LogisticRegExer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>Department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years Department  \\\n",
       "0                   3              0     1                      0      sales   \n",
       "1                   6              0     1                      0      sales   \n",
       "2                   4              0     1                      0      sales   \n",
       "3                   5              0     1                      0      sales   \n",
       "4                   3              0     1                      0      sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"HR_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3571, 10)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_left = df[df.left==1]\n",
    "emp_left.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11428, 10)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_retained = df[df.left==0]\n",
    "emp_retained.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</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>0.666810</td>\n",
       "      <td>0.715473</td>\n",
       "      <td>3.786664</td>\n",
       "      <td>199.060203</td>\n",
       "      <td>3.380032</td>\n",
       "      <td>0.175009</td>\n",
       "      <td>0.026251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.440098</td>\n",
       "      <td>0.718113</td>\n",
       "      <td>3.855503</td>\n",
       "      <td>207.419210</td>\n",
       "      <td>3.876505</td>\n",
       "      <td>0.047326</td>\n",
       "      <td>0.005321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      satisfaction_level  last_evaluation  number_project  \\\n",
       "left                                                        \n",
       "0               0.666810         0.715473        3.786664   \n",
       "1               0.440098         0.718113        3.855503   \n",
       "\n",
       "      average_montly_hours  time_spend_company  Work_accident  \\\n",
       "left                                                            \n",
       "0               199.060203            3.380032       0.175009   \n",
       "1               207.419210            3.876505       0.047326   \n",
       "\n",
       "      promotion_last_5years  \n",
       "left                         \n",
       "0                  0.026251  \n",
       "1                  0.005321  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('left').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary'>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.salary, df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Department'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Department, df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>satisfaction_level</th>\n",
       "      <th>salary</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>low</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>medium</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>medium</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>low</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>low</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  salary  average_montly_hours  promotion_last_5years\n",
       "0                0.38     low                   157                      0\n",
       "1                0.80  medium                   262                      0\n",
       "2                0.11  medium                   272                      0\n",
       "3                0.72     low                   223                      0\n",
       "4                0.37     low                   159                      0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdf = df[['satisfaction_level', 'salary', 'average_montly_hours', 'promotion_last_5years']]\n",
    "subdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_dummies = pd.get_dummies(subdf.salary, prefix='salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_dummies = pd.concat([subdf, salary_dummies], axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>satisfaction_level</th>\n",
       "      <th>salary</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>low</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>medium</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>medium</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>low</td>\n",
       "      <td>223</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>0.37</td>\n",
       "      <td>low</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  salary  average_montly_hours  promotion_last_5years  \\\n",
       "0                0.38     low                   157                      0   \n",
       "1                0.80  medium                   262                      0   \n",
       "2                0.11  medium                   272                      0   \n",
       "3                0.72     low                   223                      0   \n",
       "4                0.37     low                   159                      0   \n",
       "\n",
       "   salary_high  salary_low  salary_medium  \n",
       "0            0           1              0  \n",
       "1            0           0              1  \n",
       "2            0           0              1  \n",
       "3            0           1              0  \n",
       "4            0           1              0  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_dummies.drop('salary', axis='columns', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</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>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                0.38                   157                      0   \n",
       "1                0.80                   262                      0   \n",
       "2                0.11                   272                      0   \n",
       "3                0.72                   223                      0   \n",
       "4                0.37                   159                      0   \n",
       "\n",
       "   salary_high  salary_low  salary_medium  \n",
       "0            0           1              0  \n",
       "1            0           0              1  \n",
       "2            0           0              1  \n",
       "3            0           1              0  \n",
       "4            0           1              0  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</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>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                0.38                   157                      0   \n",
       "1                0.80                   262                      0   \n",
       "2                0.11                   272                      0   \n",
       "3                0.72                   223                      0   \n",
       "4                0.37                   159                      0   \n",
       "\n",
       "   salary_high  salary_low  salary_medium  \n",
       "0            0           1              0  \n",
       "1            0           0              1  \n",
       "2            0           0              1  \n",
       "3            0           1              0  \n",
       "4            0           1              0  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features=df_with_dummies\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = df.left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features, label, train_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7752380952380953"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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    "name": "ipython",
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