>result</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>jundice</th>\n",
       "      <th>austim</th>\n",
       "      <th>relation</th>\n",
       "      <th>Class/ASD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>699</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>700</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>701</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>703</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>704 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
       "0           1         1         1         1         0         0         1   \n",
       "1           1         1         0         1         0         0         0   \n",
       "2           1         1         0         1         1         0         1   \n",
       "3           1         1         0         1         0         0         1   \n",
       "4           1         0         0         0         0         0         0   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "699         0         1         0         1         1         0         1   \n",
       "700         1         0         0         0         0         0         0   \n",
       "701         1         0         1         1         1         0         1   \n",
       "702         1         0         0         1         1         0         1   \n",
       "703         1         0         1         1         1         0         1   \n",
       "\n",
       "     A8_Score  A9_Score  A10_Score  age  result  gender  ethnicity  jundice  \\\n",
       "0           1         0          0   26       6       0          9        0   \n",
       "1           1         0          1   24       5       1          3        0   \n",
       "2           1         1          1   27       8       1          3        1   \n",
       "3           1         0          1   35       6       0          9        0   \n",
       "4           1         0          0   40       2       0          9        0   \n",
       "..        ...       ...        ...  ...     ...     ...        ...      ...   \n",
       "699         1         1          1   25       7       0          9        0   \n",
       "700         1         0          1   34       3       1          2        0   \n",
       "701         1         0          1   24       7       0          9        0   \n",
       "702         0         1          1   35       6       1          7        0   \n",
       "703         1         1          1   26       8       0          9        0   \n",
       "\n",
       "     austim  relation  Class/ASD  \n",
       "0         0         4          0  \n",
       "1         1         4          0  \n",
       "2         1         2          1  \n",
       "3         1         4          0  \n",
       "4         0         4          0  \n",
       "..      ...       ...        ...  \n",
       "699       0         4          1  \n",
       "700       0         2          0  \n",
       "701       0         4          1  \n",
       "702       0         4          0  \n",
       "703       0         4          1  \n",
       "\n",
       "[704 rows x 18 columns]"
      ]
     },
     "execution_count": 1080,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1081,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1_Score</th>\n",
       "      <th>A2_Score</th>\n",
       "      <th>A3_Score</th>\n",
       "      <th>A4_Score</th>\n",
       "      <th>A5_Score</th>\n",
       "      <th>A6_Score</th>\n",
       "      <th>A7_Score</th>\n",
       "      <th>A8_Score</th>\n",
       "      <th>A9_Score</th>\n",
       "      <th>A10_Score</th>\n",
       "      <th>age</th>\n",
       "      <th>result</th>\n",
       "      <th>gender</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>jundice</th>\n",
       "      <th>austim</th>\n",
       "      <th>relation</th>\n",
       "      <th>Class/ASD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1_Score</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011539</td>\n",
       "      <td>0.074096</td>\n",
       "      <td>0.127814</td>\n",
       "      <td>0.169369</td>\n",
       "      <td>0.110199</td>\n",
       "      <td>0.217538</td>\n",
       "      <td>0.147640</td>\n",
       "      <td>0.145452</td>\n",
       "      <td>0.118413</td>\n",
       "      <td>0.025379</td>\n",
       "      <td>0.397454</td>\n",
       "      <td>-0.075011</td>\n",
       "      <td>0.053684</td>\n",
       "      <td>-0.019077</td>\n",
       "      <td>0.097630</td>\n",
       "      <td>0.008398</td>\n",
       "      <td>0.297628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2_Score</th>\n",
       "      <td>0.011539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.223921</td>\n",
       "      <td>0.158998</td>\n",
       "      <td>0.153821</td>\n",
       "      <td>0.185864</td>\n",
       "      <td>-0.041768</td>\n",
       "      <td>0.035408</td>\n",
       "      <td>0.205421</td>\n",
       "      <td>0.068883</td>\n",
       "      <td>0.020644</td>\n",
       "      <td>0.392540</td>\n",
       "      <td>-0.047393</td>\n",
       "      <td>0.128518</td>\n",
       "      <td>0.112615</td>\n",
       "      <td>0.074555</td>\n",
       "      <td>-0.047792</td>\n",
       "      <td>0.311382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3_Score</th>\n",
       "      <td>0.074096</td>\n",
       "      <td>0.223921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.412722</td>\n",
       "      <td>0.264927</td>\n",
       "      <td>0.268846</td>\n",
       "      <td>0.078216</td>\n",
       "      <td>0.017771</td>\n",
       "      <td>0.315113</td>\n",
       "      <td>0.168454</td>\n",
       "      <td>0.030833</td>\n",
       "      <td>0.552356</td>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.110339</td>\n",
       "      <td>0.061760</td>\n",
       "      <td>0.113697</td>\n",
       "      <td>0.029318</td>\n",
       "      <td>0.441074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4_Score</th>\n",
       "      <td>0.127814</td>\n",
       "      <td>0.158998</td>\n",
       "      <td>0.412722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.306806</td>\n",
       "      <td>0.295152</td>\n",
       "      <td>0.151236</td>\n",
       "      <td>0.008617</td>\n",
       "      <td>0.327673</td>\n",
       "      <td>0.210968</td>\n",
       "      <td>0.033967</td>\n",
       "      <td>0.586025</td>\n",
       "      <td>-0.056508</td>\n",
       "      <td>0.140787</td>\n",
       "      <td>0.064918</td>\n",
       "      <td>0.193820</td>\n",
       "      <td>-0.013804</td>\n",
       "      <td>0.469945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5_Score</th>\n",
       "      <td>0.169369</td>\n",
       "      <td>0.153821</td>\n",
       "      <td>0.264927</td>\n",
       "      <td>0.306806</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.392354</td>\n",
       "      <td>0.238589</td>\n",
       "      <td>0.102086</td>\n",
       "      <td>0.396582</td>\n",
       "      <td>0.267561</td>\n",
       "      <td>-0.025054</td>\n",
       "      <td>0.639706</td>\n",
       "      <td>-0.039688</td>\n",
       "      <td>0.009535</td>\n",
       "      <td>0.034378</td>\n",
       "      <td>0.090009</td>\n",
       "      <td>0.048976</td>\n",
       "      <td>0.537004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A6_Score</th>\n",
       "      <td>0.110199</td>\n",
       "      <td>0.185864</td>\n",
       "      <td>0.268846</td>\n",
       "      <td>0.295152</td>\n",
       "      <td>0.392354</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.175489</td>\n",
       "      <td>0.100123</td>\n",
       "      <td>0.479422</td>\n",
       "      <td>0.294435</td>\n",
       "      <td>0.035617</td>\n",
       "      <td>0.630012</td>\n",
       "      <td>-0.083615</td>\n",
