Churn_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Telecom_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>Phone</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>...</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Churn?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>415</td>\n",
       "      <td>382-4657</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>...</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>415</td>\n",
       "      <td>371-7191</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>...</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>415</td>\n",
       "      <td>358-1921</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>...</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>375-9999</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>...</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>330-6626</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>...</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
       "0    KS             128        415  382-4657         no        yes   \n",
       "1    OH             107        415  371-7191         no        yes   \n",
       "2    NJ             137        415  358-1921         no         no   \n",
       "3    OH              84        408  375-9999        yes         no   \n",
       "4    OK              75        415  330-6626        yes         no   \n",
       "\n",
       "   VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
       "0             25     265.1        110       45.07   ...           99   \n",
       "1             26     161.6        123       27.47   ...          103   \n",
       "2              0     243.4        114       41.38   ...          110   \n",
       "3              0     299.4         71       50.90   ...           88   \n",
       "4              0     166.7        113       28.34   ...          122   \n",
       "\n",
       "   Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
       "0       16.78       244.7           91         11.01       10.0           3   \n",
       "1       16.62       254.4          103         11.45       13.7           3   \n",
       "2       10.30       162.6          104          7.32       12.2           5   \n",
       "3        5.26       196.9           89          8.86        6.6           7   \n",
       "4       12.61       186.9          121          8.41       10.1           3   \n",
       "\n",
       "   Intl Charge  CustServ Calls  Churn?  \n",
       "0         2.70               1  False.  \n",
       "1         3.70               1  False.  \n",
       "2         3.29               0  False.  \n",
       "3         1.78               2  False.  \n",
       "4         2.73               3  False.  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can easily see o use of state and phone in this so we remove them\n",
    "df.drop(['State','Phone'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Account Length</th>\n",
       "      <th>Area Code</th>\n",
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       "      <th>VMail Plan</th>\n",
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       "      <td>False.</td>\n",
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       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>False.</td>\n",
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       "      <th>2</th>\n",
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       "      <td>121.2</td>\n",
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       "      <td>10.30</td>\n",
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       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
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       "      <td>3.29</td>\n",
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       "      <td>False.</td>\n",
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       "      <th>3</th>\n",
       "      <td>84</td>\n",
       "      <td>408</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>False.</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>415</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
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       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>False.</td>\n",
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      "text/plain": [
       "   Account Length  Area Code Int'l Plan VMail Plan  VMail Message  Day Mins  \\\n",
       "0             128        415         no        yes             25     265.1   \n",
       "1             107        415         no        yes             26     161.6   \n",
       "2             137        415         no         no              0     243.4   \n",
       "3              84        408        yes         no              0     299.4   \n",
       "4              75        415        yes         no              0     166.7   \n",
       "\n",
       "   Day Calls  Day Charge  Eve Mins  Eve Calls  Eve Charge  Night Mins  \\\n",
       "0        110       45.07     197.4         99       16.78       244.7   \n",
       "1        123       27.47     195.5        103       16.62       254.4   \n",
       "2        114       41.38     121.2        110       10.30       162.6   \n",
       "3         71       50.90      61.9         88        5.26       196.9   \n",
       "4        113       28.34     148.3        122       12.61       186.9   \n",
       "\n",
       "   Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
       "0           91         11.01       10.0           3         2.70   \n",
       "1          103         11.45       13.7           3         3.70   \n",
       "2          104          7.32       12.2           5         3.29   \n",
       "3           89          8.86        6.6           7         1.78   \n",
       "4          121          8.41       10.1           3         2.73   \n",
       "\n",
       "   CustServ Calls  Churn?  \n",
       "0               1  False.  \n",
       "1               1  False.  \n",
       "2               0  False.  \n",
       "3               2  False.  \n",
       "4               3  False.  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3333 entries, 0 to 3332\n",
      "Data columns (total 19 columns):\n",
      "Account Length    3333 non-null int64\n",
      "Area Code         3333 non-null int64\n",
      "Int'l Plan        3333 non-null object\n",
      "VMail Plan        3333 non-null object\n",
      "VMail Message     3333 non-null int64\n",
      "Day Mins          3333 non-null float64\n",
      "Day Calls         3333 non-null int64\n",
      "Day Charge        3333 non-null float64\n",
      "Eve Mins          3333 non-null float64\n",
      "Eve Calls         3333 non-null int64\n",
      "Eve Charge        3333 non-null float64\n",
      "Night Mins        3333 non-null float64\n",
      "Night Calls       3333 non-null int64\n",
      "Night Charge      3333 non-null float64\n",
      "Intl Mins         3333 non-null float64\n",
      "Intl Calls        3333 non-null int64\n",
      "Intl Charge       3333 non-null float64\n",
      "CustServ Calls    3333 non-null int64\n",
      "Churn?            3333 non-null object\n",
      "dtypes: float64(8), int64(8), object(3)\n",
      "memory usage: 494.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1=df[\"Int'l Plan\"]\n",
    "s1=s1.replace(['yes','no'],[1,0])\n",
    "s2=df[\"VMail Plan\"]\n",
    "s2=s2.replace(['yes','no'],[1,0])\n",
    "s3=df[\"Churn?\"]\n",
    "s3=s3.replace(['True.','False.'],[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Exploration #removing object type data and target\n",
    "\n",
    "\n",
    "df.drop([\"Int'l Plan\",\"VMail Plan\",\"Churn?\" ],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>3333.000000</td>\n",
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       "      <td>179.775098</td>\n",
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       "      <td>30.562307</td>\n",
       "      <td>200.980348</td>\n",
       "      <td>100.114311</td>\n",
       "      <td>17.083540</td>\n",
       "      <td>200.872037</td>\n",
       "      <td>100.107711</td>\n",
       "      <td>9.039325</td>\n",
       "      <td>10.237294</td>\n",
       "      <td>4.479448</td>\n",
       "      <td>2.764581</td>\n",
       "      <td>1.562856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.822106</td>\n",
       "      <td>42.371290</td>\n",
       "      <td>13.688365</td>\n",
       "      <td>54.467389</td>\n",
       "      <td>20.069084</td>\n",
       "      <td>9.259435</td>\n",
       "      <td>50.713844</td>\n",
       "      <td>19.922625</td>\n",
       "      <td>4.310668</td>\n",
       "      <td>50.573847</td>\n",
       "      <td>19.568609</td>\n",
       "      <td>2.275873</td>\n",
       "      <td>2.791840</td>\n",
       "      <td>2.461214</td>\n",
       "      <td>0.753773</td>\n",
       "      <td>1.315491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>408.000000</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>23.200000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.040000</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>74.000000</td>\n",
       "      <td>408.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>143.700000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>24.430000</td>\n",
       "      <td>166.600000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>14.160000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.520000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>101.000000</td>\n",
