diff --git "a/Employee_Promotion_Prediction.ipynb" "b/Employee_Promotion_Prediction.ipynb"
new file mode 100644--- /dev/null
+++ "b/Employee_Promotion_Prediction.ipynb"
@@ -0,0 +1,12188 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lxWg-88vlXNr"
+   },
+   "source": [
+    "## <center>**Predict whether the Employee of an Organization should get Promotion or Not?**</center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UdHgRAPlLsBW"
+   },
+   "source": [
+    "&nbsp;\n",
+    "\n",
+    "Our client is a large MNC and they have 9 broad verticals across the organisation. One of the problem our client is facing is around identifying the right people for promotion (only for manager position and below) and prepare them in time. Currently the process, they are following is:\n",
+    "    * They first identify a set of employees based on recommendations/ past performance.\n",
+    "    * Selected employees go through the separate training and evaluation program for each vertical. These programs are based on he required skill of each vertical\n",
+    "    * At the end of the program, based on various factors such as training performance, an employee gets the promotion\n",
+    "\n",
+    "![image](https://corehr.files.wordpress.com/2013/02/wrong-promotion1.jpg?w=290)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {
+    "id": "i0gPAqj5qtGh"
+   },
+   "outputs": [],
+   "source": [
+    "# lets import all the required libraries\n",
+    "\n",
+    "# for mathematical operations\n",
+    "import numpy as np\n",
+    "# for dataframe operations\n",
+    "import pandas as pd\n",
+    "\n",
+    "# for data visualizations\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# for machine learning\n",
+    "import sklearn\n",
+    "import imblearn\n",
+    "\n",
+    "# setting up the size of the figures\n",
+    "plt.rcParams['figure.figsize'] = (16, 5)\n",
+    "# setting up the style of the plot\n",
+    "plt.style.use('fivethirtyeight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {
+    "id": "if4SIFymsqQY"
+   },
+   "outputs": [],
+   "source": [
+    "# reading the datasets\n",
+    "\n",
+    "train = pd.read_csv('train (1).csv')\n",
+    "test = pd.read_csv('test (1).csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 339
+    },
+    "id": "y-Oh5tZJM9kt",
+    "outputId": "e12efa8a-6642-4558-fa21-e8906cba6fbb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee_id</th>\n",
+       "      <th>department</th>\n",
+       "      <th>region</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>recruitment_channel</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>65438</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_7</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>f</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>49</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>65141</td>\n",
+       "      <td>Operations</td>\n",
+       "      <td>region_22</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7513</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_19</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2542</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_23</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>2</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48945</td>\n",
+       "      <td>Technology</td>\n",
+       "      <td>region_26</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   employee_id         department     region         education gender  \\\n",
+       "0        65438  Sales & Marketing   region_7  Master's & above      f   \n",
+       "1        65141         Operations  region_22        Bachelor's      m   \n",
+       "2         7513  Sales & Marketing  region_19        Bachelor's      m   \n",
+       "3         2542  Sales & Marketing  region_23        Bachelor's      m   \n",
+       "4        48945         Technology  region_26        Bachelor's      m   \n",
+       "\n",
+       "  recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
+       "0            sourcing                1   35                   5.0   \n",
+       "1               other                1   30                   5.0   \n",
+       "2            sourcing                1   34                   3.0   \n",
+       "3               other                2   39                   1.0   \n",
+       "4               other                1   45                   3.0   \n",
+       "\n",
+       "   length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
+       "0                  8              1            0                  49   \n",
+       "1                  4              0            0                  60   \n",
+       "2                  7              0            0                  50   \n",
+       "3                 10              0            0                  50   \n",
+       "4                  2              0            0                  73   \n",
+       "\n",
+       "   is_promoted  \n",
+       "0            0  \n",
+       "1            0  \n",
+       "2            0  \n",
+       "3            0  \n",
+       "4            0  "
+      ]
+     },
+     "execution_count": 266,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 285
+    },
+    "id": "sVYIq2H5NNMT",
+    "outputId": "4e0053ed-e6c7-49e4-a41b-c3cd04f7c0c4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee_id</th>\n",
+       "      <th>department</th>\n",
+       "      <th>region</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>recruitment_channel</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8724</td>\n",
+       "      <td>Technology</td>\n",
+       "      <td>region_26</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>74430</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>region_4</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>f</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>72255</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_13</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38562</td>\n",
+       "      <td>Procurement</td>\n",
+       "      <td>region_2</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>f</td>\n",
+       "      <td>other</td>\n",
+       "      <td>3</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>64486</td>\n",
+       "      <td>Finance</td>\n",
+       "      <td>region_29</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   employee_id         department     region   education gender  \\\n",
+       "0         8724         Technology  region_26  Bachelor's      m   \n",
+       "1        74430                 HR   region_4  Bachelor's      f   \n",
+       "2        72255  Sales & Marketing  region_13  Bachelor's      m   \n",
+       "3        38562        Procurement   region_2  Bachelor's      f   \n",
+       "4        64486            Finance  region_29  Bachelor's      m   \n",
+       "\n",
+       "  recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
+       "0            sourcing                1   24                   NaN   \n",
+       "1               other                1   31                   3.0   \n",
+       "2               other                1   31                   1.0   \n",
+       "3               other                3   31                   2.0   \n",
+       "4            sourcing                1   30                   4.0   \n",
+       "\n",
+       "   length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \n",
+       "0                  1              1            0                  77  \n",
+       "1                  5              0            0                  51  \n",
+       "2                  4              0            0                  47  \n",
+       "3                  9              0            0                  65  \n",
+       "4                  7              0            0                  61  "
+      ]
+     },
+     "execution_count": 267,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ScIy1lK3OdR9"
+   },
+   "source": [
+    "We have observations of 54,808 employees that we want to analyse. And there are 14 features in our dataset on the top of which we are going to build our model to predict if the employee should be promoted or not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "id": "z4ngdw_TOaCR",
+    "outputId": "ae82daac-1e98-46b0-f330-89aa927f81a2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee_id</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>54808.000000</td>\n",
+       "      <td>54808.000000</td>\n",
+       "      <td>54808.000000</td>\n",
+       "      <td>50684.000000</td>\n",
+       "      <td>54808.000000</td>\n",
+       "      <td>54808.000000</td>\n",
+       "      <td>54808.000000</td>\n",
+       "      <td>54808.000000</td>\n",
+       "      <td>54808.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>39195.830627</td>\n",
+       "      <td>1.253011</td>\n",
+       "      <td>34.803915</td>\n",
+       "      <td>3.329256</td>\n",
+       "      <td>5.865512</td>\n",
+       "      <td>0.351974</td>\n",
+       "      <td>0.023172</td>\n",
+       "      <td>63.386750</td>\n",
+       "      <td>0.085170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>22586.581449</td>\n",
+       "      <td>0.609264</td>\n",
+       "      <td>7.660169</td>\n",
+       "      <td>1.259993</td>\n",
+       "      <td>4.265094</td>\n",
+       "      <td>0.477590</td>\n",
+       "      <td>0.150450</td>\n",
+       "      <td>13.371559</td>\n",
+       "      <td>0.279137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>19669.750000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>39225.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>58730.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>78298.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        employee_id  no_of_trainings           age  previous_year_rating  \\\n",
+       "count  54808.000000     54808.000000  54808.000000          50684.000000   \n",
+       "mean   39195.830627         1.253011     34.803915              3.329256   \n",
+       "std    22586.581449         0.609264      7.660169              1.259993   \n",
+       "min        1.000000         1.000000     20.000000              1.000000   \n",
+       "25%    19669.750000         1.000000     29.000000              3.000000   \n",
+       "50%    39225.500000         1.000000     33.000000              3.000000   \n",
+       "75%    58730.500000         1.000000     39.000000              4.000000   \n",
+       "max    78298.000000        10.000000     60.000000              5.000000   \n",
+       "\n",
+       "       length_of_service  KPIs_met >80%   awards_won?  avg_training_score  \\\n",
+       "count       54808.000000   54808.000000  54808.000000        54808.000000   \n",
+       "mean            5.865512       0.351974      0.023172           63.386750   \n",
+       "std             4.265094       0.477590      0.150450           13.371559   \n",
+       "min             1.000000       0.000000      0.000000           39.000000   \n",
+       "25%             3.000000       0.000000      0.000000           51.000000   \n",
+       "50%             5.000000       0.000000      0.000000           60.000000   \n",
+       "75%             7.000000       1.000000      0.000000           76.000000   \n",
+       "max            37.000000       1.000000      1.000000           99.000000   \n",
+       "\n",
+       "        is_promoted  \n",
+       "count  54808.000000  \n",
+       "mean       0.085170  \n",
+       "std        0.279137  \n",
+       "min        0.000000  \n",
+       "25%        0.000000  \n",
+       "50%        0.000000  \n",
+       "75%        0.000000  \n",
+       "max        1.000000  "
+      ]
+     },
+     "execution_count": 268,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wF0G9Hm2O1qs",
+    "outputId": "ae4b3985-d453-481d-c6fd-c456f3e3714b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 54808 entries, 0 to 54807\n",
+      "Data columns (total 14 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   employee_id           54808 non-null  int64  \n",
+      " 1   department            54808 non-null  object \n",
+      " 2   region                54808 non-null  object \n",
+      " 3   education             52399 non-null  object \n",
+      " 4   gender                54808 non-null  object \n",
+      " 5   recruitment_channel   54808 non-null  object \n",
+      " 6   no_of_trainings       54808 non-null  int64  \n",
+      " 7   age                   54808 non-null  int64  \n",
+      " 8   previous_year_rating  50684 non-null  float64\n",
+      " 9   length_of_service     54808 non-null  int64  \n",
+      " 10  KPIs_met >80%         54808 non-null  int64  \n",
+      " 11  awards_won?           54808 non-null  int64  \n",
+      " 12  avg_training_score    54808 non-null  int64  \n",
+      " 13  is_promoted           54808 non-null  int64  \n",
+      "dtypes: float64(1), int64(8), object(5)\n",
+      "memory usage: 5.9+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "id": "47Unz5Lmzf4I",
+    "outputId": "0c7d176b-8471-4747-c4dd-42e02924f16d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee_id</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>23490.000000</td>\n",
+       "      <td>23490.000000</td>\n",
+       "      <td>23490.000000</td>\n",
+       "      <td>21678.000000</td>\n",
+       "      <td>23490.000000</td>\n",
+       "      <td>23490.000000</td>\n",
+       "      <td>23490.000000</td>\n",
+       "      <td>23490.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>39041.399149</td>\n",
+       "      <td>1.254236</td>\n",
+       "      <td>34.782929</td>\n",
+       "      <td>3.339146</td>\n",
+       "      <td>5.810387</td>\n",
+       "      <td>0.358834</td>\n",
+       "      <td>0.022776</td>\n",
+       "      <td>63.263133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>22640.809201</td>\n",
+       "      <td>0.600910</td>\n",
+       "      <td>7.679492</td>\n",
+       "      <td>1.263294</td>\n",
+       "      <td>4.207917</td>\n",
+       "      <td>0.479668</td>\n",
+       "      <td>0.149191</td>\n",
+       "      <td>13.411750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>19370.250000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>38963.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>58690.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>78295.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>34.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        employee_id  no_of_trainings           age  previous_year_rating  \\\n",
+       "count  23490.000000     23490.000000  23490.000000          21678.000000   \n",
+       "mean   39041.399149         1.254236     34.782929              3.339146   \n",
+       "std    22640.809201         0.600910      7.679492              1.263294   \n",
+       "min        3.000000         1.000000     20.000000              1.000000   \n",
+       "25%    19370.250000         1.000000     29.000000              3.000000   \n",
+       "50%    38963.500000         1.000000     33.000000              3.000000   \n",
+       "75%    58690.000000         1.000000     39.000000              4.000000   \n",
+       "max    78295.000000         9.000000     60.000000              5.000000   \n",
+       "\n",
+       "       length_of_service  KPIs_met >80%   awards_won?  avg_training_score  \n",
+       "count       23490.000000   23490.000000  23490.000000        23490.000000  \n",
+       "mean            5.810387       0.358834      0.022776           63.263133  \n",
+       "std             4.207917       0.479668      0.149191           13.411750  \n",
+       "min             1.000000       0.000000      0.000000           39.000000  \n",
+       "25%             3.000000       0.000000      0.000000           51.000000  \n",
+       "50%             5.000000       0.000000      0.000000           60.000000  \n",
+       "75%             7.000000       1.000000      0.000000           76.000000  \n",