       "      <td>0.108745</td>\n",
       "      <td>0.078366</td>\n",
       "      <td>0.114048</td>\n",
       "      <td>-0.090688</td>\n",
       "      <td>0.592091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A7_Score</th>\n",
       "      <td>0.217538</td>\n",
       "      <td>-0.041768</td>\n",
       "      <td>0.078216</td>\n",
       "      <td>0.151236</td>\n",
       "      <td>0.238589</td>\n",
       "      <td>0.175489</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.085403</td>\n",
       "      <td>0.189480</td>\n",
       "      <td>0.252107</td>\n",
       "      <td>-0.026734</td>\n",
       "      <td>0.454848</td>\n",
       "      <td>0.061900</td>\n",
       "      <td>-0.002004</td>\n",
       "      <td>0.030850</td>\n",
       "      <td>-0.008610</td>\n",
       "      <td>0.032594</td>\n",
       "      <td>0.351429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A8_Score</th>\n",
       "      <td>0.147640</td>\n",
       "      <td>0.035408</td>\n",
       "      <td>0.017771</td>\n",
       "      <td>0.008617</td>\n",
       "      <td>0.102086</td>\n",
       "      <td>0.100123</td>\n",
       "      <td>0.085403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.101733</td>\n",
       "      <td>0.100782</td>\n",
       "      <td>-0.078154</td>\n",
       "      <td>0.324020</td>\n",
       "      <td>0.064127</td>\n",
       "      <td>-0.048702</td>\n",
       "      <td>0.012101</td>\n",
       "      <td>0.034845</td>\n",
       "      <td>0.051040</td>\n",
       "      <td>0.237161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A9_Score</th>\n",
       "      <td>0.145452</td>\n",
       "      <td>0.205421</td>\n",
       "      <td>0.315113</td>\n",
       "      <td>0.327673</td>\n",
       "      <td>0.396582</td>\n",
       "      <td>0.479422</td>\n",
       "      <td>0.189480</td>\n",
       "      <td>0.101733</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.283340</td>\n",
       "      <td>0.054984</td>\n",
       "      <td>0.661205</td>\n",
       "      <td>0.006940</td>\n",
       "      <td>0.081052</td>\n",
       "      <td>0.057717</td>\n",
       "      <td>0.167644</td>\n",
       "      <td>-0.058046</td>\n",
       "      <td>0.635576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A10_Score</th>\n",
       "      <td>0.118413</td>\n",
       "      <td>0.068883</td>\n",
       "      <td>0.168454</td>\n",
       "      <td>0.210968</td>\n",
       "      <td>0.267561</td>\n",
       "      <td>0.294435</td>\n",
       "      <td>0.252107</td>\n",
       "      <td>0.100782</td>\n",
       "      <td>0.283340</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007846</td>\n",
       "      <td>0.537205</td>\n",
       "      <td>-0.055246</td>\n",
       "      <td>0.059843</td>\n",
       "      <td>0.052201</td>\n",
       "      <td>0.117969</td>\n",
       "      <td>0.027427</td>\n",
       "      <td>0.385917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.025379</td>\n",
       "      <td>0.020644</td>\n",
       "      <td>0.030833</td>\n",
       "      <td>0.033967</td>\n",
       "      <td>-0.025054</td>\n",
       "      <td>0.035617</td>\n",
       "      <td>-0.026734</td>\n",
       "      <td>-0.078154</td>\n",
       "      <td>0.054984</td>\n",
       "      <td>-0.007846</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>-0.051266</td>\n",
       "      <td>0.083560</td>\n",
       "      <td>0.065387</td>\n",
       "      <td>0.095511</td>\n",
       "      <td>-0.028200</td>\n",
       "      <td>0.060054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>result</th>\n",
       "      <td>0.397454</td>\n",
       "      <td>0.392540</td>\n",
       "      <td>0.552356</td>\n",
       "      <td>0.586025</td>\n",
       "      <td>0.639706</td>\n",
       "      <td>0.630012</td>\n",
       "      <td>0.454848</td>\n",
       "      <td>0.324020</td>\n",
       "      <td>0.661205</td>\n",
       "      <td>0.537205</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.042231</td>\n",
       "      <td>0.124213</td>\n",
       "      <td>0.094842</td>\n",
       "      <td>0.192033</td>\n",
       "      <td>-0.000760</td>\n",
       "      <td>0.821445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>-0.075011</td>\n",
       "      <td>-0.047393</td>\n",
       "      <td>0.000795</td>\n",
       "      <td>-0.056508</td>\n",
       "      <td>-0.039688</td>\n",
       "      <td>-0.083615</td>\n",
       "      <td>0.061900</td>\n",
       "      <td>0.064127</td>\n",
       "      <td>0.006940</td>\n",
       "      <td>-0.055246</td>\n",
       "      <td>-0.051266</td>\n",
       "      <td>-0.042231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.091333</td>\n",
       "      <td>-0.018842</td>\n",
       "      <td>-0.088477</td>\n",
       "      <td>-0.065708</td>\n",
       "      <td>-0.080378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ethnicity</th>\n",
       "      <td>0.053684</td>\n",
       "      <td>0.128518</td>\n",
       "      <td>0.110339</td>\n",
       "      <td>0.140787</td>\n",
       "      <td>0.009535</td>\n",
       "      <td>0.108745</td>\n",
       "      <td>-0.002004</td>\n",
       "      <td>-0.048702</td>\n",
       "      <td>0.081052</td>\n",
       "      <td>0.059843</td>\n",
       "      <td>0.083560</td>\n",
       "      <td>0.124213</td>\n",
       "      <td>-0.091333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.074951</td>\n",
       "      <td>0.117392</td>\n",
       "      <td>0.093633</td>\n",
       "      <td>0.144476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jundice</th>\n",
       "      <td>-0.019077</td>\n",
       "      <td>0.112615</td>\n",
       "      <td>0.061760</td>\n",
       "      <td>0.064918</td>\n",
       "      <td>0.034378</td>\n",
       "      <td>0.078366</td>\n",
       "      <td>0.030850</td>\n",
       "      <td>0.012101</td>\n",
       "      <td>0.057717</td>\n",
       "      <td>0.052201</td>\n",
       "      <td>0.065387</td>\n",
       "      <td>0.094842</td>\n",
       "      <td>-0.018842</td>\n",
       "      <td>0.074951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.157793</td>\n",
       "      <td>-0.098007</td>\n",
       "      <td>0.102152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>austim</th>\n",
       "      <td>0.097630</td>\n",
       "      <td>0.074555</td>\n",
       "      <td>0.113697</td>\n",
       "      <td>0.193820</td>\n",
       "      <td>0.090009</td>\n",
       "      <td>0.114048</td>\n",
       "      <td>-0.008610</td>\n",
       "      <td>0.034845</td>\n",
       "      <td>0.167644</td>\n",
       "      <td>0.117969</td>\n",
       "      <td>0.095511</td>\n",
       "      <td>0.192033</td>\n",
       "      <td>-0.088477</td>\n",
       "      <td>0.117392</td>\n",
       "      <td>0.157793</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.119433</td>\n",
       "      <td>0.177415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relation</th>\n",
       "      <td>0.008398</td>\n",
       "      <td>-0.047792</td>\n",
       "      <td>0.029318</td>\n",
       "      <td>-0.013804</td>\n",
       "      <td>0.048976</td>\n",
       "      <td>-0.090688</td>\n",
       "      <td>0.032594</td>\n",
       "      <td>0.051040</td>\n",
       "      <td>-0.058046</td>\n",
       "      <td>0.027427</td>\n",
       "      <td>-0.028200</td>\n",
       "      <td>-0.000760</td>\n",
       "      <td>-0.065708</td>\n",
       "      <td>0.093633</td>\n",
       "      <td>-0.098007</td>\n",
       "      <td>-0.119433</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class/ASD</th>\n",
       "      <td>0.297628</td>\n",
       "      <td>0.311382</td>\n",
       "      <td>0.441074</td>\n",
       "      <td>0.469945</td>\n",
       "      <td>0.537004</td>\n",
       "      <td>0.592091</td>\n",
