employeeRetentionLogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# https://www.kaggle.com/liujiaqi/hr-comma-sepcsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>sales</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  sales  \\\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/mithunkumar/Desktop/HR_comma_sep.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data exploration and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3571, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = df[df.left==1]\n",
    "left.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11428, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retained = df[df.left==0]\n",
    "retained.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Average numbers for all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.groupby('left').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above table we can draw following conclusions,\n",
    "\n",
    "**Satisfaction Level**: Satisfaction level seems to be relatively low (0.44) in employees leaving the firm vs the retained ones (0.66)\n",
    "**Average Monthly Hours**: Average monthly hours are higher in employees leaving the firm (199 vs 207)\n",
    "**Promotion Last 5 Years**: Employees who are given promotion are likely to be retained at firm\n",
    "Impact of salary on employee retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b96d490>"
      ]
     },
     "execution_count": 6,
     "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": [
    "pd.crosstab(df.salary,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above bar chart shows employees with high salaries are likely to not leave the company\n",
    "\n",
    "Department wise employee retention rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c77e190>"
      ]
     },
     "execution_count": 8,
     "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": [
    "pd.crosstab(df.sales,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From above chart there seem to be some impact of department on employee retention but it is not major hence we will ignore department in our analysis\n",
    "\n",
    "From the data analysis so far we can conclude that we will use following variables as dependant variables in our model\n",
    "**Satisfaction Level**\n",
    "**Average Monthly Hours**\n",
    "**Promotion Last 5 Years**\n",
    "**Salary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary</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>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  promotion_last_5years  salary\n",
       "0                0.38                   157                      0     low\n",
       "1                0.80                   262                      0  medium\n",
       "2                0.11                   272                      0  medium\n",
       "3                0.72                   223                      0     low\n",
       "4                0.37                   159                      0     low"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "subdf = df[['satisfaction_level','average_montly_hours','promotion_last_5years','salary']]\n",
    "subdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tackle salary dummy variable\n",
    "\n",
    "Salary has all text data. It needs to be converted to numbers and we will use dummy variable for that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "salary_dummies = pd.get_dummies(subdf.salary, prefix=\"salary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_with_dummies = pd.concat([subdf,salary_dummies],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary</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>low</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>medium</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>medium</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>low</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>low</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  salary  \\\n",
       "0                0.38                   157                      0     low   \n",
       "1                0.80                   262                      0  medium   \n",
       "2                0.11                   272                      0  medium   \n",
       "3                0.72                   223                      0     low   \n",
       "4                0.37                   159                      0     low   \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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
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       "      <td>0</td>\n",
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       "      <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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dummies.drop('salary',axis='columns',inplace=True)\n",
    "df_with_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
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       "      <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",
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       "    <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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_with_dummies\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/model_selection/_split.py:2179: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda2/lib/python2.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 0, 0])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\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>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>5906</th>\n",
       "      <td>0.70</td>\n",
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       "      <td>0</td>\n",
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       "      <th>12337</th>\n",
       "      <td>0.44</td>\n",
       "      <td>140</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>14198</th>\n",
       "      <td>0.19</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>3859</th>\n",
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       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.89</td>\n",
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       "      <td>1</td>\n",
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       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>6568</th>\n",
       "      <td>0.63</td>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>3651</th>\n",
       "      <td>0.72</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14308</th>\n",
       "      <td>0.10</td>\n",
       "      <td>279</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12046</th>\n",
       "      <td>0.40</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11166</th>\n",
       "      <td>0.48</td>\n",
       "      <td>274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0.75</td>\n",
       "      <td>259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>10449</th>\n",
       "      <td>0.46</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.89</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5716</th>\n",
       "      <td>0.62</td>\n",
       "      <td>103</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>12092</th>\n",
       "      <td>0.42</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>0.10</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3044</th>\n",
       "      <td>0.71</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479</th>\n",
       "      <td>0.39</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10344</th>\n",
       "      <td>0.53</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11287</th>\n",
       "      <td>0.97</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7631</th>\n",
       "      <td>0.77</td>\n",
       "      <td>190</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11459</th>\n",
       "      <td>0.84</td>\n",
       "      <td>197</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>0.49</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12668</th>\n",
       "      <td>0.10</td>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6471</th>\n",
       "      <td>0.60</td>\n",
       "      <td>134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11248</th>\n",
       "      <td>0.72</td>\n",
       "      <td>222</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5302</th>\n",
       "      <td>0.92</td>\n",
       "      <td>218</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5531</th>\n",
       "      <td>0.53</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13901</th>\n",
       "      <td>0.83</td>\n",
       "      <td>220</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12193</th>\n",
       "      <td>0.09</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>0.59</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9260</th>\n",
       "      <td>0.71</td>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11978</th>\n",
       "      <td>0.61</td>\n",
       "      <td>193</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14155</th>\n",
       "      <td>0.99</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4374</th>\n",
       "      <td>0.60</td>\n",
       "      <td>217</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5316</th>\n",
       "      <td>0.65</td>\n",
       "      <td>256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11787</th>\n",
       "      <td>0.58</td>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9010</th>\n",
       "      <td>0.60</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14112</th>\n",
       "      <td>0.24</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13750</th>\n",
       "      <td>0.74</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6480</th>\n",
       "      <td>0.52</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8045</th>\n",
       "      <td>0.64</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14183</th>\n",
       "      <td>0.55</td>\n",
       "      <td>239</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6446</th>\n",
       "      <td>0.69</td>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10766</th>\n",
