Human Resources Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bringing in the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from subprocess import check_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hra = pd.read_csv('./Human Resources Analytics.csv')\n",
    "hra.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns names:\n",
      "['satisfaction_level', 'last_evaluation', 'number_project', 'average_montly_hours', 'time_spend_company', 'Work_accident', 'left', 'promotion_last_5years', 'sales', 'salary']\n"
     ]
    }
   ],
   "source": [
    "columns_names=hra.columns.tolist()\n",
    "print(\"Columns names:\")\n",
    "print(columns_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hra.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>satisfaction_level</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105021</td>\n",
       "      <td>-0.142970</td>\n",
       "      <td>-0.020048</td>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.025605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_evaluation</th>\n",
       "      <td>0.105021</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>-0.008684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number_project</th>\n",
       "      <td>-0.142970</td>\n",
       "      <td>0.349333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>-0.006064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_montly_hours</th>\n",
       "      <td>-0.020048</td>\n",
       "      <td>0.339742</td>\n",
       "      <td>0.417211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>-0.003544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time_spend_company</th>\n",
       "      <td>-0.100866</td>\n",
       "      <td>0.131591</td>\n",
       "      <td>0.196786</td>\n",
       "      <td>0.127755</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>0.067433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Work_accident</th>\n",
       "      <td>0.058697</td>\n",
       "      <td>-0.007104</td>\n",
       "      <td>-0.004741</td>\n",
       "      <td>-0.010143</td>\n",
       "      <td>0.002120</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>0.039245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>-0.388375</td>\n",
       "      <td>0.006567</td>\n",
       "      <td>0.023787</td>\n",
       "      <td>0.071287</td>\n",
       "      <td>0.144822</td>\n",
       "      <td>-0.154622</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.061788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <td>0.025605</td>\n",
       "      <td>-0.008684</td>\n",
       "      <td>-0.006064</td>\n",
       "      <td>-0.003544</td>\n",
       "      <td>0.067433</td>\n",
       "      <td>0.039245</td>\n",
       "      <td>-0.061788</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       satisfaction_level  last_evaluation  number_project  \\\n",
       "satisfaction_level               1.000000         0.105021       -0.142970   \n",
       "last_evaluation                  0.105021         1.000000        0.349333   \n",
       "number_project                  -0.142970         0.349333        1.000000   \n",
       "average_montly_hours            -0.020048         0.339742        0.417211   \n",
       "time_spend_company              -0.100866         0.131591        0.196786   \n",
       "Work_accident                    0.058697        -0.007104       -0.004741   \n",
       "left                            -0.388375         0.006567        0.023787   \n",
       "promotion_last_5years            0.025605        -0.008684       -0.006064   \n",
       "\n",
       "                       average_montly_hours  time_spend_company  \\\n",
       "satisfaction_level                -0.020048           -0.100866   \n",
       "last_evaluation                    0.339742            0.131591   \n",
       "number_project                     0.417211            0.196786   \n",
       "average_montly_hours               1.000000            0.127755   \n",
       "time_spend_company                 0.127755            1.000000   \n",
       "Work_accident                     -0.010143            0.002120   \n",
       "left                               0.071287            0.144822   \n",
       "promotion_last_5years             -0.003544            0.067433   \n",
       "\n",
       "                       Work_accident      left  promotion_last_5years  \n",
       "satisfaction_level          0.058697 -0.388375               0.025605  \n",
       "last_evaluation            -0.007104  0.006567              -0.008684  \n",
       "number_project             -0.004741  0.023787              -0.006064  \n",
       "average_montly_hours       -0.010143  0.071287              -0.003544  \n",
       "time_spend_company          0.002120  0.144822               0.067433  \n",
       "Work_accident               1.000000 -0.154622               0.039245  \n",
       "left                       -0.154622  1.000000              -0.061788  \n",
       "promotion_last_5years       0.039245 -0.061788               1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hra.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      "satisfaction_level       14999 non-null float64\n",
      "last_evaluation          14999 non-null float64\n",
      "number_project           14999 non-null int64\n",
      "average_montly_hours     14999 non-null int64\n",
      "time_spend_company       14999 non-null int64\n",
      "Work_accident            14999 non-null int64\n",
      "left                     14999 non-null int64\n",
      "promotion_last_5years    14999 non-null int64\n",
      "sales                    14999 non-null object\n",