       "      <td>415.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>179.400000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>201.400000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>17.120000</td>\n",
       "      <td>201.200000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9.050000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.780000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>216.400000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>36.790000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>235.300000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>10.590000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>243.000000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>350.800000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>59.640000</td>\n",
       "      <td>363.700000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>30.910000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>17.770000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Account Length    Area Code  VMail Message     Day Mins    Day Calls  \\\n",
       "count     3333.000000  3333.000000    3333.000000  3333.000000  3333.000000   \n",
       "mean       101.064806   437.182418       8.099010   179.775098   100.435644   \n",
       "std         39.822106    42.371290      13.688365    54.467389    20.069084   \n",
       "min          1.000000   408.000000       0.000000     0.000000     0.000000   \n",
       "25%         74.000000   408.000000       0.000000   143.700000    87.000000   \n",
       "50%        101.000000   415.000000       0.000000   179.400000   101.000000   \n",
       "75%        127.000000   510.000000      20.000000   216.400000   114.000000   \n",
       "max        243.000000   510.000000      51.000000   350.800000   165.000000   \n",
       "\n",
       "        Day Charge     Eve Mins    Eve Calls   Eve Charge   Night Mins  \\\n",
       "count  3333.000000  3333.000000  3333.000000  3333.000000  3333.000000   \n",
       "mean     30.562307   200.980348   100.114311    17.083540   200.872037   \n",
       "std       9.259435    50.713844    19.922625     4.310668    50.573847   \n",
       "min       0.000000     0.000000     0.000000     0.000000    23.200000   \n",
       "25%      24.430000   166.600000    87.000000    14.160000   167.000000   \n",
       "50%      30.500000   201.400000   100.000000    17.120000   201.200000   \n",
       "75%      36.790000   235.300000   114.000000    20.000000   235.300000   \n",
       "max      59.640000   363.700000   170.000000    30.910000   395.000000   \n",
       "\n",
       "       Night Calls  Night Charge    Intl Mins   Intl Calls  Intl Charge  \\\n",
       "count  3333.000000   3333.000000  3333.000000  3333.000000  3333.000000   \n",
       "mean    100.107711      9.039325    10.237294     4.479448     2.764581   \n",
       "std      19.568609      2.275873     2.791840     2.461214     0.753773   \n",
       "min      33.000000      1.040000     0.000000     0.000000     0.000000   \n",
       "25%      87.000000      7.520000     8.500000     3.000000     2.300000   \n",
       "50%     100.000000      9.050000    10.300000     4.000000     2.780000   \n",
       "75%     113.000000     10.590000    12.100000     6.000000     3.270000   \n",
       "max     175.000000     17.770000    20.000000    20.000000     5.400000   \n",
       "\n",
       "       CustServ Calls  \n",
       "count     3333.000000  \n",
       "mean         1.562856  \n",
       "std          1.315491  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          2.000000  \n",
       "max          9.000000  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac241cb70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac23e14e0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac23f1588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac23690f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac2320908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac22f3ef0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac228dba8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac2280d30>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac21d4278>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac21919b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac215ddd8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac21215f8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac20ee748>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac20b3438>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac207f668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f4ac256f5f8>]], dtype=object)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#exploratory visualization #histogram\n",
    "df.hist(figsize=(15,15))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QJjPZTERcSp6idPLkyTFlypQ+N97d3U21ZSPBSP/84O/AzBpL0lzgSGBtpLHdSPq/wN+S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\nPxUYhKLC4CLSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac2414ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac4c09320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac4c18b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac4b372b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac4cc5438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac4afa5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1f9f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1f11ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1f00208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1e79c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9JFWcQS9JFWfQS1LFGfSSVHEGvSRVnEEvSRVn0EtSxRn0klRxBr0kVZxBL0kVZ9BLUsUZ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\nQS9JFWfQS1LF/TsYpP12Uyq3aQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1dedda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1d64ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1cd9ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1cccd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1c4beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1cd9550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3ADNCDdAM8IN0IxwAzQj3ADN/B8hRcWTP6qFEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4acde06390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in df.columns:\n",
    "    plt.boxplot(df['%s'% i])\n",
    "    plt.show()\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4acdb29710>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X19OB3wOOA145zVDXA0fMcqkHu/YngMVN5ceJwCFVdWeS04BlXf3u6+UWgNdX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac4851c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()#correlation between attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "#no missing values #feature scaling\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df[\"Int'l Plan\"] = s1\n",
    "df[\"VMail Plan\"] = s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()\n",
    "X=df.as_matrix().astype(np.float)\n",
    "X=scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.14906505, -0.52360328,  1.30794844, -0.33373776,  1.12450284,\n",
       "       -0.33401297, -0.10808036,  0.14486653, -0.10754944,  1.05857074,\n",
       "        0.14782467,  1.05938994,  1.24048169, -0.60119509,  1.2411686 ,\n",
       "       -0.42793202, -0.32758048,  1.6170861 ])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.676489</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>1.234883</td>\n",
       "      <td>1.566767</td>\n",
       "      <td>0.476643</td>\n",
       "      <td>1.567036</td>\n",
       "      <td>-0.070610</td>\n",
       "      <td>-0.055940</td>\n",
       "      <td>-0.070427</td>\n",
       "      <td>0.866743</td>\n",
       "      <td>-0.465494</td>\n",
       "      <td>0.866029</td>\n",
       "      <td>-0.085008</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>-0.085690</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.149065</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>1.307948</td>\n",
       "      <td>-0.333738</td>\n",
       "      <td>1.124503</td>\n",
       "      <td>-0.334013</td>\n",
       "      <td>-0.108080</td>\n",
       "      <td>0.144867</td>\n",
       "      <td>-0.107549</td>\n",
       "      <td>1.058571</td>\n",
       "      <td>0.147825</td>\n",
       "      <td>1.059390</td>\n",
       "      <td>1.240482</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>1.241169</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.902529</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>1.168304</td>\n",
       "      <td>0.675985</td>\n",
       "      <td>1.168464</td>\n",
       "      <td>-1.573383</td>\n",
       "      <td>0.496279</td>\n",
       "      <td>-1.573900</td>\n",
       "      <td>-0.756869</td>\n",
       "      <td>0.198935</td>\n",
       "      <td>-0.755571</td>\n",
       "      <td>0.703121</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>0.697156</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.428590</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>2.196596</td>\n",
       "      <td>-1.466936</td>\n",
       "      <td>2.196759</td>\n",
       "      <td>-2.742865</td>\n",
       "      <td>-0.608159</td>\n",
       "      <td>-2.743268</td>\n",
       "      <td>-0.078551</td>\n",
       "      <td>-0.567714</td>\n",
       "      <td>-0.078806</td>\n",
       "      <td>-1.303026</td>\n",
       "      <td>1.024263</td>\n",
       "      <td>-1.306401</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.654629</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.240090</td>\n",
       "      <td>0.626149</td>\n",
       "      <td>-0.240041</td>\n",
       "      <td>-1.038932</td>\n",
       "      <td>1.098699</td>\n",
