lec76-79-ロジスティック回帰.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series,DataFrame\n",
    "\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Logistic Function')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ロジスティック関数\n",
    "def logistic(t):\n",
    "    return 1.0 / (1 + math.exp((-1.0)*t))\n",
    "\n",
    "# tを-6から6まで500点用意\n",
    "t = np.linspace(-6,6,500)\n",
    "\n",
    "y = np.array([logistic(ele) for ele in t])\n",
    "\n",
    "plt.plot(t,y)\n",
    "plt.title('Logistic Function')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sm.datasets.fair.load_pandas().data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.666666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage   age    ...     occupation_husb   affairs\n",
       "0            3.0  32.0    ...                 5.0  0.111111\n",
       "1            3.0  27.0    ...                 4.0  3.230769\n",
       "2            4.0  22.0    ...                 5.0  1.400000\n",
       "3            4.0  37.0    ...                 5.0  0.727273\n",
       "4            5.0  27.0    ...                 4.0  4.666666\n",
       "\n",
       "[5 rows x 9 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不倫の有無を0,1で表す新しい列'Had_Affair'を作る\n",
    "def affair_check(x):\n",
    "    if x != 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "df['Had_Affair'] = df['affairs'].apply(affair_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "      <th>Had_Affair</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.230769</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.666666</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.666666</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.852174</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.826086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.799999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.266665</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.041666</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.484848</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.266665</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.361111</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.826086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.826086</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.041666</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.839996</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.545454</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.532609</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.799999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.187878</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.199999</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6336</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6337</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6338</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6339</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6340</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6341</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6342</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6343</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6344</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6345</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6346</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6347</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6348</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6349</th>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6350</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6351</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6352</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6353</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6354</th>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6355</th>\n",