      "salary                   14999 non-null object\n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "hra.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for the Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>14969</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14970</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14971</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14972</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14973</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14974</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14975</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14977</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14978</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14979</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14980</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14981</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14982</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14983</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14984</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14985</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14986</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14987</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14988</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14989</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14990</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14991</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14992</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14993</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      satisfaction_level last_evaluation number_project average_montly_hours  \\\n",
       "0                  False           False          False                False   \n",
       "1                  False           False          False                False   \n",
       "2                  False           False          False                False   \n",
       "3                  False           False          False                False   \n",
       "4                  False           False          False                False   \n",
       "5                  False           False          False                False   \n",
       "6                  False           False          False                False   \n",
       "7                  False           False          False                False   \n",
       "8                  False           False          False                False   \n",
       "9                  False           False          False                False   \n",
       "10                 False           False          False                False   \n",
       "11                 False           False          False                False   \n",
       "12                 False           False          False                False   \n",
       "13                 False           False          False                False   \n",
       "14                 False           False          False                False   \n",
       "15                 False           False          False                False   \n",
       "16                 False           False          False                False   \n",
       "17                 False           False          False                False   \n",
       "18                 False           False          False                False   \n",
       "19                 False           False          False                False   \n",
       "20                 False           False          False                False   \n",
       "21                 False           False          False                False   \n",
       "22                 False           False          False                False   \n",
       "23                 False           False          False                False   \n",
       "24                 False           False          False                False   \n",
       "25                 False           False          False                False   \n",
       "26                 False           False          False                False   \n",
       "27                 False           False          False                False   \n",
       "28                 False           False          False                False   \n",
       "29                 False           False          False                False   \n",
       "...                  ...             ...            ...                  ...   \n",
       "14969              False           False          False                False   \n",
       "14970              False           False          False                False   \n",
       "14971              False           False          False                False   \n",
       "14972              False           False          False                False   \n",
       "14973              False           False          False                False   \n",
       "14974              False           False          False                False   \n",
       "14975              False           False          False                False   \n",
       "14976              False           False          False                False   \n",
       "14977              False           False          False                False   \n",
       "14978              False           False          False                False   \n",
       "14979              False           False          False                False   \n",
       "14980              False           False          False                False   \n",
       "14981              False           False          False                False   \n",
       "14982              False           False          False                False   \n",
       "14983              False           False          False                False   \n",
       "14984              False           False          False                False   \n",