+       "max            34.000000       1.000000      1.000000           99.000000  "
+      ]
+     },
+     "execution_count": 270,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "98c03csTzgJB",
+    "outputId": "065ebca2-3ad8-40f8-8e86-4f98db73d617"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 23490 entries, 0 to 23489\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column                Non-Null Count  Dtype  \n",
+      "---  ------                --------------  -----  \n",
+      " 0   employee_id           23490 non-null  int64  \n",
+      " 1   department            23490 non-null  object \n",
+      " 2   region                23490 non-null  object \n",
+      " 3   education             22456 non-null  object \n",
+      " 4   gender                23490 non-null  object \n",
+      " 5   recruitment_channel   23490 non-null  object \n",
+      " 6   no_of_trainings       23490 non-null  int64  \n",
+      " 7   age                   23490 non-null  int64  \n",
+      " 8   previous_year_rating  21678 non-null  float64\n",
+      " 9   length_of_service     23490 non-null  int64  \n",
+      " 10  KPIs_met >80%         23490 non-null  int64  \n",
+      " 11  awards_won?           23490 non-null  int64  \n",
+      " 12  avg_training_score    23490 non-null  int64  \n",
+      "dtypes: float64(1), int64(7), object(5)\n",
+      "memory usage: 2.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "gmUkMcfgPgXd",
+    "outputId": "70b09523-a623-45a1-d055-6b8f2eb78efd"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the Training Data : (54808, 14)\n",
+      "Shape of the Test Data : (23490, 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets check the shape of the train and test datasets\n",
+    "print(\"Shape of the Training Data :\", train.shape)\n",
+    "print(\"Shape of the Test Data :\", test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4X-idqc5z0FO"
+   },
+   "source": [
+    "## Descriptive Statistics\n",
+    "\n",
+    "* Descriptive Statistics is one of the most Important Step to Understand the Data and take out Insights\n",
+    "* First we will the Descriptive Statistics for the Numerical Columns\n",
+    "* for Numerical Columns we check for stats such as Max, Min, Mean, count, standard deviation, 25 percentile, 50 percentile, and 75 percentile.\n",
+    "* Then we will check for the Descriptive Statistics for Categorical Columns\n",
+    "* for Categorical Columns we check for stats such as count, frequency, top, and unique elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 300
+    },
+    "id": "uaSiIyPSy-jE",
+    "outputId": "7e250e05-7367-4b67-ceed-1dd73e47ae79"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style  type=\"text/css\" >\n",
+       "#T_cfe23_row0_col0{\n",
+       "            background-color:  #dd8c59;\n",
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+       "        }#T_cfe23_row0_col1,#T_cfe23_row0_col2,#T_cfe23_row0_col3,#T_cfe23_row0_col4,#T_cfe23_row0_col5,#T_cfe23_row0_col6,#T_cfe23_row0_col7,#T_cfe23_row0_col8,#T_cfe23_row7_col0{\n",
+       "            background-color:  #ffc77f;\n",
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+       "        }#T_cfe23_row1_col1,#T_cfe23_row1_col2,#T_cfe23_row1_col3,#T_cfe23_row1_col4,#T_cfe23_row1_col5,#T_cfe23_row1_col6,#T_cfe23_row1_col7,#T_cfe23_row1_col8,#T_cfe23_row2_col1,#T_cfe23_row2_col2,#T_cfe23_row2_col3,#T_cfe23_row2_col4,#T_cfe23_row2_col5,#T_cfe23_row2_col6,#T_cfe23_row2_col7,#T_cfe23_row2_col8,#T_cfe23_row3_col0,#T_cfe23_row3_col1,#T_cfe23_row3_col2,#T_cfe23_row3_col3,#T_cfe23_row3_col4,#T_cfe23_row3_col5,#T_cfe23_row3_col6,#T_cfe23_row3_col7,#T_cfe23_row3_col8,#T_cfe23_row4_col1,#T_cfe23_row4_col2,#T_cfe23_row4_col3,#T_cfe23_row4_col4,#T_cfe23_row4_col5,#T_cfe23_row4_col6,#T_cfe23_row4_col7,#T_cfe23_row4_col8,#T_cfe23_row5_col1,#T_cfe23_row5_col2,#T_cfe23_row5_col3,#T_cfe23_row5_col4,#T_cfe23_row5_col5,#T_cfe23_row5_col6,#T_cfe23_row5_col7,#T_cfe23_row5_col8,#T_cfe23_row6_col1,#T_cfe23_row6_col2,#T_cfe23_row6_col3,#T_cfe23_row6_col4,#T_cfe23_row6_col5,#T_cfe23_row6_col6,#T_cfe23_row6_col7,#T_cfe23_row6_col8,#T_cfe23_row7_col1,#T_cfe23_row7_col2,#T_cfe23_row7_col3,#T_cfe23_row7_col4,#T_cfe23_row7_col5,#T_cfe23_row7_col6,#T_cfe23_row7_col7,#T_cfe23_row7_col8{\n",
+       "            background-color:  #000000;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_cfe23_row2_col0{\n",
+       "            background-color:  #5a3924;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_cfe23_row4_col0{\n",
+       "            background-color:  #4f3220;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_cfe23_row6_col0{\n",
+       "            background-color:  #ed9660;\n",
+       "            color:  #000000;\n",
+       "        }</style><table id=\"T_cfe23_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >employee_id</th>        <th class=\"col_heading level0 col1\" >no_of_trainings</th>        <th class=\"col_heading level0 col2\" >age</th>        <th class=\"col_heading level0 col3\" >previous_year_rating</th>        <th class=\"col_heading level0 col4\" >length_of_service</th>        <th class=\"col_heading level0 col5\" >KPIs_met >80%</th>        <th class=\"col_heading level0 col6\" >awards_won?</th>        <th class=\"col_heading level0 col7\" >avg_training_score</th>        <th class=\"col_heading level0 col8\" >is_promoted</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_cfe23_level0_row0\" class=\"row_heading level0 row0\" >count</th>\n",
+       "                        <td id=\"T_cfe23_row0_col0\" class=\"data row0 col0\" >54808.000000</td>\n",
+       "                        <td id=\"T_cfe23_row0_col1\" class=\"data row0 col1\" >54808.000000</td>\n",
+       "                        <td id=\"T_cfe23_row0_col2\" class=\"data row0 col2\" >54808.000000</td>\n",
+       "                        <td id=\"T_cfe23_row0_col3\" class=\"data row0 col3\" >50684.000000</td>\n",
+       "                        <td id=\"T_cfe23_row0_col4\" class=\"data row0 col4\" >54808.000000</td>\n",
+       "                        <td id=\"T_cfe23_row0_col5\" class=\"data row0 col5\" >54808.000000</td>\n",
+       "                        <td id=\"T_cfe23_row0_col6\" class=\"data row0 col6\" >54808.000000</td>\n",
+       "                        <td id=\"T_cfe23_row0_col7\" class=\"data row0 col7\" >54808.000000</td>\n",
+       "                        <td id=\"T_cfe23_row0_col8\" class=\"data row0 col8\" >54808.000000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_cfe23_level0_row1\" class=\"row_heading level0 row1\" >mean</th>\n",
+       "                        <td id=\"T_cfe23_row1_col0\" class=\"data row1 col0\" >39195.830627</td>\n",
+       "                        <td id=\"T_cfe23_row1_col1\" class=\"data row1 col1\" >1.253011</td>\n",
+       "                        <td id=\"T_cfe23_row1_col2\" class=\"data row1 col2\" >34.803915</td>\n",
+       "                        <td id=\"T_cfe23_row1_col3\" class=\"data row1 col3\" >3.329256</td>\n",
+       "                        <td id=\"T_cfe23_row1_col4\" class=\"data row1 col4\" >5.865512</td>\n",
+       "                        <td id=\"T_cfe23_row1_col5\" class=\"data row1 col5\" >0.351974</td>\n",
+       "                        <td id=\"T_cfe23_row1_col6\" class=\"data row1 col6\" >0.023172</td>\n",
+       "                        <td id=\"T_cfe23_row1_col7\" class=\"data row1 col7\" >63.386750</td>\n",
+       "                        <td id=\"T_cfe23_row1_col8\" class=\"data row1 col8\" >0.085170</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_cfe23_level0_row2\" class=\"row_heading level0 row2\" >std</th>\n",
+       "                        <td id=\"T_cfe23_row2_col0\" class=\"data row2 col0\" >22586.581449</td>\n",
+       "                        <td id=\"T_cfe23_row2_col1\" class=\"data row2 col1\" >0.609264</td>\n",
+       "                        <td id=\"T_cfe23_row2_col2\" class=\"data row2 col2\" >7.660169</td>\n",
+       "                        <td id=\"T_cfe23_row2_col3\" class=\"data row2 col3\" >1.259993</td>\n",
+       "                        <td id=\"T_cfe23_row2_col4\" class=\"data row2 col4\" >4.265094</td>\n",
+       "                        <td id=\"T_cfe23_row2_col5\" class=\"data row2 col5\" >0.477590</td>\n",
+       "                        <td id=\"T_cfe23_row2_col6\" class=\"data row2 col6\" >0.150450</td>\n",
+       "                        <td id=\"T_cfe23_row2_col7\" class=\"data row2 col7\" >13.371559</td>\n",
+       "                        <td id=\"T_cfe23_row2_col8\" class=\"data row2 col8\" >0.279137</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_cfe23_level0_row3\" class=\"row_heading level0 row3\" >min</th>\n",
+       "                        <td id=\"T_cfe23_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row3_col2\" class=\"data row3 col2\" >20.000000</td>\n",
+       "                        <td id=\"T_cfe23_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row3_col5\" class=\"data row3 col5\" >0.000000</td>\n",
+       "                        <td id=\"T_cfe23_row3_col6\" class=\"data row3 col6\" >0.000000</td>\n",
+       "                        <td id=\"T_cfe23_row3_col7\" class=\"data row3 col7\" >39.000000</td>\n",
+       "                        <td id=\"T_cfe23_row3_col8\" class=\"data row3 col8\" >0.000000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_cfe23_level0_row4\" class=\"row_heading level0 row4\" >25%</th>\n",
+       "                        <td id=\"T_cfe23_row4_col0\" class=\"data row4 col0\" >19669.750000</td>\n",
+       "                        <td id=\"T_cfe23_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row4_col2\" class=\"data row4 col2\" >29.000000</td>\n",
+       "                        <td id=\"T_cfe23_row4_col3\" class=\"data row4 col3\" >3.000000</td>\n",
+       "                        <td id=\"T_cfe23_row4_col4\" class=\"data row4 col4\" >3.000000</td>\n",
+       "                        <td id=\"T_cfe23_row4_col5\" class=\"data row4 col5\" >0.000000</td>\n",
+       "                        <td id=\"T_cfe23_row4_col6\" class=\"data row4 col6\" >0.000000</td>\n",
+       "                        <td id=\"T_cfe23_row4_col7\" class=\"data row4 col7\" >51.000000</td>\n",
+       "                        <td id=\"T_cfe23_row4_col8\" class=\"data row4 col8\" >0.000000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_cfe23_level0_row5\" class=\"row_heading level0 row5\" >50%</th>\n",
+       "                        <td id=\"T_cfe23_row5_col0\" class=\"data row5 col0\" >39225.500000</td>\n",
+       "                        <td id=\"T_cfe23_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row5_col2\" class=\"data row5 col2\" >33.000000</td>\n",
+       "                        <td id=\"T_cfe23_row5_col3\" class=\"data row5 col3\" >3.000000</td>\n",
+       "                        <td id=\"T_cfe23_row5_col4\" class=\"data row5 col4\" >5.000000</td>\n",
+       "                        <td id=\"T_cfe23_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
+       "                        <td id=\"T_cfe23_row5_col6\" class=\"data row5 col6\" >0.000000</td>\n",
+       "                        <td id=\"T_cfe23_row5_col7\" class=\"data row5 col7\" >60.000000</td>\n",
+       "                        <td id=\"T_cfe23_row5_col8\" class=\"data row5 col8\" >0.000000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_cfe23_level0_row6\" class=\"row_heading level0 row6\" >75%</th>\n",
+       "                        <td id=\"T_cfe23_row6_col0\" class=\"data row6 col0\" >58730.500000</td>\n",
+       "                        <td id=\"T_cfe23_row6_col1\" class=\"data row6 col1\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row6_col2\" class=\"data row6 col2\" >39.000000</td>\n",
+       "                        <td id=\"T_cfe23_row6_col3\" class=\"data row6 col3\" >4.000000</td>\n",
+       "                        <td id=\"T_cfe23_row6_col4\" class=\"data row6 col4\" >7.000000</td>\n",
+       "                        <td id=\"T_cfe23_row6_col5\" class=\"data row6 col5\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
+       "                        <td id=\"T_cfe23_row6_col7\" class=\"data row6 col7\" >76.000000</td>\n",
+       "                        <td id=\"T_cfe23_row6_col8\" class=\"data row6 col8\" >0.000000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_cfe23_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
+       "                        <td id=\"T_cfe23_row7_col0\" class=\"data row7 col0\" >78298.000000</td>\n",
+       "                        <td id=\"T_cfe23_row7_col1\" class=\"data row7 col1\" >10.000000</td>\n",
+       "                        <td id=\"T_cfe23_row7_col2\" class=\"data row7 col2\" >60.000000</td>\n",
+       "                        <td id=\"T_cfe23_row7_col3\" class=\"data row7 col3\" >5.000000</td>\n",
+       "                        <td id=\"T_cfe23_row7_col4\" class=\"data row7 col4\" >37.000000</td>\n",
+       "                        <td id=\"T_cfe23_row7_col5\" class=\"data row7 col5\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row7_col6\" class=\"data row7 col6\" >1.000000</td>\n",
+       "                        <td id=\"T_cfe23_row7_col7\" class=\"data row7 col7\" >99.000000</td>\n",
+       "                        <td id=\"T_cfe23_row7_col8\" class=\"data row7 col8\" >1.000000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x1d3b9bcd160>"
+      ]
+     },
+     "execution_count": 273,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lets check descriptive statistics for numerical columns\n",
+    "train.describe().style.background_gradient(cmap = 'copper')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 175
+    },
+    "id": "adu5VnFDzl1Q",
+    "outputId": "853e358d-abc0-4f9f-f1b5-34e1dafd6f12"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>department</th>\n",
+       "      <th>region</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>recruitment_channel</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>54808</td>\n",
+       "      <td>54808</td>\n",
+       "      <td>52399</td>\n",
+       "      <td>54808</td>\n",
+       "      <td>54808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>9</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_2</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>16840</td>\n",
+       "      <td>12343</td>\n",
+       "      <td>36669</td>\n",
+       "      <td>38496</td>\n",
+       "      <td>30446</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               department    region   education gender recruitment_channel\n",
+       "count               54808     54808       52399  54808               54808\n",
+       "unique                  9        34           3      2                   3\n",
+       "top     Sales & Marketing  region_2  Bachelor's      m               other\n",
+       "freq                16840     12343       36669  38496               30446"
+      ]
+     },
+     "execution_count": 274,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lets check descriptive statistics for categorical columns\n",
+    "train.describe(include = 'object')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a6JNNxXDzl4f",
+    "outputId": "5591d35e-0bb2-4b8a-e5c5-b85f910f756c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "employee_id                0\n",
+       "department                 0\n",
+       "region                     0\n",
+       "education               2409\n",
+       "gender                     0\n",
+       "recruitment_channel        0\n",
+       "no_of_trainings            0\n",
+       "age                        0\n",
+       "previous_year_rating    4124\n",
+       "length_of_service          0\n",
+       "KPIs_met >80%              0\n",
+       "awards_won?                0\n",
+       "avg_training_score         0\n",