       "      <td>0.351429</td>\n",
       "      <td>0.237161</td>\n",
       "      <td>0.635576</td>\n",
       "      <td>0.385917</td>\n",
       "      <td>0.060054</td>\n",
       "      <td>0.821445</td>\n",
       "      <td>-0.080378</td>\n",
       "      <td>0.144476</td>\n",
       "      <td>0.102152</td>\n",
       "      <td>0.177415</td>\n",
       "      <td>0.013165</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  \\\n",
       "A1_Score   1.000000  0.011539  0.074096  0.127814  0.169369  0.110199   \n",
       "A2_Score   0.011539  1.000000  0.223921  0.158998  0.153821  0.185864   \n",
       "A3_Score   0.074096  0.223921  1.000000  0.412722  0.264927  0.268846   \n",
       "A4_Score   0.127814  0.158998  0.412722  1.000000  0.306806  0.295152   \n",
       "A5_Score   0.169369  0.153821  0.264927  0.306806  1.000000  0.392354   \n",
       "A6_Score   0.110199  0.185864  0.268846  0.295152  0.392354  1.000000   \n",
       "A7_Score   0.217538 -0.041768  0.078216  0.151236  0.238589  0.175489   \n",
       "A8_Score   0.147640  0.035408  0.017771  0.008617  0.102086  0.100123   \n",
       "A9_Score   0.145452  0.205421  0.315113  0.327673  0.396582  0.479422   \n",
       "A10_Score  0.118413  0.068883  0.168454  0.210968  0.267561  0.294435   \n",
       "age        0.025379  0.020644  0.030833  0.033967 -0.025054  0.035617   \n",
       "result     0.397454  0.392540  0.552356  0.586025  0.639706  0.630012   \n",
       "gender    -0.075011 -0.047393  0.000795 -0.056508 -0.039688 -0.083615   \n",
       "ethnicity  0.053684  0.128518  0.110339  0.140787  0.009535  0.108745   \n",
       "jundice   -0.019077  0.112615  0.061760  0.064918  0.034378  0.078366   \n",
       "austim     0.097630  0.074555  0.113697  0.193820  0.090009  0.114048   \n",
       "relation   0.008398 -0.047792  0.029318 -0.013804  0.048976 -0.090688   \n",
       "Class/ASD  0.297628  0.311382  0.441074  0.469945  0.537004  0.592091   \n",
       "\n",
       "           A7_Score  A8_Score  A9_Score  A10_Score       age    result  \\\n",
       "A1_Score   0.217538  0.147640  0.145452   0.118413  0.025379  0.397454   \n",
       "A2_Score  -0.041768  0.035408  0.205421   0.068883  0.020644  0.392540   \n",
       "A3_Score   0.078216  0.017771  0.315113   0.168454  0.030833  0.552356   \n",
       "A4_Score   0.151236  0.008617  0.327673   0.210968  0.033967  0.586025   \n",
       "A5_Score   0.238589  0.102086  0.396582   0.267561 -0.025054  0.639706   \n",
       "A6_Score   0.175489  0.100123  0.479422   0.294435  0.035617  0.630012   \n",
       "A7_Score   1.000000  0.085403  0.189480   0.252107 -0.026734  0.454848   \n",
       "A8_Score   0.085403  1.000000  0.101733   0.100782 -0.078154  0.324020   \n",
       "A9_Score   0.189480  0.101733  1.000000   0.283340  0.054984  0.661205   \n",
       "A10_Score  0.252107  0.100782  0.283340   1.000000 -0.007846  0.537205   \n",
       "age       -0.026734 -0.078154  0.054984  -0.007846  1.000000  0.011561   \n",
       "result     0.454848  0.324020  0.661205   0.537205  0.011561  1.000000   \n",
       "gender     0.061900  0.064127  0.006940  -0.055246 -0.051266 -0.042231   \n",
       "ethnicity -0.002004 -0.048702  0.081052   0.059843  0.083560  0.124213   \n",
       "jundice    0.030850  0.012101  0.057717   0.052201  0.065387  0.094842   \n",
       "austim    -0.008610  0.034845  0.167644   0.117969  0.095511  0.192033   \n",
       "relation   0.032594  0.051040 -0.058046   0.027427 -0.028200 -0.000760   \n",
       "Class/ASD  0.351429  0.237161  0.635576   0.385917  0.060054  0.821445   \n",
       "\n",
       "             gender  ethnicity   jundice    austim  relation  Class/ASD  \n",
       "A1_Score  -0.075011   0.053684 -0.019077  0.097630  0.008398   0.297628  \n",
       "A2_Score  -0.047393   0.128518  0.112615  0.074555 -0.047792   0.311382  \n",
       "A3_Score   0.000795   0.110339  0.061760  0.113697  0.029318   0.441074  \n",
       "A4_Score  -0.056508   0.140787  0.064918  0.193820 -0.013804   0.469945  \n",
       "A5_Score  -0.039688   0.009535  0.034378  0.090009  0.048976   0.537004  \n",
       "A6_Score  -0.083615   0.108745  0.078366  0.114048 -0.090688   0.592091  \n",
       "A7_Score   0.061900  -0.002004  0.030850 -0.008610  0.032594   0.351429  \n",
       "A8_Score   0.064127  -0.048702  0.012101  0.034845  0.051040   0.237161  \n",
       "A9_Score   0.006940   0.081052  0.057717  0.167644 -0.058046   0.635576  \n",
       "A10_Score -0.055246   0.059843  0.052201  0.117969  0.027427   0.385917  \n",
       "age       -0.051266   0.083560  0.065387  0.095511 -0.028200   0.060054  \n",
       "result    -0.042231   0.124213  0.094842  0.192033 -0.000760   0.821445  \n",
       "gender     1.000000  -0.091333 -0.018842 -0.088477 -0.065708  -0.080378  \n",
       "ethnicity -0.091333   1.000000  0.074951  0.117392  0.093633   0.144476  \n",
       "jundice   -0.018842   0.074951  1.000000  0.157793 -0.098007   0.102152  \n",
       "austim    -0.088477   0.117392  0.157793  1.000000 -0.119433   0.177415  \n",
       "relation  -0.065708   0.093633 -0.098007 -0.119433  1.000000   0.013165  \n",
       "Class/ASD -0.080378   0.144476  0.102152  0.177415  0.013165   1.000000  "
      ]
     },
     "execution_count": 1081,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1082,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,0:17]\n",
    "y = data.iloc[:,17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1083,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>A1_Score</th>\n",
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       "<p>704 rows × 17 columns</p>\n",
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      "text/plain": [
       "     A1_Score  A2_Score  A3_Score  A4_Score  A5_Score  A6_Score  A7_Score  \\\n",
       "0           1         1         1         1         0         0         1   \n",
       "1           1         1         0         1         0         0         0   \n",
       "2           1         1         0         1         1         0         1   \n",
       "3           1         1         0         1         0         0         1   \n",
       "4           1         0         0         0         0         0         0   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "699         0         1         0         1         1         0         1   \n",
       "700         1         0         0         0         0         0         0   \n",
       "701         1         0         1         1         1         0         1   \n",
       "702         1         0         0         1         1         0         1   \n",
       "703         1         0         1         1         1         0         1   \n",
       "\n",
       "     A8_Score  A9_Score  A10_Score  age  result  gender  ethnicity  jundice  \\\n",
       "0           1         0          0   26       6       0          9        0   \n",
       "1           1         0          1   24       5       1          3        0   \n",
       "2           1         1          1   27       8       1          3        1   \n",
       "3           1         0          1   35       6       0          9        0   \n",
       "4           1         0          0   40       2       0          9        0   \n",
       "..        ...       ...        ...  ...     ...     ...        ...      ...   \n",
       "699         1         1          1   25       7       0          9        0   \n",
       "700         1         0          1   34       3       1          2        0   \n",
       "701         1         0          1   24       7       0          9        0   \n",
       "702         0         1          1   35       6       1          7        0   \n",
       "703         1         1          1   26       8       0          9        0   \n",
       "\n",
       "     austim  relation  \n",
       "0         0         4  \n",
