员工离职率-kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.max_columns',None)\n",
    "pd.set_option('display.max_rows',None)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv('train.csv')\n",
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1176, 36)\n"
     ]
    },
    {
     "data": {
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       "    }\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>user_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1374</td>\n",
       "      <td>58</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>605</td>\n",
       "      <td>Sales</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1938</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>17875</td>\n",
       "      <td>11761</td>\n",
       "      <td>4</td>\n",
       "      <td>Y</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1092</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>950</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>Technical Degree</td>\n",
       "      <td>1</td>\n",
       "      <td>1546</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>97</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>2132</td>\n",
       "      <td>4585</td>\n",
       "      <td>4</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  Age Attrition BusinessTravel  DailyRate              Department  \\\n",
       "0     1374   58        No  Travel_Rarely        605                   Sales   \n",
       "1     1092   45        No  Travel_Rarely        950  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education    EducationField  EmployeeCount  \\\n",
       "0                21          3     Life Sciences              1   \n",
       "1                28          3  Technical Degree              1   \n",
       "\n",
       "   EmployeeNumber  EnvironmentSatisfaction  Gender  HourlyRate  \\\n",
       "0            1938                        4  Female          72   \n",
       "1            1546                        4    Male          97   \n",
       "\n",
       "   JobInvolvement  JobLevel             JobRole  JobSatisfaction  \\\n",
       "0               3         4             Manager                4   \n",
       "1               3         1  Research Scientist                4   \n",
       "\n",
       "  MaritalStatus  MonthlyIncome  MonthlyRate  NumCompaniesWorked Over18  \\\n",
       "0       Married          17875        11761                   4      Y   \n",
       "1       Married           2132         4585                   4      Y   \n",
       "\n",
       "  OverTime  PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
       "0      Yes                 13                  3                         3   \n",
       "1       No                 20                  4                         4   \n",
       "\n",
       "   StandardHours  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0             80                 1                 29                      2   \n",
       "1             80                 1                  8                      3   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                2               1                   0   \n",
       "1                3               5                   4   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        0                     0  \n",
       "1                        0                     3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数据\n",
    "print(train_data.shape)\n",
    "train_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(294, 35)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>Over18</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>442</td>\n",
       "      <td>36</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>635</td>\n",
       "      <td>Sales</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>592</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>9980</td>\n",
       "      <td>15318</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1091</td>\n",
       "      <td>33</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>575</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>1545</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>4320</td>\n",
       "      <td>24152</td>\n",
       "      <td>1</td>\n",
       "      <td>Y</td>\n",
       "      <td>No</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  Age BusinessTravel  DailyRate              Department  \\\n",
       "0      442   36     Non-Travel        635                   Sales   \n",
       "1     1091   33  Travel_Rarely        575  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeCount  EmployeeNumber  \\\n",
       "0                10          4        Medical              1             592   \n",
       "1                25          3  Life Sciences              1            1545   \n",
       "\n",
       "   EnvironmentSatisfaction Gender  HourlyRate  JobInvolvement  JobLevel  \\\n",
       "0                        2   Male          32               3         3   \n",
       "1                        4   Male          44               2         2   \n",
       "\n",
       "                  JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  \\\n",
       "0         Sales Executive                4        Single           9980   \n",
       "1  Manufacturing Director                2        Single           4320   \n",
       "\n",
       "   MonthlyRate  NumCompaniesWorked Over18 OverTime  PercentSalaryHike  \\\n",
       "0        15318                   1      Y       No                 14   \n",
       "1        24152                   1      Y       No                 13   \n",
       "\n",
       "   PerformanceRating  RelationshipSatisfaction  StandardHours  \\\n",
       "0                  3                         4             80   \n",
       "1                  3                         4             80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                 0                 10                      3   \n",
       "1                 0                  5                      2   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                2              10                   3   \n",
       "1                3               5                   3   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                        9                     7  \n",
       "1                        0                     2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test_data.shape)\n",
    "test_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     988\n",
       "Yes    188\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#观看标签分类大小\n",
    "train_data['Attrition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1470 entries, 0 to 293\n",
      "Data columns (total 36 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   user_id                   1470 non-null   int64  \n",
      " 1   Age                       1470 non-null   int64  \n",
      " 2   Attrition                 1176 non-null   float64\n",
      " 3   BusinessTravel            1470 non-null   object \n",
      " 4   DailyRate                 1470 non-null   int64  \n",
      " 5   Department                1470 non-null   object \n",
      " 6   DistanceFromHome          1470 non-null   int64  \n",
      " 7   Education                 1470 non-null   int64  \n",
      " 8   EducationField            1470 non-null   object \n",
      " 9   EmployeeCount             1470 non-null   int64  \n",
      " 10  EmployeeNumber            1470 non-null   int64  \n",
      " 11  EnvironmentSatisfaction   1470 non-null   int64  \n",
      " 12  Gender                    1470 non-null   object \n",
      " 13  HourlyRate                1470 non-null   int64  \n",
      " 14  JobInvolvement            1470 non-null   int64  \n",
      " 15  JobLevel                  1470 non-null   int64  \n",
      " 16  JobRole                   1470 non-null   object \n",
      " 17  JobSatisfaction           1470 non-null   int64  \n",
      " 18  MaritalStatus             1470 non-null   object \n",
      " 19  MonthlyIncome             1470 non-null   int64  \n",
      " 20  MonthlyRate               1470 non-null   int64  \n",
      " 21  NumCompaniesWorked        1470 non-null   int64  \n",
      " 22  Over18                    1470 non-null   object \n",
      " 23  OverTime                  1470 non-null   object \n",
      " 24  PercentSalaryHike         1470 non-null   int64  \n",
      " 25  PerformanceRating         1470 non-null   int64  \n",
      " 26  RelationshipSatisfaction  1470 non-null   int64  \n",
      " 27  StandardHours             1470 non-null   int64  \n",
      " 28  StockOptionLevel          1470 non-null   int64  \n",
      " 29  TotalWorkingYears         1470 non-null   int64  \n",
      " 30  TrainingTimesLastYear     1470 non-null   int64  \n",
      " 31  WorkLifeBalance           1470 non-null   int64  \n",
      " 32  YearsAtCompany            1470 non-null   int64  \n",
      " 33  YearsInCurrentRole        1470 non-null   int64  \n",
      " 34  YearsSinceLastPromotion   1470 non-null   int64  \n",
      " 35  YearsWithCurrManager      1470 non-null   int64  \n",
      "dtypes: float64(1), int64(27), object(8)\n",
      "memory usage: 424.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#没有缺失值\n",
    "train_data['Attrition']=train_data['Attrition'].apply(lambda x:0 if x=='No' else 1)\n",
    "data=train_data.append(test_data)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
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       "      <th>EmployeeNumber</th>\n",
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       "      <th>JobInvolvement</th>\n",
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       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
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       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>734.500000</td>\n",