+       "is_promoted                0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 275,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "A_EMdxF2zl7s",
+    "outputId": "d6facf3c-3d24-4f26-f140-a7ef93f71019"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "employee_id                0\n",
+       "department                 0\n",
+       "region                     0\n",
+       "education               1034\n",
+       "gender                     0\n",
+       "recruitment_channel        0\n",
+       "no_of_trainings            0\n",
+       "age                        0\n",
+       "previous_year_rating    1812\n",
+       "length_of_service          0\n",
+       "KPIs_met >80%              0\n",
+       "awards_won?                0\n",
+       "avg_training_score         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 276,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 690
+    },
+    "id": "81FACUsszl-j",
+    "outputId": "f338be1a-0bc2-4623-926b-17fb5f915518"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 277,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import missingno as msno\n",
+    "msno.bar(train, color='magenta')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 690
+    },
+    "id": "g0NW0ot1zmEc",
+    "outputId": "34f719db-d8b8-4818-b7df-2e6b40ad629d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 278,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import missingno as msno\n",
+    "msno.bar(test, color='crimson')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vpjnR_0T1SKi"
+   },
+   "source": [
+    "## Treatment of Missing Values\n",
+    "\n",
+    "* Treatment of Missing Values is very Important Step in any Machine Learning Model Creation \n",
+    "* Missing Values can be cause due to varios reasons such as the filling incomplete forms, values not available, etc\n",
+    "* There are so many types of Missing Values such as \n",
+    "     * Missing values at Random\n",
+    "     * Missing values at not Random\n",
+    "     * Missing Values at Completely Random\n",
+    "* What can we do to Impute or Treat Missing values to make a Good Machine Learning Model\n",
+    "    * We can use Business Logic to Impute the Missing Values\n",
+    "    * We can use Statistical Methods such as Mean, Median, and Mode.\n",
+    "    * We can use ML Techniques to impute the Missing values\n",
+    "    * We can delete the Missing values, when the Missing values percentage is very High.\n",
+    "    \n",
+    "* When to use Mean, and when to use Median?\n",
+    "    * We use Mean, when we do not have Outliers in the dataset for the Numerical Variables.\n",
+    "    * We use Median, when we have outliers in the dataset for the Numerical Variables.\n",
+    "    * We use Mode, When we have Categorical Variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Y6TzWaRW3r2_"
+   },
+   "source": [
+    "## Analysing Target Column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {
+    "id": "UXSStiNdzmHS"
+   },
+   "outputs": [],
+   "source": [
+    "target = train['is_promoted'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 340
+    },
+    "id": "8HaqLwq1zmKh",
+    "outputId": "f2f69a79-1148-4288-8da1-04a7cb884540"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Define the labels and sizes for the pie chart\n",
+    "labels = ['Not Promoted', 'Promoted']\n",
+    "sizes = [50140, 4668]\n",
+    "\n",
+    "# Define the colors for each category\n",
+    "colors = ['#ffa600', '#003f5c']\n",
+    "\n",
+    "# Create the pie chart with a shadow and explosion effect\n",
+    "fig1, ax1 = plt.subplots()\n",
+    "ax1.pie(sizes, colors=colors, labels=labels, autopct='%1.1f%%', startangle=90, pctdistance=0.85, shadow=True, explode=(0.05, 0))\n",
+    "\n",
+    "# Add a circle in the middle to create a donut chart effect\n",
+    "centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
+    "fig = plt.gcf()\n",
+    "fig.gca().add_artist(centre_circle)\n",
+    "\n",
+    "# Add a title to the chart\n",
+    "ax1.set_title(\"Target Distribution\", fontsize=16)\n",
+    "\n",
+    "# Set the font size for the labels\n",
+    "plt.rcParams['font.size'] = 14\n",
+    "\n",
+    "# Remove the unnecessary border lines\n",
+    "plt.rcParams['axes.spines.right'] = False\n",
+    "plt.rcParams['axes.spines.top'] = False\n",
+    "\n",
+    "# Add a legend and adjust its position\n",
+    "plt.legend(labels, loc=\"best\", fontsize=12)\n",
+    "\n",
+    "# Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "ax1.axis('equal')\n",
+    "\n",
+    "# Display the chart\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MBXjErO9hzX5"
+   },
+   "source": [
+    "## Treatment of missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XdOXGPmYzmNp",
+    "outputId": "a94361be-7cd9-4148-d046-0fd35b4fb75d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Missing Values Left in the Training Data : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets impute the missing values in the Training Data\n",
+    "\n",
+    "train['education'] = train['education'].fillna(train['education'].mode()[0])\n",
+    "train['previous_year_rating'] = train['previous_year_rating'].fillna(train['previous_year_rating'].mode()[0])\n",
+    "\n",
+    "# lets check whether the Null values are still present or not?\n",
+    "print(\"Number of Missing Values Left in the Training Data :\", train.isnull().sum().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ymISRYxozmQ1",
+    "outputId": "b05c9a88-4897-4887-aa96-6b2238ec767b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of Missing Values Left in the Training Data : 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets impute the missing values in the Testing Data\n",
+    "\n",
+    "test['education'] = test['education'].fillna(test['education'].mode()[0])\n",
+    "test['previous_year_rating'] = test['previous_year_rating'].fillna(test['previous_year_rating'].mode()[0])\n",
+    "\n",
+    "# lets check whether the Null values are still present or not?\n",
+    "print(\"Number of Missing Values Left in the Training Data :\", test.isnull().sum().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JNDyGqyT8UoI",
+    "outputId": "89eae568-42aa-42e7-d38b-f9d800756fa6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bachelor's          39078\n",
+       "Master's & above    14925\n",
+       "Below Secondary       805\n",
+       "Name: education, dtype: int64"
+      ]
+     },
+     "execution_count": 283,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['education'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lXzaIGjHiZP7"
+   },
+   "source": [
+    "## Outlier Detection\n",
+    "\n",
+    "The presence of outliers in a classification or regression dataset can result in a poor fit and lower predictive modeling performance. Instead, automatic outlier detection methods can be used in the modeling pipeline and compared, just like other data preparation transforms that may be applied to the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "N_s_g9J2zmTl",
+    "outputId": "34ee4241-6d32-47c5-a14b-4a25a93539b5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee_id</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>65438</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>49</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>65141</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7513</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2542</td>\n",
+       "      <td>2</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>48945</td>\n",
+       "      <td>1</td>\n",
+       "      <td>45</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>73</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   employee_id  no_of_trainings  age  previous_year_rating  length_of_service  \\\n",
+       "0        65438                1   35                   5.0                  8   \n",
+       "1        65141                1   30                   5.0                  4   \n",
+       "2         7513                1   34                   3.0                  7   \n",
+       "3         2542                2   39                   1.0                 10   \n",
+       "4        48945                1   45                   3.0                  2   \n",
+       "\n",
+       "   KPIs_met >80%  awards_won?  avg_training_score  is_promoted  \n",
+       "0              1            0                  49            0  \n",
+       "1              0            0                  60            0  \n",
+       "2              0            0                  50            0  \n",
+       "3              0            0                  50            0  \n",
+       "4              0            0                  73            0  "
+      ]
+     },
+     "execution_count": 284,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Lets first analyze the Numberical Columns\n",
+    "train.select_dtypes('number').head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 392
+    },
+    "id": "vtwXIwgUzmWp",
+    "outputId": "30dc39fa-d927-46f5-af61-48e9a529d3e2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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xuW7b0K9fX20b+vXrq+1JJ52UJUuWpKKiIrfddttq5/lx2l5zzTV56aWXsuuuu+bCCy9cbdsf/vCHmT17djbaaKMMGjRole0GDx6c8ePHZ999981ZZ5212nkOGzYsTz/9dLp06ZJTTjlltW2fffbZ/PKXv8zZZ5+dvffee7Vt68OsWbPyy1/+Mj/84Q+zwQYb1Pvr1Wnk+aSTTsq5555b+rX5ggsuSI8ePbLrrrvmsssuq+8aAYAGYh8AYOWWLFmSJKmqqlqjbV966aUkyV/+8pfCtrNnz06SvP/++6ttN378+CRLw26Rp59+Okny5JNPFra9+eabkyRDhw4tbFsfHnjggfztb3/LmDFjPpPXK5s9e3adjrl6++23M2fOnGUO0dpxxx3rrbAiH/1FiE9m8L8+mE3R2R07LvO4Kfe1sVl+2cCatvwowZQpU7L99ts3UDVNw2e1D1CXZfVZbf/XxHajvrZFvkdXtKrRwZWtL5/Xtg39+vXVtqFfv77a1o4k11rdiPLHaVs76lxrdaPPtaPOtVY1+lw76lxrdaPPtaPOtVY3+lw76lyrLqPPa3KbPGvWrPTp0yeLFy/OOuusk5///Of1Pvpcp8O2Bw8enF//+tfZaKONSs+VlZXlgQceqK+6AIDPAfsAACv6aBhOVj+i/HHavrTcj2yrG33+aHBOVj36/NHgnCwNvasKzx8NzsnS0edVhefaUedaQ4cO/UwP3X7ggQdKP+rW1NRkzJgx+f73v1+vr1mn8Pz444/nv/7rv7LJJpvUazEAwOeLfQAAVmb5HwHqckj6mvTMM8+UXrOqqirPPPNMvYfnOp3z3K5dOxtNAFgL2QcAYGUqKipW+7i+7bfffqXXrKioyH777Vfvr1mnHnbu3DmDBg3KQQcdlHXXXbf0fEOe87w857itaPn3wHnifB4VXfHy0/Jd0PTfA99t9evzvg+wpr5DGvN6VN/fo6vSkN8tjXl50TSUl5evcB7zmmjbsWPHFc55XpUNNthghXOeV2bvvfde4ZznVTnwwANXOOd5VX7wgx8sc87zGWecscq29eHII48s1VpWVpajjjqq3l+zTiPPY8eOzR/+8If069cvffv2Td++fXPRRRfVd20AQAOzD0BjtvwPC6v7oaGh2zb069dX24Z+/fpqO2rUqGUer+72Ux+n7fLfr6u7VdWQIUOWebyqW1WdffbZyzxe3a2qTjvttGUer+5WVfvuu+8yI7+f9a2qNtxwwxx44IEpKyvLgQce+JncqqpOI8+/+c1v6rsOAOBzyD4AwMrVjijX5XDlj9O2dvR5daPOtWpHn1c16lyrdvR5daPOtWpHn1c36lyrdvT5sx51rnXkkUfmrbfe+kxGnZM63qpq9uzZeeSRR7JgwYLU1NSkuro606ZNyxVXXPFZ1LhSyx+u47DtFRUdtt2Ub9/kVlWfX8svG4dt17+m/h7YHtSvz3If4JPcqqq+DttuTLeqctj2mn0Pmup3hn41Lvr1+VSnkeeLL744LVq0yNSpU7PXXnvlueeey2677VbPpQEADc0+AAAsVadznt955538/Oc/z3777Zfjjjsut9xyS6ZNm1bftQEADcw+AAAsVafwvPHGGydJvvzlL2fq1KnZdNNNV7jZNwDQ9NgHAICl6nTY9oYbbpjbb789u+yyS4YNG5b11lsv8+bNq+/aAIAGZh8AAJaq08jzxRdfnObNm2e33XbLTjvtlJtvvnmFS54DAE2PfQAAWKpO4XmjjTZK9+7dkyy9T9gdd9yRli1b1mthAEDDsw8AAEutNjy/8sor6d27d/r06ZPZs2cnWXrhkAsvvDDnn3/+Z1EfANAA7AMAwLJWG54HDhyYr3/969liiy0yYsSIPPbYY/nud7+bBQsWNNg9BQGA+mcfAACWtdoLhs2bNy8nnHBClixZkmOPPTaPP/54Lrnkkhx66KGfVX0AQAOwDwAAy1pteG7RokWSpLy8PIsWLcovfvGLdOjQ4TMpDABoOPYBAGBZqz1su6ampvTvDTbYwEYTANYS9gEAYFmrHXmurq7OnDlzUlNTk5qamtK/a7Vt27beCwQAPnv2AQBgWasNz6+//noOPfTQ0sbykEMOKU0rKyvLn/70p/qtDgBoEPYBAGBZqw3P48eP/6zqAAA+R+wDAMCyVnvOMwAAACA8AwAAQCHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABYRnAAAAKCA8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUqGjoAj6pO+64o6FLAOBzwPZg7WJ5k1gPgIZh5BkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAECBioYugIZzdseODV3CZ2Zt6isAn159bDdsiwAaNyPPAAAAUEB4BgAAgALCMwAAABRwzvNa5I477mjoEj5zU6ZMyfbbb9/QZTSYtb3/AHVVn9tI38UATYORZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAEAB4RkAAAAKCM8AAABQQHgGAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAAChQNnv27JqGLgIAAAA+z4w8AwAAQAHhGQAAAAoIzwAAAFBAeAYAAIACwjMAAAAUEJ4BAACggPAMAAAABSoauoA16f3338+JJ56YIUOGpLy8PAMGDEiSbLvttrnwwgvTrFnT/q2gV69eWW+99ZIkX/ziF3PUUUflZz/7WcrLy7P33nvn1FNPbeAK69dtt92Wp59+OlVVVTnmmGOyxx57rFXrwNixYzN27NgkyaJFi/Laa69l6NCha9U6UFVVlf79++ftt99Os2bNcumll6513wWLFi3KgAEDMn369Ky33nq54IIL8sEHH6wV68FLL72UIUOG5Kabbsq0adNWutxvueWW/PGPf0x5eXn69OmTnXfeuYGrJkmqq6tz7bXXZsqUKWnevHkuvfTSfPnLX27ostaI5bfN/fr1a+CKPp26fM4ao4/2629/+1v69OlTWgePOeaYHHLIIQ1c4cdTVVWVK664ItOnT8/ixYtz8sknp3379o1+ea2sX+3atWv0y2vJkiW56qqr8sYbb6SsrCwXXXRRmjdv3uiX18r6VVVV1aiXV5MJz1VVVbn66quz7rrrJkl+8Ytf5PTTT0+nTp1y9dVX56mnnsrXvva1Bq6y/nz44YepqanJTTfdVHruhBNOyLXXXpstttgiP/7xj/O3v/0tO+ywQwNWWX8mTpyYv/zlL7n11luzcOHC3HHHHWvdOtC1a9d07do1STJw4MB8+9vfzjXXXLPWrANJ8sc//jFLlizJ8OHDM378+AwdOjRVVVVr1XrwwAMPpFWrVhkxYkTeeOONXHfddXn//feb/HowevToPProo2nZsmWSlW8DNt9880yaNCkjR47MjBkz0rdv34waNaqBKydJnnrqqSxatCgjRozIiy++mBtuuCH/+Z//2dBlfWor2zY3ZnX5nDXG79fl+/XKK6+kR48eOeGEExq4sk/u0UcfTdu2bXP55Zfngw8+SM+ePdOhQ4dGv7xW1q/evXs3+uX1P//zP0mSW2+9NRMnTszQoUNTU1PT6JfXyvr1b//2b416eTWuny9W44YbbsjRRx+dTTbZJEny6quvZo899kiS7LfffpkwYUJDllfvpkyZkoULF+bss8/OGWeckUmTJmXx4sX50pe+lLKysuyzzz557rnnGrrMevOnP/0p2223XS688MKcd955+bd/+7e1bh2o9fLLL2fq1Kk55JBD1qp1IEm23HLLLFmyJNXV1amsrExFRcVatx78/e9/z7777psk2WqrrfLKK6+sFevBl770pVx77bWlxytb7n/+85+zzz77pKysLJtttlmWLFmSWbNmNVTJfMQLL7xQWm932WWXvPLKKw1c0Zqx/Lb5xRdfbOiSPpW6fM4ao5X1a9y4cTnttNNyxRVXpLKysgGr+2S+8Y1v5Ac/+EGSpKamJuXl5U1iea2qX419eXXp0iUXX3xxkuTtt99O69atm8TyWlW/GvPyahLheezYsdlggw1KG95k6QeqrKwsSdKqVavMmzevocr7TLRo0SI9e/bMoEGDctFFF+WKK65IixYtStOb+nswe/bsvPLKK7n66qtz0UUXpV+/fqmurl6r1oFat912W0455ZRUVlaWDhVM1o73oFWrVnn77bdz3HHH5aqrrkr37t3Xuu+CDh06ZNy4campqcmLL76YefPmlUZTkqb7Hnz9619PRcX/HUy1suU+b968te4z0VhUVlamdevWpcfNmjVLVVVVA1a0Ziy/be7Xr1+j7lddPmeN0fL92nnnnXPOOedk2LBh2WKLLXLrrbc2YHWfTKtWrbLeeuulsrIyF198cU4//fQmsbxW1q+msLySpKKiIv3798/111+fww47rEksr2TFfjX25dUkDtt+8MEHU1ZWlgkTJuS1115L//79lxlNmD9/ftZff/0GrLD+bbnllqWRpa222iqtW7fOBx98UJre1N+Dtm3bZuutt84666yTrbbaKs2bN8+MGTNK05t6/2vNnTs3b7zxRvbcc8/Mmzcv8+fPL01bG96Du+66K/vss0/OOuuszJgxI2eeeWYWL15cmr42vAff/va38/e//z2nnXZadt1112y//fZZuHBhafra8B4kWea8sNo+t27deq37TDQWtTvDtWpqapYJM43V8tvmtm3b5r333ku7du0aurQ1YmWfs6agS5cupb506dKl0Z5CMGPGjFxwwQU59thjc9hhh2XIkCGlaY15eS3fr7lz5zaJ5ZUk/fv3z8yZM3PyySfnww8/LD3fmJdXsmy/br311my66aZJGufyahIjz8OGDcvNN9+cm266KR06dEj//v2z7777ZuLEiUmSZ555JrvttlvDFlnPHnzwwdxwww1JknfffTcLFy5My5Yt8+abb6ampiZ/+tOfmvR78NWvfjXPPvtsampqSv3v3LnzWrUOJMnkyZPTuXPnJEnr1q1TUVGx1qwDSdKmTZvS6FWbNm1SVVWVHXbYYa1aD15++eV07tw5t9xyS77xjW9kyy23XOvWg2TpCPzyy33XXXfNn/70p1RXV+edd95JdXV1Nthgg4YtlCRLv8OfeeaZJMmLL76YbbfdtoErWjOW3zZXVlZm4403buCq1pyVfc6agnPOOSd//etfkyQTJkzIjjvu2MAVfXzvvfdezj777Pzwhz/MEUcckaRpLK+V9aspLK9HHnkkt912W5KlR6yUlZVlp512avTLa2X96tu3b6NeXo3/Z91V+NGPfpSrrroqixcvTvv27fP1r3+9oUuqV926dcvll19euoruf/zHf6SsrCz9+vXLkiVLsvfee6djx44NXGX9OeCAAzJ58uR873vfS01NTS644IJ88YtfXKvWgSR54403ssUWW5Qe1x4muDasA0ly/PHH54orrsipp56aqqqqnHHGGdlpp53WqvVgyy23zKWXXpqRI0dm/fXXz2WXXZZ33nlnrVoPkpVvA8rLy7Pbbruld+/eqa6uzoUXXtjQZfIvXbp0yfjx49O7d+/U1NQ0+itS11rZtrkpjKjXaqr7Wn379s1//ud/pqKiIhtvvHHpnM3G5LbbbsucOXMyYsSIjBgxIknSp0+fXH/99Y16ea2sX+eee25+/vOfN+rl9bWvfS0DBgzIaaedVroa9dZbb93oP18r61e7du0a9eerbPbs2TUNXQQAAAB8njWJw7YBAACgPgnPAAAAUEB4BgAAgALCMwAAABQQngEAAKCA8EyTVFVVlX//93/POeec09Cl1MncuXNzwgkn5IQTTsjRRx+dAw44oPR40KBBdZ7Pyy+/nIsuuqiw3QknnJC5c+d+mpJLqqqqMmjQoPTo0aP038iRI1NT40L+ADQO06dPz0EHHfSZvNbLL7+cq6++OkkyceLEfPe73/1U85sxY0a++93vpkePHvnLX/6yzLTKyspceeWVOf7449OjR4/07NkzDzzwwKd6vY96991307t37zU2P/i8azo3G4SPeOKJJ7Lddtvl1Vdfzd///ve0b9++oUtarfXXXz933nlnkqUb0uuuu670+OP4yle+kmuuuaaw3SeZ96r86le/yvTp0zN69OhUVFRk3rx5OfPMM7PBBhvkqKOOWmOvAwBNwdSpU/PPf/5zjc3v+eefz8Ybb5xf/vKXK0z75S9/mZYtW+auu+5KWVlZ3n333Zx88snZbLPNss8++3zq195kk00yfPjwTz0faCyEZ5qk+++/P4cccki+/OUv5+67707fvn1zxBFHZODAgfnKV76SJLn00kuz++6759hjj82IESPyxBNPpLq6Optvvnn69u2bTTbZJKeffnratGmTN954I0cffXS+8pWvZPDgwVm8eHFmzpyZvfbaK//xH/+RJBk7dmxGjRqVddddN3vuuWfuueeePPvss0myyvnXVbdu3bLzzjvn//2//5czzjgjFRUVue2227J48eLMmjUrhx9+eE4//fRS8L777rtz+eWXZ7311svrr7+eGTNmZKuttsqVV16ZVq1aZa+99srvf//7jBs3Lk8++WTKysoybdq0rLPOOunfv3+23XbbTJs2LVdccUXmzJmTL3zhC6mpqcm3vvWtdO3adZnaZs6cmaqqqixevDgVFRVp3bp1+vfvXxp5njlzZq655pq88cYbadasWY4++uh07949M2bMyLXXXpu33347NTU1Ofzww9OrV69Mnz49p512Wtq3b5/p06fn5ptvzvTp0zN48OAsXLgwZWVlOfXUU3PAAQesiVUFAFZr8eLFGTx4cCZPnpwlS5Zkhx12yHnnnZfWrVunW7duOfzwwzNhwoTMmDEjBx98cOmot1GjRuXBBx9Mq1atsvvuu+epp57KsGHDcvPNN2fevHkZMGBADj/88CxYsCCXXnpp/vGPf2TRokW55JJLsvvuu69Qx5gxY3LPPfekWbNm2XjjjXP++efn3XffzU033ZR58+bljDPOyNChQ5f5m5kzZ2ajjTZKVVVV1llnnWyyySa59tpr07Zt2yTJP//5z1x33XWZMWNGqqqqcsghh+T73//+Ctvir371q2nZsmUuuOCCJMmzzz6bYcOGlUa1n3rqqVRVVWXw4MEZN25cKioqsssuu6Rv375ZZ511PvV+EHxeOGybJmfq1Kl56aWXcvDBB+fwww/PI488kjlz5uSII47Iww8/nCSZM2dOxo8fn8MOOywPP/xwXn/99YwcOTJ33nln9t9//1x55ZWl+a2//vq555570r1799xzzz35wQ9+kJEjR+aee+7J//zP/+SVV17J1KlTM2TIkAwZMiR33HFH1ltvvSxZsiRJCudfV9tuu21+/etfp0uXLrnrrrvyk5/8JKNHj86IESMyatSozJ49e4W/efXVV3PDDTfk17/+dWbOnJk//OEPK7SZNGlSzj///Nx9993Zddddc/vttydJfvKTn+TQQw/N3XffnfPOOy8vvvjiSuvq0aNH/vnPf+bQQw/N6aefnhtvvDGLFy/OtttumyQZOHBgttxyy9x7770ZPnx4xowZk2nTpqVfv37p1KlTfvWrX+XWW2/No48+mt///vdJlm7MTz755Nx///1p3rx5BgwYkMsvvzy33357rr/++lx77bV55513PvZ7CAAf16hRo1JRUZHRo0fnrrvuyiabbLLMKO+CBQtyyy235NZbb829996bt956K88++2zGjh2b2267LaNHj05lZWWSpF27dvnBD36Q3XbbLf369UuydJt3/PHH584778xRRx2VW265ZYUaJkyYkNtvvz1Dhw7NXXfdlW9+85u58MIL06lTp9L8lg/OSXLqqadmwoQJOfTQQ3POOedk+PDhWW+99bLFFlskWbqtP+KIIzJ69OiMHDkyzz33XB577LFSXbXb4t69e+exxx7L4sWLkyQPPfRQunXrtsxr3X///Xn11Vdz55135le/+lXmz5+fxx57bI3tB8HngZFnmpz7778/+++/f9q2bZu2bdvmi1/8YsaMGZNvf/vb+d73vpdzzz03v//973PAAQekdevWGTduXF5++eWcdNJJSZLq6uosXLiwNL+P/vr7k5/8JH/84x8zcuTI/OMf/8jChQuzYMGCTJ48OXvvvXfatWuXJPnOd75T2vgVzb+udttttyRJWVlZrr/++owbNy6/+93v8o9//CM1NTVZsGDBCn+z7777pnnz5kmWhu85c+as0GbHHXcs1b3jjjvmiSeeyJw5c/Lyyy9n2LBhSZL27dunc+fOK62rXbt2GT16dKZOnZqJEydm4sSJ6d27d84999wce+yxmTBhQs4+++wkSevWrXP33XdnwYIF+ctf/pLBgweXnu/atWueeeaZdOzYMeXl5dlll12SJC+++GLee++90q/dte/BlClTstlmm33s9xEAPo5x48Zl7ty5GT9+fJKl1/rYcMMNS9MPPPDAJMmmm26aDTfcMHPmzMkzzzyTb3zjG1l//fWTJMcdd1yef/75lc5/iy22SMeOHZMkHTp0yEMPPbRCm2effTYHH3xw6XW7du2a66+/PtOnT19t7dtvv33uu+++vPrqq5k0aVKee+65jBw5MldffXX23HPPTJ48OXPmzMlNN92UZOkPAVOmTMnOO++8zLZ4iy22yPbbb5+nn346nTt3zoQJE3LZZZct88P9c889l29961tp0aJFkuSqq65Kklx88cVrZD8IPg+EZ5qUBQsW5JFHHsm6665b+kW0srIy9913X3r16pUddtgh48aNy0MPPZQ+ffokWfol3qtXrxx77LFJkkWLFi0TMlu2bFn692mnnZbtt98+++67bw4++OD89a9/TU1NTcrLy5e5QFZ5eXnp30Xzr6vaOhYsWJBevXrloIMOyu67754jjjgiTz311Eov0LXuuuuW/l1WVlbnNs2aLT0o5aPta59b3qBBg9KtW7dss8022WabbXLcccfl0UcfzahRo3LsscemvLw8ZWVlpfZvvfVW2rZtu0ItNTU1qaqqSpI0b948FRVLv56qq6uz9dZbZ+TIkaW277777jI7LgBQX6qrq3Peeedlv/32S5LMnz8/ixYtKk1f2XZ0+f2CVW1Dk5S2dx/9++Wt6iKctdvNVU0bOHBgzjrrrOy0007ZaaedcsIJJ2T48OH5r//6r+y+++6pqanJ8OHDS4F39uzZad68een/H62tW7dueeSRR/L++++nS5cuadWq1TLhefnt/XvvvZeampo1th8EnwcO26ZJ+e1vf5sNNtggDz/8cH7zm9/kN7/5TcaMGZP58+fn8ccfz5FHHpnRo0dn4cKF+epXv5ok2WefffLggw9m3rx5SZKbb745/fv3X2Hec+bMySuvvJIf/vCH+drXvpZ33303b775ZpYsWZJ99tknzz33XOkCIL/5zW9Kf1fX+dfVtGnTUllZmTPOOCMHHHBAJk2alEWLFqW6uvoTz3N5rVu3zq677lr69futt95a5S/ms2bNys0331z6FbmmpiZvvPFGdthhhyRJ586dS/OpvZjYtGnT0rFjx9x7772l5x9++OHsvffeK8y/Y8eOmTZtWiZNmpQkee2113LMMcfk3XffXWP9BYBV2WeffXLvvfdm8eLFqa6uzlVXXbXSi3N91P77758nnniitO1/8MEHS9PKy8tXG3pXVcPjjz+eWbNmJVl62HSbNm3y5S9/eZV/U1FRkf/93//N8OHDS69XVVWVt956KzvuuGNat26djh07li4iOnfu3Jxyyil5+umnVzq/Ll265NVXX80DDzyQI488coXpe+21V373u9+V9kmuvfba/O53v1vj+0HQkIw806Tcf//96dGjxzIjv+uvv366d++eX/3qVxk+fHgGDhyYXr16laZ369ZtmVstbLbZZqXzkD6qTZs2Oemkk9KrV6+0bds2G2ywQXbddde8+eab2WuvvfLjH/8455xzTpo3b54OHTqUfsWt6/zrarvttsv++++f4447Luuvv36+9KUvpX379pk2bVrpEO01oX///vnpT3+a++67L5tuumk233zzUp8+qm/fvrnxxhvTo0ePrLPOOlmyZEn23HPPXHjhhUmSCy64INdee2169OiR6urqfO9738tOO+2UAQMG5LrrrsvYsWOzePHiHHbYYenatWvefvvtZea/4YYb5pprrsngwYPz4YcfpqamJpdffnk233zzNdZXAFiwYMEKt6saPnx4Tj755AwaNCg9e/ZMdXV1OnTokB/96EernVfnzp3TrVu39O7dO+uuu2622Wab0jZ0l112ydChQ3PBBRfU+TZVe++9d44//viceeaZqa6uzoYbbpif/exnqx3RTlLafh5zzDGl1z/ooINK+yRXXHFFrrvuuhx//PFZvHhxDj300Bx22GErPRy8efPmOfjggzNhwoTsvPPOK0w/6qijMn369Jx44ompqalJp06d0r179zRr1myN7gdBQyqbPXu2m7HCp/TWW2/lkUceSe/evdOsWbM88cQTpYtvNFYjRozI17/+9Wy99daZN29eevTokV/84hfZZpttGro0APhce/nll/Piiy+me/fuSZbeIvKvf/1r6TxgoHEy8gxrQLt27TJz5swcf/zxKS8vT+vWrXPZZZc1dFmfypZbbplLLrkkzZo1y5IlS3LiiScKzgBQB1tuuWVGjx6dMWPGpKysLJtttlkuvvjihi4L+JSMPAMAAEABFwwDAACAAsIzAAAAFBCeAQAAoIDwDAAAAAWEZwAAACggPAMAAECB/w965eOLZCQb0wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets check the boxplots for the columns where we suspect for outliers\n",
+    "plt.rcParams['figure.figsize'] = (15, 5)\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "\n",
+    "# Box plot for average training score\n",
+    "plt.subplot(1, 2, 1)\n",
+    "sns.boxplot(train['avg_training_score'], color = 'red')\n",
+    "plt.xlabel('Average Training Score', fontsize = 12)\n",
+    "plt.ylabel('Range', fontsize = 12)\n",
+    "\n",
+    "# Box plot for length of service\n",
+    "plt.subplot(1, 2, 2)\n",
+    "sns.boxplot(train['length_of_service'], color = 'red')\n",
+    "plt.xlabel('Length of Service', fontsize = 12)\n",
+    "plt.ylabel('Range', fontsize = 12)\n",
+    "\n",
+    "plt.suptitle('Box Plot', fontsize = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "r82qOjFsitxI"
+   },
+   "source": [
+    "From the above box plots we can see that we have outliers in our Length of Service column. Let's handle this outliers in the next step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0tM2IOc68h89",
+    "outputId": "e4639868-b127-471c-90be-ea817595edcf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bachelor's          39078\n",
+       "Master's & above    14925\n",
+       "Below Secondary       805\n",
+       "Name: education, dtype: int64"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['education'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sales & Marketing    16840\n",
+       "Operations           11348\n",
+       "Procurement           7138\n",
+       "Technology            7138\n",
+       "Analytics             5352\n",
+       "Finance               2536\n",
+       "HR                    2418\n",
+       "Legal                 1039\n",
+       "R&D                    999\n",
+       "Name: department, dtype: int64"
+      ]
+     },
+     "execution_count": 287,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['department'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WiuAXbNV8kSO",
+    "outputId": "287c5fdb-2296-4525-aa81-b0846fb52ae5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37       1\n",
+       "34       4\n",
+       "33       9\n",
+       "32      10\n",
+       "30      12\n",
+       "31      20\n",
+       "29      30\n",
+       "28      30\n",
+       "27      36\n",
+       "26      41\n",
+       "25      51\n",
+       "22      61\n",
+       "23      65\n",
+       "24      70\n",
+       "21      78\n",
+       "20     128\n",
+       "19     329\n",
+       "18     392\n",
+       "17     432\n",
+       "16     548\n",
+       "14     549\n",
+       "15     593\n",
+       "13     687\n",
+       "12     794\n",
+       "11     916\n",
+       "10    2193\n",
+       "9     2629\n",
+       "8     2883\n",
+       "1     4547\n",
+       "6     4734\n",
+       "7     5551\n",
+       "5     5832\n",
+       "2     6684\n",
+       "4     6836\n",
+       "3     7033\n",
+       "Name: length_of_service, dtype: int64"
+      ]
+     },
+     "execution_count": 288,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['length_of_service'].value_counts().sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {
+    "id": "syfxlTCgihr9"
+   },
+   "outputs": [],
+   "source": [
+    "# lets remove the outliers from the length of service column\n",
+    "\n",
+    "train = train[train['length_of_service'] <= 16]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dbYm2GRf9H01",
+    "outputId": "5c68b87a-e0e5-4b9c-83a3-fc4aec1590da"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bachelor's          38194\n",
+       "Master's & above    14010\n",
+       "Below Secondary       805\n",
+       "Name: education, dtype: int64"
+      ]
+     },
+     "execution_count": 290,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['education'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nzLqgLeCi95U"
+   },
+   "source": [
+    "## Univariate Analysis\n",
+    "\n",
+    "Univariate analysis is perhaps the simplest form of statistical analysis. Like other forms of statistics, it can be inferential or descriptive. The key fact is that only one variable is involved. Univariate analysis can yield misleading results in cases in which multivariate analysis is more appropriate.\n",
+    "\n",
+    "* This is an Essential step, to understand the variables present in the dataset one by one.\n",
+    "* First, we will check the Univariate Analysis for Numerical Columns to check for Outliers by using Box plots.\n",
+    "* Then, we will use Distribution plots to check the distribution of the Numerical Columns in the Dataset.\n",
+    "* After that we will check the Univariate Analysis for Categorical Columns using Pie charts, and Count plots.\n",
+    "* We Use Pie charts, when we have very few categories in the categorical column, and we use count plots we have more categorises in the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 343
+    },
+    "id": "ZKC2yNvvi41y",
+    "outputId": "c60e5938-3af2-4506-87c9-b4022b617dd4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets plot pie chart for the columns where we have very few categories\n",
+    "plt.rcParams['figure.figsize'] = (16,5)\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "\n",
+    "# plotting a pie chart to represent share of Previous year Rating of the Employees\n",
+    "plt.subplot(1, 3, 1)\n",
+    "labels = ['0','1']\n",
+    "sizes = train['KPIs_met >80%'].value_counts()\n",
+    "explode = [0, 0]    #explode parameter specifies how far each slice is separated from the center of the chart\n",
+    "\n",
+    "plt.pie(sizes, labels = labels, explode = explode, shadow = True, startangle = 90)\n",
+    "plt.title('KPIs Met > 80%', fontsize = 20)\n",
+    "\n",
+    "# plotting a pie chart to represent share of Previous year Rating of the Employees\n",
+    "plt.subplot(1, 3, 2)\n",
+    "labels = ['1', '2', '3', '4', '5']\n",
+    "sizes = train['previous_year_rating'].value_counts()\n",
+    "explode = [0, 0, 0, 0, 0.1]\n",
+    "\n",
+    "plt.pie(sizes, labels = labels, explode = explode, shadow = True, startangle = 90)\n",
+    "plt.title('Previous year Ratings', fontsize = 20)\n",
+    "\n",
+    "# plotting a pie chart to represent share of Previous year Rating of the Employees\n",
+    "plt.subplot(1, 3, 3)\n",
+    "labels = ['0', '1']\n",
+    "sizes = train['awards_won?'].value_counts()\n",
+    "explode = [0,0.1]\n",
+    "\n",
+    "plt.pie(sizes, labels = labels, explode = explode, shadow = True, startangle = 90)\n",
+    "plt.title('Awards Won?', fontsize = 20)\n",
+    "\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bTlTCReu23IG",
+    "outputId": "0e2a07e9-c40e-4c09-9087-669db4aeec72"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1     42806\n",
+       "2      7808\n",
+       "3      1744\n",
+       "4       459\n",
+       "5       124\n",
+       "6        42\n",
+       "7        11\n",
+       "8         5\n",
+       "9         5\n",
+       "10        5\n",
+       "Name: no_of_trainings, dtype: int64"
+      ]
+     },
+     "execution_count": 292,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['no_of_trainings'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 402
+    },
+    "id": "wcVZWzUVi5vh",
+    "outputId": "9759cec2-f35b-49a9-f310-ee01a7534802"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x396 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get the counts for each category\n",
+    "counts = train['no_of_trainings'].value_counts()\n",
+    "\n",
+    "# Set the style of the plot\n",
+    "plt.style.use('seaborn')\n",
+    "\n",
+    "# Create a bar chart with custom colors\n",
+    "plt.bar(counts.index, counts.values, color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2'])\n",
+    "\n",
+    "# Add labels and title with custom fonts and sizes\n",
+    "plt.xlabel('Number of Trainings', fontsize=12, fontweight='bold')\n",
+    "plt.ylabel('Count', fontsize=12, fontweight='bold')\n",
+    "plt.title('Training Counts', fontsize=14, fontweight='bold')\n",
+    "\n",
+    "# Customize the tick labels\n",
+    "plt.xticks(counts.index, fontsize=10)\n",
+    "plt.yticks(fontsize=10)\n",
+    "\n",
+    "# Remove the top and right spines\n",
+    "plt.gca().spines['top'].set_visible(False)\n",
+    "plt.gca().spines['right'].set_visible(False)\n",
+    "\n",
+    "# Add grid lines and set the alpha level\n",
+    "plt.grid(axis='y', alpha=0.5)\n",
+    "\n",
+    "# Show the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XqjqPcki6uxP",
+    "outputId": "d25d017c-dbed-4857-b9ec-3fb9ef425c4d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30    3665\n",
+       "32    3534\n",
+       "31    3534\n",
+       "29    3405\n",
+       "33    3210\n",
+       "28    3147\n",
+       "34    3076\n",
+       "27    2827\n",
+       "35    2711\n",
+       "36    2517\n",
+       "37    2165\n",
+       "26    2060\n",
+       "38    1922\n",
+       "39    1694\n",
+       "40    1659\n",
+       "25    1299\n",
+       "41    1284\n",
+       "42    1130\n",
+       "43     956\n",
+       "24     845\n",
+       "44     796\n",
+       "45     671\n",
+       "46     555\n",
+       "47     443\n",
+       "23     428\n",
+       "48     394\n",
+       "50     350\n",
+       "49     308\n",
+       "51     275\n",
+       "53     265\n",
+       "52     247\n",
+       "22     231\n",
+       "54     222\n",
+       "55     218\n",
+       "56     177\n",
+       "57     165\n",
+       "60     143\n",
+       "58     137\n",
+       "59     133\n",
+       "20     113\n",
+       "21      98\n",
+       "Name: age, dtype: int64"
+      ]
+     },
+     "execution_count": 294,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['age'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 312
+    },
+    "id": "Eshkgplx320Y",
+    "outputId": "c88796b6-267a-4843-d63c-a3fee05bf162"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets check the Age of the Employees\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (8, 4)\n",
+    "plt.hist(train['age'], color = 'black')\n",
+    "plt.title('Distribution of Age among the Employees', fontsize = 15)\n",
+    "plt.xlabel('Age of the Employees')\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 415
+    },
+    "id": "UEVtG9re4sO5",
+    "outputId": "814ce31a-1f26-4cd2-af27-ef5edb14d7ca"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets check different Departments\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (12, 6)\n",
+    "sns.countplot(y = train['department'], palette = 'cividis', orient = 'v')\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel('Department Name')\n",
+    "plt.title('Distribution of Employees in Different Departments', fontsize = 15)\n",
+    "plt.grid()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D59Tw21a47RA"
+   },
+   "source": [
+    "Company has maximum number of employees in the sales and marketing department"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 958
+    },
+    "id": "JL_41Jj_42qr",
+    "outputId": "de892bfa-3d81-4c68-edd2-f264b59e2512"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x1080 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets check distribution of different Regions\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (12,15)\n",
+    "plt.style.use('fivethirtyeight')\n",
+    "sns.countplot(y = train['region'], palette = 'inferno', orient = 'v')\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel('Region')\n",
+    "plt.title('Different Regions', fontsize = 15)\n",
+    "plt.xticks(rotation = 90)\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HCL24p_a5JCj"
+   },
+   "source": [
+    "region 2 has the maximum number of employees working there"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "i-AWORwq5dle",
+    "outputId": "96b517e7-7330-43c8-d5e5-06ded0fcf522"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([\"Master's & above\", \"Bachelor's\", 'Below Secondary'], dtype=object)"
+      ]
+     },
+     "execution_count": 298,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['education'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 343