       "      <td>0.72</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>0.88</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6218</th>\n",
       "      <td>0.65</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8950</th>\n",
       "      <td>0.53</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9519</th>\n",
       "      <td>0.83</td>\n",
       "      <td>199</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5500</th>\n",
       "      <td>0.76</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3437</th>\n",
       "      <td>0.92</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3026</th>\n",
       "      <td>0.74</td>\n",
       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>0.10</td>\n",
       "      <td>297</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6097</th>\n",
       "      <td>0.62</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2807</th>\n",
       "      <td>0.91</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10500 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "5906                 0.70                   263                      0   \n",
       "12337                0.44                   140                      0   \n",
       "5287                 0.71                   194                      0   \n",
       "14198                0.19                   229                      0   \n",
       "929                  0.83                   243                      0   \n",
       "3859                 0.53                   264                      0   \n",
       "9330                 0.89                   146                      0   \n",
       "3962                 0.64                   254                      0   \n",
       "6568                 0.63                   228                      0   \n",
       "3651                 0.72                   151                      0   \n",
       "14308                0.10                   279                      0   \n",
       "12046                0.40                   147                      0   \n",
       "11166                0.48                   274                      0   \n",
       "13192                0.75                   259                      0   \n",
       "10449                0.46                   189                      0   \n",
       "4653                 0.89                   138                      0   \n",
       "5716                 0.62                   103                      0   \n",
       "12793                0.62                   156                      0   \n",
       "12092                0.42                   143                      0   \n",
       "470                  0.10                   260                      0   \n",
       "11635                0.59                   247                      0   \n",
       "3044                 0.71                   262                      0   \n",
       "1479                 0.39                   142                      0   \n",
       "10344                0.53                   182                      0   \n",
       "11287                0.97                   135                      0   \n",
       "7631                 0.77                   190                      0   \n",
       "11459                0.84                   197                      1   \n",
       "2004                 0.49                   214                      0   \n",
       "12668                0.10                   258                      0   \n",
       "6471                 0.60                   134                      0   \n",
       "...                   ...                   ...                    ...   \n",
       "11248                0.72                   222                      0   \n",
       "5302                 0.92                   218                      0   \n",
       "5531                 0.53                   143                      1   \n",
       "13901                0.83                   220                      0   \n",
       "12193                0.09                   249                      0   \n",
       "3603                 0.59                   156                      0   \n",
       "9260                 0.71                   207                      0   \n",
       "11978                0.61                   193                      0   \n",
       "14155                0.99                   264                      0   \n",
       "4374                 0.60                   217                      0   \n",
       "5316                 0.65                   256                      0   \n",
       "11787                0.58                   209                      0   \n",
       "9010                 0.60                   229                      0   \n",
       "14112                0.24                   199                      0   \n",
       "13750                0.74                   233                      0   \n",
       "6480                 0.52                   164                      0   \n",
       "8045                 0.64                   179                      0   \n",
       "14183                0.55                   239                      0   \n",
       "6446                 0.69                   228                      0   \n",
       "10766                0.72                   164                      0   \n",
       "327                  0.88                   262                      0   \n",
       "6218                 0.65                   111                      0   \n",
       "8950                 0.53                   159                      0   \n",
       "9519                 0.83                   199                      0   \n",
       "5500                 0.76                   149                      0   \n",
       "3437                 0.92                   185                      0   \n",
       "3026                 0.74                   221                      0   \n",
       "1108                 0.10                   297                      0   \n",
       "6097                 0.62                   180                      0   \n",
       "2807                 0.91                   164                      0   \n",
       "\n",
       "       salary_high  salary_low  salary_medium  \n",
       "5906             0           0              1  \n",
       "12337            0           0              1  \n",
       "5287             0           1              0  \n",
       "14198            0           0              1  \n",
       "929              0           0              1  \n",
       "3859             0           1              0  \n",
       "9330             0           1              0  \n",
       "3962             0           0              1  \n",
       "6568             0           0              1  \n",
       "3651             0           0              1  \n",
       "14308            0           1              0  \n",
       "12046            0           1              0  \n",
       "11166            0           0              1  \n",
       "13192            0           1              0  \n",
       "10449            0           1              0  \n",
       "4653             0           0              1  \n",
       "5716             0           0              1  \n",
       "12793            0           0              1  \n",
       "12092            0           1              0  \n",
       "470              0           1              0  \n",
       "11635            1           0              0  \n",
       "3044             0           1              0  \n",
       "1479             0           0              1  \n",
       "10344            0           1              0  \n",
       "11287            0           0              1  \n",
       "7631             0           1              0  \n",
       "11459            0           1              0  \n",
       "2004             0           1              0  \n",
       "12668            0           1              0  \n",
       "6471             0           1              0  \n",
       "...            ...         ...            ...  \n",
       "11248            0           0              1  \n",
       "5302             0           1              0  \n",
       "5531             0           1              0  \n",
       "13901            0           1              0  \n",
       "12193            0           0              1  \n",
       "3603             0           1              0  \n",
       "9260             0           1              0  \n",
       "11978            0           0              1  \n",
       "14155            0           0              1  \n",
       "4374             0           1              0  \n",
       "5316             0           0              1  \n",
       "11787            1           0              0  \n",
       "9010             0           0              1  \n",
       "14112            0           0              1  \n",
       "13750            0           0              1  \n",
       "6480             0           0              1  \n",
       "8045             0           1              0  \n",
       "14183            1           0              0  \n",
       "6446             0           0              1  \n",
       "10766            0           1              0  \n",
       "327              0           1              0  \n",
       "6218             0           0              1  \n",
       "8950             0           1              0  \n",
       "9519             0           1              0  \n",
       "5500             0           0              1  \n",
       "3437             0           1              0  \n",
       "3026             0           0              1  \n",
       "1108             0           1              0  \n",
       "6097             1           0              0  \n",
       "2807             0           0              1  \n",
       "\n",
       "[10500 rows x 6 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7800952380952381"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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