       "      <td>36.923810</td>\n",
       "      <td>0.159864</td>\n",
       "      <td>802.485714</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1024.865306</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>65.891156</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>2.728571</td>\n",
       "      <td>6502.931293</td>\n",
       "      <td>14313.103401</td>\n",
       "      <td>2.693197</td>\n",
       "      <td>15.209524</td>\n",
       "      <td>3.153741</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.793878</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>4.123129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>424.496761</td>\n",
       "      <td>9.135373</td>\n",
       "      <td>0.366636</td>\n",
       "      <td>403.509100</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>602.024335</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>20.329428</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>1.102846</td>\n",
       "      <td>4707.956783</td>\n",
       "      <td>7117.786044</td>\n",
       "      <td>2.498009</td>\n",
       "      <td>3.659938</td>\n",
       "      <td>0.360824</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.852077</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>1.289271</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "      <td>3.568136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>2094.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>367.250000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>491.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2911.000000</td>\n",
       "      <td>8047.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>734.500000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1020.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4919.000000</td>\n",
       "      <td>14235.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1101.750000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1555.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8379.000000</td>\n",
       "      <td>20461.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1469.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2068.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19999.000000</td>\n",
       "      <td>26999.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           user_id          Age    Attrition    DailyRate  DistanceFromHome  \\\n",
       "count  1470.000000  1470.000000  1176.000000  1470.000000       1470.000000   \n",
       "mean    734.500000    36.923810     0.159864   802.485714          9.192517   \n",
       "std     424.496761     9.135373     0.366636   403.509100          8.106864   \n",
       "min       0.000000    18.000000     0.000000   102.000000          1.000000   \n",
       "25%     367.250000    30.000000     0.000000   465.000000          2.000000   \n",
       "50%     734.500000    36.000000     0.000000   802.000000          7.000000   \n",
       "75%    1101.750000    43.000000     0.000000  1157.000000         14.000000   \n",
       "max    1469.000000    60.000000     1.000000  1499.000000         29.000000   \n",
       "\n",
       "         Education  EmployeeCount  EmployeeNumber  EnvironmentSatisfaction  \\\n",
       "count  1470.000000         1470.0     1470.000000              1470.000000   \n",
       "mean      2.912925            1.0     1024.865306                 2.721769   \n",
       "std       1.024165            0.0      602.024335                 1.093082   \n",
       "min       1.000000            1.0        1.000000                 1.000000   \n",
       "25%       2.000000            1.0      491.250000                 2.000000   \n",
       "50%       3.000000            1.0     1020.500000                 3.000000   \n",
       "75%       4.000000            1.0     1555.750000                 4.000000   \n",
       "max       5.000000            1.0     2068.000000                 4.000000   \n",
       "\n",
       "        HourlyRate  JobInvolvement     JobLevel  JobSatisfaction  \\\n",
       "count  1470.000000     1470.000000  1470.000000      1470.000000   \n",
       "mean     65.891156        2.729932     2.063946         2.728571   \n",
       "std      20.329428        0.711561     1.106940         1.102846   \n",
       "min      30.000000        1.000000     1.000000         1.000000   \n",
       "25%      48.000000        2.000000     1.000000         2.000000   \n",
       "50%      66.000000        3.000000     2.000000         3.000000   \n",
       "75%      83.750000        3.000000     3.000000         4.000000   \n",
       "max     100.000000        4.000000     5.000000         4.000000   \n",
       "\n",
       "       MonthlyIncome   MonthlyRate  NumCompaniesWorked  PercentSalaryHike  \\\n",
       "count    1470.000000   1470.000000         1470.000000        1470.000000   \n",
       "mean     6502.931293  14313.103401            2.693197          15.209524   \n",
       "std      4707.956783   7117.786044            2.498009           3.659938   \n",
       "min      1009.000000   2094.000000            0.000000          11.000000   \n",
       "25%      2911.000000   8047.000000            1.000000          12.000000   \n",
       "50%      4919.000000  14235.500000            2.000000          14.000000   \n",
       "75%      8379.000000  20461.500000            4.000000          18.000000   \n",
       "max     19999.000000  26999.000000            9.000000          25.000000   \n",
       "\n",
       "       PerformanceRating  RelationshipSatisfaction  StandardHours  \\\n",
       "count        1470.000000               1470.000000         1470.0   \n",
       "mean            3.153741                  2.712245           80.0   \n",
       "std             0.360824                  1.081209            0.0   \n",
       "min             3.000000                  1.000000           80.0   \n",
       "25%             3.000000                  2.000000           80.0   \n",
       "50%             3.000000                  3.000000           80.0   \n",
       "75%             3.000000                  4.000000           80.0   \n",
       "max             4.000000                  4.000000           80.0   \n",
       "\n",
       "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "count       1470.000000        1470.000000            1470.000000   \n",
       "mean           0.793878          11.279592               2.799320   \n",
       "std            0.852077           7.780782               1.289271   \n",
       "min            0.000000           0.000000               0.000000   \n",
       "25%            0.000000           6.000000               2.000000   \n",
       "50%            1.000000          10.000000               3.000000   \n",
       "75%            1.000000          15.000000               3.000000   \n",
       "max            3.000000          40.000000               6.000000   \n",
       "\n",
       "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "count      1470.000000     1470.000000         1470.000000   \n",
       "mean          2.761224        7.008163            4.229252   \n",
       "std           0.706476        6.126525            3.623137   \n",
       "min           1.000000        0.000000            0.000000   \n",
       "25%           2.000000        3.000000            2.000000   \n",
       "50%           3.000000        5.000000            3.000000   \n",
       "75%           3.000000        9.000000            7.000000   \n",
       "max           4.000000       40.000000           18.000000   \n",
       "\n",
       "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count              1470.000000           1470.000000  \n",
       "mean                  2.187755              4.123129  \n",
       "std                   3.222430              3.568136  \n",
       "min                   0.000000              0.000000  \n",
       "25%                   0.000000              2.000000  \n",
       "50%                   1.000000              3.000000  \n",
       "75%                   3.000000              7.000000  \n",
       "max                  15.000000             17.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#观察可知EmploymentNumber\\StandardHors\\Over18为无用数据列\n",
    "#无缺失值以及异常值\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除无意义列\n",
    "train_data.drop(columns=['EmployeeNumber','StandardHours','Over18'],inplace=True)\n",
    "test_data.drop(columns=['EmployeeNumber','StandardHours','Over18'],inplace=True)\n",
    "data.drop(columns=['EmployeeNumber','StandardHours','Over18'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.BusinessTravel特征的类型数量是:3\n",
      "2.Department特征的类型数量是:3\n",
      "3.EducationField特征的类型数量是:6\n",
      "4.Gender特征的类型数量是:2\n",
      "5.JobRole特征的类型数量是:9\n",
      "6.MaritalStatus特征的类型数量是:3\n",
      "7.OverTime特征的类型数量是:2\n"
     ]
    }
   ],
   "source": [
    "#获取定类变量\n",
    "cat_list=[]\n",
    "for i in train_data.columns:\n",
    "    if train_data[i].dtype=='O':\n",
    "        cat_list.append(i)\n",
    "for n,i in enumerate(cat_list):\n",
    "    cat_num=train_data[i].value_counts().index.shape[0]\n",
    "    print('{0}.{1}特征的类型数量是:{2}'.format(n+1,i,cat_num))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对各个定类变量分别与标签进行可视化；\n",
    "#观察可知经常出差的离职率是最高的；研究部门的人数占比最多，但是销售部门的人员流动性更高，即离职率更高；男性且单身的离职率相对较高\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 中文字体设置-黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题\n",
    "sns.set(font='SimHei',font_scale=1.1)  # 解决Seaborn中文显示问题并调整字体大小\n",
    "f,[ax1,ax2,ax3]=plt.subplots(1,3,figsize=(20,5))\n",
    "sns.countplot(x='BusinessTravel',hue='Attrition',data=train_data,ax=ax1)\n",
    "sns.countplot(x='Department',hue='Attrition',data=train_data,ax=ax2)\n",
    "sns.countplot(x='Gender',hue='Attrition',data=train_data,ax=ax3)\n",
    "ax1.set_title('Business特征分析')\n",
    "ax2.set_title('Depeartment特征分析')\n",
    "ax3.set_title('Gender特征分析')\n",