+    },
+    "id": "z135NmLH5BQw",
+    "outputId": "acc8cddc-3a8a-408b-ca7c-ef4d05c6629c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets plot pie chart for the columns where we have very few categories\n",
+    "plt.rcParams['figure.figsize'] = (16,5)\n",
+    "\n",
+    "# plotting a pie chart to represent share of Previous year Rating of the Employees\n",
+    "plt.subplot(1, 3, 1)\n",
+    "labels = train['education'].value_counts().index\n",
+    "sizes = train['education'].value_counts()\n",
+    "colors = plt.cm.copper(np.linspace(0, 1, 5))\n",
+    "explode = [0, 0, 0.1]\n",
+    "\n",
+    "plt.pie(sizes, labels = labels, colors = colors, explode = explode, shadow = True, startangle = 90)\n",
+    "plt.title('Education', fontsize = 20)\n",
+    "\n",
+    "# plotting a pie chart to represent share of Previous year Rating of the Employees\n",
+    "plt.subplot(1, 3, 2)\n",
+    "labels = train['gender'].value_counts().index\n",
+    "sizes = train['gender'].value_counts()\n",
+    "colors = plt.cm.copper(np.linspace(0, 1, 5))\n",
+    "explode = [0, 0]\n",
+    "\n",
+    "plt.pie(sizes, labels = labels, colors = colors, explode = explode, shadow = True, startangle = 90)\n",
+    "plt.title('Gender', fontsize = 20)\n",
+    "\n",
+    "# plotting a pie chart to represent share of Previous year Rating of the Employees\n",
+    "plt.subplot(1, 3, 3)\n",
+    "labels = train['recruitment_channel'].value_counts().index\n",
+    "sizes = train['recruitment_channel'].value_counts()\n",
+    "colors = plt.cm.copper(np.linspace(0, 1, 5))\n",
+    "explode = [0,0,0.1]\n",
+    "\n",
+    "plt.pie(sizes, labels = labels, colors = colors, explode = explode, shadow = True, startangle = 90)\n",
+    "plt.title('Recruitment Channel', fontsize = 20)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qFf0Inym9nCi"
+   },
+   "source": [
+    "## Bivariate Analysis\n",
+    "\n",
+    "Bivariate analysis is one of the simplest forms of quantitative analysis. It involves the analysis of two variables, for the purpose of determining the empirical relationship between them. Bivariate analysis can be helpful in testing simple hypotheses of association.\n",
+    "\n",
+    "* Types of Bivariate Analysis\n",
+    "    * Categorical vs Categorical \n",
+    "    * Categorical vs Numerical\n",
+    "    * Numerical vs Numerical\n",
+    "    \n",
+    "* First, we will perform Categorical vs Categorical Analysis using Stacked and Grouped Bar Charts with the help of crosstab function.\n",
+    "* Second, we will perform Categorical vs Numerical Analysis using Bar Charts, Box plots, Strip plots, Swarm plots, Boxen plots, Violin Plots, etc\n",
+    "* Atlast, we will perform Numerical vs Numerical Analysis using Scatter plots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "88IMsJjMBnWD"
+   },
+   "source": [
+    "## For Gender vs Promotion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 257
+    },
+    "id": "KK_bsZ3T5Yl4",
+    "outputId": "6bc72768-d2a3-440f-d0d3-432a72bf9004"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (15, 3)\n",
+    "x = pd.crosstab(train['gender'], train['is_promoted'])\n",
+    "colors = plt.cm.viridis(np.linspace(0, 1, 5))\n",
+    "x.div(x.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = False, color = colors) #stacked parameter is set to False to create a horizontal bar chart with bars side by side\n",
+    "plt.title('Effect of Gender on Promotion', fontsize = 15)\n",
+    "plt.xlabel('Gender')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 143
+    },
+    "id": "czihxpcN9qGy",
+    "outputId": "cb5e166e-e77a-42cd-cae5-316f0f2f9c12"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>is_promoted</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>f</th>\n",
+       "      <td>14321</td>\n",
+       "      <td>1428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>m</th>\n",
+       "      <td>34141</td>\n",
+       "      <td>3119</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "is_promoted      0     1\n",
+       "gender                  \n",
+       "f            14321  1428\n",
+       "m            34141  3119"
+      ]
+     },
+     "execution_count": 301,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h = pd.crosstab(train['gender'], train['is_promoted'])\n",
+    "h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 143
+    },
+    "id": "y8SgS96aAMcx",
+    "outputId": "305c91d0-886d-4d03-e600-50d7c47ac05a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>is_promoted</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>f</th>\n",
+       "      <td>0.909328</td>\n",
+       "      <td>0.090672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>m</th>\n",
+       "      <td>0.916291</td>\n",
+       "      <td>0.083709</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "is_promoted         0         1\n",
+       "gender                         \n",
+       "f            0.909328  0.090672\n",
+       "m            0.916291  0.083709"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h.div(x.sum(1).astype(float), axis = 0)   #This normalizes the contingency table by dividing each row by the sum of its values. \n",
+    "                                          #This converts the frequency counts into proportions that can be easily compared across categories\n",
+    "                                          # i.e. 14321/(14321+1428) and 1428/(14321+1428)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "W6e8teZhBsBK"
+   },
+   "source": [
+    "## Departments vs Promotion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 671
+    },
+    "id": "gC2fbemQBCNL",
+    "outputId": "44559106-de45-4781-f783-359244e56576"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets compare the effect of different Departments and Promotion\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (15,4)\n",
+    "x = pd.crosstab(train['department'], train['is_promoted'])\n",
+    "colors = plt.cm.autumn(np.linspace(0, 1, 3))\n",
+    "x.div(x.sum(1).astype(float), axis = 0).plot(kind = 'bar', stacked = False, color = colors)\n",
+    "plt.title('Effect of Department on Promotion', fontsize = 15)\n",
+    "plt.xticks(rotation = 20)\n",
+    "plt.xlabel(' ')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (15,4)\n",
+    "x = pd.crosstab(train['department'], train['is_promoted'])\n",
+    "colors = plt.cm.autumn(np.linspace(0, 1, 3))\n",
+    "x.div(x.sum(1).astype(float), axis = 0).plot(kind = 'area', stacked = False, color = colors)\n",
+    "plt.title('Effect of Department on Promotion', fontsize = 15)\n",
+    "plt.xticks(rotation = 20)\n",
+    "plt.xlabel(' ')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1qJW5MhoDWxo"
+   },
+   "source": [
+    "## Age vs Promotion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 545
+    },
+    "id": "psp8l51iByVA",
+    "outputId": "96be0c14-2058-40c5-f6ed-b93b7a12fd7f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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AAFBIkQgAAAAAFFIkAgAAAACFFIkAAAAAQCFFIgAAAABQSJEIAAAAABRSJAIAAAAAhRSJAAAAAEAhRSIAAAAAUKiyVCeeOnVqHnjggTQ2Nuawww7LjjvumNGjRydJNttss5x11lnp1EmPCXQcdXV1+cEPfpCFCxc276uoqMjhhx+eL3/5y2VMBgDAstTV1WXixImZO3dui/3du3fPkCFD0q9fvzIlA2hbJWnyHn300fz1r3/N5MmTM2HChLzyyiu54oorMnTo0Fx33XVpamrK9OnTSzE0QLtVW1vbokRMkqamptx6661lSgQAwPKora39QImYJHPnzk1tbW0ZEgGUR0lWJP7hD3/I5ptvnrPOOivz5s3LKaeckp///OfZcccdkyR77rln/vjHP6Z///6F55oxY0YpIgK0ufr6+g/dv3jxYj/rAADasaXN45YcM5cDViV9+/Zd6rGSFIkNDQ15+eWXM378+MyZMydnnHFGFi9enIqKiiTJWmutlbfffnu5zrWs8ACrCj/rAABWXuZyQEdRkiKxe/fu6d27d1ZbbbX06tUrq6++el555ZXm4/Pnz0+3bt1KMTQAAAAAUAIluUfidtttl4cffjhNTU2pr6/Pu+++m1122SWPPvpokuShhx7K9ttvX4qhAQAAAIASKMmKxL333juPPfZYjjvuuDQ1NeXMM8/MhhtumDFjxmThwoXp06dP9t1331IMDQAAAACUQEmKxCQ59dRTP7Bv4sSJpRoOAAAAACihklzaDAAAAACsWhSJAAAAAEAhRSIAAAAAUEiRCFBidXV1GT58+DJfM3z48NTV1bVRIgAAlsfyzOMSczmg41AkApRYbW1t6uvrl/ma+vr61NbWtlEiAACWx/LM4xJzOaDjUCQClNjyTD4/yusAAGgbH2V+Zi4HdASKRAAAAACgUGW5AwB0NFePvqb545Mv+HYZkwAA8FFM+fboFtuDrrmgTEkAysOKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAoVFnuAACrmrq6utTW1qa+vv5Dj598wbeX+t7q6uokSVVVVWpqatKvX7+SZAQAAICPyopEgFa2rBJxedXX16e2traVEgEAAMDHp0gEaGUft0Rs7fMAAABAa1AkAgAAAACF3CMRoMSu+M4Vy/3aYZcOK1kOAAAA+DisSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQopEAAAAAKCQIhEAAAAAKKRIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAoVFnuAAAAANAe1NXVpba2NvX19R96fNA1Fyzz/dXV1UmSqqqq1NTUpF+/fq2eEaCcrEgEAACAZJkl4kdRX1+f2traVkgE0L4oEgEAACBplRKxFOcCaC8UiQAAAABAIfdIBAAAgA8x4esjPtLrh94yrkRJANoHKxIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCJXtqc3V1ddZee+0kyYYbbpi99947V111VTbYYIMkyQknnJAdd9yxVMMDlExdXV1qa2tTX1+/XK8fdumwFR6rurr6Q/dXVVWlpqYm/fr1W+FzAwAAwEdRkiJxwYIFaWpqyoQJE5r3XXvttTnllFOy7777lmJIgDbzUUrEUqmvr09tbW3Gjx9f1hwAAAB0HCUpEmfMmJF33303p5xyShobG3PSSSflqaeeyj/+8Y/cfPPN2XbbbXPyySensrJ4+BkzZpQiIsAKK3eJuER9fb2fkQAA7Zi5GrAy6tu371KPlaRIXGONNXLMMcdkwIABmT17doYNG5ZDDz00++23XzbccMNcdtll+dnPfpavf/3rhedaVniAjs7PSACA9stcDVjVlKRI3GSTTbLRRhuloqIivXr1Svfu3fOFL3yh+f6In//853P//feXYmiANve9M8e12Vhnfm9Em40FAAAA/6kkT23+xS9+kSuvvDLJvy+9e/vtt1NTU5NXXnklSfLII49k6623LsXQAAAAAEAJlGRF4oABA3LRRRdl8ODBSZILLrgg8+fPz9lnn50uXbqkT58++epXv1qKoQEAAACAEihJkbjaaqvlkksu+cD+3XffvRTDAQAAAAAlVpJLmwEAAACAVYsiEQAAAAAopEgEAAAAAAqV5B6JAAAA0N7U1dWltrY29fX1y/X6obeM+1jjVVdXf+j+qqqq1NTUpF+/fh/r/ABtzYpEAAAAOoSPUiKWUn19fWpra8sdA+AjUyQCAADQIbSHEnGJ9pQFYHkpEgEAAACAQu6RCAAAQId0Vf8T2nS8U38zqU3HA2htViQCAAAAAIUUiQAAAABAIUUiAAAAAFBIkQgAAAAAFFIkAgAAAACFFIkAAAAAQCFFIgAAAABQSJEIAAAAABRSJAIAAAAAhRSJAAAAAEAhRSIAAAAAUEiRCAAAAAAUUiQCAAAAAIUUiQAAAABAIUUiAAAAAFBIkQgAAAAAFKosdwCAtlZXV5fa2trU19e3yvnO/N6IVjnPiqiurl6h91VVVaWmpib9+vVr5UQAAACsqqxIBDqc1iwRV1b19fWpra0tdwwAAABWIopEoMPp6CXiEr4OAAAAfBSKRAAAAACgkHskAh3e2FMuK3eENjPyh+eUOwIAAAArKSsSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQopEAAAAAKCQIhEAAAAAKKRIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAApVlurE1dXVWXvttZMkG264YQ499NCMHz8+nTt3zm677ZbBgweXamgAAABWUXV1damtrU19ff3HPtepv5nUColWXHV19Ud+T1VVVWpqatKvX78SJAJYtpIUiQsWLEhTU1MmTJjQvO/oo4/O5Zdfnk9/+tM5/fTT8/TTT2fLLbcsxfAAAACsolqrRFxZ1dfXp7a2NuPHjy93FKADKkmROGPGjLz77rs55ZRT0tjYmMGDB2fhwoXZaKONkiS77757/vSnPy1XkThjxoxSRAQgfsYCACufjlwiLlFfX28eB5RM3759l3qsJEXiGmuskWOOOSYDBgzI7NmzM2zYsHTr1q35+FprrZV//vOfy3WuZYUH4OPxMxYAYOVkHgeUQ0mKxE022SQbbbRRKioq0qtXr3Tt2jVz585tPj5//vwWxSIAAACsiHHbfqPcEdrEiL/9pNwRAErz1OZf/OIXufLKK5P8e8n1u+++mzXXXDMvvvhimpqa8oc//CHbb799KYYGAAAAAEqgJCsSBwwYkIsuuqj5ycznn39+KioqcsEFF2TRokXZbbfd8pnPfKYUQwMAAAAAJVCSInG11VbLJZdc8oH9U6ZMKcVwAAAAAECJleTSZgAAAABg1aJIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQopEAAAAAKCQIhEAAAAAKKRIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQopEAAAAAKBQZbkDAB1TXV1damtrU19fX+4oGfnDc8odoWyqq6vbfMyqqqrU1NSkX79+bT42AAAAK86KRKAs2kuJSNurr69PbW1tuWMAAADwESkSgbJQInZsvv8AAAArH0UiAAAAAFDIPRKBduHiIReXOwIldv7E88sdAQAAgI/BikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQopEAAAAAKCQIhEAAAAAKKRIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQiUrEt94440ccsghef755/P000/nS1/6UoYOHZqhQ4fm3nvvLdWwAAAAAEAJVJbipI2NjRk7dmy6dOmSJHnyySdz1FFH5eijjy7FcAAAALSRurq61NbWpr6+vtxRkiQj/vaTckcoi+rq6rKMW1VVlZqamvTr168s4wPlVZIViVdeeWW+9rWvpaqqKkny1FNP5Xe/+11OOOGEXHzxxZk3b14phgUAAKDE2lOJSNurr69PbW1tuWMAZdLqKxLvuOOO9OjRI3vssUeuv/76JMm2226bAQMGZOutt86UKVMyefLknHbaact1vhkzZrR2RADaAT/fAWDlpESkvr7eXA5WYX379l3qsVYvEn/xi1+koqIijzzySP7xj39k1KhRGTduXNZbb70kyT777JNx48Yt9/mWFR6AlZef7wAAKy9zOeiYWr1InDRpUvPHQ4cOzTnnnJMzzzwzI0aMyLbbbptHHnkkW221VWsPCwAAQBmMWf/gckegDZz76l3ljgC0AyV52Mr7nX322Rk3blwqKyvTs2fPjBw5si2GBQAAAABaSUmLxAkTJjR/PHny5FIOBQAAAACUUEme2gwAAAAArFoUiQAAAABAIUUiAAAAAFBouYrE2bNn5/e//31eeeWVNDU1lToTAAAAANDOFD5s5ZZbbslvf/vbvPnmm/nSl76UF198MWeeeWZbZAMAAAAA2onCFYn33ntvrr766nTr1i1HHnlknnjiibbIBQAAAAC0I4VF4uLFi1NRUdG8vfrqq5c0EAAAAADQ/hRe2vyFL3whJ5xwQl5++eUMGzYs//Vf/9UWuQAAAACAdqSwSPz617+eXXbZJTNnzkyvXr3St2/ftsgFAAAAALQjhUXiq6++mtra2vzrX//KfvvtlwULFuQzn/lMW2QDAAAAANqJwnskjh07Nl/+8pfT2NiYHXbYIePHj2+LXAAAAABAO1JYJL777rvZZZddkiS9evXysBUAAAAA6IAKi8QuXbrk4YcfzuLFi1NXV6dIBAAAAIAOqLBIHDlyZO644440NDTkpptuytlnn90WuQAAAACAdqTwYSv33ntvRowYkU984hNtkQcAAAAAaIcKi8Q111wzZ511Vnr27JmvfOUr2WOPPVJRUdEW2QAAAACAdqLw0ubDDjss1113XQYPHpw777wzX/nKVzJp0qS8+eabbZEPAAAAAGgHCovEt956K//zP/+TMWPG5O23387w4cOz2WabZfjw4W2RDwAAAABoBwovbT7uuONy0EEH5dJLL80nP/nJ5v1PP/10SYMBAAAAAO1HYZF466235tlnn80TTzyRN998M1tssUWS5KSTTip5OAAAAACgfSgsEm+55Zbcc889+cxnPpMbb7wx+++/f4455pi2yAYAAAAAtBOFReI999yTSZMmpbKyMo2NjTn++OMViQAAAADQwRQ+bCVJKisrm/+75GMAAAAAoOMobAW32267nHPOOdl+++3z+OOPZ7vttmuLXAAAAABAO1LR0NDQVPSi3/3ud3n++efTp0+ffO5zn2uLXNAh1NXVpba2NvX19eWOArSBqqqq1NTUpF+/fuWOAgArrLq6usX2mPUPLlMS2tK5r97VYnvatGllSgKU01Ivbb7tttua/9TX12fttdfOq6++mttuu60t88EqTYkIHUt9fX1qa2vLHQMAAGCFLPXS5tdee63FdkVFRZqaChcvAh+BEhE6Hv/uAQCAldVSi8TBgwcnSZqamvLb3/42s2fPzqabbpq99967zcIBAAAAAO1D4cNWLr300rzzzjvp169f7rzzzvz5z3/O6aef3hbZoMO54LgLyx0BKIHRUy8qdwQAAICPrbBInDlzZvP9nL7xjW9k0KBBJQ8FAAAAALQvS33YyhIbbbRR/vnPfyZJ3njjjWywwQYlDwUAAAAAtC+FKxLr6ury9a9/PZ/85Cfz6quvZvXVV8/BBx+cioqK3HnnnW2REQAAAAAos8Ii8fbbb2+DGAAAAABAe1ZYJD744IP55S9/mffee6953xVXXFHKTAAAAABAO1NYJF555ZUZOXJkunXr1hZ5AAAAAIB2qLBI3HTTTbPTTju1RRYAAICVRl1dXWpra1NfX1/uKGV17qt3lTsCZVBdXV3uCG2qqqoqNTU16devX7mjQFkVFomf//znM2jQoPTp06d53/nnn1/SUAAAAO2dEhE6jvr6+tTW1mb8+PHljgJlVVgk3nLLLamurk7Xrl3bIg8AAMBKQYkIHYt/87AcRWLPnj1zwAEHtEUWAAAAAKCdKiwSu3TpklNPPTVbbrllKioqkiQnnXRSyYMBAACsTEZVfK7cEYBWNqrp9+WOAO1KYZG41157tUUOAAAAAKAdW2qR+PTTT2fLLbfMIYcc0mL/9OnTSx4KAAAAAGhfOi3twBVXXNH88cknn9z88U9+8pOSBgIAAAAA2p+lFon/adGiRaXOAQAAAAC0Y8tVJAIAAAAAHdtSi8SmpqY0Njbmvffe+8DHy+ONN97IIYcckueffz4vvPBCBg8enMGDB+eyyy7L4sWLW+0TAAAAAABKb6kPW3nppZcycODAJP8uFQcOHJimpqZUVFQUnrSxsTFjx45Nly5dkvz7fotDhw7NTjvtlLFjx2b69Onp379/K30KAAAAAECpLbVI/PnPf77CJ73yyivzta99Lddff32S5KmnnsqOO+6YJNlzzz3zxz/+UZEIAAAAACuRpRaJK+qOO+5Ijx49ssceezQXif+5knGttdbK22+/vdznmzFjRmtHBICy8rsNAGDlZB5HR9C3b9+lHmv1IvEXv/hFKioq8sgjj+Qf//hHRo0alX/961/Nx+fPn59u3bot9/mWFR4AVkZ+twEArJzM4+jolqtIfPvtt/PSSy9lo402ypprrrnM106aNKn546FDh+acc87JVVddlUcffTQ77bRTHnrooey8884fLzUAAAAA0KYKi8Rf//rXqa2tzaJFi7L//vsnSY4//viPNMhpp52WMWPGZOHChenTp0/23XffFUsLAAAAAJRFYZF48803Z8qUKTnttNMyaNCgfPOb31zuInHChAnNH0+cOHHFUwIAAAAAZdWp8AWdOmX11VdPklRUVGSNNdYoeSgAAAAAoH0pLBK33377nHfeeXn11VczduzYbLPNNm2RCwAAAABoRwovbT7ppJPy8MMPZ8stt0zv3r2z9957t0UuAAAAAKAdKVyROGfOnMycOTMLFizI008/ncmTJ7dFLgAAAACgHSksEs8777y88847WXfddZv/AAAAAAAdS+GlzWussUYGDx7cFlkAAAAAgHZqqUXirFmzkiTrrrtu7rnnnmy11VbNx3r16lX6ZAAAAABAu7HUIvGyyy5r/vj2229vcezaa68tWSAAAAAAoP1ZapG4pCx88MEHWzyp+d577y19KgAAAACgXVlqkfjggw+mrq4u99xzT+rq6pIkixcvzgMPPJADDjigzQICAAAAAOW31CJxiy22yNy5c9OlS5dssskmSZJOnTrlwAMPbLNwAAAAAED7sNQicYMNNsghhxySL37xi+nUqVNbZgIAAAAA2pnChlCJCAAAAAAstSWcNWtWW+YAAAAAANqxpV7aPGrUqNTW1ubMM8/M9773vbbM1GHV1dWltrY29fX15Y5CmYyeelG5IwBtoLq6utwRaCNVVVWpqalJv379yh0FAAA+tqUWiZ/+9KfzhS98IW+//Xa++MUvpqmpKUlSUVGRO++8s80CdiRKRABYtdTX16e2tjbjx48vdxQAAPjYllokXnLJJUmS7373uznrrLPaLFBHpkQEgFWP3+8AAKwqllokLnHSSSflqquuynPPPZeNN944xx9/fLp3794W2QAAAACAdqKwSLzkkkuyww475KCDDspf/vKXjB49Ot///vfbIluHd+6R3yl3BABgBYy5+dJyRwAAgFZXWCTOnTs3RxxxRJJkiy22yK9//euShwIAAAAA2pdORS9YsGBBXnvttSTJ66+/nsWLF5c8FAAAAADQvhSuSBwyZEi+9a1vpWvXrpk3b17OPffctsgFAAAAALQjhUXibrvtlttvvz0NDQ3p0aNHG0QCAAAAANqbwkubl1AiAgAAAEDHVbgiEQAAWLa6urrU1tamvr6+3FEoo1FNvy93BKDEqquryx2BNlJVVZWampr069ev3FHaleVakTh79uz8/ve/zyuvvJKmpqZSZwIAgJWKEhEAVi319fWpra0td4x2p3BF4i233JLf/va3efPNN/OlL30pL774Ys4888y2yAYAACsFJSIArHr8fv+gwhWJ9957b66++up069YtRx55ZJ544om2yAUAAAAAtCOFKxIXL16cioqK5u3VV1+9pIEAAGBld+5r7qcEACujMevVlTtCu1ZYJB544IE54YQT8vLLL2fYsGH5r//6r7bIBQAAAAC0I4VF4hFHHJFdd901M2fOTO/evbP55pu3RS4AAAAAoB0pLBIvvvji5o8ffvjhVFZWZv3118/hhx+eddZZp6ThAAAAAID2ofBhKwsWLMh6662X/fffP5/85Cfz6quvZuHChbnooovaIh8AAAAA0A4UFon/+te/cuKJJ2aPPfbI4MGD09jYmKFDh+att95qi3wAAAAAQDtQWCTOmzcvzz//fJLkueeey/z589PQ0JB33nmn1NkAAAAAgHai8B6JZ555Zi644IK89tpr6dKlSw455JDcd999qampaYt8AAAAAEA7ULgicdttt83ZZ5+dXXbZJe+8807eeOONDBw4MPvuu29b5AMAAAAA2oGlrkhcuHBh7rnnntx6661ZbbXVMm/evNx+++1ZY4012jIfAAAAANAOLHVF4oABA/LMM89k9OjRue6661JVVaVEBAAAAIAOaqkrEr/xjW/k7rvvzksvvZQBAwakqampLXMBAAAAAO3IUlckHnvssfnxj3+cI444Ivfcc0/+/ve/54c//GFmzpzZlvkAAAAAgHag8KnNO+64Y3bccce89dZbufPOO3PhhRfmxhtvbItsAAAAAEA7UVgkLtGtW7ccccQROeKII0qZBwAAAABoh5Z6aTMAAAAAwBKKRAAAAACg0HJf2vxRLFq0KGPGjMmsWbNSUVGRc845J42NjRk+