       "14985              False           False          False                False   \n",
       "14986              False           False          False                False   \n",
       "14987              False           False          False                False   \n",
       "14988              False           False          False                False   \n",
       "14989              False           False          False                False   \n",
       "14990              False           False          False                False   \n",
       "14991              False           False          False                False   \n",
       "14992              False           False          False                False   \n",
       "14993              False           False          False                False   \n",
       "14994              False           False          False                False   \n",
       "14995              False           False          False                False   \n",
       "14996              False           False          False                False   \n",
       "14997              False           False          False                False   \n",
       "14998              False           False          False                False   \n",
       "\n",
       "      time_spend_company Work_accident   left promotion_last_5years  sales  \\\n",
       "0                  False         False  False                 False  False   \n",
       "1                  False         False  False                 False  False   \n",
       "2                  False         False  False                 False  False   \n",
       "3                  False         False  False                 False  False   \n",
       "4                  False         False  False                 False  False   \n",
       "5                  False         False  False                 False  False   \n",
       "6                  False         False  False                 False  False   \n",
       "7                  False         False  False                 False  False   \n",
       "8                  False         False  False                 False  False   \n",
       "9                  False         False  False                 False  False   \n",
       "10                 False         False  False                 False  False   \n",
       "11                 False         False  False                 False  False   \n",
       "12                 False         False  False                 False  False   \n",
       "13                 False         False  False                 False  False   \n",
       "14                 False         False  False                 False  False   \n",
       "15                 False         False  False                 False  False   \n",
       "16                 False         False  False                 False  False   \n",
       "17                 False         False  False                 False  False   \n",
       "18                 False         False  False                 False  False   \n",
       "19                 False         False  False                 False  False   \n",
       "20                 False         False  False                 False  False   \n",
       "21                 False         False  False                 False  False   \n",
       "22                 False         False  False                 False  False   \n",
       "23                 False         False  False                 False  False   \n",
       "24                 False         False  False                 False  False   \n",
       "25                 False         False  False                 False  False   \n",
       "26                 False         False  False                 False  False   \n",
       "27                 False         False  False                 False  False   \n",
       "28                 False         False  False                 False  False   \n",
       "29                 False         False  False                 False  False   \n",
       "...                  ...           ...    ...                   ...    ...   \n",
       "14969              False         False  False                 False  False   \n",
       "14970              False         False  False                 False  False   \n",
       "14971              False         False  False                 False  False   \n",
       "14972              False         False  False                 False  False   \n",
       "14973              False         False  False                 False  False   \n",
       "14974              False         False  False                 False  False   \n",
       "14975              False         False  False                 False  False   \n",
       "14976              False         False  False                 False  False   \n",
       "14977              False         False  False                 False  False   \n",
       "14978              False         False  False                 False  False   \n",
       "14979              False         False  False                 False  False   \n",
       "14980              False         False  False                 False  False   \n",
       "14981              False         False  False                 False  False   \n",
       "14982              False         False  False                 False  False   \n",
       "14983              False         False  False                 False  False   \n",
       "14984              False         False  False                 False  False   \n",
       "14985              False         False  False                 False  False   \n",
       "14986              False         False  False                 False  False   \n",
       "14987              False         False  False                 False  False   \n",
       "14988              False         False  False                 False  False   \n",