       "      <td>4.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6356</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6357</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6358</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6359</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6360</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6361</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6362</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6363</th>\n",
       "      <td>5.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6364</th>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6365</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6366 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rate_marriage   age     ...        affairs  Had_Affair\n",
       "0               3.0  32.0     ...       0.111111           1\n",
       "1               3.0  27.0     ...       3.230769           1\n",
       "2               4.0  22.0     ...       1.400000           1\n",
       "3               4.0  37.0     ...       0.727273           1\n",
       "4               5.0  27.0     ...       4.666666           1\n",
       "5               4.0  27.0     ...       4.666666           1\n",
       "6               5.0  37.0     ...       0.852174           1\n",
       "7               5.0  37.0     ...       1.826086           1\n",
       "8               3.0  22.0     ...       4.799999           1\n",
       "9               3.0  27.0     ...       1.333333           1\n",
       "10              2.0  27.0     ...       3.266665           1\n",
       "11              5.0  27.0     ...       2.041666           1\n",
       "12              3.0  37.0     ...       0.484848           1\n",
       "13              5.0  27.0     ...       2.000000           1\n",
       "14              4.0  22.0     ...       3.266665           1\n",
       "15              4.0  37.0     ...       1.361111           1\n",
       "16              4.0  27.0     ...       2.000000           1\n",
       "17              1.0  37.0     ...       1.826086           1\n",
       "18              2.0  42.0     ...       1.826086           1\n",
       "19              4.0  37.0     ...       2.041666           1\n",
       "20              5.0  22.0     ...       7.839996           1\n",
       "21              3.0  37.0     ...       2.545454           1\n",
       "22              3.0  42.0     ...       0.532609           1\n",
       "23              2.0  27.0     ...       0.622222           1\n",
       "24              4.0  27.0     ...       0.583333           1\n",
       "25              5.0  27.0     ...       4.799999           1\n",
       "26              2.0  27.0     ...       0.166667           1\n",
       "27              5.0  37.0     ...       0.615385           1\n",
       "28              2.0  32.0     ...       1.187878           1\n",
       "29              3.0  27.0     ...      11.199999           1\n",
       "...             ...   ...     ...            ...         ...\n",
       "6336            5.0  42.0     ...       0.000000           0\n",