fHg23njjJMlhhx2WAw44oBTDAwAAAACtrCRF4oMPPpgkmTx5ch599NFce+212WuvvXLUUUfl6KOPLsWQAAAAAEAJlaRI3GeffbLXXnslSV566aV07do1Tz31VGbNmpXp06dn4403zvDhw7P22muXYngAAAAAoJWVpEhMksrKyowaNSrTp0/P2LFjU19fnwEDBmTrrbfOlClTMnny5Jx22mmF55kxY0apIgIAtAnzGQCAlVNHnMf17dt3qcdKViQmyahRo/Laa69l0KBBmTx5ctZff/0k/16xOG7cuOU6x7LCAwCsDMxnAABWTuZxLZXkqc133nlnpk6dmiRZY401UlFRkbPPPjt/+9vfkiSPPPJIttpqq1IMDQAAAACUQElWJPbv3z+jR4/OCSec0Py05g022CDjxo1LZWVlevbsmZEjR5ZiaAAAAACgBEpSJK655poZO3bsB/ZPnjy5FMMBAAAAACVWkkubAQAAAIBViyIRAAAAACikSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQopEAAAAAKCQIhEAAAAAKKRIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAAChUWe4AfNAbb7yRJLnrobvKnAQAWBFLfpevu+66ZU5CW2uex73zjzInAQBWhHncslmRCAAAAAAUUiQCAAAAAIUUiQAAAABAIfdIbIeWXId/8J4HlzkJALAiHpv1l3JHoEya53GLtyhzEgBgRTy27oJyR2jXrEgEAAAAAAopEgEAAACAQopEAAAAAKCQIhEAAAAAKKRIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQopEAAAAAKCQIhEAAAAAKKRIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQpWlOOmiRYsyZsyYzJo1KxUVFTnnnHOy+uqrZ/To0UmSzTbbLGeddVY6ddJjAgAAAMDKoCRF4oMPPpgkmTx5ch599NFce+21aWpqytChQ7PTTjtl7NixmT59evr371+K4QEAAACAVlaSJYH77LNPRo4cmSR56aWX0rVr1zz11FPZcccdkyR77rlnHnnkkVIMDQAAAACUQElWJCZJZWVlRo0alenTp2fs2LH505/+lIqKiiTJWmutlbfffnu5zjNjxoxSRQQAaBPmMwAAK6eOOI/r27fvUo+VrEhMklGjRuW1117LoEGDsmDBgub98+fPT7du3ZbrHMsKDwCwMjCfAQBYOZnHtVSSS5vvvPPOTJ06NUmyxhprpKKiIltvvXUeffTRJMlDDz2U7bffvhRDAwAAAAAlUJIVif3798/o0aNzwgknpLGxMcOHD0/v3r0zZsyYLFy4MH369Mm+++5biqEBAAAAgBIoSZG45pprZuzYsR/YP3HixFIMBwAAAACUWEkubQYAAAAAVi2KRAAAAACgkCIRAAAAAChUknsk0jrG3HxpuSMAALACxqxXV+4IAACtzopEAAAAAKCQIrEdqaqqKncEAKCV+f3eMfg+A8Cqx+/3D1IktiM1NTX+kgLAKqSqqio1NTXljkEbMI8DgFWLedyHq2hoaGgqdwjoqKqrq1tsX3DchWVKApTS6KkXtdieNm1amZIA0JreP5cbVfG5MiUBSmVU0+9bbJvH0dFZkQgAAAAAFFIkAgAAAACFFIkAAAAAQCFFIgAAAABQSJEIAAAAABRSJAIAAAAAhRSJAAAAAEAhRSIAAAAAUEiRCAAAAAAUUiQCAAAAAIUUiQAAAABAIUUiAAAAAFBIkQgAAAAAFFIkAgAAAACFFIkAAAAAQCFFIgAAAABQqLLcAYD/Z/TUi8odAQCAFTSq6ffljgAAJWVFIgAAAABQSJEIZVRVVVXuCEAb8+8eYNXhZzp0LP7NgyIRyqqmpsYvI+hAqqqqUlNTU+4YALQScznoOMzj4N8qGhoamsodAuh4qqurW2xfPOTiMiWhrZw/8fwW29OmTStTEgDg43j/PG7M+geXKQlt6dxX72qxbS4HHZMViQAAAABAIUUiAAAAAFBIkQgAAAAAFFIkAgAAAACFFIkAAAAAQCFFIgAAAABQqLLcAQCS5PyJ55c7AgAAK+DcV+8qdwQA2ogViQAAAABAIUUiUBZVVVXljkAZ+f4DwMrL73H8HYCOS5EIlEVNTY0JSAdVVVWVmpqacscAAFaQeVzHZi4HHVtFQ0NDU7lDALSl6urqFttjT7msTEna3sgfntNie9q0aWVKAgCwYt4/lxu37TfKlKRtjfjbT1psm8cB5WBFIgAAAABQSJEIAAAAABRSJAIAAAAAhRSJAAAAAEAhRSIAAAAAUKiytU/Y2NiYiy++OHPmzMnChQszaNCgbLDBBhk+fHg23njjJMlhhx2WAw44oLWHBgAAAABKpNWLxLvuuivdu3fPRRddlLlz5+aYY47J8ccfn6OOOipHH310aw8HAABABzbibz8pdwSADqPVi8T99tsv++67b5KkqakpnTt3zlNPPZVZs2Zl+vTp2XjjjTN8+PCsvfbarT00AAAAAFAirV4krrXWWkmSefPmZeTIkRk6dGgWLlyYAQMGZOutt86UKVMyefLknHbaact1vhkzZrR2RAD+f37GAgArmx49eqShoaHcMcqqR48e5nFAyfTt23epx1q9SEySV155JWeeeWYGDhyYgw46KG+99Va6deuWJNlnn30ybty45T7XssID8PH4GQsArGxOOOGE1NbWpr6+vtxRyqKqqio1NTXmcUBZtHqR+Prrr+eUU07JiBEjsuuuuyZJTj311IwYMSLbbrttHnnkkWy11VatPSwAAAAdQL9+/TJ+/PgVem91dXWL7av6n9AakZbbqb+Z1GJ72rRpbTo+wMfV6kXi1KlT8+abb2bKlCmZMmVKkmTYsGH5wQ9+kMrKyvTs2TMjR45s7WEBAAAAgBJq9SLxjDPOyBlnnPGB/ZMnT27toQAAAACANtKp3AEAAAAAgPZPkQgAAAAAFFIkAgAAAACFWv0eiQArm5E/PKfcEQAAKIP3P0UZgGWzIhEAAAAAKKRIBDqcqqqqckdoF3wdAICOpj3Nf9pTFoDlpUgEOpyampoOP3GrqqpKTU1NuWMAALSp9jIPNBcDVlYVDQ0NTeUOAbAyqa6ubrH9vTPHtdnYZ35vRIvtadOmtdnYAACruvfP8yZ8fcRSXvnhht7Scl5orgasaqxIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACikSAQAAAIBCleUOALCyO/N7I8odAQCAEhh6y7hyRwBoV6xIBAAAAAAKKRIBPqKqqqpyR0jSfnIAAKwqWnN+Za4GrIoUiQAfUU1NTdknhlVVVampqSlrBgCAVU1rzfPM1YBVVUVDQ0NTuUMArEqqq6tbbF/xnSuW+73DLh3WYnvatGmtkAgAgBXx/nndlG+PbrE96JoLWmybuwGrOisSAQAAAIBCikQAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQpXlDgCwqnv/k5gBAFg5vf8pzQAdjRWJAAAAAEAhRSJAK6uqqmpX5wEAYMV8lPmYuRvQESgSAVpZTU3Nx55IVlVVpaamppUSAQCwIpZ3XmfuBnQUFQ0NDU3lDgGwKquurm6xffXoa5o/PvmCb7c4Nm3atDbJBADA8hs+fHjq6+s/9FhVVVXGjx/fxokAysOKRAAAAFiGZa02tBIR6EgUiQAAALAM/fr1W6FjAKsaRSIAAAAAUEiRCAAAAAAUUiQCAAAAAIUUiQAAAABAocpyBwDoaE6+4NvljgAAAAAfmRWJAAAAAEAhRSJAiVVVVbXq6wAAaHsfNlczfwM6GkUiQInV1NQUTjKrqqpSU1PTRokAAPio3j+nM38DOqKKhoaGpnKHAOgIqqurl3ps2rRpbZgEAAAAPjorEgEAAACAQopEAAAAAKCQIhEAAAAAKKRIBAAAgOXU2NiY448/Po2NjeWOAtDmFIkAAACwnIYNG5b33nsvp59+ermjALS5ytY+YWNjYy6++OLMmTMnCxcuzKBBg9KnT5+MHj06SbLZZpvlrLPOSqdOOkwAAABWHs8880zmzp2bJGloaMgzzzyTzTffvMypANpOq7d5d911V7p3757rrrsuV155Zb73ve/liiuuyNChQ3Pdddelqakp06dPb+1hAQAAoKSWLJBZ4uKLLy5TEoDyaPUicb/99suQIUOSJE1NTencuXOeeuqp7LjjjkmSPffcM4888khrDwsAAAAls2RhzH9avHhxfvSjH5UpEUDba/VLm9daa60kybx58zJy5MgMHTo0V111VSoqKpqPv/3228t9vhkzZrR2RICy6NGjRxoaGj6wv1OnTn7WAQC0cw888MCH7v/tb3+bz3/+822cBqB0+vbtu9RjrV4kJskrr7ySM888MwMHDsxBBx2Uq6++uvnY/Pnz061bt+U+17LCA6xMTjjhhPzgBz/IwoULm/dVVFRk4MCBftYBALRz++yzT377299+YH///v3N5YAOo9WLxNdffz2nnHJKRowYkV133TVJssUWW+TRRx/NTjvtlIceeig777xzaw8L0O7169cvU6ZMKXcMAABWwPHHH5/p06e3uLy5U6dOGTRoUBlTAbStioaGhqbily2/73//+7n33nvTu3fv5n3Dhw/P97///SxcuDB9+vTJueeem86dO7fmsAAAAFBSzzzzTC666KLm7QsvvNBTm4EOpdWLRAAAAFhVnXzyyZk7d2569OiRH/7wh+WOA9CmFIkAAACwnBobGzNkyJBMnDgxlZUleewAQLulSAQAAAAACnUqdwAAAAAAoP1TJAIAAAAAhRSJAAAAAEAhRSIAAAAAUEiRCAAAAAAUUiQCAAAAAIUUiQAAAABAIUUiAAAAAFBIkQgAAAAAFFIkAgAAAACFFIkAAAAAQCFFIgAAAABQSJEIAAAAABRSJAIAAAAAhRSJAAAAAEAhRSIAAAAAUEiRCAAAAAAUUiQCAAAAAIUUiQAAAABAIUUiAAAAAFBIkQgAAAAAFFIkAgAAAACFFIkAbWjx4sUZO3ZsBg0alKFDh+aFF14odyQAAD6CJ554IkOHDi13DICyUCQCtKHp06fnvffey5QpU/Ltb387V155ZbkjAQCwnG644YZceumlee+998odBaAsFIkAbejxxx/PHnvskSTp169fnnzyyTInAgBgeW200Ua5/PLLyx0DoGwUiQBtaN68eenatWvzdqdOndLY2FjGRAAALK999903lZWV5Y4BUDaKRIA2tPbaa2fevHnN201NTSajAAAArBQUiQBtaLvttstDDz2UJKmrq8tmm21W5kQAAACwfCyDAWhD++yzT/74xz/m+OOPT1NTUy644IJyRwIAAIDlUtHQ0NBU7hAAAAAAQPvm0mYAAAAAoJAiEQAAAAAopEgEAAAAAAopEgEAAACAQopEAAAAAKBQZbkDAACsyubMmZPzzjsvU6ZMKXztX/7yl3Tr1i19+/bNQQcdlLvvvvsjj3X00Udnyy23bLH/mmuuSefOnT/SuZYYOnRozjnnnPTu3XuF3v9R7LnnnvnsZz+bioqKNDY2pnfv3jn77LNTWVm6KevcuXPz8MMP56CDDlqu1w8aNCiXXHJJ1lprrZx//vlZsGBBFi1alDPOOCPbbLNNyXICALQHikQAgHbil7/8ZQ488MD07dt3hc/Rp0+fTJgwoRVTtZ111lmnRfZzzz03Dz30UD7/+c+XbMxnnnkmDz744HIXiUvU1tZm9913z9FHH50nn3wyY8eOzQ033FCilAAA7YMiEQCgjdx666351a9+lYqKimyzzTYZMWJE87Enn3wyDz/8cJ5++un06dMnCxcuzHnnnZdXXnkl3bt3z2WXXZZ33303l1xySebOnZskOeOMM7L55psv19gXXXRRKisr8/LLL+e9997LAQcckN/97nd5+eWXM27cuLzyyiupra1NRUVFXn/99Rx66KE5/PDDm9//1ltv5YILLsi8efOyaNGiDB06NOuvv34uvPDCTJ06Ncm/i7+jjz46CxYsyLXXXptOnTplo402ysiRI5MkY8eOzQsvvJCmpqYMHTo0O+2001LzNjY25p133smaa66ZSZMmpa6uLvPnz895552Xhx56KP/7v/+bzp07Z4cddsgpp5ySSZMm5cUXX0xDQ0Pmzp2bww8/PPfff39mz56dCy+8MP369ctNN930gffV1tZmxowZue2227LnnntmzJgxWbBgQbp06ZJzzz03G2ywQf77v/87f/jDH7L++uunoaEhSfLNb34za621VpKkc+fOzR8DAKzKFIkAAG3kl7/8Zc4+++xss802ufXWW9PY2Nh82e7WW2+dPfbYIwceeGA++clPZv78+TnppJOy4YYbZujQoXn66adz//33Z+edd87AgQMze/bsXHzxxbnuuutajPHcc89l6NChzdtbbbVVhg0bliT51Kc+le985zsZO3Zs5syZkyuuuCKTJk3Kgw8+mC222CL19fWZNm1aFi9enKOOOir77bdf83mmTJmS3XbbLd/4xjfy6quvZvDgwbn99tvTpUuXPPvss+nZs2fmzJmTbbbZJgMHDsx1112XddddNxMmTMgdd9yRxsbG9OjRI+eff34aGhoyZMiQ/PSnP22R/c0338zQoUNTUVGR5N+XOu+yyy557LHH0rt375xxxhl55plnct999+VHP/pROnfunLPPPjsPPvhgkqRLly656qqrcv311+f3v/99xo8fn1/+8pe59957s+aaa37o+2pqavKzn/0shx56aM4999wcccQR2XPPPfOnP/0pV199dY488sg89thjmTp1aubPn5+BAwcmSdZdd93mr/eoUaNy8cUXt+LfFACA9kmRCADQRi644ILceOONmTNnTvr167fM13bv3j0bbrhhkqRnz5559913M3PmzPz5z3/Offfdl+Tfxdv7LevS5q222ipJ0q1bt+Z7Hnbr1i3vvfdekqRfv35ZffXVkySbbrppXnzxxeb3Pv/8882X/66//vpZe+2188Ybb+SrX/1q7rjjjnzyk5/MwQcfnH/96195/fXXc+655yZJFixYkF133TVvvvlmHn/88fztb39LkixatCgNDQ3p0aNH8xjvv7T5P/Xq1as5x2c+85nmAnb77bfPs88+myTN94bs2rVr+vTp0/z5LViwYKnv+8xnPtM8xsyZM1NbW5sbbrghTU1NqayszOzZs7P11lunU6dO6dq1azbbbLMWuX74wx/mvPPO+8B+AIBVkSIRAKCN3H777TnnnHPSpUuXnHLKKfnrX/+aHXfcsfl4p06dsnjx4qW+v1evXjnooINy0EEH5Y033sjPf/7zjzT+kpV+S/OPf/wjixYtysKFC/Pcc89lk002aT7Wu3fvPP7449lyyy3z6quv5q233kr37t2z77775sYbb0z37t0zduzYdOvWLeuvv37GjRuXrl275oEHHsiaa66ZZ599Nuuvv35qamry7rvvpra2Nuuss85Hzt67d+/8+Mc/TmNjYzp37pzHHnssX/ziFzNjxoxlfn5Le19FRUXz17xXr1455phj8tnPfjbPP/98/vKXv2TTTTfNrbfemsWLF2fBggV57rnnWpx39913z9Zbb73cnwcAwMpMkQgA0EY222yznHDCCVlrrbWy/vrrZ9ttt21xfNttt80111zTvBLx/WpqanLJJZfk9ttvz7x58zJ48OAPvOb9lzYnyfnnn79c+RobGzNs2LDMnTs3NTU1LVYLHnfccbn44ovz61//OgsWLMi5556bysrKVFZWZocddkhDQ0O6d++eJBk+fHhOP/30LF68OGuvvXZGjRqV7bbbLpdeemmGDBmSefPmZeDAgenUqdNy5fpPm2++efbbb78MHjw4ixcvznbbbZd99tknM2bMWKH31dfXZ+bMmbn55ptz2mmn5bLLLst7772XBQsWZPjw4dliiy2yxx575Ljjjst6662XT3ziEy3O++c//zlf+MIXmj93AIBVWUVDQ0NTuUMAAFBejz76aH72s5/l0ksv/cjv/e53v5v+/ftnl112KUEyAADai4/+v4EBAOD/d8opp+TNN99UIgIAdABWJAIAAAAAhaxIBAAAAAAKKRIBAAAAgEKKRAAAAACgkCIRAAAAACikSAQAAAAACv1/MMa62CxEXzAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Effect of Age on the Promotion\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (20, 8)\n",
+    "sns.boxenplot(x='is_promoted', y='age', data=train, palette='PuRd')\n",
+    "plt.title('Effect of Age on Promotion', fontsize=15)\n",
+    "plt.xlabel('Is the Employee Promoted?', fontsize=10)\n",
+    "plt.ylabel('Age of the Employee', fontsize=10)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5j7hvZLRGcdC"
+   },
+   "source": [
+    "## Department Vs Average Training Score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 487
+    },
+    "id": "zHHN4u0EEPNU",
+    "outputId": "c72400d6-870d-4b20-cfb5-f31b3a647bd7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Department Vs Average Training Score\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (16, 7)\n",
+    "sns.boxplot(x = 'department', y = 'avg_training_score',data=train, palette = 'autumn')\n",
+    "plt.title('Average Training Scores from each Department', fontsize = 15)\n",
+    "plt.ylabel('Promoted or not', fontsize = 10)\n",
+    "plt.xlabel('Departments', fontsize = 10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qD4e62KaHmHd"
+   },
+   "source": [
+    "## Multivariate Analysis\n",
+    "\n",
+    "Multivariate analysis is based on the principles of multivariate statistics, which involves observation and analysis of more than one statistical outcome variable at a time.\n",
+    "\n",
+    "* First, we will use the Correlation Heatmap to check the correlation between the Numerical Columns\n",
+    "* Then we will check the ppscore or the Predictive Score to check the correlation between all the columns present in the data.\n",
+    "* Then, we will use Bubble Charts, split Violin plots, Hue with Bivariate Plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 615
+    },
+    "id": "Wca9y5aPGfDi",
+    "outputId": "0c3c42e2-2546-4427-b33b-1fb8e38502ca"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets check the Heat Map for the Data with respect to correlation.\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (15, 8)\n",
+    "sns.heatmap(train.corr(), annot = True, linewidth = 0.5, cmap = 'Wistia')\n",
+    "plt.title('Correlation Heat Map', fontsize = 15)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pNVxExZFHt4v"
+   },
+   "source": [
+    "## Department vs Promotion vs Won awards?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-vbntAU7IQKk",
+    "outputId": "8a2b9c11-78dc-4250-e1a4-48004254b33c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        0\n",
+       "1        0\n",
+       "2        0\n",
+       "3        0\n",
+       "4        0\n",
+       "        ..\n",
+       "54802    0\n",
+       "54804    0\n",
+       "54805    0\n",
+       "54806    0\n",
+       "54807    0\n",
+       "Name: awards_won?, Length: 53009, dtype: int64"
+      ]
+     },
+     "execution_count": 307,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['awards_won?']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 487
+    },
+    "id": "ii5T0QC3HopM",
+    "outputId": "9e0a93fe-121b-4ff1-fb42-bcbcc545b717"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# lets check the relation of Departments and Promotions when they won awards ?\n",
+    "\n",
+    "plt.rcParams['figure.figsize'] = (16, 7)\n",
+    "sns.barplot(x = 'department', y = 'is_promoted',data=train, hue = train['awards_won?'], palette = 'cool')\n",
+    "plt.title('Chances of Promotion in each Department when they have won some Awards too', fontsize = 15)\n",
+    "plt.ylabel('Promoted or not', fontsize = 10)\n",
+    "plt.xlabel('Departments', fontsize = 10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "VeapLdGohlAh"
+   },
+   "source": [
+    "## **Answering Some Key Questions**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "StWmelfOZe3q"
+   },
+   "source": [
+    "### Does Older Employees getting more Promotion than Younger Employees?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HFVTlIxEaQEv",
+    "outputId": "d4f5f0fc-e3d0-4a80-fc40-3a1fa63502d9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    53009.000000\n",
+       "mean        34.254315\n",
+       "std          7.120853\n",
+       "min         20.000000\n",
+       "25%         29.000000\n",
+       "50%         33.000000\n",
+       "75%         38.000000\n",
+       "max         60.000000\n",
+       "Name: age, dtype: float64"
+      ]
+     },
+     "execution_count": 309,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['age'].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 661
+    },
+    "id": "MLRIc1VuZkVF",
+    "outputId": "6aef5b05-62ba-4cf1-926b-9a34cd614d8f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee_id</th>\n",
+       "      <th>department</th>\n",
+       "      <th>region</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>recruitment_channel</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>49017</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_7</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>f</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>58304</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_28</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>33</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>24040</td>\n",
+       "      <td>Technology</td>\n",
+       "      <td>region_22</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75</th>\n",
+       "      <td>54782</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_2</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>49</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>47498</td>\n",
+       "      <td>Operations</td>\n",
+       "      <td>region_11</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>f</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>42</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54722</th>\n",
+       "      <td>47608</td>\n",
+       "      <td>Procurement</td>\n",
+       "      <td>region_10</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>72</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54734</th>\n",
+       "      <td>11685</td>\n",
+       "      <td>Operations</td>\n",
+       "      <td>region_15</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54757</th>\n",
+       "      <td>14502</td>\n",
+       "      <td>Technology</td>\n",
+       "      <td>region_7</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>54</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54792</th>\n",
+       "      <td>994</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_14</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>59</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54796</th>\n",
+       "      <td>12592</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_25</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2997 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       employee_id         department     region         education gender  \\\n",
+       "11           49017  Sales & Marketing   region_7        Bachelor's      f   \n",
+       "39           58304  Sales & Marketing  region_28        Bachelor's      m   \n",
+       "71           24040         Technology  region_22  Master's & above      m   \n",
+       "75           54782  Sales & Marketing   region_2  Master's & above      m   \n",
+       "85           47498         Operations  region_11  Master's & above      f   \n",
+       "...            ...                ...        ...               ...    ...   \n",
+       "54722        47608        Procurement  region_10  Master's & above      m   \n",
+       "54734        11685         Operations  region_15        Bachelor's      m   \n",
+       "54757        14502         Technology   region_7  Master's & above      m   \n",