       "14989              False         False  False                 False  False   \n",
       "14990              False         False  False                 False  False   \n",
       "14991              False         False  False                 False  False   \n",
       "14992              False         False  False                 False  False   \n",
       "14993              False         False  False                 False  False   \n",
       "14994              False         False  False                 False  False   \n",
       "14995              False         False  False                 False  False   \n",
       "14996              False         False  False                 False  False   \n",
       "14997              False         False  False                 False  False   \n",
       "14998              False         False  False                 False  False   \n",
       "\n",
       "      salary  \n",
       "0      False  \n",
       "1      False  \n",
       "2      False  \n",
       "3      False  \n",
       "4      False  \n",
       "5      False  \n",
       "6      False  \n",
       "7      False  \n",
       "8      False  \n",
       "9      False  \n",
       "10     False  \n",
       "11     False  \n",
       "12     False  \n",
       "13     False  \n",
       "14     False  \n",
       "15     False  \n",
       "16     False  \n",
       "17     False  \n",
       "18     False  \n",
       "19     False  \n",
       "20     False  \n",
       "21     False  \n",
       "22     False  \n",
       "23     False  \n",
       "24     False  \n",
       "25     False  \n",
       "26     False  \n",
       "27     False  \n",
       "28     False  \n",
       "29     False  \n",
       "...      ...  \n",
       "14969  False  \n",
       "14970  False  \n",
       "14971  False  \n",
       "14972  False  \n",
       "14973  False  \n",
       "14974  False  \n",
       "14975  False  \n",
       "14976  False  \n",
       "14977  False  \n",
       "14978  False  \n",
       "14979  False  \n",
       "14980  False  \n",
       "14981  False  \n",
       "14982  False  \n",
       "14983  False  \n",
       "14984  False  \n",
       "14985  False  \n",
       "14986  False  \n",
       "14987  False  \n",
       "14988  False  \n",
       "14989  False  \n",
       "14990  False  \n",
       "14991  False  \n",
       "14992  False  \n",
       "14993  False  \n",
       "14994  False  \n",
       "14995  False  \n",
       "14996  False  \n",
       "14997  False  \n",
       "14998  False  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hra.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a908110908>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FhWnFihVKTExUcnKycubMqe3bt1sdK11ly5ZVSEiIYmNjtXTpUpUsWdLqSJmy+mfs0sL0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a908110240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(hra.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the information that is given with the Human Resources Analytics Dataset, there is no null values involved in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a9081b3748>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ccTbp33Fqxn/Z3ngWO5rMYlereXg9VQq3Ul4AlOtZl+v/PfXI93Xr6FW21ZrM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a8IQHn/wUT//lOh20T//hNuPd4BtRpTW3tcO/UmevtmUm5GkHDmOGW79wdA4+iMQ73GJJw+Ssze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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a908110c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = hra.corr()\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(correlation, vmax=1, square=True,annot=True,cmap='gnuplot')\n",
    "\n",
    "plt.title('Correlation between different fearures')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization of Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sales', 'accounting', 'hr', 'technical', 'support', 'management',\n",
       "       'IT', 'product_mng', 'marketing', 'RandD'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hra['sales'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</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>IT</th>\n",
       "      <td>758.46</td>\n",
       "      <td>879.55</td>\n",
       "      <td>4683</td>\n",
       "      <td>248119</td>\n",
       "      <td>4256</td>\n",
       "      <td>164</td>\n",
       "      <td>273</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>487.80</td>\n",
       "      <td>560.44</td>\n",
       "      <td>3033</td>\n",
       "      <td>158030</td>\n",
       "      <td>2650</td>\n",
       "      <td>134</td>\n",
       "      <td>121</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>446.51</td>\n",
       "      <td>550.49</td>\n",
       "      <td>2934</td>\n",
       "      <td>154292</td>\n",
       "      <td>2702</td>\n",
       "      <td>96</td>\n",
       "      <td>204</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>442.52</td>\n",
       "      <td>523.84</td>\n",
       "      <td>2701</td>\n",
       "      <td>146828</td>\n",
       "      <td>2480</td>\n",
       "      <td>89</td>\n",
       "      <td>215</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>391.45</td>\n",
       "      <td>456.12</td>\n",
       "      <td>2432</td>\n",
       "      <td>126787</td>\n",
       "      <td>2711</td>\n",
       "      <td>103</td>\n",
       "      <td>91</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>530.76</td>\n",
       "      <td>614.23</td>\n",
       "      <td>3164</td>\n",
       "      <td>171073</td>\n",
       "      <td>3063</td>\n",
       "      <td>138</td>\n",
       "      <td>203</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>558.91</td>\n",
       "      <td>644.71</td>\n",
       "      <td>3434</td>\n",
       "      <td>180369</td>\n",
       "      <td>3135</td>\n",