       "6337            5.0  27.0     ...       0.000000           0\n",
       "6338            5.0  42.0     ...       0.000000           0\n",
       "6339            4.0  32.0     ...       0.000000           0\n",
       "6340            5.0  27.0     ...       0.000000           0\n",
       "6341            5.0  27.0     ...       0.000000           0\n",
       "6342            4.0  22.0     ...       0.000000           0\n",
       "6343            5.0  17.5     ...       0.000000           0\n",
       "6344            4.0  32.0     ...       0.000000           0\n",
       "6345            5.0  27.0     ...       0.000000           0\n",
       "6346            4.0  22.0     ...       0.000000           0\n",
       "6347            5.0  22.0     ...       0.000000           0\n",
       "6348            5.0  27.0     ...       0.000000           0\n",
       "6349            5.0  37.0     ...       0.000000           0\n",
       "6350            5.0  32.0     ...       0.000000           0\n",
       "6351            4.0  22.0     ...       0.000000           0\n",
       "6352            5.0  42.0     ...       0.000000           0\n",
       "6353            5.0  22.0     ...       0.000000           0\n",
       "6354            5.0  42.0     ...       0.000000           0\n",
       "6355            4.0  27.0     ...       0.000000           0\n",
       "6356            5.0  32.0     ...       0.000000           0\n",
       "6357            5.0  32.0     ...       0.000000           0\n",
       "6358            3.0  27.0     ...       0.000000           0\n",
       "6359            4.0  22.0     ...       0.000000           0\n",
       "6360            5.0  22.0     ...       0.000000           0\n",
       "6361            5.0  32.0     ...       0.000000           0\n",
       "6362            4.0  32.0     ...       0.000000           0\n",
       "6363            5.0  22.0     ...       0.000000           0\n",
       "6364            5.0  32.0     ...       0.000000           0\n",
       "6365            4.0  22.0     ...       0.000000           0\n",
       "\n",
       "[6366 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occupation</th>\n",
       "      <th>occupation_husb</th>\n",
       "      <th>affairs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Had_Affair</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.329701</td>\n",
       "      <td>28.390679</td>\n",
       "      <td>7.989335</td>\n",
       "      <td>1.238813</td>\n",
       "      <td>2.504521</td>\n",
       "      <td>14.322977</td>\n",
       "      <td>3.405286</td>\n",
       "      <td>3.833758</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.647345</td>\n",
       "      <td>30.537019</td>\n",
       "      <td>11.152460</td>\n",
       "      <td>1.728933</td>\n",
       "      <td>2.261568</td>\n",
       "      <td>13.972236</td>\n",
       "      <td>3.463712</td>\n",
       "      <td>3.884559</td>\n",
       "      <td>2.187243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rate_marriage        age    ...     occupation_husb   affairs\n",
       "Had_Affair                              ...                              \n",
       "0                4.329701  28.390679    ...            3.833758  0.000000\n",
       "1                3.647345  30.537019    ...            3.884559  2.187243\n",
       "\n",
       "[2 rows x 9 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不倫の有無でグループ分けして、平均する\n",