+       "54792          994  Sales & Marketing  region_14        Bachelor's      m   \n",
+       "54796        12592  Sales & Marketing  region_25  Master's & above      m   \n",
+       "\n",
+       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
+       "11               sourcing                1   35                   5.0   \n",
+       "39               sourcing                1   33                   5.0   \n",
+       "71                  other                1   34                   5.0   \n",
+       "75               sourcing                1   38                   4.0   \n",
+       "85               sourcing                1   42                   5.0   \n",
+       "...                   ...              ...  ...                   ...   \n",
+       "54722            sourcing                1   34                   5.0   \n",
+       "54734            sourcing                1   31                   3.0   \n",
+       "54757               other                1   54                   4.0   \n",
+       "54792               other                1   59                   3.0   \n",
+       "54796               other                1   34                   3.0   \n",
+       "\n",
+       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
+       "11                     3              1            0                  50   \n",
+       "39                     6              1            0                  51   \n",
+       "71                     7              1            0                  78   \n",
+       "75                     2              1            0                  49   \n",
+       "85                    11              1            0                  60   \n",
+       "...                  ...            ...          ...                 ...   \n",
+       "54722                  2              1            0                  72   \n",
+       "54734                  1              1            0                  56   \n",
+       "54757                  7              0            0                  81   \n",
+       "54792                 11              0            0                  65   \n",
+       "54796                  7              0            0                  60   \n",
+       "\n",
+       "       is_promoted  \n",
+       "11               1  \n",
+       "39               1  \n",
+       "71               1  \n",
+       "75               1  \n",
+       "85               1  \n",
+       "...            ...  \n",
+       "54722            1  \n",
+       "54734            1  \n",
+       "54757            1  \n",
+       "54792            1  \n",
+       "54796            1  \n",
+       "\n",
+       "[2997 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 310,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "old = train[(train['age']>30) & (train['is_promoted']==1)]\n",
+    "old"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 557
+    },
+    "id": "cxsHE3xuZkey",
+    "outputId": "ccecd830-8bf8-4814-9f66-451ac2b95016"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee_id</th>\n",
+       "      <th>department</th>\n",
+       "      <th>region</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>recruitment_channel</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>77981</td>\n",
+       "      <td>Finance</td>\n",
+       "      <td>region_22</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>58</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>16502</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_22</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>45624</td>\n",
+       "      <td>Analytics</td>\n",
+       "      <td>region_31</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>79</th>\n",
+       "      <td>59147</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_22</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>58</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>84</th>\n",
+       "      <td>44575</td>\n",
+       "      <td>Legal</td>\n",
+       "      <td>region_7</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54624</th>\n",
+       "      <td>57131</td>\n",
+       "      <td>Finance</td>\n",
+       "      <td>region_2</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54713</th>\n",
+       "      <td>56396</td>\n",
+       "      <td>Procurement</td>\n",
+       "      <td>region_22</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>67</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54720</th>\n",
+       "      <td>38719</td>\n",
+       "      <td>Analytics</td>\n",
+       "      <td>region_4</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>88</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54730</th>\n",
+       "      <td>51059</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_2</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>58</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54761</th>\n",
+       "      <td>8278</td>\n",
+       "      <td>Procurement</td>\n",
+       "      <td>region_13</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>f</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>86</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1550 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       employee_id         department     region   education gender  \\\n",
+       "66           77981            Finance  region_22  Bachelor's      m   \n",
+       "67           16502  Sales & Marketing  region_22  Bachelor's      m   \n",
+       "69           45624          Analytics  region_31  Bachelor's      m   \n",
+       "79           59147  Sales & Marketing  region_22  Bachelor's      m   \n",
+       "84           44575              Legal   region_7  Bachelor's      m   \n",
+       "...            ...                ...        ...         ...    ...   \n",
+       "54624        57131            Finance   region_2  Bachelor's      m   \n",
+       "54713        56396        Procurement  region_22  Bachelor's      m   \n",
+       "54720        38719          Analytics   region_4  Bachelor's      m   \n",
+       "54730        51059  Sales & Marketing   region_2  Bachelor's      m   \n",
+       "54761         8278        Procurement  region_13  Bachelor's      f   \n",
+       "\n",
+       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
+       "66                  other                1   27                   3.0   \n",
+       "67               sourcing                1   27                   3.0   \n",
+       "69                  other                1   30                   3.0   \n",
+       "79               sourcing                1   30                   3.0   \n",
+       "84                  other                1   29                   3.0   \n",
+       "...                   ...              ...  ...                   ...   \n",
+       "54624               other                1   24                   3.0   \n",
+       "54713            sourcing                1   30                   4.0   \n",
+       "54720            sourcing                1   29                   2.0   \n",
+       "54730               other                1   29                   5.0   \n",
+       "54761            sourcing                1   30                   4.0   \n",
+       "\n",
+       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
+       "66                     1              1            1                  58   \n",
+       "67                     1              0            0                  61   \n",
+       "69                     7              1            0                  84   \n",
+       "79                     3              0            0                  58   \n",
+       "84                     1              0            0                  65   \n",
+       "...                  ...            ...          ...                 ...   \n",
+       "54624                  1              0            0                  71   \n",
+       "54713                  4              1            0                  67   \n",
+       "54720                  3              1            0                  88   \n",
+       "54730                  4              0            0                  58   \n",
+       "54761                  2              1            0                  86   \n",
+       "\n",
+       "       is_promoted  \n",
+       "66               1  \n",
+       "67               1  \n",
+       "69               1  \n",
+       "79               1  \n",
+       "84               1  \n",
+       "...            ...  \n",
+       "54624            1  \n",
+       "54713            1  \n",
+       "54720            1  \n",
+       "54730            1  \n",
+       "54761            1  \n",
+       "\n",
+       "[1550 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 311,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "young = train[(train['is_promoted']==1) & (train['age']<=30)]\n",
+    "young"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 609
+    },
+    "id": "8foRTcOaZklA",
+    "outputId": "155bfaac-b30d-452c-9207-4bd25a71a48b"
+   },
+   "outputs": [
+    {
+     "data": {
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee_id</th>\n",
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+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
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+       "      <td>5.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>51</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>77981</td>\n",
+       "      <td>Finance</td>\n",
+       "      <td>region_22</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>58</td>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>16502</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69</th>\n",
+       "      <td>45624</td>\n",
+       "      <td>Analytics</td>\n",
+       "      <td>region_31</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>54734</th>\n",
+       "      <td>11685</td>\n",
+       "      <td>Operations</td>\n",
+       "      <td>region_15</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54757</th>\n",
+       "      <td>14502</td>\n",
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+       "      <td>Master's &amp; above</td>\n",
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+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>4.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54761</th>\n",
+       "      <td>8278</td>\n",
+       "      <td>Procurement</td>\n",
+       "      <td>region_13</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>f</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>4.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>86</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54792</th>\n",
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+       "      <td>region_14</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>59</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54796</th>\n",
+       "      <td>12592</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_25</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4547 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       employee_id         department     region         education gender  \\\n",
+       "11           49017  Sales & Marketing   region_7        Bachelor's      f   \n",
+       "39           58304  Sales & Marketing  region_28        Bachelor's      m   \n",
+       "66           77981            Finance  region_22        Bachelor's      m   \n",
+       "67           16502  Sales & Marketing  region_22        Bachelor's      m   \n",
+       "69           45624          Analytics  region_31        Bachelor's      m   \n",
+       "...            ...                ...        ...               ...    ...   \n",
+       "54734        11685         Operations  region_15        Bachelor's      m   \n",
+       "54757        14502         Technology   region_7  Master's & above      m   \n",
+       "54761         8278        Procurement  region_13        Bachelor's      f   \n",
+       "54792          994  Sales & Marketing  region_14        Bachelor's      m   \n",
+       "54796        12592  Sales & Marketing  region_25  Master's & above      m   \n",
+       "\n",
+       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
+       "11               sourcing                1   35                   5.0   \n",
+       "39               sourcing                1   33                   5.0   \n",
+       "66                  other                1   27                   3.0   \n",
+       "67               sourcing                1   27                   3.0   \n",
+       "69                  other                1   30                   3.0   \n",
+       "...                   ...              ...  ...                   ...   \n",
+       "54734            sourcing                1   31                   3.0   \n",
+       "54757               other                1   54                   4.0   \n",
+       "54761            sourcing                1   30                   4.0   \n",
+       "54792               other                1   59                   3.0   \n",
+       "54796               other                1   34                   3.0   \n",
+       "\n",
+       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
+       "11                     3              1            0                  50   \n",
+       "39                     6              1            0                  51   \n",
+       "66                     1              1            1                  58   \n",
+       "67                     1              0            0                  61   \n",
+       "69                     7              1            0                  84   \n",
+       "...                  ...            ...          ...                 ...   \n",
+       "54734                  1              1            0                  56   \n",
+       "54757                  7              0            0                  81   \n",
+       "54761                  2              1            0                  86   \n",
+       "54792                 11              0            0                  65   \n",
+       "54796                  7              0            0                  60   \n",
+       "\n",
+       "       is_promoted  \n",
+       "11               1  \n",
+       "39               1  \n",
+       "66               1  \n",
+       "67               1  \n",
+       "69               1  \n",
+       "...            ...  \n",
+       "54734            1  \n",
+       "54757            1  \n",
+       "54761            1  \n",
+       "54792            1  \n",
+       "54796            1  \n",
+       "\n",
+       "[4547 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 312,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[train['is_promoted']==1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lXyva9GtbaJV"
+   },
+   "source": [
+    "Out of the total 4547 employees which got promotion, older employees with age greater than 30 years got promoted more than the younger employees between the age 20 t0 30."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Vfr_CfqecpCY"
+   },
+   "source": [
+    "### What is the Probability to get Promoted, If an employeed has won an award?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 609
+    },
+    "id": "OS_3E_YhcuH8",
+    "outputId": "57a0146c-8400-4a54-c9ce-38e0e46b4d9f"
+   },
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
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+       "      <th></th>\n",
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+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>51164</td>\n",
+       "      <td>Technology</td>\n",
+       "      <td>region_14</td>\n",
+       "      <td>Bachelor's</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>78</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>7606</td>\n",
+       "      <td>Technology</td>\n",