    "f.suptitle('定类数据类型特征分析',size=20,y=1.1)\n",
    "f,[ax1,ax2]=plt.subplots(1,2,figsize=(20,5))\n",
    "sns.countplot(x='MaritalStatus',hue='Attrition',data=train_data,ax=ax1)\n",
    "sns.countplot(x='OverTime',hue='Attrition',data=train_data,ax=ax2)\n",
    "ax1.set_title('MaritalStatus特征分析')\n",
    "ax2.set_title('OverTime特征分析')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8]),\n",
       " <a list of 9 Text major ticklabel objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,[ax1,ax2]=plt.subplots(2,1,figsize=(17,10))\n",
    "sns.countplot(x='EducationField',hue='Attrition',data=train_data,ax=ax1)\n",
    "sns.countplot(x='JobRole',hue='Attrition',data=train_data,ax=ax2)\n",
    "ax1.set_title(u'EducationField特征分析')\n",
    "ax2.set_title(u'JobRole特征分析')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c8ef0131c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 923.93x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在不同出差频率下，男性和女性在单身情况的数量对比\n",
    "grid=sns.FacetGrid(train_data,row='Gender',col='BusinessTravel',hue='Attrition',palette='seismic',size=4)\n",
    "grid.map(sns.countplot,'MaritalStatus',alpha=0.8)\n",
    "grid.add_legend()\n",
    "#经常出差的单身女性离职率很高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8eef34b88>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#工资提成的离职情况分布\n",
    "plt.figure(figsize=(14,5))\n",
    "sns.barplot(x='PercentSalaryHike',y='Attrition',data=train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8ef167fc8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#年龄的离职情况分布\n",
    "plt.figure(figsize=(14,5))\n",
    "sns.barplot(x='Age',y='Attrition',data=train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8efc09dc8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#开始工作年龄\n",
    "data['StartWorking']=data['Age']-data['TotalWorkingYears']\n",
    "plt.figure(figsize=(14,5))\n",
    "sns.barplot(x='StartWorking',y='Attrition',data=data.iloc[:train_data.shape[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据可视化图重设年龄分组便于后续分类\n",
    "def resetAge(input):\n",
    "    if ((input < 24) & (input > 18)) | (input == 58):\n",
    "        return 1\n",
    "    elif (input == 18) | (input == 48) |(input == 54) | (input == 57) | (input > 58) :\n",
    "        return 0\n",
    "    else:\n",
    "        return 2\n",
    "#根据可视化图重设薪酬分组便于后续分类\n",
    "def resetSalary(input):\n",
    "    if (input>0) & (input<3725):\n",
    "        return 0\n",
    "    elif (input>=3725) & (input<111250):\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "#根据可视化图重设工资提高百分比分组便于后续分类\n",
    "def resetPerHike(input):\n",
    "    if (input>=22) & (input<25):\n",
    "        return 0\n",
    "    elif ((input>=11) & (input<14)) |((input>14) & (input <22)):\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "#根据可视化图重设开始工作年龄分组\n",
    "def resetStart(input):\n",
    "    if (input==49)|(input==51):\n",
    "        return 3\n",
    "    elif (input==22)|(input==27)|(input==31)|(input==40):\n",
    "        return 2\n",
    "    elif (input==42)|((input>43)&(input<49))|(input==50)|(input>=52):\n",
    "        return 0\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Age']=data['Age'].apply(resetAge)\n",
    "data['MonthlyIncome']=data['MonthlyIncome'].apply(resetSalary)\n",
    "data['PercentSalaryHike']=data['PercentSalaryHike'].apply(resetPerHike)\n",
    "data['StartWorking']=data['StartWorking'].apply(resetStart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#独热编码\n",
    "cata_result = pd.DataFrame()\n",
    "for i in data.columns:\n",
    "    if data[i].dtype == 'O':\n",
    "        cata = pd.DataFrame()\n",
    "        cata = pd.get_dummies(data[i],prefix=i)\n",
    "        cata_result = pd.concat([cata_result,cata],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除点object变量\n",
    "for i in data.columns:\n",
    "    if data[i].dtype == 'O':\n",
    "        data = data.drop(i,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([data,cata_result],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>MonthlyRate</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>StartWorking</th>\n",
       "      <th>BusinessTravel_Non-Travel</th>\n",
       "      <th>BusinessTravel_Travel_Frequently</th>\n",
       "      <th>BusinessTravel_Travel_Rarely</th>\n",
       "      <th>Department_Human Resources</th>\n",
       "      <th>Department_Research &amp; Development</th>\n",
       "      <th>Department_Sales</th>\n",
       "      <th>EducationField_Human Resources</th>\n",
       "      <th>EducationField_Life Sciences</th>\n",
       "      <th>EducationField_Marketing</th>\n",
       "      <th>EducationField_Medical</th>\n",
       "      <th>EducationField_Other</th>\n",
       "      <th>EducationField_Technical Degree</th>\n",
       "      <th>Gender_Female</th>\n",
       "      <th>Gender_Male</th>\n",
       "      <th>JobRole_Healthcare Representative</th>\n",
       "      <th>JobRole_Human Resources</th>\n",
       "      <th>JobRole_Laboratory Technician</th>\n",
       "      <th>JobRole_Manager</th>\n",
       "      <th>JobRole_Manufacturing Director</th>\n",
       "      <th>JobRole_Research Director</th>\n",
       "      <th>JobRole_Research Scientist</th>\n",
       "      <th>JobRole_Sales Executive</th>\n",
       "      <th>JobRole_Sales Representative</th>\n",
       "      <th>MaritalStatus_Divorced</th>\n",
       "      <th>MaritalStatus_Married</th>\n",
       "      <th>MaritalStatus_Single</th>\n",
       "      <th>OverTime_No</th>\n",
       "      <th>OverTime_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1374</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>605</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>11761</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1092</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>950</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4585</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>768</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>74</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22217</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>569</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1434</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>14229</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>911</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>599</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>8040</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  Age  Attrition  DailyRate  DistanceFromHome  Education  \\\n",
       "0     1374    2        0.0        605                21          3   \n",
       "1     1092    2        0.0        950                28          3   \n",
       "2      768    2        0.0        300                26          3   \n",
       "3      569    2        0.0       1434                 8          4   \n",
       "4      911    2        1.0        599                24          1   \n",
       "\n",
       "   EmployeeCount  EnvironmentSatisfaction  HourlyRate  JobInvolvement  \\\n",
       "0              1                        4          72               3   \n",
       "1              1                        4          97               3   \n",
       "2              1                        3          74               3   \n",
       "3              1                        1          76               2   \n",
       "4              1                        3          73               1   \n",
       "\n",
       "   JobLevel  JobSatisfaction  MonthlyIncome  MonthlyRate  NumCompaniesWorked  \\\n",
       "0         4                4              2        11761                   4   \n",
       "1         1                4              0         4585                   4   \n",
       "2         2                1              2        22217                   1   \n",
       "3         3                1              2        14229                   1   \n",
       "4         1                4              0         8040                   1   \n",
       "\n",
       "   PercentSalaryHike  PerformanceRating  RelationshipSatisfaction  \\\n",
       "0                  2                  3                         3   \n",
       "1                  2                  4                         4   \n",
       "2                  2                  3                         2   \n",
       "3                  2                  3                         2   \n",
       "4                  2                  3                         4   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                 1                 29                      2   \n",
       "1                 1                  8                      3   \n",
       "2                 1                  8                      3   \n",
       "3                 0                 10                      1   \n",
       "4                 0                  1                      4   \n",
       "\n",
       "   WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                2               1                   0   \n",
       "1                3               5                   4   \n",
       "2                2               7                   7   \n",
       "3                3              10                   7   \n",
       "4                3               1                   0   \n",
       "\n",
       "   YearsSinceLastPromotion  YearsWithCurrManager  StartWorking  \\\n",
       "0                        0                     0             1   \n",
       "1                        0                     3             1   \n",
       "2                        7                     5             1   \n",
       "3                        0                     9             1   \n",
       "4                        1                     0             1   \n",
       "\n",
       "   BusinessTravel_Non-Travel  BusinessTravel_Travel_Frequently  \\\n",