       "      <td>132</td>\n",
       "      <td>198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>2543.81</td>\n",
       "      <td>2938.23</td>\n",
       "      <td>15634</td>\n",
       "      <td>831773</td>\n",
       "      <td>14631</td>\n",
       "      <td>587</td>\n",
       "      <td>1014</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>1378.19</td>\n",
       "      <td>1611.81</td>\n",
       "      <td>8479</td>\n",
       "      <td>447490</td>\n",
       "      <td>7563</td>\n",
       "      <td>345</td>\n",
       "      <td>555</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>1653.48</td>\n",
       "      <td>1961.39</td>\n",
       "      <td>10548</td>\n",
       "      <td>550793</td>\n",
       "      <td>9279</td>\n",
       "      <td>381</td>\n",
       "      <td>697</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             satisfaction_level  last_evaluation  number_project  \\\n",
       "sales                                                              \n",
       "IT                       758.46           879.55            4683   \n",
       "RandD                    487.80           560.44            3033   \n",
       "accounting               446.51           550.49            2934   \n",
       "hr                       442.52           523.84            2701   \n",
       "management               391.45           456.12            2432   \n",
       "marketing                530.76           614.23            3164   \n",
       "product_mng              558.91           644.71            3434   \n",
       "sales                   2543.81          2938.23           15634   \n",
       "support                 1378.19          1611.81            8479   \n",
       "technical               1653.48          1961.39           10548   \n",
       "\n",
       "             average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "sales                                                                        \n",
       "IT                         248119                4256            164   273   \n",
       "RandD                      158030                2650            134   121   \n",
       "accounting                 154292                2702             96   204   \n",
       "hr                         146828                2480             89   215   \n",
       "management                 126787                2711            103    91   \n",
       "marketing                  171073                3063            138   203   \n",
       "product_mng                180369                3135            132   198   \n",
       "sales                      831773               14631            587  1014   \n",
       "support                    447490                7563            345   555   \n",
       "technical                  550793                9279            381   697   \n",
       "\n",
       "             promotion_last_5years  \n",
       "sales                               \n",
       "IT                               3  \n",
       "RandD                           27  \n",
       "accounting                      14  \n",
       "hr                              15  \n",
       "management                      69  \n",
       "marketing                       43  \n",
       "product_mng                      0  \n",
       "sales                          100  \n",
       "support                         20  \n",
       "technical                       28  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales=hra.groupby('sales').sum()\n",
    "sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</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>IT</th>\n",
       "      <td>0.618142</td>\n",
       "      <td>0.716830</td>\n",
       "      <td>3.816626</td>\n",
       "      <td>202.215974</td>\n",
       "      <td>3.468623</td>\n",
       "      <td>0.133659</td>\n",
       "      <td>0.222494</td>\n",
       "      <td>0.002445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>0.619822</td>\n",
       "      <td>0.712122</td>\n",
       "      <td>3.853875</td>\n",
       "      <td>200.800508</td>\n",
       "      <td>3.367217</td>\n",
       "      <td>0.170267</td>\n",
       "      <td>0.153748</td>\n",
       "      <td>0.034307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>0.582151</td>\n",
       "      <td>0.717718</td>\n",
       "      <td>3.825293</td>\n",
       "      <td>201.162973</td>\n",
       "      <td>3.522816</td>\n",
       "      <td>0.125163</td>\n",
       "      <td>0.265971</td>\n",
       "      <td>0.018253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>0.598809</td>\n",
       "      <td>0.708850</td>\n",
       "      <td>3.654939</td>\n",
       "      <td>198.684709</td>\n",
       "      <td>3.355886</td>\n",
       "      <td>0.120433</td>\n",
       "      <td>0.290934</td>\n",
       "      <td>0.020298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>0.621349</td>\n",
       "      <td>0.724000</td>\n",
       "      <td>3.860317</td>\n",
       "      <td>201.249206</td>\n",
       "      <td>4.303175</td>\n",
       "      <td>0.163492</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.109524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>0.618601</td>\n",
       "      <td>0.715886</td>\n",
       "      <td>3.687646</td>\n",
       "      <td>199.385781</td>\n",
       "      <td>3.569930</td>\n",
       "      <td>0.160839</td>\n",
       "      <td>0.236597</td>\n",
       "      <td>0.050117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>0.619634</td>\n",
       "      <td>0.714756</td>\n",
       "      <td>3.807095</td>\n",
       "      <td>199.965632</td>\n",
       "      <td>3.475610</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.614447</td>\n",
       "      <td>0.709717</td>\n",
       "      <td>3.776329</td>\n",