    "df.groupby('Had_Affair').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1a92dc18>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 年齢分布\n",
    "sns.countplot('age',data=df.sort_values('age'),hue='Had_Affair',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1ac93d68>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 結婚年月\n",
    "sns.countplot('yrs_married',data=df.sort_values('yrs_married'),hue='Had_Affair',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1a895550>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEICAYAAABBBrPDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAGzhJREFUeJzt3X2UVNWd7vFvd9N0t6F9IyQzcxGQmPzSK5Pg0L5gVOSGKBGSi+J1TSdL7qgxGTNmDJobzRId0OV1ogNkDL5EUMNodJQXnWjiC5dEBFHQWwMipvwpML6QBMVWkI7Q9EvdP+q0Fk317gLqVFVXP5+1WNTZZ59z9qa1nt7n1N5VkUqlEBER6UllsRsgIiKlTUEhIiJBCgoREQlSUIiISJCCQkREghQUIiISpKAQEZGgAXGc1MyqgbuBEUANcD3we2ABkAI2AJe4e6eZzQAmAe3ANHd/3syOyVY3jraKiEhYXCOK84Bmdz8VOBO4BZgDXB2VVQCTzWw0cBpwItAE3Bodv0/dmNopIiK9iGVEASwCFmdstwONwNPR9uPAGYADS909BbxpZgPMbEgPdR/u6WLr1q1L1dTU5LcHIiJl7sMPP3y3sbFxSG/1YgkKd28BMLN60oFxNTArCgSAncBhwKFAc8ahXeUVWeqKiEh+vZFLpbhGFJjZUaRHAbe5+/1mdlPG7npgO/BB9Lp7eWeWsh7V1NTQ0NCQl3aLiPQXiUQip3qxPKMws08DS4Er3f3uqHitmY2LXp8JrARWARPMrNLMhgGV7v5uD3VFRKQI4hpRXAUcAVxjZtdEZT8AfmZmA4EksNjdO8xsJfAc6dC6JKr7Q2B+Zt2Y2ikiIr2oKIdlxpPJZEq3nkTkYLW1tbFlyxZ2795d7KbkVW1tLUOHDqW6unqv8kQikWhsbDyut+Nje0YhItLXbNmyhfr6ekaMGEFFRUWxm5MXqVSK5uZmtmzZwtFHH31A59DMbBGRyO7duxk8eHDZhARARUUFgwcPPqhRkoJCRCRDOYVEl4Ptk4JCRESCFBQiIhJU1kHR0RnPJ7riOq+IlJY1a9Zw2WWX7VU2a9YsHnrooV6PXbFiBT/+8Y97rTdv3jxOOeUUWltbPypbv349kyZNYvbs2Sxbtoyvf/3r3HPPPT1e58EHH+z1OgejrD/1VFVZwWMvtOT9vBOPH5T3c4pI//Too48yceJEfvOb3zBlyhQAnnnmGZqampg6dSrTp0/n8ssv5ytf+UrW48eOHRt7G8s6KERE4tDR0cH06dPZunUr77//PmPHjmXatGls2rSJq666irq6Ourq6jjssPAydWvWrGHYsGE0NTXxox/9iClTprB+/XoWL15MdXU1gwYNYvny5axfv54jjjiCl19+maVLl9Le3k59fT1z587l17/+NZs3b6apqYnvfe97HH744YwdO5bvfOc7eeuvgkJEJGD16tVMnTr1o+233nqLSy+9lGOPPZZzzz2X1tbWj4Li5ptv5tJLL+Xkk09m3rx5bN68OXjuRYsWce655zJy5EgGDhzIiy++yKhRozj77LP55Cc/ydlnn82aNWuYOHEio0aNYtWqVSxYsIDKykq+/e1v89JLL+11vm3btrFkyRIGDhyY138DBYWISMCYMWP46U9/+tH2rFmzaGlpYePGjaxevZpBgwaxZ88eAF577TW+9KUvATB69OhgUOzYsYMVK1bw3nvvce+999LS0sIvf/lLRo0albV+ZWUl1dXVXH755RxyyCFs3bqV9vb2veoMHTo07yEBCgoRkQNSX1/PddddxxtvvMHChQtJpVKMHDmStWvXMnbsWDZs2BA8/pFHHuGcc87hyiuvBGDXrl2MHz+e9957L2v9V155hWXLlrFo0SJ27drFlClT6L4EU2VlPJ9PUlCIiOynqqoqVqxYQSKRoK6ujuHDh/POO+8wY8YMLrvsMu666y6OPPJIQl+otmjRIm666eNvX6irq+OMM85g4cKFWesPHz6curo6pkyZwsCBAxkyZAjvvPNO3vuWTdkvCqhPPYlIrpLJZNl+t022vmlRQBGREjBz5kw2bdq0T/n8+fOpra0tQov2n4JCRCRGM2fOLHYTDlpZz8wWEZGDp6AQEZEgBYWIyAHI95pvpbyGnJ5RiIgcgHyvJdfbpyk7OzuZOXMm7s7AgQO5/vrrGT58eN6uHxJbUJjZicCN7j7OzB4A/iLaNQJY7e5NZvYIMBhoA3a5+5lmdgywAEgBG4BL3L0zrnaKiPQFy5YtY8+ePTz44IOsW7eOn/zkJ9x+++0FuXYsQWFmVwBTgT8DuHtTVH4E8BTQtW7vMcAX3D1zzDUHuNrdl5vZz4HJwMNxtFNEpK9IJBKceuqpABx77LG9zvzOp7ieUWwCpmQpvxaY6+5/MrNPA4cDj5rZM2b29ahOI/B09Ppx4KsxtVFEpM9oaWlh0KCPb09VVVXts9ZTXGIZUbj7EjMbkVlmZp8CxvPxaGIgMBu4GTgSWGVmzwMVGSOMnUB4nV6gtbWVZDK5T3mcMyyzXU9E+ra2tjZ27dqVU926urq8Xz907draWt5///2P6nR0dNDW1kZbW1tO525razvg961CPsz+n8D97t4RbW8Ffu7u7cA7ZrYWMCDzeUQ9sL23E9fU1BR82n25TvMX6c+SyWQsAZCr0LVPOOEEnnrqKc466yzWrVuHme1XW6urq7Mt4ZHTsYUMiq8C13fb/j4wycwGAX8NJIG1ZjbO3ZcDZ5J+piEiUlI6OlN5XfetozNFVWVFj/tPP/10Vq1aRVNTE6lUihtuuCFv1+5NIYPCgI8WZ3f3x81sgpmtJj2KuMrd3zWzHwLzzWwg6eBYXMA2iojkJPSmHsf5Kisrue666/J6zVzFFhTu/jowJmP7C1nqTMtS9ipwWlztEhGR/aOZ2SIiEqSgEBGRIAWFiIgEKShERCRIQSEicgBSHR29Vyri+fJJq8eKiByAiqoqmn/7QN7ON3h8U071XnzxRWbNmsW9996bt2v3RkEhItJHzJ8/n0ceeaTgs8d160lEpI8YNmwYc+fOLfh1FRQiIn3EhAkTGDCg8DeCFBQiIhKkoBARkSA9zBYROQCpjo6cP6mU6/kqqqrydr580ohCROQA5PtNPdfzDR06lIULF+b12r1RUIiISJCCQkREghQUIiIZUqlUsZuQdwfbJwWFiEiktraW5ubmsgqLVCpFc3MztbW1B3wOfepJRCQydOhQtmzZwrZt24rdlLyqra1l6NChB3y8gkJEJFJdXc3RRx9d7GaUnNiCwsxOBG5093FmNhp4FHgt2n27uz9oZjOASUA7MM3dnzezY4AFQArYAFzi7p1xtVNERMJiCQozuwKYCvw5KhoNzHH32Rl1RgOnAScCRwFLgOOBOcDV7r7czH4OTAYejqOdIiLSu7hGFJuAKUDXgumNgJnZZNKjimnAKcBSd08Bb5rZADMbEtV9OjruceAMFBQiIkUTS1C4+xIzG5FR9Dxwp7snzGw6MAPYDjRn1NkJHAZUROGRWRbU2tpKMpncp7yhoeHAOpCDbNcTESlHhXqY/bC7b+96DcwFfgXUZ9SpJx0enVnKgmpqamINhWwKfT0RkXxLJBI51SvUPIonzeyE6PV4IAGsAiaYWaWZDQMq3f1dYK2ZjYvqngmsLFAbRUQki0KNKL4H3GJme4CtwHfd/QMzWwk8RzqwLonq/hCYb2YDgSSwuEBtFBGRLCrKYQZiMplM9XQr6LEXWvJ+vYnHD8r7OUVECi2RSCQaGxuP662elvAQEZEgBYWIiAQpKEREJEhBISIiQQoKEREJUlCIiEiQgkJERIIUFCIiEqSgEBGRIAWFiIgEKShERCRIQSEiIkEKChERCVJQiIhIkIJCRESCFBQiIhKkoBARkSAFhYiIBCkoREQkaEBcJzazE4Eb3X2cmR0LzAU6gFbgf7n722b2M+BkYGd02GSgGrgfqAP+CFzg7h/G1U4REQmLZURhZlcAdwK1UdHNwD+6+zjgIeDKqHw0MMHdx0V/dgD/BNzv7qcCa4G/j6ONIiKSm7huPW0CpmRsN7n7uuj1AGC3mVUCnwXmmdkqM7sw2n8K8ET0+nHgqzG1UUREchDLrSd3X2JmIzK2/wRgZl8Gvg+MBT5B+nbUHKAKeMrM/h9wKLAjOnQncFhv12ttbSWZTO5T3tDQcFD9CMl2PRGRchTbM4ruzOxvgenAJHffZmZVwM1dzx/M7HfAKOADoB7YFf29vbdz19TUxBoK2RT6eiIi+ZZIJHKqV5BPPZnZeaRHEuPcfXNU/DngGTOrMrNq0rec/hNYBUyM6pwJrCxEG0VEJLvYgyIaOfyM9OjgITNbbmbXunsSuA9YDTwN3OPuLwPXA01mtgo4Cbgl7jaKiEjPYrv15O6vA2OizSN7qHMTcFO3sreBr8XVLhER2T+acCciIkEKChERCVJQiIhIkIJCRESCFBQiIhKkoBARkSAFhYiIBCkoREQkSEEhIiJBCgoREQlSUIiISJCCQkREgnIKCjO7qNv2pfE0R0RESk1w9Vgz+ybwP4D/bmZfiYqrgL8mvXS4iIiUud6WGX8C+BMwGLgjKusk/Z3YIiLSDwSDwt3fB5YDy83sU0BtLseJiEj5yOkN38xuBSYBfwQqgBTw5RjbJSIiJSLXkcGJwEh374yzMSIiUnpy/XjsRj6+7SQiIv1IriOKYcAbZrYx2k65e/DWk5mdCNzo7uPM7BhgAelbVhuAS9y908xmkL6l1Q5Mc/fne6q7n/0SEZE8yXVE8U3gOKAp+vPNUGUzuwK4k49HIXOAq939VNLPOCab2WjgNNK3tZqAW3uqm3NvREQk73IdUfxdlrLrAvU3AVOAe6PtRuDp6PXjwBmAA0vdPQW8aWYDzGxID3UfzrGdIiKSZ7kGxdvR3xXAaHoZibj7EjMbkVFUEQUCwE7gMOBQoDmjTld5trpBra2tJJPJfcobGhp6O/SAZbueiEg5yiko3P2OzG0ze3w/r5P5jKEe2A58EL3uXp6tblBNTU2soZBNoa8nIpJviUQip3q5rvX0uYw/p5F+uL0/1prZuOj1mcBKYBUwwcwqzWwYUOnu7/ZQV0REiiTXW0+ZI4rdwP/ez+v8EJhvZgOBJLDY3TvMbCXwHOnAuqSnuvt5LRERyaOKVCrVey3AzAYDnwE2R7/5l4xkMpnq6VbQYy+05P16E48flPdziogUWiKRSDQ2Nh7XW71cbz2dCzwLXAWsNrPzDrJ9IiLSR+Q6j+JyoNHdzwL+BvhBfE0SEZFSkmtQdLp7C4C77yT9nEJERPqBXB9mbzKz2cAK4FT0fRQiIv1GriOKecB7wOnABcAtsbVIRERKSq5BMQd42N2/DxwfbYuISD+Qa1C0u/vvAdx9M3vPnhYRkTKW6zOKN8zsBtKT404A/hBfk0REpJTkOqK4AHgHmAhsAy6MrUUiIlJScl0UcDfwrzG3RWQvHZ0pqior+sx5RcpVrreeRAquqrJCS7CIlIBcbz2JiEg/paAQEZEgBYWIiAQpKEREJEhBISIiQQoKEREJUlCIiEiQgkJERIIKNuHOzM4Hzo82a4FjgW8B/wK8FZXPAFYCtwGjgFbgInffWKh2iojI3goWFO6+AFgAYGa3AncDo4Er3H1JVz0zmwLUuvtJZjYGmA1MLlQ7RURkbwW/9WRmxwFfcPd5QCNwoZmtNLPZZjYAOAV4AsDdVwPHFbqNIiLysWI8o7gKuDZ6/X+BfwTGAoOAi4FDgR0Z9TuiABERkSIo6BuwmR0OfN7dn4qK7nb37dG+XwHnkA6J+ozDKt29PXTe1tZWksnkPuUNDQ15aXc22a5XaCM/cww1A6vzft7WPW1s3lT8x0Ll/vMT6SsK/Zv6WGAZgJlVAOvN7MvuvgUYDySAt4FvAAujZxQv9XbSmpqaWN9Usin09XoS1+qqpdK/uJR7/0RykUgkcqpX6KAwYDOAu6fM7CLgITPbBfwemA90AKeb2bNABekvTRIRkSIpaFC4+790214KLM1S9eLCtEhERHqjCXciIhKkoBARkSAFhYiIBCkoREQkSEEhIiJBCgoREQlSUIiISJCCQkREghQUIiISpKAQEZEgBYWIiAQpKKTfSXV09KnzihSbvhBI+p2Kqiqaf/tA3s87eHxT3s8pUgo0ohARkSAFhYiIBCkoREQkSEEhIiJBCgoREQlSUIiISFBBPx5rZmuBHdHmfwF3ADcD7cBSd7/WzCqB24BRQCtwkbtvLGQ7RUTkYwULCjOrBXD3cRll64BzgM3Ab8xsNDACqHX3k8xsDDAbmFyodoqIyN4KOaIYBRxiZkuj684Eatx9E4CZPQmMB/4SeALA3Veb2XEFbKOIiHRTyGcUHwKzgAnAxcAvorIuO4HDgEP5+PYUQIeZldQM8jiXatAyECJSagr5BvwqsNHdU8CrZrYDODJjfz2wHTgket2l0t3bQydubW0lmUzuU97Q0HDQjc4mriUgIL0MRLa+ZBNX/4Cc2xCnOPsXl1L4dxPJt0IGxYXAF4F/MLO/Ih0Ifzazz5B+RjEBuBYYCnwDWBg9o3iptxPX1NT0yTeVnpRCX0qhDX