       "      <td>-1.037939</td>\n",
       "      <td>-0.276311</td>\n",
       "      <td>1.067803</td>\n",
       "      <td>-0.276562</td>\n",
       "      <td>-0.049184</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>-0.045885</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.425335</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.801056</td>\n",
       "      <td>-0.121381</td>\n",
       "      <td>0.801216</td>\n",
       "      <td>0.386928</td>\n",
       "      <td>0.044463</td>\n",
       "      <td>0.386648</td>\n",
       "      <td>0.059881</td>\n",
       "      <td>0.914473</td>\n",
       "      <td>0.061821</td>\n",
       "      <td>-1.410498</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>-1.412550</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.500681</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>1.161817</td>\n",
       "      <td>0.705572</td>\n",
       "      <td>-0.619735</td>\n",
       "      <td>0.705083</td>\n",
       "      <td>2.909300</td>\n",
       "      <td>0.395875</td>\n",
       "      <td>2.908677</td>\n",
       "      <td>0.231933</td>\n",
       "      <td>0.914473</td>\n",
       "      <td>0.233209</td>\n",
       "      <td>-0.980610</td>\n",
       "      <td>1.024263</td>\n",
       "      <td>-0.974686</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.153683</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.418205</td>\n",
       "      <td>-1.068253</td>\n",
       "      <td>-0.418264</td>\n",
       "      <td>-1.930341</td>\n",
       "      <td>-0.306949</td>\n",
       "      <td>-1.931206</td>\n",
       "      <td>0.216112</td>\n",
       "      <td>-0.209945</td>\n",
       "      <td>0.215631</td>\n",
       "      <td>-1.123906</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>-1.120641</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.400220</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.086760</td>\n",
       "      <td>-0.171217</td>\n",
       "      <td>0.087242</td>\n",
       "      <td>2.970436</td>\n",
       "      <td>-1.009773</td>\n",
       "      <td>2.971322</td>\n",
       "      <td>0.295216</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.294733</td>\n",
       "      <td>-0.550721</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>-0.550091</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.002990</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>2.111671</td>\n",
       "      <td>1.447412</td>\n",
       "      <td>-0.819076</td>\n",
       "      <td>1.447141</td>\n",
       "      <td>0.414538</td>\n",
       "      <td>0.546480</td>\n",
       "      <td>0.414490</td>\n",
       "      <td>2.482445</td>\n",
       "      <td>-0.158835</td>\n",
       "      <td>2.483233</td>\n",
       "      <td>0.344880</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>0.338904</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.905784</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.930515</td>\n",
       "      <td>1.822198</td>\n",
       "      <td>-0.930251</td>\n",
       "      <td>0.542727</td>\n",
       "      <td>-0.859168</td>\n",
       "      <td>0.542099</td>\n",
       "      <td>0.156784</td>\n",
       "      <td>0.556704</td>\n",
       "      <td>0.158501</td>\n",
       "      <td>0.882241</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>0.882917</td>\n",
       "      <td>1.852927</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.679745</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.145520</td>\n",
       "      <td>1.323844</td>\n",
       "      <td>0.145570</td>\n",
       "      <td>-0.741139</td>\n",
       "      <td>2.403944</td>\n",
       "      <td>-0.740957</td>\n",
       "      <td>-0.096350</td>\n",
       "      <td>-0.312165</td>\n",
       "      <td>-0.096384</td>\n",
       "      <td>-0.407425</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>-0.404137</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.681107</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.936023</td>\n",
       "      <td>-0.221052</td>\n",
       "      <td>-0.935652</td>\n",
       "      <td>-1.894843</td>\n",
       "      <td>-1.461588</td>\n",
       "      <td>-1.894084</td>\n",
       "      <td>-1.182054</td>\n",
       "      <td>1.425573</td>\n",
       "      <td>-1.181845</td>\n",
       "      <td>0.344880</td>\n",
       "      <td>-1.007560</td>\n",
       "      <td>0.338904</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-0.152320</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.425550</td>\n",
       "      <td>-0.619735</td>\n",
       "      <td>-0.425825</td>\n",
       "      <td>0.919407</td>\n",
       "      <td>-1.260782</td>\n",
       "      <td>0.920288</td>\n",
       "      <td>-0.169521</td>\n",
       "      <td>0.761144</td>\n",
       "      <td>-0.171092</td>\n",
       "      <td>0.738945</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>0.736962</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.981130</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.084758</td>\n",
       "      <td>-1.516771</td>\n",
       "      <td>-1.084711</td>\n",
       "      <td>2.094805</td>\n",
       "      <td>-1.210580</td>\n",
       "      <td>2.094296</td>\n",
       "      <td>0.042083</td>\n",
       "      <td>-0.056615</td>\n",
       "      <td>0.044242</td>\n",
       "      <td>1.025537</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>1.028871</td>\n",
       "      <td>1.852927</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.505299</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>2.811735</td>\n",
       "      <td>-1.666277</td>\n",
       "      <td>2.811359</td>\n",
       "      <td>2.303852</td>\n",
       "      <td>-0.156344</td>\n",
       "      <td>2.303112</td>\n",
       "      <td>-0.796421</td>\n",
       "      <td>1.425573</td>\n",
       "      <td>-0.795122</td>\n",
       "      <td>-1.732915</td>\n",
       "      <td>1.836992</td>\n",
       "      <td>-1.730996</td>\n",
       "      <td>1.852927</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.403475</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>1.381014</td>\n",
       "      <td>0.305273</td>\n",
       "      <td>1.921869</td>\n",
       "      <td>0.305431</td>\n",
       "      <td>1.576131</td>\n",
       "      <td>-0.507756</td>\n",
       "      <td>1.576897</td>\n",
       "      <td>-2.206452</td>\n",
       "      <td>-1.283253</td>\n",
       "      <td>-2.205781</td>\n",
       "      <td>1.276306</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>1.280974</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.202551</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.200607</td>\n",
       "      <td>0.675985</td>\n",
       "      <td>0.200657</td>\n",
       "      <td>0.339596</td>\n",
       "      <td>0.546480</td>\n",
       "      <td>0.340244</td>\n",
       "      <td>-1.409478</td>\n",
       "      <td>1.067803</td>\n",
       "      <td>-1.410363</td>\n",
       "      <td>-0.765665</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>-0.762389</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.629514</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>1.819408</td>\n",
       "      <td>0.182245</td>\n",
       "      <td>-1.716113</td>\n",
       "      <td>0.182295</td>\n",
       "      <td>0.233101</td>\n",
       "      <td>-1.762799</td>\n",
       "      <td>0.233516</td>\n",
       "      <td>-0.695563</td>\n",
       "      <td>0.403374</td>\n",
       "      <td>-0.694047</td>\n",
       "      <td>-0.085008</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>-0.085690</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.704860</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.819419</td>\n",
       "      <td>-0.520064</td>\n",
       "      <td>0.819578</td>\n",
       "      <td>-0.818052</td>\n",
       "      <td>-0.608159</td>\n",
       "      <td>-0.817523</td>\n",
       "      <td>-0.159633</td>\n",
       "      <td>-1.334363</td>\n",
       "      <td>-0.157908</td>\n",
       "      <td>0.989713</td>\n",
       "      <td>-1.007560</td>\n",
       "      <td>0.989065</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.153683</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.453093</td>\n",
       "      <td>0.825491</td>\n",
       "      <td>-0.452829</td>\n",
       "      <td>0.763607</td>\n",
       "      <td>-0.357151</td>\n",
       "      <td>0.762516</td>\n",
       "      <td>0.156784</td>\n",
       "      <td>1.681122</td>\n",
       "      <td>0.158501</td>\n",
       "      <td>0.129936</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>0.126607</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.604398</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-2.155284</td>\n",
       "      <td>-0.569899</td>\n",
       "      <td>-2.155132</td>\n",
       "      <td>-0.612949</td>\n",
       "      <td>1.048497</td>\n",
       "      <td>-0.613347</td>\n",
       "      <td>0.172604</td>\n",
       "      <td>-1.845462</td>\n",
       "      <td>0.171685</td>\n",
       "      <td>-1.625442</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>-1.624847</td>\n",
       "      <td>2.613214</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.726720</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.059217</td>\n",
       "      <td>0.576314</td>\n",
       "      <td>0.059159</td>\n",
       "      <td>-2.525929</td>\n",
       "      <td>-0.055940</td>\n",
       "      <td>-2.525171</td>\n",
       "      <td>-0.377169</td>\n",
       "      <td>-1.129923</td>\n",