       "0                          0                                 0   \n",
       "1                          0                                 0   \n",
       "2                          0                                 0   \n",
       "3                          1                                 0   \n",
       "4                          0                                 1   \n",
       "\n",
       "   BusinessTravel_Travel_Rarely  Department_Human Resources  \\\n",
       "0                             1                           0   \n",
       "1                             1                           0   \n",
       "2                             1                           0   \n",
       "3                             0                           0   \n",
       "4                             0                           0   \n",
       "\n",
       "   Department_Research & Development  Department_Sales  \\\n",
       "0                                  0                 1   \n",
       "1                                  1                 0   \n",
       "2                                  0                 1   \n",
       "3                                  0                 1   \n",
       "4                                  0                 1   \n",
       "\n",
       "   EducationField_Human Resources  EducationField_Life Sciences  \\\n",
       "0                               0                             1   \n",
       "1                               0                             0   \n",
       "2                               0                             0   \n",
       "3                               0                             1   \n",
       "4                               0                             1   \n",
       "\n",
       "   EducationField_Marketing  EducationField_Medical  EducationField_Other  \\\n",
       "0                         0                       0                     0   \n",
       "1                         0                       0                     0   \n",
       "2                         1                       0                     0   \n",
       "3                         0                       0                     0   \n",
       "4                         0                       0                     0   \n",
       "\n",
       "   EducationField_Technical Degree  Gender_Female  Gender_Male  \\\n",
       "0                                0              1            0   \n",
       "1                                1              0            1   \n",
       "2                                0              0            1   \n",
       "3                                0              0            1   \n",
       "4                                0              0            1   \n",
       "\n",
       "   JobRole_Healthcare Representative  JobRole_Human Resources  \\\n",
       "0                                  0                        0   \n",
       "1                                  0                        0   \n",
       "2                                  0                        0   \n",
       "3                                  0                        0   \n",
       "4                                  0                        0   \n",
       "\n",
       "   JobRole_Laboratory Technician  JobRole_Manager  \\\n",
       "0                              0                1   \n",
       "1                              0                0   \n",
       "2                              0                0   \n",
       "3                              0                0   \n",
       "4                              0                0   \n",
       "\n",
       "   JobRole_Manufacturing Director  JobRole_Research Director  \\\n",
       "0                               0                          0   \n",
       "1                               0                          0   \n",
       "2                               0                          0   \n",
       "3                               0                          0   \n",
       "4                               0                          0   \n",
       "\n",
       "   JobRole_Research Scientist  JobRole_Sales Executive  \\\n",
       "0                           0                        0   \n",
       "1                           1                        0   \n",
       "2                           0                        1   \n",
       "3                           0                        1   \n",
       "4                           0                        0   \n",
       "\n",
       "   JobRole_Sales Representative  MaritalStatus_Divorced  \\\n",
       "0                             0                       0   \n",
       "1                             0                       0   \n",
       "2                             0                       0   \n",
       "3                             0                       0   \n",
       "4                             1                       0   \n",
       "\n",
       "   MaritalStatus_Married  MaritalStatus_Single  OverTime_No  OverTime_Yes  \n",
       "0                      1                     0            0             1  \n",
       "1                      1                     0            1             0  \n",
       "2                      1                     0            1             0  \n",
       "3                      0                     1            1             0  \n",
       "4                      0                     1            0             1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rfc的auc为：0.784\n",
      "gdbt的auc为：0.792\n",
      "cart的auc为：0.586\n",
      "knn的auc为：0.508\n",
      "svm的auc为：0.562\n",
      "lr的auc为：0.727\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "num=train_data.shape[0]\n",
    "train_val_y=train_data.iloc[:num]['Attrition']\n",
    "train_val_x=data.iloc[:num].drop(columns=['Attrition'])\n",
    "test_x=data.iloc[num:].drop('Attrition',axis=1)\n",
    "\n",
    "train_x,val_x,train_y,val_y=train_test_split(train_val_y,train_val_x,test_size=0.25,\n",
    "                                            random_state=2020)\n",
    "\n",
    "model={}\n",
    "model['rfc']=RandomForestClassifier()\n",
    "model['gdbt']=GradientBoostingClassifier()\n",
    "model['cart']=DecisionTreeClassifier()\n",
    "model['knn']=KNeighborsClassifier()\n",
    "model['svm']=svm.SVC()\n",
    "model['lr']=LogisticRegression()\n",
    "for i in model:\n",
    "    model[i].fit(train_val_x,train_val_y)\n",
    "    score=cross_val_score(model[i],train_val_x,train_val_y,cv=5,scoring='roc_auc')\n",
    "    print('%s的auc为：%.3f'%(i,score.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rfc :\n",
      "best parameters: {'max_depth': 7, 'min_samples_leaf': 4, 'n_estimators': 100}\n",
      "gbdt :\n",
      "best parameters: {'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 300}\n",
      "lr :\n",
      "best parameters: {'C': 0.5, 'solver': 'newton-cg'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "model=['rfc','gbdt','lr']\n",
    "\n",
    "temp=[]\n",
    "rfc=RandomForestClassifier(random_state=0)\n",
    "params={'n_estimators':[50,100,150,200,250],'max_depth':[3,5,7],'min_samples_leaf':[2,4,6]}\n",
    "temp.append([rfc,params])\n",
    "\n",
    "gbt=GradientBoostingClassifier(random_state=0)\n",
    "params={'learning_rate':[0.01,0.05,0.1,0.15,0.2],'n_estimators':[100,300,500],\n",
    "       'max_depth':[3,5,7]}\n",
    "temp.append([gbt,params])\n",
    "\n",
    "\n",
    "lr=LogisticRegression()\n",
    "params={'C':[0.01,0.1,0.5,1,2,3,4,5,6],'solver':['newton-cg','lbfgs','sag']}\n",
    "temp.append([lr,params])\n",
    "\n",
    "for i in range(len(model)):\n",
    "    best_model=GridSearchCV(temp[i][0],param_grid=temp[i][1],refit=True,cv=5).fit(train_val_x,train_val_y)\n",
    "    print(model[i],':')\n",
    "    print('best parameters:',best_model.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rfc的auc为：0.772\n",
      "gdbt的auc为：0.792\n",
      "lr的auc为：0.826\n"
     ]
    }
   ],
   "source": [
    "model={}\n",
    "model['rfc']=RandomForestClassifier(max_depth=7,min_samples_leaf=4,n_estimators=50)\n",
    "model['gdbt']=GradientBoostingClassifier(learning_rate=0.1,max_depth=3,n_estimators=100)\n",
    "model['lr']=LogisticRegression(C=0.5,solver='newton-cg')\n",
    "for i in model:\n",
    "    model[i].fit(train_val_x,train_val_y)\n",
    "    score=cross_val_score(model[i],train_val_x,train_val_y,cv=5,scoring='roc_auc')\n",
    "    print('%s的auc为：%.3f'%(i,score.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.823\n"
     ]
    }
   ],
   "source": [
    "#使用集成模型中的voting\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "voting_clf = VotingClassifier(estimators=[\n",
    "    ('rfc',RandomForestClassifier(max_depth=7,min_samples_leaf=4,n_estimators=50)),\n",
    "   ('gdbt',GradientBoostingClassifier(learning_rate=0.1,max_depth=3,n_estimators=100)),\n",
    "     ('lr',LogisticRegression(C=0.5,solver='newton-cg'))\n",
    "], voting='soft')\n",
    "scores=cross_val_score(voting_clf,train_val_x,train_val_y,cv=5,scoring='roc_auc')\n",
    "print('%.3f'%(scores.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [0.47535   , 0.52465   ],\n",
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       "       [0.9860326 , 0.0139674 ],\n",
       "       [0.19440297, 0.80559703],\n",
       "       [0.2856007 , 0.7143993 ],\n",
       "       [0.75336565, 0.24663435],\n",
       "       [0.82079107, 0.17920893],\n",
       "       [0.98608472, 0.01391528],\n",
       "       [0.72857742, 0.27142258],\n",
       "       [0.89662642, 0.10337358],\n",
       "       [0.88337869, 0.11662131],\n",
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       "       [0.66590142, 0.33409858],\n",
       "       [0.15654294, 0.84345706],\n",
       "       [0.47948097, 0.52051903],\n",
       "       [0.45218082, 0.54781918],\n",
       "       [0.29528671, 0.70471329],\n",
       "       [0.97546305, 0.02453695],\n",