       "1         1         4  \n",
       "2         1         2  \n",
       "3         1         4  \n",
       "4         0         4  \n",
       "..      ...       ...  \n",
       "699       0         4  \n",
       "700       0         2  \n",
       "701       0         4  \n",
       "702       0         4  \n",
       "703       0         4  \n",
       "\n",
       "[704 rows x 17 columns]"
      ]
     },
     "execution_count": 1083,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1084,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "699    1\n",
       "700    0\n",
       "701    1\n",
       "702    0\n",
       "703    1\n",
       "Name: Class/ASD, Length: 704, dtype: int64"
      ]
     },
     "execution_count": 1084,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1085,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x155c622b1c8>"
      ]
     },
     "execution_count": 1085,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1086,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1087,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1088,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    515\n",
       "1    189\n",
       "Name: Class/ASD, dtype: int64"
      ]
     },
     "execution_count": 1088,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Class/ASD'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1089,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x155c8044108>"
      ]
     },
     "execution_count": 1089,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['Class/ASD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "from sklearn.model_selection import KFold\n",
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV,train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1235,
   "metadata": {},
   "outputs": [],
   "source": [
    "log = LogisticRegression(max_iter = 500)\n",
    "grid = {'C':[0.001,0.01,0.1],'penalty':['l1','l2'],'solver' : ['saga']}\n",
    "cv = KFold(n_splits = 5,random_state = None,shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1236,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = x1,x,y1,y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    515\n",
       "1    189\n",
       "Name: Class/ASD, dtype: int64"
      ]
     },
     "execution_count": 1237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    728\n",
       "0    326\n",
       "Name: Class/ASD Traits , dtype: int64"
      ]
     },
     "execution_count": 1238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
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       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>Age_Mons</th>\n",
       "      <th>Qchat-10-Score</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Jaundice</th>\n",
       "      <th>Family_mem_with_ASD</th>\n",
       "      <th>Who completed the test</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1054 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      A1  A2  A3  A4  A5  A6  A7  A8  A9  A10  Age_Mons  Qchat-10-Score  Sex  \\\n",
       "0      0   0   0   0   0   0   1   1   0    1         2               3    0   \n",
       "1      1   1   0   0   0   1   1   0   0    0         3               4    1   \n",
       "2      1   0   0   0   0   0   1   1   0    1         3               4    1   \n",
       "3      1   1   1   1   1   1   1   1   1    1         2              10    1   \n",
       "4      1   1   0   1   1   1   1   1   1    1         1               9    0   \n",
       "...   ..  ..  ..  ..  ..  ..  ..  ..  ..  ...       ...             ...  ...   \n",
       "1049   0   0   0   0   0   0   0   0   0    1         2               1    0   \n",
       "1050   0   0   1   1   1   0   1   0   1    0         1               5    1   \n",
       "1051   1   0   1   1   1   1   1   1   1    1         1               9    1   \n",
       "1052   1   0   0   0   0   0   0   1   0    1         1               3    1   \n",
       "1053   1   1   0   0   1   1   0   1   1    0         2               6    1   \n",
       "\n",
       "      Ethnicity  Jaundice  Family_mem_with_ASD  Who completed the test  \n",
       "0             8         0                    1                       4  \n",
       "1             5         0                    1                       4  \n",
       "2             8         0                    1                       4  \n",
       "3             0         1                    1                       4  \n",
       "4             5         1                    0                       4  \n",
       "...         ...       ...                  ...                     ...  \n",
       "1049          5         1                    0                       4  \n",
       "1050          7         0                    1                       4  \n",
       "1051          8         0                    1                       4  \n",
       "1052          5         1                    0                       4  \n",
       "1053          6         0                    0                       4  \n",
       "\n",
       "[1054 rows x 17 columns]"
      ]
     },
     "execution_count": 1239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       1\n",
       "       ..\n",
       "1049    0\n",
       "1050    1\n",
       "1051    1\n",
       "1052    0\n",
       "1053    1\n",
       "Name: Class/ASD Traits , Length: 1054, dtype: int64"
      ]
     },
     "execution_count": 1240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=True),\n",
       "             error_score=nan,\n",
       "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
       "                                          fit_intercept=True,\n",
       "                                          intercept_scaling=1, l1_ratio=None,\n",
       "                                          max_iter=500, multi_class='auto',\n",
       "                                          n_jobs=None, penalty='l2',\n",
       "                                          random_state=None, solver='lbfgs',\n",
       "                                          tol=0.0001, verbose=0,\n",
       "                                          warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'C': [0.001, 0.01, 0.1], 'penalty': ['l1', 'l2'],\n",
       "                         'solver': ['saga']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 1241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GridSearchCV(log,grid,cv = cv,n_jobs = -1,refit=True)\n",
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1242,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "      dtype=int64)"
      ]
     },
     "execution_count": 1243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[231 284]\n",
      " [  0 189]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5965909090909091\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.45      0.62       515\n",
      "           1       0.40      1.00      0.57       189\n",
      "\n",
      "    accuracy                           0.60       704\n",
      "   macro avg       0.70      0.72      0.60       704\n",
      "weighted avg       0.84      0.60      0.61       704\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma=1000, kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 1247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf_svc = SVC(kernel = 'rbf',gamma = 1000)\n",