       "      <td>-0.377637</td>\n",
       "      <td>-0.264128</td>\n",
       "      <td>5.900637</td>\n",
       "      <td>-0.258182</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.249527</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.273891</td>\n",
       "      <td>0.127796</td>\n",
       "      <td>-1.273736</td>\n",
       "      <td>-1.255868</td>\n",
       "      <td>0.094665</td>\n",
       "      <td>-1.256036</td>\n",
       "      <td>-0.222916</td>\n",
       "      <td>0.250045</td>\n",
       "      <td>-0.223827</td>\n",
       "      <td>-0.908961</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>-0.908343</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.776951</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.811908</td>\n",
       "      <td>-0.719406</td>\n",
       "      <td>-1.811647</td>\n",
       "      <td>0.872075</td>\n",
       "      <td>-1.411387</td>\n",
       "      <td>0.871564</td>\n",
       "      <td>0.714468</td>\n",
       "      <td>0.761144</td>\n",
       "      <td>0.716613</td>\n",
       "      <td>0.022464</td>\n",
       "      <td>-1.007560</td>\n",
       "      <td>0.020458</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.831800</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.018654</td>\n",
       "      <td>-1.217759</td>\n",
       "      <td>-1.018823</td>\n",
       "      <td>1.501189</td>\n",
       "      <td>0.596682</td>\n",
       "      <td>1.500331</td>\n",
       "      <td>0.985399</td>\n",
       "      <td>0.761144</td>\n",
       "      <td>0.984682</td>\n",
       "      <td>1.885315</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>1.891330</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1.106707</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>2.257803</td>\n",
       "      <td>0.610088</td>\n",
       "      <td>0.725820</td>\n",
       "      <td>0.610031</td>\n",
       "      <td>-0.194855</td>\n",
       "      <td>0.596682</td>\n",
       "      <td>-0.195716</td>\n",
       "      <td>-0.359371</td>\n",
       "      <td>0.761144</td>\n",
       "      <td>-0.360059</td>\n",
       "      <td>-0.264128</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>-0.258182</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-1.182054</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.835030</td>\n",
       "      <td>-1.367265</td>\n",
       "      <td>-0.835199</td>\n",
       "      <td>-0.896938</td>\n",
       "      <td>-0.005739</td>\n",
       "      <td>-0.896409</td>\n",
       "      <td>-1.953319</td>\n",
       "      <td>-1.641023</td>\n",
       "      <td>-1.955291</td>\n",
       "      <td>1.598722</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>1.599421</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-2.035979</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.187753</td>\n",
       "      <td>0.426808</td>\n",
       "      <td>0.187695</td>\n",
       "      <td>1.128454</td>\n",
       "      <td>-0.808966</td>\n",
       "      <td>1.129103</td>\n",
       "      <td>-0.383102</td>\n",
       "      <td>0.096715</td>\n",
       "      <td>-0.382032</td>\n",
       "      <td>-1.410498</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>-1.412550</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-1.307631</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.110466</td>\n",
       "      <td>0.825491</td>\n",
       "      <td>-1.110635</td>\n",
       "      <td>0.278460</td>\n",
       "      <td>0.446077</td>\n",
       "      <td>0.277599</td>\n",
       "      <td>-0.438475</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>-0.439161</td>\n",
       "      <td>0.309056</td>\n",
       "      <td>-1.413924</td>\n",
       "      <td>0.312367</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</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>3303</th>\n",
       "      <td>0.324873</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>1.307948</td>\n",
       "      <td>-0.783616</td>\n",
       "      <td>-0.619735</td>\n",
       "      <td>-0.783352</td>\n",
       "      <td>-0.892994</td>\n",
       "      <td>1.249304</td>\n",
       "      <td>-0.894089</td>\n",
       "      <td>0.924094</td>\n",
       "      <td>-0.312165</td>\n",
       "      <td>0.923158</td>\n",
       "      <td>0.452353</td>\n",
       "      <td>1.024263</td>\n",
       "      <td>0.458322</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3304</th>\n",
       "      <td>-0.755091</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.116140</td>\n",
       "      <td>0.675985</td>\n",
       "      <td>0.116406</td>\n",
       "      <td>-0.046944</td>\n",
       "      <td>2.002330</td>\n",
       "      <td>-0.047225</td>\n",
       "      <td>0.111299</td>\n",
       "      <td>-1.027703</td>\n",
       "      <td>0.110161</td>\n",
       "      <td>1.276306</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>1.280974</td>\n",
       "      <td>1.852927</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3305</th>\n",
       "      <td>-1.081592</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>1.015686</td>\n",
       "      <td>0.813910</td>\n",
       "      <td>1.323844</td>\n",
       "      <td>0.814178</td>\n",
       "      <td>0.745858</td>\n",
       "      <td>-0.758764</td>\n",
       "      <td>0.746275</td>\n",
       "      <td>-0.527467</td>\n",
       "      <td>-0.721044</td>\n",
       "      <td>-0.527053</td>\n",
       "      <td>0.452353</td>\n",
       "      <td>1.024263</td>\n",
       "      <td>0.458322</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>0.123950</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>1.527146</td>\n",
       "      <td>-1.766002</td>\n",
       "      <td>1.523186</td>\n",
       "      <td>-1.766281</td>\n",
       "      <td>0.057580</td>\n",
       "      <td>1.550515</td>\n",
       "      <td>0.057183</td>\n",
       "      <td>0.566148</td>\n",
       "      <td>-1.385473</td>\n",
       "      <td>0.567197</td>\n",
       "      <td>-0.765665</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>-0.762389</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3307</th>\n",
       "      <td>1.781569</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.442990</td>\n",
       "      <td>0.426808</td>\n",
       "      <td>0.442609</td>\n",
       "      <td>0.651195</td>\n",
       "      <td>1.148901</td>\n",
       "      <td>0.651148</td>\n",
       "      <td>-0.794444</td>\n",
       "      <td>-1.794352</td>\n",
       "      <td>-0.795122</td>\n",
       "      <td>2.709268</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>2.713982</td>\n",
       "      <td>1.852927</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3308</th>\n",
       "      <td>-1.408093</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.578872</td>\n",
       "      <td>-0.669570</td>\n",
       "      <td>0.578707</td>\n",
       "      <td>-0.695779</td>\n",
       "      <td>-0.156344</td>\n",
       "      <td>-0.696874</td>\n",
       "      <td>1.285995</td>\n",
       "      <td>-1.436583</td>\n",
       "      <td>1.287908</td>\n",
       "      <td>1.097185</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>1.095214</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>-0.026743</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.727607</td>\n",
       "      <td>0.576314</td>\n",
       "      <td>0.727766</td>\n",
       "      <td>0.487507</td>\n",
       "      <td>0.094665</td>\n",
       "      <td>0.486415</td>\n",
       "      <td>1.076369</td>\n",
       "      <td>-0.261055</td>\n",
       "      <td>1.076968</td>\n",
       "      <td>0.631473</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>0.630813</td>\n",
       "      <td>1.852927</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3310</th>\n",
       "      <td>-0.177436</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.195098</td>\n",
       "      <td>-0.470229</td>\n",
       "      <td>0.195256</td>\n",
       "      <td>-2.149249</td>\n",
       "      <td>0.345673</td>\n",
       "      <td>-2.149303</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.403374</td>\n",
       "      <td>0.474911</td>\n",
       "      <td>1.204658</td>\n",
       "      <td>5.087908</td>\n",
       "      <td>1.201363</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>0.676489</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.588975</td>\n",
       "      <td>-0.320723</td>\n",
       "      <td>-0.588926</td>\n",
       "      <td>1.623462</td>\n",
       "      <td>-0.859168</td>\n",
       "      <td>1.623301</td>\n",
       "      <td>-0.248625</td>\n",
       "      <td>1.221133</td>\n",
       "      <td>-0.250194</td>\n",
       "      <td>-1.195554</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>-1.200252</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3312</th>\n",
       "      <td>2.007608</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.920412</td>\n",
       "      <td>1.473350</td>\n",
       "      <td>0.920031</td>\n",
       "      <td>-1.115846</td>\n",
       "      <td>-0.357151</td>\n",
       "      <td>-1.116825</td>\n",
       "      <td>1.216779</td>\n",
       "      <td>0.505594</td>\n",
       "      <td>1.217595</td>\n",
       "      <td>1.419602</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>1.413660</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3313</th>\n",
       "      <td>0.651374</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.413445</td>\n",
       "      <td>1.373680</td>\n",
       "      <td>-1.413074</td>\n",
       "      <td>-1.129651</td>\n",