       "      <td>200.911353</td>\n",
       "      <td>3.534058</td>\n",
       "      <td>0.141787</td>\n",
       "      <td>0.244928</td>\n",
       "      <td>0.024155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>support</th>\n",
       "      <td>0.618300</td>\n",
       "      <td>0.723109</td>\n",
       "      <td>3.803948</td>\n",
       "      <td>200.758188</td>\n",
       "      <td>3.393001</td>\n",
       "      <td>0.154778</td>\n",
       "      <td>0.248991</td>\n",
       "      <td>0.008973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>0.607897</td>\n",
       "      <td>0.721099</td>\n",
       "      <td>3.877941</td>\n",
       "      <td>202.497426</td>\n",
       "      <td>3.411397</td>\n",
       "      <td>0.140074</td>\n",
       "      <td>0.256250</td>\n",
       "      <td>0.010294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             satisfaction_level  last_evaluation  number_project  \\\n",
       "sales                                                              \n",
       "IT                     0.618142         0.716830        3.816626   \n",
       "RandD                  0.619822         0.712122        3.853875   \n",
       "accounting             0.582151         0.717718        3.825293   \n",
       "hr                     0.598809         0.708850        3.654939   \n",
       "management             0.621349         0.724000        3.860317   \n",
       "marketing              0.618601         0.715886        3.687646   \n",
       "product_mng            0.619634         0.714756        3.807095   \n",
       "sales                  0.614447         0.709717        3.776329   \n",
       "support                0.618300         0.723109        3.803948   \n",
       "technical              0.607897         0.721099        3.877941   \n",
       "\n",
       "             average_montly_hours  time_spend_company  Work_accident  \\\n",
       "sales                                                                  \n",
       "IT                     202.215974            3.468623       0.133659   \n",
       "RandD                  200.800508            3.367217       0.170267   \n",
       "accounting             201.162973            3.522816       0.125163   \n",
       "hr                     198.684709            3.355886       0.120433   \n",
       "management             201.249206            4.303175       0.163492   \n",
       "marketing              199.385781            3.569930       0.160839   \n",
       "product_mng            199.965632            3.475610       0.146341   \n",
       "sales                  200.911353            3.534058       0.141787   \n",
       "support                200.758188            3.393001       0.154778   \n",
       "technical              202.497426            3.411397       0.140074   \n",
       "\n",
       "                 left  promotion_last_5years  \n",
       "sales                                         \n",
       "IT           0.222494               0.002445  \n",
       "RandD        0.153748               0.034307  \n",
       "accounting   0.265971               0.018253  \n",
       "hr           0.290934               0.020298  \n",
       "management   0.144444               0.109524  \n",
       "marketing    0.236597               0.050117  \n",
       "product_mng  0.219512               0.000000  \n",
       "sales        0.244928               0.024155  \n",
       "support      0.248991               0.008973  \n",
       "technical    0.256250               0.010294  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_sales=hra.groupby('sales').mean()\n",
    "groupby_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.60789705882352951"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IT=groupby_sales['satisfaction_level'].IT\n",
    "RandD=groupby_sales['satisfaction_level'].RandD\n",
    "accounting=groupby_sales['satisfaction_level'].accounting\n",
    "hr=groupby_sales['satisfaction_level'].hr\n",
    "management=groupby_sales['satisfaction_level'].management\n",
    "marketing=groupby_sales['satisfaction_level'].marketing\n",
    "product_mng=groupby_sales['satisfaction_level'].product_mng\n",
    "sales=groupby_sales['satisfaction_level'].sales\n",
    "support=groupby_sales['satisfaction_level'].support\n",
    "technical=groupby_sales['satisfaction_level'].technical\n",
    "technical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a908370fd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z0/N0lQpfxcbaPYFtqiYlwdq1do9hGyfnd3J2UH7l9y3/cY8Hxo/X+ReoVPis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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a908188908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "department_name=('sales', 'accounting', 'hr', 'technical', 'support', 'management',\n",
    "       'IT', 'product_mng', 'marketing', 'RandD')\n",
    "department=(sales, accounting, hr, technical, support, management,\n",
    "       IT, product_mng, marketing, RandD)\n",
    "y_pos = np.arange(len(department))\n",
    "x=np.arange(0,1,0.1)\n",
    "\n",
    "plt.barh(y_pos, department, align='center', alpha=0.5,color='red')\n",
    "plt.yticks(y_pos,department_name )\n",
    "plt.xlabel('Satisfaction level')\n",
    "plt.title('Mean Satisfaction Level of each department')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bb89vfvOb7LXXXnnzzTc3+ji77bZbTj311Pz+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\nNm1a97vHHnssAwcOrHvD6Oyzzy70Gn9LqFe7vb8lBACwHbv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AAAAorGG877C/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rzy+Xy69tprtW7dOiOrOXYk6fjx4/rmm280fvx4SSff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\nAZjRKRbF0nZUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9083c3f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr  = plt.subplots(2,2,figsize=(20,20))\n",