    "clf_svc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 150 candidates, totalling 750 fits\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=2, gamma=1, kernel=rbf, score=0.730, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=2, gamma=1, kernel=rbf, score=0.682, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=2, gamma=1, kernel=rbf, score=0.668, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=2, gamma=1, kernel=rbf, score=0.701, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, degree=2, gamma=1, kernel=rbf, score=0.733, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=rbf, score=0.972, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=rbf, score=0.976, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=rbf .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=rbf, score=0.986, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=rbf, score=0.834, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=rbf, score=0.720, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=rbf, score=0.754, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=rbf, score=0.782, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=rbf, score=0.867, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=poly, score=0.720, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=poly, score=0.664, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=poly, score=0.668, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=poly, score=0.687, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=2, gamma=0.001, kernel=poly, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=2, gamma=100, kernel=rbf, score=0.730, total=   0.1s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=rbf ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, degree=2, gamma=100, kernel=rbf, score=0.664, total=   0.1s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=2, gamma=100, kernel=rbf, score=0.668, total=   0.1s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=2, gamma=100, kernel=rbf, score=0.701, total=   0.1s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=2, gamma=100, kernel=rbf, score=0.714, total=   0.1s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=3, gamma=1, kernel=rbf, score=0.730, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=3, gamma=1, kernel=rbf, score=0.682, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=3, gamma=1, kernel=rbf, score=0.668, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=3, gamma=1, kernel=rbf, score=0.701, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, degree=3, gamma=1, kernel=rbf, score=0.733, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=rbf, score=0.972, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=rbf, score=0.976, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=rbf .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=rbf, score=0.986, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=poly, score=0.981, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=poly, score=0.981, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=poly, score=0.976, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=rbf, score=0.834, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=rbf, score=0.720, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=rbf, score=0.754, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=rbf, score=0.782, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=rbf, score=0.867, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=poly, score=0.720, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=poly, score=0.664, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=poly, score=0.668, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=poly, score=0.687, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=3, gamma=0.001, kernel=poly, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=3, gamma=100, kernel=rbf, score=0.730, total=   0.1s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=rbf ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, degree=3, gamma=100, kernel=rbf, score=0.664, total=   0.1s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=3, gamma=100, kernel=rbf, score=0.668, total=   0.1s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=3, gamma=100, kernel=rbf, score=0.701, total=   0.1s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=rbf ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, degree=3, gamma=100, kernel=rbf, score=0.714, total=   0.1s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=3, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=4, gamma=1, kernel=rbf, score=0.730, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=4, gamma=1, kernel=rbf, score=0.682, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=4, gamma=1, kernel=rbf, score=0.668, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=0.1, degree=4, gamma=1, kernel=rbf, score=0.701, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, degree=4, gamma=1, kernel=rbf, score=0.733, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=0.1, degree=4, gamma=1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=rbf, score=0.972, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=rbf, score=0.976, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=rbf, score=0.986, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=poly, score=0.967, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=poly, score=0.986, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=poly, score=0.948, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=poly, score=0.967, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.01, kernel=poly, score=0.981, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=rbf, score=0.834, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=rbf, score=0.720, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=rbf, score=0.754, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=rbf, score=0.782, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=rbf, score=0.867, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=poly, score=0.720, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=poly, score=0.664, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=poly, score=0.668, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=poly, score=0.687, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=0.1, degree=4, gamma=0.001, kernel=poly, score=0.714, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=4, gamma=100, kernel=rbf, score=0.730, total=   0.1s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=4, gamma=100, kernel=rbf, score=0.664, total=   0.1s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=rbf ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.1, degree=4, gamma=100, kernel=rbf, score=0.668, total=   0.1s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=4, gamma=100, kernel=rbf, score=0.701, total=   0.1s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=0.1, degree=4, gamma=100, kernel=rbf, score=0.714, total=   0.1s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=0.1, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=0.1, degree=4, gamma=100, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=rbf ..............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=1, degree=2, gamma=1, kernel=rbf, score=0.877, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=2, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=2, gamma=1, kernel=rbf, score=0.886, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=2, gamma=1, kernel=rbf, score=0.853, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=2, gamma=1, kernel=rbf, score=0.919, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=rbf ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, degree=2, gamma=0.001, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=2, gamma=0.001, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=2, gamma=0.001, kernel=poly, score=0.735, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=2, gamma=0.001, kernel=poly, score=0.664, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=2, gamma=0.001, kernel=poly, score=0.668, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=2, gamma=0.001, kernel=poly, score=0.697, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=2, gamma=0.001, kernel=poly, score=0.738, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=2, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, degree=2, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=2, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=2, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=2, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=poly ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=2, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=3, gamma=1, kernel=rbf, score=0.877, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=3, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=3, gamma=1, kernel=rbf, score=0.886, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=3, gamma=1, kernel=rbf, score=0.853, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=3, gamma=1, kernel=rbf, score=0.919, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.01, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.01, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=3, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=3, gamma=0.01, kernel=poly, score=0.986, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=poly ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, degree=3, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=poly, score=0.720, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=poly, score=0.664, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=poly, score=0.668, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=poly, score=0.687, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=3, gamma=0.001, kernel=poly, score=0.714, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, degree=3, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=3, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=3, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, degree=3, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=3, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=3, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=rbf ..............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] .. C=1, degree=4, gamma=1, kernel=rbf, score=0.877, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=4, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=4, gamma=1, kernel=rbf, score=0.886, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=4, gamma=1, kernel=rbf, score=0.853, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=rbf ..............................\n",
      "[CV] .. C=1, degree=4, gamma=1, kernel=rbf, score=0.919, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=1, kernel=poly .............................\n",
      "[CV] . C=1, degree=4, gamma=1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=rbf ............................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.1, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=0.1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=4, gamma=0.01, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=rbf ...........................\n",
      "[CV]  C=1, degree=4, gamma=0.01, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=rbf ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.01, kernel=poly, score=0.986, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.01, kernel=poly, score=0.981, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.01, kernel=poly ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=rbf, score=0.991, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=rbf ..........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=poly, score=0.720, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=poly, score=0.664, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=poly, score=0.668, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=poly, score=0.687, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=0.001, kernel=poly .........................\n",
      "[CV]  C=1, degree=4, gamma=0.001, kernel=poly, score=0.714, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=4, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, degree=4, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=4, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=rbf ............................\n",
      "[CV]  C=1, degree=4, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1, degree=4, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1, degree=4, gamma=100, kernel=poly ...........................\n",
      "[CV]  C=1, degree=4, gamma=100, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=2, gamma=1, kernel=rbf, score=0.882, total=   0.1s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=2, gamma=1, kernel=rbf, score=0.910, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=rbf .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=10, degree=2, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=2, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=2, gamma=1, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=rbf .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=10, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=2, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=rbf ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=10, degree=2, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=2, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=2, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=rbf ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=10, degree=2, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=2, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=3, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=3, gamma=1, kernel=rbf, score=0.910, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=3, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=rbf .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=10, degree=3, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=3, gamma=1, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=3, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.1, kernel=poly ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=10, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=3, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=poly, score=0.796, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=poly, score=0.668, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=poly, score=0.697, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=poly, score=0.720, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=3, gamma=0.001, kernel=poly, score=0.786, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=3, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=3, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=3, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=rbf ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=10, degree=3, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=3, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=3, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=4, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=4, gamma=1, kernel=rbf, score=0.910, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=rbf .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=10, degree=4, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=4, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=rbf .............................\n",
      "[CV] . C=10, degree=4, gamma=1, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=1, kernel=poly ............................\n",
      "[CV]  C=10, degree=4, gamma=1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.1, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=rbf ..........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=poly, score=0.991, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.01, kernel=poly .........................\n",
      "[CV]  C=10, degree=4, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=rbf .........................\n",
      "[CV]  C=10, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=4, gamma=0.001, kernel=poly, score=0.720, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=4, gamma=0.001, kernel=poly, score=0.664, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=4, gamma=0.001, kernel=poly, score=0.668, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=poly ........................\n",