       "       [0.68941091, 0.31058909],\n",
       "       [0.61556549, 0.38443451],\n",
       "       [0.5495915 , 0.4504085 ],\n",
       "       [0.64110546, 0.35889454],\n",
       "       [0.9981017 , 0.0018983 ],\n",
       "       [0.45229715, 0.54770285],\n",
       "       [0.98103098, 0.01896902],\n",
       "       [0.87034375, 0.12965625],\n",
       "       [0.56552651, 0.43447349],\n",
       "       [0.15646354, 0.84353646],\n",
       "       [0.88074027, 0.11925973],\n",
       "       [0.67272195, 0.32727805]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 因此我们最后的选择是lr\n",
    "model=LogisticRegression(C=0.5,solver='newton-cg').fit(train_val_x,train_val_y)\n",
    "result=model.predict_proba(test_x)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.664495</td>\n",
       "      <td>0.335505</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.641529</td>\n",
       "      <td>0.358471</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.116863</td>\n",
       "      <td>0.883137</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.502931</td>\n",
       "      <td>0.497069</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.070202</td>\n",
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       "      <th>5</th>\n",
       "      <td>0.810523</td>\n",
       "      <td>0.189477</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.200230</td>\n",
       "      <td>0.799770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.847326</td>\n",
       "      <td>0.152674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.989581</td>\n",
       "      <td>0.010419</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.258427</td>\n",
       "      <td>0.741573</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.639620</td>\n",
       "      <td>0.360380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.627105</td>\n",
       "      <td>0.372895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.914504</td>\n",
       "      <td>0.085496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.222901</td>\n",
       "      <td>0.777099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.715887</td>\n",
       "      <td>0.284113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.952467</td>\n",
       "      <td>0.047533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.561779</td>\n",
       "      <td>0.438221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.761188</td>\n",
       "      <td>0.238812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.841472</td>\n",
       "      <td>0.158528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.214250</td>\n",
       "      <td>0.785750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.597538</td>\n",
       "      <td>0.402462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.911433</td>\n",
       "      <td>0.088567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.893643</td>\n",
       "      <td>0.106357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.748360</td>\n",
       "      <td>0.251640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.095825</td>\n",
       "      <td>0.904175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.286530</td>\n",
       "      <td>0.713470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.527500</td>\n",
       "      <td>0.472500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.835947</td>\n",
       "      <td>0.164053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.115131</td>\n",
       "      <td>0.884869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.787115</td>\n",
       "      <td>0.212885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.968460</td>\n",
       "      <td>0.031540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.568594</td>\n",
       "      <td>0.431406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.862119</td>\n",
       "      <td>0.137881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.674878</td>\n",
       "      <td>0.325122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.841139</td>\n",
       "      <td>0.158861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.936312</td>\n",
       "      <td>0.063688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.272538</td>\n",
       "      <td>0.727462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.695845</td>\n",
       "      <td>0.304155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.838151</td>\n",
       "      <td>0.161849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.444435</td>\n",
       "      <td>0.555565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.860587</td>\n",
       "      <td>0.139413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.918908</td>\n",
       "      <td>0.081092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.986631</td>\n",
       "      <td>0.013369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.968540</td>\n",
       "      <td>0.031460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.976555</td>\n",
       "      <td>0.023445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.128455</td>\n",
       "      <td>0.871545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.201104</td>\n",
       "      <td>0.798896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.995464</td>\n",
       "      <td>0.004536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.274841</td>\n",
       "      <td>0.725159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.259746</td>\n",
       "      <td>0.740254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.702658</td>\n",
       "      <td>0.297342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0.049212</td>\n",
       "      <td>0.950788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>0.906114</td>\n",
       "      <td>0.093886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>0.396850</td>\n",
       "      <td>0.603150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0.158361</td>\n",
       "      <td>0.841639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.704500</td>\n",
       "      <td>0.295500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.953149</td>\n",
       "      <td>0.046851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.188512</td>\n",
       "      <td>0.811488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.970308</td>\n",
       "      <td>0.029692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.348684</td>\n",
       "      <td>0.651316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.891990</td>\n",
       "      <td>0.108010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>0.669000</td>\n",
       "      <td>0.331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.516448</td>\n",
       "      <td>0.483552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>0.931304</td>\n",
       "      <td>0.068696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.532100</td>\n",
       "      <td>0.467900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.892861</td>\n",
       "      <td>0.107139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>0.278714</td>\n",
       "      <td>0.721286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.690262</td>\n",
       "      <td>0.309738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.498703</td>\n",
       "      <td>0.501297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.379997</td>\n",
       "      <td>0.620003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.760896</td>\n",
       "      <td>0.239104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.511635</td>\n",
       "      <td>0.488365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.838155</td>\n",
       "      <td>0.161845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.750629</td>\n",
       "      <td>0.249371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.707486</td>\n",
       "      <td>0.292514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.787400</td>\n",
       "      <td>0.212600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.750908</td>\n",
       "      <td>0.249092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.734279</td>\n",
       "      <td>0.265721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.183329</td>\n",
       "      <td>0.816671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.891416</td>\n",
       "      <td>0.108584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.942116</td>\n",
       "      <td>0.057884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.894979</td>\n",
       "      <td>0.105021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.234338</td>\n",
       "      <td>0.765662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.904967</td>\n",
       "      <td>0.095033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.945808</td>\n",
       "      <td>0.054192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.566610</td>\n",
       "      <td>0.433390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.984058</td>\n",
       "      <td>0.015942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.817222</td>\n",
       "      <td>0.182778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.955420</td>\n",
       "      <td>0.044580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.734511</td>\n",
       "      <td>0.265489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.767918</td>\n",
       "      <td>0.232082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.769071</td>\n",
       "      <td>0.230929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.344679</td>\n",
       "      <td>0.655321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>0.490452</td>\n",
       "      <td>0.509548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0.991579</td>\n",
       "      <td>0.008421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.709002</td>\n",
       "      <td>0.290998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.105369</td>\n",