       "      <td>-0.256747</td>\n",
       "      <td>-1.130746</td>\n",
       "      <td>-0.187319</td>\n",
       "      <td>-0.158835</td>\n",
       "      <td>-0.188670</td>\n",
       "      <td>-0.085008</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>-0.085690</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3314</th>\n",
       "      <td>-0.303013</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.019741</td>\n",
       "      <td>-0.968582</td>\n",
       "      <td>-0.019692</td>\n",
       "      <td>0.645279</td>\n",
       "      <td>-1.310983</td>\n",
       "      <td>0.644187</td>\n",
       "      <td>-1.363993</td>\n",
       "      <td>1.016693</td>\n",
       "      <td>-1.362022</td>\n",
       "      <td>-0.407425</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>-0.404137</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>1.203914</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>0.723423</td>\n",
       "      <td>-0.574285</td>\n",
       "      <td>0.277302</td>\n",
       "      <td>-0.573804</td>\n",
       "      <td>-1.705517</td>\n",
       "      <td>0.295472</td>\n",
       "      <td>-1.706150</td>\n",
       "      <td>-0.446385</td>\n",
       "      <td>-0.107725</td>\n",
       "      <td>-0.447950</td>\n",
       "      <td>-1.338850</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>-1.332938</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3316</th>\n",
       "      <td>0.048603</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>1.527146</td>\n",
       "      <td>-0.287832</td>\n",
       "      <td>0.526479</td>\n",
       "      <td>-0.287567</td>\n",
       "      <td>0.357346</td>\n",
       "      <td>-0.206546</td>\n",
       "      <td>0.356485</td>\n",
       "      <td>0.384208</td>\n",
       "      <td>0.403374</td>\n",
       "      <td>0.382625</td>\n",
       "      <td>0.738945</td>\n",
       "      <td>1.836992</td>\n",
       "      <td>0.736962</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3317</th>\n",
       "      <td>1.555530</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.319962</td>\n",
       "      <td>-0.520064</td>\n",
       "      <td>0.319473</td>\n",
       "      <td>-0.246130</td>\n",
       "      <td>0.646884</td>\n",
       "      <td>-0.246760</td>\n",
       "      <td>0.202269</td>\n",
       "      <td>-0.312165</td>\n",
       "      <td>0.202447</td>\n",
       "      <td>-0.873137</td>\n",
       "      <td>1.430627</td>\n",
       "      <td>-0.868537</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>-1.232285</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.007637</td>\n",
       "      <td>1.523186</td>\n",
       "      <td>-1.008021</td>\n",
       "      <td>1.962671</td>\n",
       "      <td>0.897892</td>\n",
       "      <td>1.962046</td>\n",
       "      <td>-0.165566</td>\n",
       "      <td>0.301154</td>\n",
       "      <td>-0.166697</td>\n",
       "      <td>0.488177</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>0.484859</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>-0.303013</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.182079</td>\n",
       "      <td>-0.071546</td>\n",
       "      <td>-1.181924</td>\n",
       "      <td>0.175908</td>\n",
       "      <td>0.747287</td>\n",
       "      <td>0.175512</td>\n",
       "      <td>1.582636</td>\n",
       "      <td>0.607814</td>\n",
       "      <td>1.582345</td>\n",
       "      <td>2.028611</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>2.024015</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>0.525797</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.730365</td>\n",
       "      <td>0.028125</td>\n",
       "      <td>-0.730425</td>\n",
       "      <td>-0.090331</td>\n",
       "      <td>-1.160378</td>\n",
       "      <td>-0.091308</td>\n",
       "      <td>-1.597350</td>\n",
       "      <td>1.681122</td>\n",
       "      <td>-1.599330</td>\n",
       "      <td>-0.192480</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>-0.191839</td>\n",
       "      <td>1.852927</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3321</th>\n",
       "      <td>-1.031361</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.259367</td>\n",
       "      <td>0.875326</td>\n",
       "      <td>0.258985</td>\n",
       "      <td>-2.287299</td>\n",
       "      <td>0.496279</td>\n",
       "      <td>-2.286193</td>\n",
       "      <td>0.182492</td>\n",
       "      <td>1.732232</td>\n",
       "      <td>0.180474</td>\n",
       "      <td>1.061361</td>\n",
       "      <td>1.430627</td>\n",
       "      <td>1.055408</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3322</th>\n",
       "      <td>-0.981130</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>2.595059</td>\n",
       "      <td>0.227466</td>\n",
       "      <td>2.595331</td>\n",
       "      <td>1.272420</td>\n",
       "      <td>1.098699</td>\n",
       "      <td>1.272954</td>\n",
       "      <td>-0.402878</td>\n",
       "      <td>-1.436583</td>\n",
       "      <td>-0.404005</td>\n",
       "      <td>0.452353</td>\n",
       "      <td>-1.007560</td>\n",
       "      <td>0.458322</td>\n",
       "      <td>1.852927</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3323</th>\n",
       "      <td>0.400220</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.126992</td>\n",
       "      <td>1.274009</td>\n",
       "      <td>-1.126837</td>\n",
       "      <td>0.952933</td>\n",
       "      <td>-0.156344</td>\n",
       "      <td>0.952770</td>\n",
       "      <td>0.516707</td>\n",
       "      <td>-2.254342</td>\n",
       "      <td>0.518857</td>\n",
       "      <td>1.204658</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>1.201363</td>\n",
       "      <td>2.613214</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3324</th>\n",
       "      <td>1.455068</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.183166</td>\n",
       "      <td>0.675985</td>\n",
       "      <td>-0.182793</td>\n",
       "      <td>-0.064693</td>\n",
       "      <td>0.245270</td>\n",
       "      <td>-0.065786</td>\n",
       "      <td>-0.141834</td>\n",
       "      <td>-0.925484</td>\n",
       "      <td>-0.140330</td>\n",
       "      <td>0.488177</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>0.484859</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3325</th>\n",
       "      <td>-0.579283</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.250185</td>\n",
       "      <td>-0.071546</td>\n",
       "      <td>0.250344</td>\n",
       "      <td>-1.658186</td>\n",
       "      <td>-0.608159</td>\n",
       "      <td>-1.657426</td>\n",
       "      <td>0.839057</td>\n",
       "      <td>0.454484</td>\n",
       "      <td>0.839661</td>\n",
       "      <td>-0.335777</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>-0.337794</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3326</th>\n",
       "      <td>-0.127205</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-1.343668</td>\n",
       "      <td>1.373680</td>\n",
       "      <td>-1.343945</td>\n",
       "      <td>1.653044</td>\n",
       "      <td>-0.658361</td>\n",
       "      <td>1.653463</td>\n",
       "      <td>-0.434520</td>\n",
       "      <td>-0.414384</td>\n",
       "      <td>-0.434767</td>\n",
       "      <td>1.670370</td>\n",
       "      <td>1.024263</td>\n",
       "      <td>1.665764</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3327</th>\n",
       "      <td>-0.554168</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.827685</td>\n",
       "      <td>-0.121381</td>\n",
       "      <td>-0.827638</td>\n",
       "      <td>-0.222465</td>\n",
       "      <td>-1.612194</td>\n",
       "      <td>-0.223558</td>\n",
       "      <td>0.405962</td>\n",
       "      <td>1.425573</td>\n",
       "      <td>0.404598</td>\n",
       "      <td>0.559825</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>0.564470</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3328</th>\n",
       "      <td>2.283878</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>2.038605</td>\n",
       "      <td>-0.432895</td>\n",
       "      <td>-1.167924</td>\n",
       "      <td>-0.433386</td>\n",
       "      <td>0.286348</td>\n",
       "      <td>1.299506</td>\n",
       "      <td>0.286880</td>\n",
       "      <td>1.547039</td>\n",
       "      <td>-0.874374</td>\n",
       "      <td>1.547188</td>\n",
       "      <td>-0.120832</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>-0.125496</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3329</th>\n",
       "      <td>-0.830437</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.942447</td>\n",
       "      <td>-2.164631</td>\n",
       "      <td>0.942714</td>\n",
       "      <td>-0.938353</td>\n",
       "      <td>-2.264816</td>\n",
       "      <td>-0.938172</td>\n",
       "      <td>-0.189297</td>\n",
       "      <td>1.170023</td>\n",
       "      <td>-0.188670</td>\n",
       "      <td>-0.228304</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>-0.231645</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3330</th>\n",
       "      <td>-1.835055</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.018820</td>\n",
       "      <td>0.426808</td>\n",
       "      <td>0.019193</td>\n",
       "      <td>1.731930</td>\n",
       "      <td>-2.114211</td>\n",
       "      <td>1.732349</td>\n",
       "      <td>-0.177431</td>\n",
       "      <td>-0.465494</td>\n",
       "      <td>-0.175486</td>\n",
       "      <td>1.383778</td>\n",
       "      <td>0.617898</td>\n",