      "[CV]  C=10, degree=4, gamma=0.001, kernel=poly, score=0.687, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=0.001, kernel=poly ........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=10, degree=4, gamma=0.001, kernel=poly, score=0.714, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=4, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=4, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=rbf ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=10, degree=4, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=4, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=rbf ...........................\n",
      "[CV]  C=10, degree=4, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=10, degree=4, gamma=100, kernel=poly ..........................\n",
      "[CV]  C=10, degree=4, gamma=100, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=2, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=2, gamma=1, kernel=rbf, score=0.910, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=2, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=2, gamma=1, kernel=rbf, score=0.882, total=   0.1s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=2, gamma=1, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=rbf .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=rbf ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=2, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=2, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=2, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=rbf ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=2, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=2, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=2, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=3, gamma=1, kernel=rbf, score=0.882, total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] C=100, degree=3, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=3, gamma=1, kernel=rbf, score=0.910, total=   0.1s\n",
      "[CV] C=100, degree=3, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=3, gamma=1, kernel=rbf, score=0.891, total=   0.1s\n",
      "[CV] C=100, degree=3, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=3, gamma=1, kernel=rbf, score=0.882, total=   0.1s\n",
      "[CV] C=100, degree=3, gamma=1, kernel=rbf ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=3, gamma=1, kernel=rbf, score=0.924, total=   0.1s\n",
      "[CV] C=100, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=poly .......................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=3, gamma=0.001, kernel=poly, score=0.981, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=3, gamma=0.001, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=3, gamma=0.001, kernel=poly, score=0.981, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=3, gamma=0.001, kernel=poly, score=0.976, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=3, gamma=0.001, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=3, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=rbf ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=3, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=3, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=3, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=3, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=poly .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=3, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=rbf, score=0.910, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=rbf ............................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=1, kernel=poly ...........................\n",
      "[CV]  C=100, degree=4, gamma=1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.1, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=0.1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=rbf .........................\n",
      "[CV]  C=100, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=poly ........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.01, kernel=poly ........................\n",
      "[CV]  C=100, degree=4, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=rbf ........................\n",
      "[CV]  C=100, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=4, gamma=0.001, kernel=poly, score=0.801, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=4, gamma=0.001, kernel=poly, score=0.692, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=poly .......................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=4, gamma=0.001, kernel=poly, score=0.720, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=4, gamma=0.001, kernel=poly, score=0.744, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=0.001, kernel=poly .......................\n",
      "[CV]  C=100, degree=4, gamma=0.001, kernel=poly, score=0.795, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=4, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=4, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=rbf ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=100, degree=4, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=4, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=rbf ..........................\n",
      "[CV]  C=100, degree=4, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=100, degree=4, gamma=100, kernel=poly .........................\n",
      "[CV]  C=100, degree=4, gamma=100, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=rbf ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=2, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=2, gamma=1, kernel=rbf, score=0.910, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=2, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=2, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=2, gamma=1, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=poly ..........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=2, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=rbf ........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=0.001, kernel=poly ......................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=2, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=2, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=2, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=2, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=rbf .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=2, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=2, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=2, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=2, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=3, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=3, gamma=1, kernel=rbf, score=0.910, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=rbf ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=3, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=3, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=3, gamma=1, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=3, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=poly ........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=3, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=3, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=rbf .......................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=poly, score=0.986, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=3, gamma=0.001, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=3, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=rbf .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=3, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=3, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=3, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=rbf .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=3, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=3, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=3, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=4, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=4, gamma=1, kernel=rbf, score=0.910, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=4, gamma=1, kernel=rbf, score=0.891, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=rbf ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=4, gamma=1, kernel=rbf, score=0.882, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=rbf ...........................\n",