       "      <td>0.894631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.299121</td>\n",
       "      <td>0.700879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.574440</td>\n",
       "      <td>0.425560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.685823</td>\n",
       "      <td>0.314177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.656519</td>\n",
       "      <td>0.343481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0.146745</td>\n",
       "      <td>0.853255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.058923</td>\n",
       "      <td>0.941077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0.968910</td>\n",
       "      <td>0.031090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.825725</td>\n",
       "      <td>0.174275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.927224</td>\n",
       "      <td>0.072776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0.631879</td>\n",
       "      <td>0.368121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>0.502090</td>\n",
       "      <td>0.497910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>0.742942</td>\n",
       "      <td>0.257058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>0.178929</td>\n",
       "      <td>0.821071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>0.826577</td>\n",
       "      <td>0.173423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>0.640640</td>\n",
       "      <td>0.359360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>0.966367</td>\n",
       "      <td>0.033633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>0.341349</td>\n",
       "      <td>0.658651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>0.865250</td>\n",
       "      <td>0.134750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0.716563</td>\n",
       "      <td>0.283437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.939149</td>\n",
       "      <td>0.060851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.841335</td>\n",
       "      <td>0.158665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>0.971657</td>\n",
       "      <td>0.028343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>0.989294</td>\n",
       "      <td>0.010706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0.500986</td>\n",
       "      <td>0.499014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>0.748734</td>\n",
       "      <td>0.251266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>0.680688</td>\n",
       "      <td>0.319312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>0.034716</td>\n",
       "      <td>0.965284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0.917634</td>\n",
       "      <td>0.082366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.978456</td>\n",
       "      <td>0.021544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>0.942304</td>\n",
       "      <td>0.057696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>0.612132</td>\n",
       "      <td>0.387868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0.438404</td>\n",
       "      <td>0.561596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>0.938608</td>\n",
       "      <td>0.061392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>0.956723</td>\n",
       "      <td>0.043277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>0.264926</td>\n",
       "      <td>0.735074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>0.069818</td>\n",
       "      <td>0.930182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>0.449365</td>\n",
       "      <td>0.550635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>0.937009</td>\n",
       "      <td>0.062991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0.344712</td>\n",
       "      <td>0.655288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>0.153887</td>\n",
       "      <td>0.846113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>0.202785</td>\n",
       "      <td>0.797215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.434223</td>\n",
       "      <td>0.565777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.473686</td>\n",
       "      <td>0.526314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.851692</td>\n",
       "      <td>0.148308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.747446</td>\n",
       "      <td>0.252554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0.829631</td>\n",
       "      <td>0.170369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0.548497</td>\n",
       "      <td>0.451503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>0.126130</td>\n",
       "      <td>0.873870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0.815932</td>\n",
       "      <td>0.184068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0.309211</td>\n",
       "      <td>0.690789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.968727</td>\n",
       "      <td>0.031273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.657254</td>\n",
       "      <td>0.342746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.623139</td>\n",
       "      <td>0.376861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>0.972459</td>\n",
       "      <td>0.027541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>0.585738</td>\n",
       "      <td>0.414262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.668344</td>\n",
       "      <td>0.331656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.224494</td>\n",
       "      <td>0.775506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>0.948562</td>\n",
       "      <td>0.051438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0.904115</td>\n",
       "      <td>0.095885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>0.386965</td>\n",
       "      <td>0.613035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0.231657</td>\n",
       "      <td>0.768343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.932050</td>\n",
       "      <td>0.067950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.925471</td>\n",
       "      <td>0.074529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.053943</td>\n",
       "      <td>0.946057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.970930</td>\n",
       "      <td>0.029070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>0.866964</td>\n",
       "      <td>0.133036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>0.023188</td>\n",
       "      <td>0.976812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>0.912740</td>\n",
       "      <td>0.087260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0.597873</td>\n",
       "      <td>0.402127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>0.439278</td>\n",
       "      <td>0.560722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>0.529444</td>\n",
       "      <td>0.470556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>0.907941</td>\n",
       "      <td>0.092059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>0.987287</td>\n",
       "      <td>0.012713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>0.217237</td>\n",
       "      <td>0.782763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0.618697</td>\n",
       "      <td>0.381303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>0.920429</td>\n",
       "      <td>0.079571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>0.546031</td>\n",
       "      <td>0.453969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0.798680</td>\n",
       "      <td>0.201320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0.729930</td>\n",
       "      <td>0.270070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0.864891</td>\n",
       "      <td>0.135109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>0.826681</td>\n",
       "      <td>0.173319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0.818295</td>\n",
       "      <td>0.181705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0.580277</td>\n",
       "      <td>0.419723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.936642</td>\n",
       "      <td>0.063358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.675768</td>\n",
       "      <td>0.324232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.986607</td>\n",
       "      <td>0.013393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.041111</td>\n",
       "      <td>0.958889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.524990</td>\n",
       "      <td>0.475010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>0.605328</td>\n",
       "      <td>0.394672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0.107326</td>\n",
       "      <td>0.892674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.950450</td>\n",
       "      <td>0.049550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>0.160885</td>\n",
       "      <td>0.839115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>0.410303</td>\n",
       "      <td>0.589697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.710722</td>\n",
       "      <td>0.289278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>0.276115</td>\n",
       "      <td>0.723885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.552563</td>\n",
       "      <td>0.447437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.873441</td>\n",
       "      <td>0.126559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.819505</td>\n",
       "      <td>0.180495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>0.512620</td>\n",
       "      <td>0.487380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.884349</td>\n",
       "      <td>0.115651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.893083</td>\n",
       "      <td>0.106917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>0.395456</td>\n",
       "      <td>0.604544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.885198</td>\n",