    "sns.countplot(x='salary', data=hra, ax=axarr[0,0],palette='viridis')\n",
    "sns.countplot(x='sales', data=hra, ax=axarr[0,1],palette='hot')\n",
    "sns.countplot(x='satisfaction_level', data=hra, ax=axarr[1,0],palette='brg')\n",
    "sns.countplot(x='number_project', data=hra, ax=axarr[1,1],palette='terrain')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wYP8rnn766dccb9++PXv27BnUY0f7eFLNuKlm3FwznloznnFoXPs9gYlix44d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jxb/2+NNTIy0nzuWJM/1oSKdQE46yJy1uNZj3Xy+N1ut3mNrHWuqakB/vczw6pqtZ6j+Ph4qqqq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\nQ0NDVq1ala6urmzZs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JQBAAAAAChLTaULAAAAAPio2Lp1a5555plK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\nAmUAAAAAAMoiUAYAAAAAoCz/H0QliJwZuwHBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9083dae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr  = plt.subplots(2,2,figsize=(20,20))\n",
    "sns.countplot(x='time_spend_company', data=hra, ax=axarr[0,0],palette='viridis')\n",
    "sns.countplot(x='average_montly_hours', data=hra, ax=axarr[0,1],palette='hot')\n",
    "sns.countplot(x='Work_accident', data=hra, ax=axarr[1,0],palette='brg')\n",
    "sns.countplot(x='promotion_last_5years', data=hra, ax=axarr[1,1],palette='terrain')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Principal Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hra.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping the Sales and Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  \n",
       "0                   3              0     1                      0  \n",
       "1                   6              0     1                      0  \n",
       "2                   4              0     1                      0  \n",
       "3                   5              0     1                      0  \n",
       "4                   3              0     1                      0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hra_drop=hra.drop(labels=['sales','salary'],axis=1)\n",
    "hra_drop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dropping the Tolist Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['satisfaction_level',\n",
       " 'last_evaluation',\n",
       " 'number_project',\n",
       " 'average_montly_hours',\n",
       " 'time_spend_company',\n",
       " 'Work_accident',\n",
       " 'left',\n",
       " 'promotion_last_5years']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = hra_drop.columns.tolist()\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['left',\n",
       " 'satisfaction_level',\n",
       " 'last_evaluation',\n",
       " 'number_project',\n",
       " 'average_montly_hours',\n",
       " 'time_spend_company',\n",
       " 'Work_accident',\n",
       " 'promotion_last_5years']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols.insert(0, cols.pop(cols.index('left')))\n",
    "cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting the List to Columns Again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hra_drop = hra_drop.reindex(columns= cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.38,  0.53,  2.  , ...,  3.  ,  0.  ,  0.  ],\n",
       "       [ 0.8 ,  0.86,  5.  , ...,  6.  ,  0.  ,  0.  ],\n",
       "       [ 0.11,  0.88,  7.  , ...,  4.  ,  0.  ,  0.  ],\n",
       "       ..., \n",
       "       [ 0.37,  0.53,  2.  , ...,  3.  ,  0.  ,  0.  ],\n",
       "       [ 0.11,  0.96,  6.  , ...,  4.  ,  0.  ,  0.  ],\n",
       "       [ 0.37,  0.52,  2.  , ...,  3.  ,  0.  ,  0.  ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = hra_drop.iloc[:,1:8].values\n",
    "y = hra_drop.iloc[:,0].values\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 7)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X_std = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance matrix \n",
      "[[ 1.00006668  0.10502822 -0.14297912 -0.02004945 -0.1008728   0.05870115\n",
      "   0.02560689]\n",
      " [ 0.10502822  1.00006668  0.34935588  0.33976445  0.1315995  -0.00710476\n",
      "  -0.00868435]\n",
      " [-0.14297912  0.34935588  1.00006668  0.41723845  0.19679901 -0.00474086\n",
      "  -0.00606436]\n",
      " [-0.02004945  0.33976445  0.41723845  1.00006668  0.12776343 -0.01014356\n",
      "  -0.00354465]\n",
      " [-0.1008728   0.1315995   0.19679901  0.12776343  1.00006668  0.00212056\n",
      "   0.06743742]\n",
      " [ 0.05870115 -0.00710476 -0.00474086 -0.01014356  0.00212056  1.00006668\n",
      "   0.03924805]\n",
      " [ 0.02560689 -0.00868435 -0.00606436 -0.00354465  0.06743742  0.03924805\n",
      "   1.00006668]]\n"
     ]
    }
   ],
   "source": [
    "mean_vec = np.mean(X_std, axis=0)\n",
    "cov_mat = (X_std - mean_vec).T.dot((X_std - mean_vec)) / (X_std.shape[0]-1)\n",
    "print('Covariance matrix \\n%s' %cov_mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NumPy covariance matrix: \n",
      "[[ 1.00006668  0.10502822 -0.14297912 -0.02004945 -0.1008728   0.05870115\n",
      "   0.02560689]\n",
      " [ 0.10502822  1.00006668  0.34935588  0.33976445  0.1315995  -0.00710476\n",
      "  -0.00868435]\n",
      " [-0.14297912  0.34935588  1.00006668  0.41723845  0.19679901 -0.00474086\n",
      "  -0.00606436]\n",
      " [-0.02004945  0.33976445  0.41723845  1.00006668  0.12776343 -0.01014356\n",
      "  -0.00354465]\n",
      " [-0.1008728   0.1315995   0.19679901  0.12776343  1.00006668  0.00212056\n",
      "   0.06743742]\n",
      " [ 0.05870115 -0.00710476 -0.00474086 -0.01014356  0.00212056  1.00006668\n",
      "   0.03924805]\n",
      " [ 0.02560689 -0.00868435 -0.00606436 -0.00354465  0.06743742  0.03924805\n",
      "   1.00006668]]\n"
     ]
    }
   ],
   "source": [