      "[CV]  C=1000, degree=4, gamma=1, kernel=rbf, score=0.924, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=4, gamma=1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=1, kernel=poly ..........................\n",
      "[CV]  C=1000, degree=4, gamma=1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=rbf, score=0.995, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=poly ........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.1, kernel=poly ........................\n",
      "[CV]  C=1000, degree=4, gamma=0.1, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=rbf ........................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.01, kernel=poly .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.01, kernel=poly, score=0.995, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=rbf .......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=rbf, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=poly, score=0.967, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=poly, score=0.986, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=poly, score=0.948, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=poly, score=0.967, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=0.001, kernel=poly ......................\n",
      "[CV]  C=1000, degree=4, gamma=0.001, kernel=poly, score=0.981, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=4, gamma=100, kernel=rbf, score=0.787, total=   0.1s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=4, gamma=100, kernel=rbf, score=0.754, total=   0.1s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=rbf .........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=1000, degree=4, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=4, gamma=100, kernel=rbf, score=0.739, total=   0.1s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=rbf .........................\n",
      "[CV]  C=1000, degree=4, gamma=100, kernel=rbf, score=0.814, total=   0.1s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=4, gamma=100, kernel=poly, score=1.000, total=   0.0s\n",
      "[CV] C=1000, degree=4, gamma=100, kernel=poly ........................\n",
      "[CV]  C=1000, degree=4, gamma=100, kernel=poly, score=0.995, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n",
      "[Parallel(n_jobs=1)]: Done 750 out of 750 | elapsed:   15.1s finished\n",
      "C:\\Users\\Personal\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\_base.py:231: ConvergenceWarning: Solver terminated early (max_iter=700).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=None, shuffle=True),\n",
       "             error_score=nan,\n",
       "             estimator=SVC(C=1.0, break_ties=False, cache_size=300,\n",
       "                           class_weight=None, coef0=0.0,\n",
       "                           decision_function_shape='ovr', degree=3,\n",
       "                           gamma='scale', kernel='rbf', max_iter=700,\n",
       "                           probability=False, random_state=None, shrinking=True,\n",
       "                           tol=0.001, verbose=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'C': [0.1, 1, 10, 100, 1000], 'degree': [2, 3, 4],\n",
       "                         'gamma': [1, 0.1, 0.01, 0.001, 100],\n",
       "                         'kernel': ['rbf', 'poly']},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=3)"
      ]
     },
     "execution_count": 1248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C': [0.1, 1, 10, 100, 1000], \n",
    "'gamma': [1, 0.1, 0.01, 0.001, 100],\n",
    "'degree':[2,3,4],\n",
    "'kernel': ['rbf','poly']} \n",
    "cv = KFold(n_splits = 5,random_state = None,shuffle = True)\n",
    "grid = GridSearchCV(SVC(cache_size = 300,max_iter = 700,), param_grid, cv = cv,refit = True, verbose = 3) \n",
    "\n",
    "grid.fit(x_train, y_train) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0,\n",
       "       0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1,\n",
       "       0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,\n",
       "       1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 1249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_pred = grid.predict(x_test)\n",
    "grid_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.73      0.84       515\n",
      "           1       0.57      0.99      0.73       189\n",
      "\n",
      "    accuracy                           0.80       704\n",
      "   macro avg       0.79      0.86      0.79       704\n",
      "weighted avg       0.88      0.80      0.81       704\n",
      "\n",
      "[[376 139]\n",
      " [  1 188]]\n",
      "0.8011363636363636\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,grid_pred))\n",
    "print(confusion_matrix(y_test,grid_pred))\n",
    "print(accuracy_score(y_test,grid_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 1262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "d_clf = DecisionTreeClassifier()\n",
    "d_clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1260,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_pred = d_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.45      1.00      0.62       231\n",
      "           1       1.00      0.40      0.57       473\n",
      "\n",
      "    accuracy                           0.60       704\n",
      "   macro avg       0.72      0.70      0.60       704\n",
      "weighted avg       0.82      0.60      0.59       704\n",
      "\n",
      "0.5965909090909091\n",
      "[[231   0]\n",
      " [284 189]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(d_pred,y_test))\n",
    "print(accuracy_score(d_pred,y_test))\n",
    "print(confusion_matrix(d_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1264,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_clf = RandomForestClassifier().fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1267,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_pred = r_clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.45      1.00      0.62       231\n",
      "           1       1.00      0.40      0.57       473\n",
      "\n",
      "    accuracy                           0.60       704\n",
      "   macro avg       0.72      0.70      0.60       704\n",
      "weighted avg       0.82      0.60      0.59       704\n",
      "\n",
      "0.5965909090909091\n",
      "[[231   0]\n",
      " [284 189]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(r_pred,y_test))\n",
    "print(accuracy_score(r_pred,y_test))\n",
    "print(confusion_matrix(r_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    704.000000\n",
       "mean      29.673295\n",
       "std       16.490476\n",
       "min       17.000000\n",
       "25%       21.000000\n",
       "50%       27.000000\n",
       "75%       35.000000\n",
       "max      383.000000\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 1113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      26\n",
       "1      24\n",
       "2      27\n",
       "3      35\n",
       "4      40\n",
       "       ..\n",
       "699    25\n",
       "700    34\n",
       "701    24\n",
       "702    35\n",
       "703    26\n",
       "Name: age, Length: 704, dtype: int32"
      ]
     },
     "execution_count": 1114,
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