       "      <td>0.114802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0.264827</td>\n",
       "      <td>0.735173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0.317010</td>\n",
       "      <td>0.682990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0.210320</td>\n",
       "      <td>0.789680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.860826</td>\n",
       "      <td>0.139174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.616095</td>\n",
       "      <td>0.383905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.914501</td>\n",
       "      <td>0.085499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.298543</td>\n",
       "      <td>0.701457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0.994044</td>\n",
       "      <td>0.005956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0.590983</td>\n",
       "      <td>0.409017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0.844515</td>\n",
       "      <td>0.155485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0.635090</td>\n",
       "      <td>0.364910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>0.278657</td>\n",
       "      <td>0.721343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>0.683438</td>\n",
       "      <td>0.316562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>0.215956</td>\n",
       "      <td>0.784044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>0.638086</td>\n",
       "      <td>0.361914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>0.963660</td>\n",
       "      <td>0.036340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>0.766654</td>\n",
       "      <td>0.233346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>0.896778</td>\n",
       "      <td>0.103222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>0.813480</td>\n",
       "      <td>0.186520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>0.715710</td>\n",
       "      <td>0.284290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>0.550215</td>\n",
       "      <td>0.449785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>0.134092</td>\n",
       "      <td>0.865908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>0.192791</td>\n",
       "      <td>0.807209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>0.242417</td>\n",
       "      <td>0.757583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>0.330733</td>\n",
       "      <td>0.669267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>0.963291</td>\n",
       "      <td>0.036709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>0.756283</td>\n",
       "      <td>0.243717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>0.641852</td>\n",
       "      <td>0.358148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.046468</td>\n",
       "      <td>0.953532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>0.631361</td>\n",
       "      <td>0.368639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>0.312686</td>\n",
       "      <td>0.687314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>0.747434</td>\n",
       "      <td>0.252566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>0.836149</td>\n",
       "      <td>0.163851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>0.897668</td>\n",
       "      <td>0.102332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>0.834481</td>\n",
       "      <td>0.165519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>0.670669</td>\n",
       "      <td>0.329331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>0.647236</td>\n",
       "      <td>0.352764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>0.991752</td>\n",
       "      <td>0.008248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>0.885110</td>\n",
       "      <td>0.114890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>0.978297</td>\n",
       "      <td>0.021703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>0.453562</td>\n",
       "      <td>0.546438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>0.389915</td>\n",
       "      <td>0.610085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0.906622</td>\n",
       "      <td>0.093378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>0.799534</td>\n",
       "      <td>0.200466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>0.728706</td>\n",
       "      <td>0.271294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0.919014</td>\n",
       "      <td>0.080986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>0.523618</td>\n",
       "      <td>0.476382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>0.207387</td>\n",
       "      <td>0.792613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>0.754300</td>\n",
       "      <td>0.245700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>0.758276</td>\n",
       "      <td>0.241724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>0.336193</td>\n",
       "      <td>0.663807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0.627377</td>\n",
       "      <td>0.372623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0.220238</td>\n",
       "      <td>0.779762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>0.877831</td>\n",
       "      <td>0.122169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0.495161</td>\n",
       "      <td>0.504839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>0.758219</td>\n",
       "      <td>0.241781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>0.948168</td>\n",
       "      <td>0.051832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>0.184359</td>\n",
       "      <td>0.815641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>0.095241</td>\n",
       "      <td>0.904759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>0.486673</td>\n",
       "      <td>0.513327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0.519742</td>\n",
       "      <td>0.480258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>0.822768</td>\n",
       "      <td>0.177232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>0.475350</td>\n",
       "      <td>0.524650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>0.776179</td>\n",
       "      <td>0.223821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>0.914720</td>\n",
       "      <td>0.085280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>0.459822</td>\n",
       "      <td>0.540178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>0.986033</td>\n",
       "      <td>0.013967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>0.194403</td>\n",
       "      <td>0.805597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>0.285601</td>\n",
       "      <td>0.714399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>0.753366</td>\n",
       "      <td>0.246634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>0.820791</td>\n",
       "      <td>0.179209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>0.986085</td>\n",
       "      <td>0.013915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>0.728577</td>\n",
       "      <td>0.271423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>0.896626</td>\n",
       "      <td>0.103374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0.883379</td>\n",
       "      <td>0.116621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>0.901294</td>\n",
       "      <td>0.098706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>0.665901</td>\n",
       "      <td>0.334099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>0.156543</td>\n",
       "      <td>0.843457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>0.479481</td>\n",
       "      <td>0.520519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>0.452181</td>\n",
       "      <td>0.547819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0.295287</td>\n",
       "      <td>0.704713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>0.975463</td>\n",
       "      <td>0.024537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>0.689411</td>\n",
       "      <td>0.310589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>0.615565</td>\n",
       "      <td>0.384435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>0.549591</td>\n",
       "      <td>0.450409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>0.641105</td>\n",
       "      <td>0.358895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>0.998102</td>\n",
       "      <td>0.001898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.452297</td>\n",
       "      <td>0.547703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>0.981031</td>\n",
       "      <td>0.018969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>0.870344</td>\n",
       "      <td>0.129656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>0.565527</td>\n",
       "      <td>0.434473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>0.156464</td>\n",
       "      <td>0.843536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>0.880740</td>\n",
       "      <td>0.119260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>0.672722</td>\n",
       "      <td>0.327278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1\n",
       "0    0.664495  0.335505\n",
       "1    0.641529  0.358471\n",
       "2    0.116863  0.883137\n",
       "3    0.502931  0.497069\n",
       "4    0.070202  0.929798\n",
       "5    0.810523  0.189477\n",
       "6    0.200230  0.799770\n",
       "7    0.847326  0.152674\n",
       "8    0.989581  0.010419\n",
       "9    0.258427  0.741573\n",
       "10   0.639620  0.360380\n",
       "11   0.627105  0.372895\n",
       "12   0.914504  0.085496\n",
       "13   0.222901  0.777099\n",
       "14   0.715887  0.284113\n",
       "15   0.952467  0.047533\n",
       "16   0.561779  0.438221\n",
       "17   0.761188  0.238812\n",
       "18   0.841472  0.158528\n",
       "19   0.214250  0.785750\n",
       "20   0.597538  0.402462\n",
       "21   0.911433  0.088567\n",