2R/t2kL0kkEjnVK2RQ3AUsMLNngBTp4OgE7gOqSH/qaY2ZvQCcbmbPAhXABQVso4iIdFOwoHD3PcC3suwa061eJ+lnGCIiUgI04U6kCDo6U33y3NI/ldSniUT6i6rKCh57oSWWc088flAs55X+SyMKEREJUlCIiEiQgkJERIIUFLKPuGaHa9a5SN+kh9myj7hmng8e35T3c4pI/DSiEBGRIAWFiIgEKShERCRIQSEiIkEKChERCVJQiIhIkIJCRESCFBQiIhKkoBApM5pZL/mmmdkiZUYz6yXfNKIQEZEgBYWIiAQpKEREJKhgzyjMrBq4GxgB1ADXA1uAR4HXomq3u/uDZjYDmAS0A9Pc/flCtVNERPZWyIfZ5wHN7j7VzAYDa4HrgDnuPrurkpmNBk4DTgSOApYAxxewnSIikqGQQbEIWJyx3Q40AmZmk0mPKqYBpwBL3T0FvGlmA8xsiLtvK2BbRUQkUrCgcPcWADOrJx0YV5O+BXWnuyfMbDowA9gONGccuhM4DOgxKFpbW0kmk/uUNzQ05K39hZStL9n0xf7l2jco7/71xb7B/v38pHwUdB6FmR0FPAzc5u73m9nh7r492v0wMBf4FVCfcVg96fDoUU1NTZ/9Hy+bcupLd+XcN1D/pG9JJBI51SvYp57M7NPAUuBKd787Kn7SzE6IXo8HEsAqYIKZVZrZMKDS3d8tVDtFRGRvhRxRXAUcAVxjZtdEZZcD/2pme4CtwHfd/QMzWwk8RzrILilgG0VEpJtCPqP4AfCDLLu+nKXuTGBmzE0SEZEcaMKdiORdR2eqT55bstOigCKSd1WVFTz2Qkss5554/KBYzis904hCRESCFBQiIhKkoBARkSAFhYiIBCkoREQkSEEhIiJBCgoR6VNSHR1FP29cczlKdY6I5lGISJ9SUVVF828fyPt5B49vyrluXPNESnWOiEYUIiISpKAQEZEgBYWIiAQpKERESkRcD+oP9tx6mC0iUiLielAP+/ewvjuNKEREJEhBISIiQQoKEREJUlCIiEhQST7MNrNK4DZgFNAKXOTuG4vbKhGR/qlURxRnAbXufhLwY2B2kdsjItJvlWpQnAI8AeDuq4HjitscEZH+qyKVKr3VCs3sTmCJuz8ebb8JjHT39mz1E4nENuCNAjZRRKQcDG9sbBzSW6WSfEYBfADUZ2xX9hQSALl0VEREDkyp3npaBUwEMLMxwEvFbY6ISP9VqiOKh4HTzexZoAK4oMjtERHpt0ryGYWIiJSOUr31JCIiJUJBISIiQaX6jKIk9DZD3My+A/w90A5c7+6/LkpDD4KZnQjc6O7jupV/A/gn0n27293nF6F5B8zMqoG7gRFADemfzyMZ+/t6/6qA+YABHcAF7r4pY3+f7l8XM/sUkABOd/dXMsr7fP/MbC2wI9r8L3e/IGPfz4CTgZ1R0WR330GRaEQR1uMMcTP7C+BS0j/MCcA/m1lNUVp5gMzsCuBOoLZbeTXwU+AM4DTgu1F/+5LzgGZ3PxU4E7ila0eZ9O8bAO5+Muk3zDldO8qkf139uAPYlaW8T/fPzGoB3H1c9Kf7B3ZGAxMy9hctJEBB0ZvQDPETgFXu3hr9EDcCXyp8Ew/KJmBKlvIGYKO7v+/ue4BngFML2rKDtwi4JmM7cx5On++fu/8H8N1oczjwdsbuPt+/yCzg58Afu5WXQ/9GAYeY2VIz+100DQD46E7GZ4F5ZrbKzC4sWisjCoqwQ/l4aAjQYWYDeti3EzisUA3LB3dfArRl2VUOfWtx951mVg8sBq7O2N3n+wfg7u1m9m/AXNJ97NLn+2dm5wPb3P3JLLv7fP+AD0kH4QTgYuC+jPeWT5D+mZ4HfA34BzMr6i+hCoqw0Azx7vvqge2FaljMyqJvZnYU8BRwr7vfn7GrLPoH4O5/B3wOmG9mn4iKy6F/F5KeS7UcOBa4J+P2Ujn071Xgl+6ecvdXgWbgL6N9HwI3u/uH7r4T+B3pEUjR6GF22CrS94IXZpkh/jzwf6J7jTWkh8MbCt/EWCSBz5rZkUALMJb0bz99hpl9GlgKfN/df9ttdzn0byow1N3/mfQbSyfph9pQBv1z97Fdr6OwuNjdt0ZFfb5/pIPwi6RHC39FepT0p2jf54AHzGw06V/mTwH+rSitjCgowvaZIW5ml5O+P/pI9MmElaR/mNPdfXcR23rQzOxbwCB3nxf180nSfbvb3f9Q3Nbtt6uAI4BrzKzrWcV84BNl0r+HgF+Y2QqgGpgGTDGzcvn57aPM/vu8C1hgZs8AKdLBcamZdb233AesJn1r+B53f7mIbdXMbBERCdMzChERCVJQiIhIkIJCRESCFBQiIhKkoBARkSAFhch+MLPzzewnWcofMLOBZrbAzL7WbV+tmb1eqDaK5JvmUYjkgbs3AZhZsZsikncKCpEAM6sDfkF64b1qYAkwxsyWAkOA26MJYK8Dn884bhBwH+lJf5lL0y8HtkXlk0gvY/9Z0qP7q919uZmtB54mvchkiiIvMS2iW08iYRcDr0dLzZ9PesnrNtKLuZ1NekZ0NucDG6KlKO7otu9+d/8q6dm470Z1JgO3RvsPBf7d3U8D/kB6mXSRolFQiIQZ8ByAu28gvfjcf7p7CtgKHNLDcV8gvR4Y7r6GvVfp9ejvLwITo1HGEmCAmQ2O9q2N/n6Lbt8XIlJoCgqRsCRwPICZjQRuIH07qDevACdFx/0N6dtWXToz6vx79O2CZ5L+Do33o31aW0dKhoJCJOwOYKSZPQ3cQ8Y3yfXiVuC/RYu+XUL6q3Sznfvz0bmfBd5w984s9USKSosCiohIkEYUIiISpKAQEZEgBYWIiAQpKEREJEhBISIiQQoKEREJUlCIiEiQgkJERIL+P2hwefa3iLZSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 子供の数\n",
    "sns.countplot('children',data=df.sort_values('children'),hue='Had_Affair',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1073b68d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 学歴\n",
    "sns.countplot('educ',data=df.sort_values('educ'),hue='Had_Affair',palette='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   1.0  2.0  3.0  4.0  5.0  6.0\n",
       "0    0    1    0    0    0    0\n",
       "1    0    0    1    0    0    0\n",
       "2    0    0    1    0    0    0\n",
       "3    0    0    0    0    1    0\n",