    "print('NumPy covariance matrix: \\n%s' %np.cov(X_std.T))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a908e064e0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aJX2MVrDYIiIiIkWEdR+kdAqyYLFFREREiiiVpbMlfYjWsNgiIiIiRYQms7NF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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a90aaa6908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,9))\n",
    "sns.heatmap(cov_mat, vmax=1, square=True,annot=True,cmap='Accent',alpha=0.5)\n",
    "\n",
    "plt.title('Correlation Among the Different Features')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eigenvectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvectors \n",
      "[[ 0.08797699  0.29189921  0.27784886  0.33637135  0.79752505  0.26786864\n",
      "  -0.09438973]\n",
      " [-0.50695734 -0.30996609 -0.70780994  0.07393548  0.33180877  0.1101505\n",
      "  -0.13499526]\n",
      " [-0.5788351   0.77736008 -0.00657105 -0.19677589 -0.10338032 -0.10336241\n",
      "  -0.02293518]\n",
      " [-0.54901653 -0.45787675  0.63497294 -0.25170987  0.10388959 -0.01034922\n",
      "  -0.10714981]\n",
      " [-0.31354922 -0.05287224  0.12200054  0.78782241 -0.28404472  0.04036861\n",
      "   0.42547869]\n",
      " [ 0.01930249 -0.04433104 -0.03622859 -0.05762997  0.37489883 -0.8048393\n",
      "   0.45245222]\n",
      " [-0.00996933 -0.00391698 -0.04873036 -0.39411153  0.10557298  0.50589173\n",
      "   0.75836313]]\n",
      "\n",
      "Eigenvalues \n",
      "[ 1.83017431  0.54823098  0.63363587  0.84548166  1.12659606  0.95598647\n",
      "  1.06036136]\n"
     ]
    }
   ],
   "source": [
    "eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n",
    "\n",
    "print('Eigenvectors \\n%s' %eig_vecs)\n",
    "print('\\nEigenvalues \\n%s' %eig_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Principal Components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Eigenvalues in descending order:\n",
      "1.83017431388\n",
      "1.12659606399\n",
      "1.06036136228\n",
      "0.955986474007\n",
      "0.845481663714\n",
      "0.633635874483\n",
      "0.548230976542\n"
     ]
    }
   ],
   "source": [
    "# Make a list of (eigenvalue, eigenvector) tuples\n",
    "eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i]) for i in range(len(eig_vals))]\n",
    "\n",
    "# Sort the (eigenvalue, eigenvector) tuples from high to low\n",
    "eig_pairs.sort(key=lambda x: x[0], reverse=True)\n",
    "\n",
    "# Visually confirm that the list is correctly sorted by decreasing eigenvalues\n",
    "print('Eigenvalues in descending order:')\n",
    "for i in eig_pairs:\n",
    "    print(i[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tot = sum(eig_vals)\n",
    "var_exp = [(i / tot)*100 for i in sorted(eig_vals, reverse=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IKtDr8kXhX0nuleKswr1CestCI+TqK1aKIYk5ehOz9A7m6D3M0juYo3fkd458eAcAAACM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\n/wE+pp/Yt5kwDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a908daf3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('dark_background'):\n",
    "    plt.figure(figsize=(10, 8))\n",
    "\n",
    "    plt.bar(range(7), var_exp, alpha=1.0, align='center',\n",
    "            label='individual explained variance',color='orange')\n",
    "    plt.ylabel('Explained Variance Ratio')\n",
    "    plt.xlabel('Principal Components')\n",
    "    plt.legend(loc='best')\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projection Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix W:\n",
      " [[ 0.08797699  0.79752505]\n",
      " [-0.50695734  0.33180877]\n",
      " [-0.5788351  -0.10338032]\n",
      " [-0.54901653  0.10388959]\n",
      " [-0.31354922 -0.28404472]\n",
      " [ 0.01930249  0.37489883]\n",
      " [-0.00996933  0.10557298]]\n"
     ]
    }
   ],
   "source": [
    "matrix_w = np.hstack((eig_pairs[0][1].reshape(7,1), \n",
    "                      eig_pairs[1][1].reshape(7,1)\n",
    "                    ))\n",
    "print('Matrix W:\\n', matrix_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.90035018, -1.12083103],\n",
       "       [-2.1358322 ,  0.2493369 ],\n",
       "       [-3.05891625, -1.68312693],\n",
       "       ..., \n",
       "       [ 2.0507165 , -1.182032  ],\n",
       "       [-2.91418496, -1.42752606],\n",
       "       [ 1.91543672, -1.17021407]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = X_std.dot(matrix_w)\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA is Scikit Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1a908345a58>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UwjK/du0aVq1ahfT0dAiCULR848aNSg1GJCZBEBD0exBmHJmByjqVscNrBzwb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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a908ea8eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA().fit(X_std)\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_),color='green')\n",
    "plt.xlim(0,7,1)\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cumulative Explained Variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sklearn_pca = PCA(n_components=6)\n",
    "Y_sklearn = sklearn_pca.fit_transform(X_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.90035018 -1.12083103 -0.0797787   0.03228437 -0.07256447  0.06063013]\n",
      " [ 2.1358322   0.2493369   0.0936161   0.50676925  1.2487747  -0.61378158]\n",
      " [ 3.05891625 -1.68312693 -0.301682   -0.4488635  -1.12495888  0.29066929]\n",
      " ..., \n",
      " [-2.0507165  -1.182032   -0.04594506  0.02441143 -0.01553247  0.24980658]\n",
      " [ 2.91418496 -1.42752606 -0.36333357 -0.31517759 -0.97107375  0.51444624]\n",
      " [-1.91543672 -1.17021407 -0.07024077  0.01486762 -0.09545357  0.01773844]]\n"
     ]
    }
   ],
   "source": [
    "print(Y_sklearn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 6)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_sklearn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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