       "      <td>1.387123</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>2.082955</td>\n",
       "      <td>1.718817</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>0.624778</td>\n",
       "      <td>0.227466</td>\n",
       "      <td>0.625153</td>\n",
       "      <td>-0.816080</td>\n",
       "      <td>-0.808966</td>\n",
       "      <td>-0.815203</td>\n",
       "      <td>-1.219628</td>\n",
       "      <td>1.885562</td>\n",
       "      <td>-1.221396</td>\n",
       "      <td>-1.876211</td>\n",
       "      <td>2.243356</td>\n",
       "      <td>-1.876950</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3332</th>\n",
       "      <td>-0.679745</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>1.234883</td>\n",
       "      <td>1.003042</td>\n",
       "      <td>0.626149</td>\n",
       "      <td>1.003202</td>\n",
       "      <td>1.280309</td>\n",
       "      <td>-0.909370</td>\n",
       "      <td>1.279915</td>\n",
       "      <td>0.801482</td>\n",
       "      <td>-1.181033</td>\n",
       "      <td>0.800110</td>\n",
       "      <td>1.240482</td>\n",
       "      <td>-0.194831</td>\n",
       "      <td>1.241169</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3333 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6   \\\n",
       "0     0.676489 -0.523603  1.234883  1.566767  0.476643  1.567036 -0.070610   \n",
       "1     0.149065 -0.523603  1.307948 -0.333738  1.124503 -0.334013 -0.108080   \n",
       "2     0.902529 -0.523603 -0.591760  1.168304  0.675985  1.168464 -1.573383   \n",
       "3    -0.428590 -0.688834 -0.591760  2.196596 -1.466936  2.196759 -2.742865   \n",
       "4    -0.654629 -0.523603 -0.591760 -0.240090  0.626149 -0.240041 -1.038932   \n",
       "5     0.425335  1.718817 -0.591760  0.801056 -0.121381  0.801216  0.386928   \n",
       "6     0.500681  1.718817  1.161817  0.705572 -0.619735  0.705083  2.909300   \n",
       "7     1.153683 -0.523603 -0.591760 -0.418205 -1.068253 -0.418264 -1.930341   \n",
       "8     0.400220 -0.688834 -0.591760  0.086760 -0.171217  0.087242  2.970436   \n",
       "9     1.002990 -0.523603  2.111671  1.447412 -0.819076  1.447141  0.414538   \n",
       "10   -0.905784 -0.523603 -0.591760 -0.930515  1.822198 -0.930251  0.542727   \n",
       "11   -0.679745 -0.523603 -0.591760  0.145520  1.323844  0.145570 -0.741139   \n",
       "12    1.681107 -0.688834 -0.591760 -0.936023 -0.221052 -0.935652 -1.894843   \n",
       "13   -0.152320  1.718817 -0.591760 -0.425550 -0.619735 -0.425825  0.919407   \n",
       "14   -0.981130 -0.523603 -0.591760 -1.084758 -1.516771 -1.084711  2.094805   \n",
       "15    1.505299 -0.523603 -0.591760  2.811735 -1.666277  2.811359  2.303852   \n",
       "16   -0.403475 -0.688834  1.381014  0.305273  1.921869  0.305431  1.576131   \n",
       "17   -0.202551  1.718817 -0.591760  0.200607  0.675985  0.200657  0.339596   \n",
       "18   -0.629514  1.718817  1.819408  0.182245 -1.716113  0.182295  0.233101   \n",
       "19   -0.704860 -0.523603 -0.591760  0.819419 -0.520064  0.819578 -0.818052   \n",
       "20    1.153683 -0.523603 -0.591760 -0.453093  0.825491 -0.452829  0.763607   \n",
       "21   -0.604398 -0.688834 -0.591760 -2.155284 -0.569899 -2.155132 -0.612949   \n",
       "22    0.726720 -0.523603 -0.591760  0.059217  0.576314  0.059159 -2.525929   \n",
       "23    0.249527 -0.523603 -0.591760 -1.273891  0.127796 -1.273736 -1.255868   \n",
       "24    0.776951  1.718817 -0.591760 -1.811908 -0.719406 -1.811647  0.872075   \n",
       "25    1.831800 -0.523603 -0.591760 -1.018654 -1.217759 -1.018823  1.501189   \n",
       "26   -1.106707 -0.688834  2.257803  0.610088  0.725820  0.610031 -0.194855   \n",
       "27   -1.182054 -0.688834 -0.591760 -0.835030 -1.367265 -0.835199 -0.896938   \n",
       "28   -2.035979 -0.523603 -0.591760  0.187753  0.426808  0.187695  1.128454   \n",
       "29   -1.307631  1.718817 -0.591760 -1.110466  0.825491 -1.110635  0.278460   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "3303  0.324873 -0.523603  1.307948 -0.783616 -0.619735 -0.783352 -0.892994   \n",
       "3304 -0.755091  1.718817 -0.591760  0.116140  0.675985  0.116406 -0.046944   \n",
       "3305 -1.081592 -0.523603  1.015686  0.813910  1.323844  0.814178  0.745858   \n",
       "3306  0.123950 -0.688834  1.527146 -1.766002  1.523186 -1.766281  0.057580   \n",
       "3307  1.781569 -0.688834 -0.591760  0.442990  0.426808  0.442609  0.651195   \n",
       "3308 -1.408093 -0.523603 -0.591760  0.578872 -0.669570  0.578707 -0.695779   \n",
       "3309 -0.026743 -0.688834 -0.591760  0.727607  0.576314  0.727766  0.487507   \n",
       "3310 -0.177436 -0.523603 -0.591760  0.195098 -0.470229  0.195256 -2.149249   \n",
       "3311  0.676489 -0.523603 -0.591760 -0.588975 -0.320723 -0.588926  1.623462   \n",
       "3312  2.007608 -0.688834 -0.591760  0.920412  1.473350  0.920031 -1.115846   \n",
       "3313  0.651374 -0.688834 -0.591760 -1.413445  1.373680 -1.413074 -1.129651   \n",
       "3314 -0.303013 -0.523603 -0.591760 -0.019741 -0.968582 -0.019692  0.645279   \n",
       "3315  1.203914 -0.523603  0.723423 -0.574285  0.277302 -0.573804 -1.705517   \n",
       "3316  0.048603  1.718817  1.527146 -0.287832  0.526479 -0.287567  0.357346   \n",
       "3317  1.555530 -0.523603 -0.591760  0.319962 -0.520064  0.319473 -0.246130   \n",
       "3318 -1.232285 -0.523603 -0.591760 -1.007637  1.523186 -1.008021  1.962671   \n",
       "3319 -0.303013 -0.523603 -0.591760 -1.182079 -0.071546 -1.181924  0.175908   \n",
       "3320  0.525797  1.718817 -0.591760 -0.730365  0.028125 -0.730425 -0.090331   \n",
       "3321 -1.031361 -0.523603 -0.591760  0.259367  0.875326  0.258985 -2.287299   \n",
       "3322 -0.981130 -0.688834 -0.591760  2.595059  0.227466  2.595331  1.272420   \n",
       "3323  0.400220 -0.523603 -0.591760 -1.126992  1.274009 -1.126837  0.952933   \n",
       "3324  1.455068 -0.523603 -0.591760 -0.183166  0.675985 -0.182793 -0.064693   \n",
       "3325 -0.579283 -0.688834 -0.591760  0.250185 -0.071546  0.250344 -1.658186   \n",
       "3326 -0.127205 -0.523603 -0.591760 -1.343668  1.373680 -1.343945  1.653044   \n",
       "3327 -0.554168 -0.523603 -0.591760 -0.827685 -0.121381 -0.827638 -0.222465   \n",
       "3328  2.283878 -0.523603  2.038605 -0.432895 -1.167924 -0.433386  0.286348   \n",
       "3329 -0.830437 -0.523603 -0.591760  0.942447 -2.164631  0.942714 -0.938353   \n",
       "3330 -1.835055  1.718817 -0.591760  0.018820  0.426808  0.019193  1.731930   \n",
       "3331  2.082955  1.718817 -0.591760  0.624778  0.227466  0.625153 -0.816080   \n",
       "3332 -0.679745 -0.523603  1.234883  1.003042  0.626149  1.003202  1.280309   \n",
       "\n",
       "            7         8         9         10        11        12        13  \\\n",
       "0    -0.055940 -0.070427  0.866743 -0.465494  0.866029 -0.085008 -0.601195   \n",
       "1     0.144867 -0.107549  1.058571  0.147825  1.059390  1.240482 -0.601195   \n",
       "2     0.496279 -1.573900 -0.756869  0.198935 -0.755571  0.703121  0.211534   \n",
       "3    -0.608159 -2.743268 -0.078551 -0.567714 -0.078806 -1.303026  1.024263   \n",
       "4     1.098699 -1.037939 -0.276311  1.067803 -0.276562 -0.049184 -0.601195   \n",
       "5     0.044463  0.386648  0.059881  0.914473  0.061821 -1.410498  0.617898   \n",
       "6     0.395875  2.908677  0.231933  0.914473  0.233209 -0.980610  1.024263   \n",
       "7    -0.306949 -1.931206  0.216112 -0.209945  0.215631 -1.123906  0.617898   \n",
       "8    -1.009773  2.971322  0.295216 -0.516604  0.294733 -0.550721 -0.194831   \n",
       "9     0.546480  0.414490  2.482445 -0.158835  2.483233  0.344880  0.211534   \n",
       "10   -0.859168  0.542099  0.156784  0.556704  0.158501  0.882241  0.617898   \n",
       "11    2.403944 -0.740957 -0.096350 -0.312165 -0.096384 -0.407425  0.211534   \n",
       "12   -1.461588 -1.894084 -1.182054  1.425573 -1.181845  0.344880 -1.007560   \n",
       "13   -1.260782  0.920288 -0.169521  0.761144 -0.171092  0.738945  0.211534   \n",
       "14   -1.210580  2.094296  0.042083 -0.056615  0.044242  1.025537  0.617898   \n",
       "15   -0.156344  2.303112 -0.796421  1.425573 -0.795122 -1.732915  1.836992   \n",
       "16   -0.507756  1.576897 -2.206452 -1.283253 -2.205781  1.276306 -0.194831   \n",
       "17    0.546480  0.340244 -1.409478  1.067803 -1.410363 -0.765665 -0.601195   \n",
       "18   -1.762799  0.233516 -0.695563  0.403374 -0.694047 -0.085008  0.211534   \n",
       "19   -0.608159 -0.817523 -0.159633 -1.334363 -0.157908  0.989713 -1.007560   \n",
       "20   -0.357151  0.762516  0.156784  1.681122  0.158501  0.129936 -0.194831   \n",
       "21    1.048497 -0.613347  0.172604 -1.845462  0.171685 -1.625442  0.617898   \n",
       "22   -0.055940 -2.525171 -0.377169 -1.129923 -0.377637 -0.264128  5.900637   \n",
       "23    0.094665 -1.256036 -0.222916  0.250045 -0.223827 -0.908961  0.617898   \n",
       "24   -1.411387  0.871564  0.714468  0.761144  0.716613  0.022464 -1.007560   \n",
       "25    0.596682  1.500331  0.985399  0.761144  0.984682  1.885315  0.211534   \n",
       "26    0.596682 -0.195716 -0.359371  0.761144 -0.360059 -0.264128 -0.601195   \n",
       "27   -0.005739 -0.896409 -1.953319 -1.641023 -1.955291  1.598722 -0.194831   \n",
       "28   -0.808966  1.129103 -0.383102  0.096715 -0.382032 -1.410498  0.617898   \n",
       "29    0.446077  0.277599 -0.438475 -0.516604 -0.439161  0.309056 -1.413924   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "3303  1.249304 -0.894089  0.924094 -0.312165  0.923158  0.452353  1.024263   \n",
       "3304  2.002330 -0.047225  0.111299 -1.027703  0.110161  1.276306  0.211534   \n",