       "22   0.893643  0.106357\n",
       "23   0.748360  0.251640\n",
       "24   0.095825  0.904175\n",
       "25   0.286530  0.713470\n",
       "26   0.527500  0.472500\n",
       "27   0.835947  0.164053\n",
       "28   0.115131  0.884869\n",
       "29   0.787115  0.212885\n",
       "30   0.968460  0.031540\n",
       "31   0.568594  0.431406\n",
       "32   0.862119  0.137881\n",
       "33   0.674878  0.325122\n",
       "34   0.841139  0.158861\n",
       "35   0.936312  0.063688\n",
       "36   0.272538  0.727462\n",
       "37   0.695845  0.304155\n",
       "38   0.838151  0.161849\n",
       "39   0.444435  0.555565\n",
       "40   0.860587  0.139413\n",
       "41   0.918908  0.081092\n",
       "42   0.986631  0.013369\n",
       "43   0.968540  0.031460\n",
       "44   0.976555  0.023445\n",
       "45   0.128455  0.871545\n",
       "46   0.201104  0.798896\n",
       "47   0.995464  0.004536\n",
       "48   0.274841  0.725159\n",
       "49   0.259746  0.740254\n",
       "50   0.702658  0.297342\n",
       "51   0.049212  0.950788\n",
       "52   0.906114  0.093886\n",
       "53   0.396850  0.603150\n",
       "54   0.158361  0.841639\n",
       "55   0.704500  0.295500\n",
       "56   0.953149  0.046851\n",
       "57   0.188512  0.811488\n",
       "58   0.970308  0.029692\n",
       "59   0.348684  0.651316\n",
       "60   0.891990  0.108010\n",
       "61   0.669000  0.331000\n",
       "62   0.516448  0.483552\n",
       "63   0.931304  0.068696\n",
       "64   0.532100  0.467900\n",
       "65   0.892861  0.107139\n",
       "66   0.278714  0.721286\n",
       "67   0.690262  0.309738\n",
       "68   0.498703  0.501297\n",
       "69   0.379997  0.620003\n",
       "70   0.760896  0.239104\n",
       "71   0.511635  0.488365\n",
       "72   0.838155  0.161845\n",
       "73   0.750629  0.249371\n",
       "74   0.707486  0.292514\n",
       "75   0.787400  0.212600\n",
       "76   0.750908  0.249092\n",
       "77   0.734279  0.265721\n",
       "78   0.183329  0.816671\n",
       "79   0.891416  0.108584\n",
       "80   0.942116  0.057884\n",
       "81   0.894979  0.105021\n",
       "82   0.234338  0.765662\n",
       "83   0.904967  0.095033\n",
       "84   0.945808  0.054192\n",
       "85   0.566610  0.433390\n",
       "86   0.984058  0.015942\n",
       "87   0.817222  0.182778\n",
       "88   0.955420  0.044580\n",
       "89   0.734511  0.265489\n",
       "90   0.767918  0.232082\n",
       "91   0.769071  0.230929\n",
       "92   0.344679  0.655321\n",
       "93   0.490452  0.509548\n",
       "94   0.991579  0.008421\n",
       "95   0.709002  0.290998\n",
       "96   0.105369  0.894631\n",
       "97   0.299121  0.700879\n",
       "98   0.574440  0.425560\n",
       "99   0.685823  0.314177\n",
       "100  0.656519  0.343481\n",
       "101  0.146745  0.853255\n",
       "102  0.058923  0.941077\n",
       "103  0.968910  0.031090\n",
       "104  0.825725  0.174275\n",
       "105  0.927224  0.072776\n",
       "106  0.631879  0.368121\n",
       "107  0.502090  0.497910\n",
       "108  0.742942  0.257058\n",
       "109  0.178929  0.821071\n",
       "110  0.826577  0.173423\n",
       "111  0.640640  0.359360\n",
       "112  0.966367  0.033633\n",
       "113  0.341349  0.658651\n",
       "114  0.865250  0.134750\n",
       "115  0.716563  0.283437\n",
       "116  0.939149  0.060851\n",
       "117  0.841335  0.158665\n",
       "118  0.971657  0.028343\n",
       "119  0.989294  0.010706\n",
       "120  0.500986  0.499014\n",
       "121  0.748734  0.251266\n",
       "122  0.680688  0.319312\n",
       "123  0.034716  0.965284\n",
       "124  0.917634  0.082366\n",
       "125  0.978456  0.021544\n",
       "126  0.942304  0.057696\n",
       "127  0.612132  0.387868\n",
       "128  0.438404  0.561596\n",
       "129  0.938608  0.061392\n",
       "130  0.956723  0.043277\n",
       "131  0.264926  0.735074\n",
       "132  0.069818  0.930182\n",
       "133  0.449365  0.550635\n",
       "134  0.937009  0.062991\n",
       "135  0.344712  0.655288\n",
       "136  0.153887  0.846113\n",
       "137  0.202785  0.797215\n",
       "138  0.434223  0.565777\n",
       "139  0.473686  0.526314\n",
       "140  0.851692  0.148308\n",
       "141  0.747446  0.252554\n",
       "142  0.829631  0.170369\n",
       "143  0.548497  0.451503\n",
       "144  0.126130  0.873870\n",
       "145  0.815932  0.184068\n",
       "146  0.309211  0.690789\n",
       "147  0.968727  0.031273\n",
       "148  0.657254  0.342746\n",
       "149  0.623139  0.376861\n",
       "150  0.972459  0.027541\n",
       "151  0.585738  0.414262\n",
       "152  0.668344  0.331656\n",
       "153  0.224494  0.775506\n",
       "154  0.948562  0.051438\n",
       "155  0.904115  0.095885\n",
       "156  0.386965  0.613035\n",
       "157  0.231657  0.768343\n",
       "158  0.932050  0.067950\n",
       "159  0.925471  0.074529\n",
       "160  0.053943  0.946057\n",
       "161  0.970930  0.029070\n",
       "162  0.866964  0.133036\n",
       "163  0.023188  0.976812\n",
       "164  0.912740  0.087260\n",
       "165  0.597873  0.402127\n",
       "166  0.439278  0.560722\n",
       "167  0.529444  0.470556\n",
       "168  0.907941  0.092059\n",
       "169  0.987287  0.012713\n",
       "170  0.217237  0.782763\n",
       "171  0.618697  0.381303\n",
       "172  0.920429  0.079571\n",
       "173  0.546031  0.453969\n",
       "174  0.798680  0.201320\n",
       "175  0.729930  0.270070\n",
       "176  0.864891  0.135109\n",
       "177  0.826681  0.173319\n",
       "178  0.818295  0.181705\n",
       "179  0.580277  0.419723\n",
       "180  0.936642  0.063358\n",
       "181  0.675768  0.324232\n",
       "182  0.986607  0.013393\n",
       "183  0.041111  0.958889\n",
       "184  0.524990  0.475010\n",
       "185  0.605328  0.394672\n",
       "186  0.107326  0.892674\n",
       "187  0.950450  0.049550\n",
       "188  0.160885  0.839115\n",
       "189  0.410303  0.589697\n",
       "190  0.710722  0.289278\n",
       "191  0.276115  0.723885\n",
       "192  0.552563  0.447437\n",
       "193  0.873441  0.126559\n",
       "194  0.819505  0.180495\n",
       "195  0.512620  0.487380\n",
       "196  0.884349  0.115651\n",
       "197  0.893083  0.106917\n",
       "198  0.395456  0.604544\n",
       "199  0.885198  0.114802\n",
       "200  0.264827  0.735173\n",
       "201  0.317010  0.682990\n",
       "202  0.210320  0.789680\n",
       "203  0.860826  0.139174\n",
       "204  0.616095  0.383905\n",
       "205  0.914501  0.085499\n",
       "206  0.298543  0.701457\n",
       "207  0.994044  0.005956\n",
       "208  0.590983  0.409017\n",
       "209  0.844515  0.155485\n",
       "210  0.635090  0.364910\n",
       "211  0.278657  0.721343\n",
       "212  0.683438  0.316562\n",
       "213  0.215956  0.784044\n",
       "214  0.638086  0.361914\n",
       "215  0.963660  0.036340\n",
       "216  0.766654  0.233346\n",
       "217  0.896778  0.103222\n",
       "218  0.813480  0.186520\n",
       "219  0.715710  0.284290\n",
       "220  0.550215  0.449785\n",
       "221  0.134092  0.865908\n",
       "222  0.192791  0.807209\n",
       "223  0.242417  0.757583\n",
       "224  0.330733  0.669267\n",
       "225  0.963291  0.036709\n",
       "226  0.756283  0.243717\n",
       "227  0.641852  0.358148\n",
       "228  0.046468  0.953532\n",
       "229  0.631361  0.368639\n",
       "230  0.312686  0.687314\n",
       "231  0.747434  0.252566\n",
       "232  0.836149  0.163851\n",
       "233  0.897668  0.102332\n",
       "234  0.834481  0.165519\n",
       "235  0.670669  0.329331\n",
       "236  0.647236  0.352764\n",
       "237  0.991752  0.008248\n",
       "238  0.885110  0.114890\n",
       "239  0.978297  0.021703\n",
       "240  0.453562  0.546438\n",
       "241  0.389915  0.610085\n",
       "242  0.906622  0.093378\n",
       "243  0.799534  0.200466\n",
       "244  0.728706  0.271294\n",
       "245  0.919014  0.080986\n",
       "246  0.523618  0.476382\n",
       "247  0.207387  0.792613\n",
       "248  0.754300  0.245700\n",
       "249  0.758276  0.241724\n",
       "250  0.336193  0.663807\n",
       "251  0.627377  0.372623\n",
       "252  0.220238  0.779762\n",
       "253  0.877831  0.122169\n",
       "254  0.495161  0.504839\n",
       "255  0.758219  0.241781\n",
       "256  0.948168  0.051832\n",
       "257  0.184359  0.815641\n",
       "258  0.095241  0.904759\n",
       "259  0.486673  0.513327\n",
       "260  0.519742  0.480258\n",
       "261  0.822768  0.177232\n",
       "262  0.475350  0.524650\n",
       "263  0.776179  0.223821\n",
       "264  0.914720  0.085280\n",
       "265  0.459822  0.540178\n",
       "266  0.986033  0.013967\n",
       "267  0.194403  0.805597\n",
       "268  0.285601  0.714399\n",
       "269  0.753366  0.246634\n",
       "270  0.820791  0.179209\n",
       "271  0.986085  0.013915\n",
       "272  0.728577  0.271423\n",
       "273  0.896626  0.103374\n",
       "274  0.883379  0.116621\n",
       "275  0.901294  0.098706\n",
       "276  0.665901  0.334099\n",
       "277  0.156543  0.843457\n",
       "278  0.479481  0.520519\n",
       "279  0.452181  0.547819\n",
       "280  0.295287  0.704713\n",
       "281  0.975463  0.024537\n",
       "282  0.689411  0.310589\n",
       "283  0.615565  0.384435\n",
       "284  0.549591  0.450409\n",
       "285  0.641105  0.358895\n",
       "286  0.998102  0.001898\n",
       "287  0.452297  0.547703\n",
       "288  0.981031  0.018969\n",
       "289  0.870344  0.129656\n",
       "290  0.565527  0.434473\n",
       "291  0.156464  0.843536\n",
       "292  0.880740  0.119260\n",
       "293  0.672722  0.327278"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_predict=pd.DataFrame(result)\n",
    "data_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_predict.to_csv('sample1.csv')"
   ]
  }
 ],
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