       "4    0    0    1    0    0    0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# カテゴリーを表す変数をダミー変数化する\n",
    "occ_dummies = pd.get_dummies(df['occupation'])\n",
    "hus_occ_dummies = pd.get_dummies(df['occupation_husb'])\n",
    "\n",
    "occ_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 列名を付ける\n",
    "occ_dummies.columns = ['occ1','occ2','occ3','occ4','occ5','occ6']\n",
    "hus_occ_dummies.columns = ['hocc1','hocc2','hocc3','hocc4','hocc5','hocc6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 不要な列を削除して説明変数を作る\n",
    "X = df.drop(['occupation','occupation_husb','Had_Affair'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ダミー変数をまとめる\n",
    "dummies = pd.concat([occ_dummies,hus_occ_dummies],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>affairs</th>\n",
       "      <th>occ1</th>\n",
       "      <th>occ2</th>\n",
       "      <th>occ3</th>\n",
       "      <th>occ4</th>\n",
       "      <th>occ5</th>\n",
       "      <th>occ6</th>\n",
       "      <th>hocc1</th>\n",
       "      <th>hocc2</th>\n",
       "      <th>hocc3</th>\n",
       "      <th>hocc4</th>\n",
       "      <th>hocc5</th>\n",
       "      <th>hocc6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.230769</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.400000</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.727273</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.666666</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage   age  yrs_married  children  ...    hocc3  hocc4  hocc5  hocc6\n",
       "0            3.0  32.0          9.0       3.0  ...        0      0      1      0\n",
       "1            3.0  27.0         13.0       3.0  ...        0      1      0      0\n",
       "2            4.0  22.0          2.5       0.0  ...        0      0      1      0\n",
       "3            4.0  37.0         16.5       4.0  ...        0      0      1      0\n",
       "4            5.0  27.0          9.0       1.0  ...        0      1      0      0\n",
       "\n",
       "[5 rows x 19 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 説明変数XのDataFrame\n",
    "X = pd.concat([X,dummies],axis=1)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Had_Affair, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df.Had_Affair\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rate_marriage</th>\n",
       "      <th>age</th>\n",
       "      <th>yrs_married</th>\n",
       "      <th>children</th>\n",
       "      <th>religious</th>\n",
       "      <th>educ</th>\n",
       "      <th>occ2</th>\n",
       "      <th>occ3</th>\n",
       "      <th>occ4</th>\n",
       "      <th>occ5</th>\n",
       "      <th>occ6</th>\n",
       "      <th>hocc2</th>\n",
       "      <th>hocc3</th>\n",
       "      <th>hocc4</th>\n",
       "      <th>hocc5</th>\n",
       "      <th>hocc6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rate_marriage   age  yrs_married  children  ...    hocc3  hocc4  hocc5  hocc6\n",
       "0            3.0  32.0          9.0       3.0  ...        0      0      1      0\n",
       "1            3.0  27.0         13.0       3.0  ...        0      1      0      0\n",
       "2            4.0  22.0          2.5       0.0  ...        0      0      1      0\n",
       "3            4.0  37.0         16.5       4.0  ...        0      0      1      0\n",
       "4            5.0  27.0          9.0       1.0  ...        0      1      0      0\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 多重共線性を排除\n",