       "3305 -0.758764  0.746275 -0.527467 -0.721044 -0.527053  0.452353  1.024263   \n",
       "3306  1.550515  0.057183  0.566148 -1.385473  0.567197 -0.765665 -0.601195   \n",
       "3307  1.148901  0.651148 -0.794444 -1.794352 -0.795122  2.709268 -0.194831   \n",
       "3308 -0.156344 -0.696874  1.285995 -1.436583  1.287908  1.097185  0.617898   \n",
       "3309  0.094665  0.486415  1.076369 -0.261055  1.076968  0.631473 -0.194831   \n",
       "3310  0.345673 -2.149303  0.473200  0.403374  0.474911  1.204658  5.087908   \n",
       "3311 -0.859168  1.623301 -0.248625  1.221133 -0.250194 -1.195554  0.211534   \n",
       "3312 -0.357151 -1.116825  1.216779  0.505594  1.217595  1.419602 -0.194831   \n",
       "3313 -0.256747 -1.130746 -0.187319 -0.158835 -0.188670 -0.085008  0.211534   \n",
       "3314 -1.310983  0.644187 -1.363993  1.016693 -1.362022 -0.407425 -0.194831   \n",
       "3315  0.295472 -1.706150 -0.446385 -0.107725 -0.447950 -1.338850 -0.194831   \n",
       "3316 -0.206546  0.356485  0.384208  0.403374  0.382625  0.738945  1.836992   \n",
       "3317  0.646884 -0.246760  0.202269 -0.312165  0.202447 -0.873137  1.430627   \n",
       "3318  0.897892  1.962046 -0.165566  0.301154 -0.166697  0.488177 -0.194831   \n",
       "3319  0.747287  0.175512  1.582636  0.607814  1.582345  2.028611  0.617898   \n",
       "3320 -1.160378 -0.091308 -1.597350  1.681122 -1.599330 -0.192480 -0.194831   \n",
       "3321  0.496279 -2.286193  0.182492  1.732232  0.180474  1.061361  1.430627   \n",
       "3322  1.098699  1.272954 -0.402878 -1.436583 -0.404005  0.452353 -1.007560   \n",
       "3323 -0.156344  0.952770  0.516707 -2.254342  0.518857  1.204658 -0.601195   \n",
       "3324  0.245270 -0.065786 -0.141834 -0.925484 -0.140330  0.488177 -0.194831   \n",
       "3325 -0.608159 -1.657426  0.839057  0.454484  0.839661 -0.335777 -0.194831   \n",
       "3326 -0.658361  1.653463 -0.434520 -0.414384 -0.434767  1.670370  1.024263   \n",
       "3327 -1.612194 -0.223558  0.405962  1.425573  0.404598  0.559825  0.211534   \n",
       "3328  1.299506  0.286880  1.547039 -0.874374  1.547188 -0.120832  0.617898   \n",
       "3329 -2.264816 -0.938172 -0.189297  1.170023 -0.188670 -0.228304 -0.194831   \n",
       "3330 -2.114211  1.732349 -0.177431 -0.465494 -0.175486  1.383778  0.617898   \n",
       "3331 -0.808966 -0.815203 -1.219628  1.885562 -1.221396 -1.876211  2.243356   \n",
       "3332 -0.909370  1.279915  0.801482 -1.181033  0.800110  1.240482 -0.194831   \n",
       "\n",
       "            14        15        16        17  \n",
       "0    -0.085690 -0.427932 -0.327580  1.617086  \n",
       "1     1.241169 -0.427932 -0.327580  1.617086  \n",
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       "4    -0.045885  1.092641  3.052685 -0.618396  \n",
       "5    -1.412550 -1.188218  3.052685 -0.618396  \n",
       "6    -0.974686  1.092641 -0.327580  1.617086  \n",
       "7    -1.120641 -1.188218  3.052685 -0.618396  \n",
       "8    -0.550091 -0.427932 -0.327580 -0.618396  \n",
       "9     0.338904 -1.188218  3.052685  1.617086  \n",
       "10    0.882917  1.852927 -0.327580 -0.618396  \n",
       "11   -0.404137 -1.188218 -0.327580 -0.618396  \n",
       "12    0.338904 -0.427932 -0.327580 -0.618396  \n",
       "13    0.736962  1.092641 -0.327580 -0.618396  \n",
       "14    1.028871  1.852927 -0.327580 -0.618396  \n",
       "15   -1.730996  1.852927 -0.327580 -0.618396  \n",
       "16    1.280974 -0.427932 -0.327580  1.617086  \n",
       "17   -0.762389  1.092641 -0.327580 -0.618396  \n",
       "18   -0.085690 -0.427932 -0.327580  1.617086  \n",
       "19    0.989065 -0.427932 -0.327580 -0.618396  \n",
       "20    0.126607 -1.188218 -0.327580 -0.618396  \n",
       "21   -1.624847  2.613214 -0.327580 -0.618396  \n",
       "22   -0.258182 -1.188218 -0.327580 -0.618396  \n",
       "23   -0.908343  0.332354 -0.327580 -0.618396  \n",
       "24    0.020458 -1.188218 -0.327580 -0.618396  \n",
       "25    1.891330  1.092641 -0.327580 -0.618396  \n",
       "26   -0.258182 -1.188218 -0.327580  1.617086  \n",
       "27    1.599421  1.092641 -0.327580 -0.618396  \n",
       "28   -1.412550 -1.188218 -0.327580 -0.618396  \n",
       "29    0.312367 -0.427932 -0.327580 -0.618396  \n",
       "...        ...       ...       ...       ...  \n",
       "3303  0.458322  0.332354 -0.327580  1.617086  \n",
       "3304  1.280974  1.852927  3.052685 -0.618396  \n",
       "3305  0.458322  0.332354 -0.327580  1.617086  \n",
       "3306 -0.762389 -0.427932 -0.327580  1.617086  \n",
       "3307  2.713982  1.852927 -0.327580 -0.618396  \n",
       "3308  1.095214 -0.427932 -0.327580 -0.618396  \n",
       "3309  0.630813  1.852927  3.052685 -0.618396  \n",
       "3310  1.201363  0.332354 -0.327580 -0.618396  \n",
       "3311 -1.200252  0.332354 -0.327580 -0.618396  \n",
       "3312  1.413660  0.332354 -0.327580 -0.618396  \n",
       "3313 -0.085690 -0.427932 -0.327580 -0.618396  \n",
       "3314 -0.404137 -0.427932 -0.327580 -0.618396  \n",
       "3315 -1.332938 -1.188218 -0.327580  1.617086  \n",
       "3316  0.736962 -1.188218 -0.327580  1.617086  \n",
       "3317 -0.868537 -0.427932  3.052685 -0.618396  \n",
       "3318  0.484859  0.332354 -0.327580 -0.618396  \n",
       "3319  2.024015  1.092641 -0.327580 -0.618396  \n",
       "3320 -0.191839  1.852927  3.052685 -0.618396  \n",
       "3321  1.055408  1.092641 -0.327580 -0.618396  \n",
       "3322  0.458322  1.852927 -0.327580 -0.618396  \n",
       "3323  1.201363  2.613214 -0.327580 -0.618396  \n",
       "3324  0.484859 -0.427932 -0.327580 -0.618396  \n",
       "3325 -0.337794  0.332354 -0.327580 -0.618396  \n",
       "3326  1.665764 -0.427932 -0.327580 -0.618396  \n",
       "3327  0.564470  0.332354 -0.327580 -0.618396  \n",
       "3328 -0.125496  0.332354 -0.327580  1.617086  \n",
       "3329 -0.231645  1.092641 -0.327580 -0.618396  \n",
       "3330  1.387123  0.332354 -0.327580 -0.618396  \n",
       "3331 -1.876950  0.332354  3.052685 -0.618396  \n",
       "3332  1.241169 -1.188218 -0.327580  1.617086  \n",
       "\n",
       "[3333 rows x 18 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.columns=df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>Account Length</th>\n",
       "      <th>Area Code</th>\n",
       "      <th>VMail Message</th>\n",
       "      <th>Day Mins</th>\n",
       "      <th>Day Calls</th>\n",
       "      <th>Day Charge</th>\n",
       "      <th>Eve Mins</th>\n",
       "      <th>Eve Calls</th>\n",
       "      <th>Eve Charge</th>\n",
       "      <th>Night Mins</th>\n",
       "      <th>Night Calls</th>\n",
       "      <th>Night Charge</th>\n",
       "      <th>Intl Mins</th>\n",
       "      <th>Intl Calls</th>\n",
       "      <th>Intl Charge</th>\n",
       "      <th>CustServ Calls</th>\n",
       "      <th>Int'l Plan</th>\n",
       "      <th>VMail Plan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.676489</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>1.234883</td>\n",
       "      <td>1.566767</td>\n",
       "      <td>0.476643</td>\n",
       "      <td>1.567036</td>\n",
       "      <td>-0.070610</td>\n",
       "      <td>-0.055940</td>\n",
       "      <td>-0.070427</td>\n",
       "      <td>0.866743</td>\n",
       "      <td>-0.465494</td>\n",
       "      <td>0.866029</td>\n",
       "      <td>-0.085008</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>-0.085690</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.149065</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>1.307948</td>\n",
       "      <td>-0.333738</td>\n",
       "      <td>1.124503</td>\n",
       "      <td>-0.334013</td>\n",
       "      <td>-0.108080</td>\n",
       "      <td>0.144867</td>\n",
       "      <td>-0.107549</td>\n",
       "      <td>1.058571</td>\n",
       "      <td>0.147825</td>\n",
       "      <td>1.059390</td>\n",
       "      <td>1.240482</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>1.241169</td>\n",
       "      <td>-0.427932</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>1.617086</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.902529</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>1.168304</td>\n",
       "      <td>0.675985</td>\n",
       "      <td>1.168464</td>\n",
       "      <td>-1.573383</td>\n",
       "      <td>0.496279</td>\n",
       "      <td>-1.573900</td>\n",
       "      <td>-0.756869</td>\n",
       "      <td>0.198935</td>\n",
       "      <td>-0.755571</td>\n",
       "      <td>0.703121</td>\n",
       "      <td>0.211534</td>\n",
       "      <td>0.697156</td>\n",
       "      <td>-1.188218</td>\n",
       "      <td>-0.327580</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.428590</td>\n",
       "      <td>-0.688834</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>2.196596</td>\n",
       "      <td>-1.466936</td>\n",
       "      <td>2.196759</td>\n",
       "      <td>-2.742865</td>\n",
       "      <td>-0.608159</td>\n",
       "      <td>-2.743268</td>\n",
       "      <td>-0.078551</td>\n",
       "      <td>-0.567714</td>\n",
       "      <td>-0.078806</td>\n",
       "      <td>-1.303026</td>\n",
       "      <td>1.024263</td>\n",
       "      <td>-1.306401</td>\n",
       "      <td>0.332354</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.654629</td>\n",
       "      <td>-0.523603</td>\n",
       "      <td>-0.591760</td>\n",
       "      <td>-0.240090</td>\n",
       "      <td>0.626149</td>\n",
       "      <td>-0.240041</td>\n",
       "      <td>-1.038932</td>\n",
       "      <td>1.098699</td>\n",
       "      <td>-1.037939</td>\n",
       "      <td>-0.276311</td>\n",
       "      <td>1.067803</td>\n",
       "      <td>-0.276562</td>\n",
       "      <td>-0.049184</td>\n",
       "      <td>-0.601195</td>\n",
       "      <td>-0.045885</td>\n",
       "      <td>1.092641</td>\n",
       "      <td>3.052685</td>\n",
       "      <td>-0.618396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Account Length  Area Code  VMail Message  Day Mins  Day Calls  Day Charge  \\\n",
       "0        0.676489  -0.523603       1.234883  1.566767   0.476643    1.567036   \n",
       "1        0.149065  -0.523603       1.307948 -0.333738   1.124503   -0.334013   \n",
       "2        0.902529  -0.523603      -0.591760  1.168304   0.675985    1.168464   \n",
       "3       -0.428590  -0.688834      -0.591760  2.196596  -1.466936    2.196759   \n",
       "4       -0.654629  -0.523603      -0.591760 -0.240090   0.626149   -0.240041   \n",
       "\n",
       "   Eve Mins  Eve Calls  Eve Charge  Night Mins  Night Calls  Night Charge  \\\n",
       "0 -0.070610  -0.055940   -0.070427    0.866743    -0.465494      0.866029   \n",
       "1 -0.108080   0.144867   -0.107549    1.058571     0.147825      1.059390   \n",
       "2 -1.573383   0.496279   -1.573900   -0.756869     0.198935     -0.755571   \n",
       "3 -2.742865  -0.608159   -2.743268   -0.078551    -0.567714     -0.078806   \n",
       "4 -1.038932   1.098699   -1.037939   -0.276311     1.067803     -0.276562   \n",
       "\n",
       "   Intl Mins  Intl Calls  Intl Charge  CustServ Calls  Int'l Plan  VMail Plan  \n",
       "0  -0.085008   -0.601195    -0.085690       -0.427932   -0.327580    1.617086  \n",
       "1   1.240482   -0.601195     1.241169       -0.427932   -0.327580    1.617086  \n",
       "2   0.703121    0.211534     0.697156       -1.188218   -0.327580   -0.618396  \n",
       "3  -1.303026    1.024263    -1.306401        0.332354    3.052685   -0.618396  \n",
       "4  -0.049184   -0.601195    -0.045885        1.092641    3.052685   -0.618396  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestClassifier(n_estimators=1000)\n",
    "model.fit(c,target)\n",
    "importance=model.feature_importances_\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance=pd.DataFrame(importance)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "importance.index=c.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Account Length</th>\n",
       "      <td>0.029438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Area Code</th>\n",
       "      <td>0.008084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VMail Message</th>\n",
       "      <td>0.030950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day Mins</th>\n",
       "      <td>0.136856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day Calls</th>\n",
       "      <td>0.030607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day Charge</th>\n",
       "      <td>0.141424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eve Mins</th>\n",
       "      <td>0.067220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eve Calls</th>\n",
       "      <td>0.027446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eve Charge</th>\n",
       "      <td>0.066522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Night Mins</th>\n",
       "      <td>0.038044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Night Calls</th>\n",
       "      <td>0.029738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Night Charge</th>\n",
       "      <td>0.036983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intl Mins</th>\n",
       "      <td>0.041748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intl Calls</th>\n",
       "      <td>0.050580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intl Charge</th>\n",
       "      <td>0.042137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CustServ Calls</th>\n",
       "      <td>0.118585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Int'l Plan</th>\n",
       "      <td>0.081532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VMail Plan</th>\n",
       "      <td>0.022106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0\n",
       "Account Length  0.029438\n",
       "Area Code       0.008084\n",
       "VMail Message   0.030950\n",
       "Day Mins        0.136856\n",
       "Day Calls       0.030607\n",
       "Day Charge      0.141424\n",
       "Eve Mins        0.067220\n",
       "Eve Calls       0.027446\n",
       "Eve Charge      0.066522\n",
       "Night Mins      0.038044\n",
       "Night Calls     0.029738\n",
       "Night Charge    0.036983\n",
       "Intl Mins       0.041748\n",
       "Intl Calls      0.050580\n",
       "Intl Charge     0.042137\n",
       "CustServ Calls  0.118585\n",
       "Int'l Plan      0.081532\n",
       "VMail Plan      0.022106"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nuXp29RuT3HPim/akPDvJ70/03FOy3cvFdi8X271cbPdysd3LxXYvF9u9XGz3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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac4c90320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importance.plot(kind='bar',stacked=True,figsize=(20,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.95508982  0.94610778  0.95508982  0.93693694  0.93393393  0.94294294\n",
      "  0.96396396  0.94894895  0.94294294  0.91291291]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import KFold, cross_val_score\n",
    "k_fold = KFold(len(target), n_folds=10, shuffle=True, random_state=0)\n",
    "clf = RandomForestClassifier()\n",
    "print (cross_val_score(clf, c, target, cv=k_fold, n_jobs=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95272727272727276"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(c,target, test_size=0.33)\n",
    "modell=RandomForestClassifier(n_estimators=1000)\n",
    "modell.fit(X_train,y_train)\n",
    "modell.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[923   5]\n",
      " [ 47 125]]\n"
     ]
    }
   ],
   "source": [
    "y_predict=modell.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "print(confusion_matrix(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.99      0.97       928\n",
      "          1       0.96      0.73      0.83       172\n",
      "\n",
      "avg / total       0.95      0.95      0.95      1100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that it has average F1 Score of 0.95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.86067812750601447"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZtUp6RtLQijF/rWjVLjPrVLRNc76K81yGPncAKK1sPLr7gqTnJb0q6YykV9z9DTN70cye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\n+cZ3ZxEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4ac1ee5cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "fpr, tpr, thresholds = metrics.roc_curve(y_test, y_predict)\n",
    "plt.plot(fpr,tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.851818181818\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model3=GaussianNB()\n",
    "model3.fit(X_train,y_train)\n",
    "print(model3.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COMBINING BOTH OF MODELS WITH EACH HAVING EQUAL WEIGHTAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "s11=model3.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "s12=modell.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.90777475,  0.09222525],\n",
       "       [ 0.92549436,  0.07450564],\n",
       "       [ 0.18978665,  0.81021335],\n",
       "       ..., \n",
       "       [ 0.9967481 ,  0.0032519 ],\n",
       "       [ 0.97801159,  0.02198841],\n",
       "       [ 0.69133869,  0.30866131]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s11"
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  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.988,  0.012],\n",
       "       [ 0.942,  0.058],\n",
       "       [ 0.142,  0.858],\n",
       "       ..., \n",
       "       [ 0.989,  0.011],\n",
       "       [ 0.992,  0.008],\n",
       "       [ 0.649,  0.351]])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "s13=0.5*s11 + 0.5*s12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/spartan/.local/lib/python3.5/site-packages/ipykernel_launcher.py:1: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modell.predict(X_test.iloc[1].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.94788737,  0.05211263],\n",
       "       [ 0.93374718,  0.06625282],\n",
       "       [ 0.16589332,  0.83410668],\n",
       "       ..., \n",
       "       [ 0.99287405,  0.00712595],\n",
       "       [ 0.98500579,  0.01499421],\n",
       "       [ 0.67016934,  0.32983066]])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.93374718,  0.06625282])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s13[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1100"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s13)"
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  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1100):\n",
    "    y_predicted.append(int(s13[i][0]<s13[i][1]))\n",
    "    "
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  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
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      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "y_predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[878,  50],\n",
       "       [ 72, 100]])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_predicted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "score= (878+100)/(878+50+72+100) = 89%\n",
    "\n",
    "So,\n",
    "Score(random forest) = 95%\n",
    "Score(gaussianNB) = 85%\n",
    "Score(combined) = 89%"
   ]
  }
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