    "X = X.drop('occ1',axis=1)\n",
    "X = X.drop('hocc1',axis=1)\n",
    "\n",
    "X = X.drop('affairs',axis=1)\n",
    "\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yを一次元のArrayにする\n",
    "Y = np.ravel(Y)\n",
    "\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7258875274897895"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X,Yでモデルを作り、X.Yでモデルの精度を取得\n",
    "log_model = LogisticRegression()\n",
    "\n",
    "log_model.fit(X,Y)\n",
    "\n",
    "log_model.score(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3224945020420987"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 変数名とその係数を格納するDataFrame作成\n",
    "coeff_df = DataFrame([X.columns,log_model.coef_[0]]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rate_marriage</td>\n",
       "      <td>-0.697846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>age</td>\n",
       "      <td>-0.0563363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yrs_married</td>\n",
       "      <td>0.103893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>children</td>\n",
       "      <td>0.0181853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>religious</td>\n",
       "      <td>-0.368507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>educ</td>\n",
       "      <td>0.00864737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>occ2</td>\n",
       "      <td>0.298119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>occ3</td>\n",
       "      <td>0.60815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>occ4</td>\n",
       "      <td>0.346511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>occ5</td>\n",
       "      <td>0.94226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>occ6</td>\n",
       "      <td>0.91815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hocc2</td>\n",
       "      <td>0.219957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hocc3</td>\n",
       "      <td>0.324766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hocc4</td>\n",
       "      <td>0.189354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>hocc5</td>\n",
       "      <td>0.213093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>hocc6</td>\n",
       "      <td>0.21418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0           1\n",
       "0   rate_marriage   -0.697846\n",
       "1             age  -0.0563363\n",
       "2     yrs_married    0.103893\n",
       "3        children   0.0181853\n",
       "4       religious   -0.368507\n",
       "5            educ  0.00864737\n",
       "6            occ2    0.298119\n",
       "7            occ3     0.60815\n",
       "8            occ4    0.346511\n",
       "9            occ5     0.94226\n",
       "10           occ6     0.91815\n",
       "11          hocc2    0.219957\n",
       "12          hocc3    0.324766\n",
       "13          hocc4    0.189354\n",
       "14          hocc5    0.213093\n",
       "15          hocc6     0.21418"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 学習用データのみでモデル作成\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y)\n",
    "log_model2 = LogisticRegression()\n",
    "log_model2.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7261306532663316\n"
     ]
    }
   ],
   "source": [
    "# テストデータを用いて予測\n",
    "class_predict = log_model2.predict(X_test)\n",
    "\n",
    "# 精度を計算\n",
    "print(metrics.accuracy_score(Y_test,class_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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