+       "      <td>region_29</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>f</td>\n",
+       "      <td>other</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>76</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>53630</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_23</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>f</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>54641</th>\n",
+       "      <td>2467</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_27</td>\n",
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+       "      <td>1</td>\n",
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+       "      <td>2.0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54702</th>\n",
+       "      <td>4952</td>\n",
+       "      <td>Operations</td>\n",
+       "      <td>region_28</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>62</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>54772</th>\n",
+       "      <td>34501</td>\n",
+       "      <td>Operations</td>\n",
+       "      <td>region_27</td>\n",
+       "      <td>Master's &amp; above</td>\n",
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+       "      <td>57</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>54797</th>\n",
+       "      <td>62450</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_11</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54799</th>\n",
+       "      <td>68093</td>\n",
+       "      <td>Procurement</td>\n",
+       "      <td>region_2</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>f</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>67</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1253 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       employee_id         department     region         education gender  \\\n",
+       "66           77981            Finance  region_22        Bachelor's      m   \n",
+       "122          38052        Procurement  region_34  Master's & above      m   \n",
+       "138          51164         Technology  region_14        Bachelor's      f   \n",
+       "140           7606         Technology  region_29        Bachelor's      f   \n",
+       "200          53630  Sales & Marketing  region_23  Master's & above      f   \n",
+       "...            ...                ...        ...               ...    ...   \n",
+       "54641         2467  Sales & Marketing  region_27        Bachelor's      m   \n",
+       "54702         4952         Operations  region_28        Bachelor's      m   \n",
+       "54772        34501         Operations  region_27  Master's & above      m   \n",
+       "54797        62450  Sales & Marketing  region_11        Bachelor's      m   \n",
+       "54799        68093        Procurement   region_2  Master's & above      f   \n",
+       "\n",
+       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
+       "66                  other                1   27                   3.0   \n",
+       "122              sourcing                1   37                   5.0   \n",
+       "138                 other                1   31                   4.0   \n",
+       "140                 other                2   30                   3.0   \n",
+       "200              sourcing                1   34                   4.0   \n",
+       "...                   ...              ...  ...                   ...   \n",
+       "54641               other                1   42                   2.0   \n",
+       "54702               other                1   28                   3.0   \n",
+       "54772               other                1   37                   5.0   \n",
+       "54797            sourcing                1   28                   5.0   \n",
+       "54799               other                1   50                   5.0   \n",
+       "\n",
+       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
+       "66                     1              1            1                  58   \n",
+       "122                    3              0            1                  92   \n",
+       "138                    4              1            1                  78   \n",
+       "140                    7              1            1                  76   \n",
+       "200                    5              1            1                  94   \n",
+       "...                  ...            ...          ...                 ...   \n",
+       "54641                  4              0            1                  47   \n",
+       "54702                  2              0            1                  62   \n",
+       "54772                  2              1            1                  57   \n",
+       "54797                  3              1            1                  47   \n",
+       "54799                  6              1            1                  67   \n",
+       "\n",
+       "       is_promoted  \n",
+       "66               1  \n",
+       "122              1  \n",
+       "138              0  \n",
+       "140              0  \n",
+       "200              1  \n",
+       "...            ...  \n",
+       "54641            0  \n",
+       "54702            0  \n",
+       "54772            0  \n",
+       "54797            0  \n",
+       "54799            0  \n",
+       "\n",
+       "[1253 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 313,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "award_won = train[train['awards_won?']==1]\n",
+    "award_won"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 644
+    },
+    "id": "zv8Lrii1ct86",
+    "outputId": "c2a1c259-26b0-4763-fee0-edccf37a8c39"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>employee_id</th>\n",
+       "      <th>department</th>\n",
+       "      <th>region</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>recruitment_channel</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>77981</td>\n",
+       "      <td>Finance</td>\n",
+       "      <td>region_22</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>58</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>38052</td>\n",
+       "      <td>Procurement</td>\n",
+       "      <td>region_34</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>37</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>92</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>200</th>\n",
+       "      <td>53630</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_23</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>f</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>62923</td>\n",
+       "      <td>Operations</td>\n",
+       "      <td>region_13</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>f</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>93</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>317</th>\n",
+       "      <td>22865</td>\n",
+       "      <td>Analytics</td>\n",
+       "      <td>region_20</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>40</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>83</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54017</th>\n",
+       "      <td>69878</td>\n",
+       "      <td>Analytics</td>\n",
+       "      <td>region_26</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>41</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>90</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54024</th>\n",
+       "      <td>3752</td>\n",
+       "      <td>HR</td>\n",
+       "      <td>region_2</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>60</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54146</th>\n",
+       "      <td>37302</td>\n",
+       "      <td>Operations</td>\n",
+       "      <td>region_2</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>37</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>79</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54503</th>\n",
+       "      <td>10882</td>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>region_25</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>sourcing</td>\n",
+       "      <td>2</td>\n",
+       "      <td>32</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>92</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54507</th>\n",
+       "      <td>43314</td>\n",
+       "      <td>Analytics</td>\n",
+       "      <td>region_25</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>m</td>\n",
+       "      <td>other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>552 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       employee_id         department     region         education gender  \\\n",
+       "66           77981            Finance  region_22        Bachelor's      m   \n",
+       "122          38052        Procurement  region_34  Master's & above      m   \n",
+       "200          53630  Sales & Marketing  region_23  Master's & above      f   \n",
+       "230          62923         Operations  region_13  Master's & above      f   \n",
+       "317          22865          Analytics  region_20  Master's & above      m   \n",
+       "...            ...                ...        ...               ...    ...   \n",
+       "54017        69878          Analytics  region_26  Master's & above      m   \n",
+       "54024         3752                 HR   region_2  Master's & above      m   \n",
+       "54146        37302         Operations   region_2        Bachelor's      m   \n",
+       "54503        10882  Sales & Marketing  region_25  Master's & above      m   \n",
+       "54507        43314          Analytics  region_25  Master's & above      m   \n",
+       "\n",
+       "      recruitment_channel  no_of_trainings  age  previous_year_rating  \\\n",
+       "66                  other                1   27                   3.0   \n",
+       "122              sourcing                1   37                   5.0   \n",
+       "200              sourcing                1   34                   4.0   \n",
+       "230                 other                1   38                   5.0   \n",
+       "317                 other                1   40                   5.0   \n",
+       "...                   ...              ...  ...                   ...   \n",
+       "54017            sourcing                1   41                   5.0   \n",
+       "54024            sourcing                1   60                   5.0   \n",
+       "54146               other                1   37                   5.0   \n",
+       "54503            sourcing                2   32                   4.0   \n",
+       "54507               other                1   35                   5.0   \n",
+       "\n",
+       "       length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
+       "66                     1              1            1                  58   \n",
+       "122                    3              0            1                  92   \n",
+       "200                    5              1            1                  94   \n",
+       "230                    2              1            1                  93   \n",
+       "317                    5              0            1                  83   \n",
+       "...                  ...            ...          ...                 ...   \n",
+       "54017                  5              1            1                  90   \n",
+       "54024                  8              1            1                  50   \n",
+       "54146                  7              0            1                  79   \n",
+       "54503                  5              1            1                  92   \n",
+       "54507                  3              0            1                  81   \n",
+       "\n",
+       "       is_promoted  \n",
+       "66               1  \n",
+       "122              1  \n",
+       "200              1  \n",
+       "230              1  \n",
+       "317              1  \n",
+       "...            ...  \n",
+       "54017            1  \n",
+       "54024            1  \n",
+       "54146            1  \n",
+       "54503            1  \n",
+       "54507            1  \n",
+       "\n",
+       "[552 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 314,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "award_won[award_won['is_promoted']==1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gU_pPk5hdRUO"
+   },
+   "source": [
+    "The Probability of getting promoted when award is won by the employee is = 552/1253 = 0.4405\n",
+    "\n",
+    "That means there is 44% chances of getting promotion if an award was won by an employee."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2cdW8hRtfuf6"
+   },
+   "source": [
+    "### What is the Average Training Score of those Employees who got Promotion?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {
+    "id": "IGzLisBMmVn0"
+   },
+   "outputs": [],
+   "source": [
+    "data = train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jVlgfHnIctzE",
+    "outputId": "847a5c3a-9407-4f7f-fb01-913111d3e3a7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Average Training Score for the Employees who got Promotion is 71\n"
+     ]
+    }
+   ],
+   "source": [
+    "promoted_employees = data[data['is_promoted'] == 1]\n",
+    "avg_training_score_promoted_emp = promoted_employees['avg_training_score'].mean()\n",
+    "print(\"The Average Training Score for the Employees who got Promotion is {0:.0f}\".format(avg_training_score_promoted_emp))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ZsNinGbPgcCL"
+   },
+   "source": [
+    "### What is the impact of gender on promotion?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5jTMHwdAggSz",
+    "outputId": "df0112eb-65b8-44cf-ce71-466477200849"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "m    37260\n",
+       "f    15749\n",
+       "Name: gender, dtype: int64"
+      ]
+     },
+     "execution_count": 317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lets check the gender gap in total employees\n",
+    "\n",
+    "data['gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DVHrbS1wggI7",
+    "outputId": "6b67464c-f1f5-48ed-e00a-d0f191c70e96"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "m    3119\n",
+       "f    1428\n",
+       "Name: gender, dtype: int64"
+      ]
+     },
+     "execution_count": 318,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lets check the Gender Gap in Promotion\n",
+    "\n",
+    "promoted_employees['gender'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Av1Jv7Fygf9A",
+    "outputId": "109e67f8-a887-4332-f031-e27c2b753971"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.08315149625935161 0.08993379107405591\n"
+     ]
+    }
+   ],
+   "source": [
+    "m_prom = 3201/38496\n",
+    "f_prom = 1467/16312\n",
+    "print(m_prom, f_prom) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GD7MVkb1gzKX"
+   },
+   "source": [
+    "### What is the Probability of Freshers getting Promoted?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zhy5WvKog4FJ",
+    "outputId": "dd1ff916-965f-4590-e5cb-247870ca3821"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    8057\n",
+       "1     743\n",
+       "Name: is_promoted, dtype: int64"
+      ]
+     },
+     "execution_count": 320,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lets consider the employees who have worked for less than equal to two years\n",
+    "\n",
+    "freshers = data[(data['length_of_service'] <= 2) & (data['age'] <= 30)]\n",
+    "freshers['is_promoted'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dkJboWxJg3-_",
+    "outputId": "5e1159af-6d93-45ab-cd6b-e3783bd9989c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability of a Fresher being Promoted is 8.44%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets check the Percentage also\n",
+    "\n",
+    "prob = 743/(8057+743)\n",
+    "print(\"Probability of a Fresher being Promoted is {0:.2f}%\".format(prob*100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LERiuw5mI2c8"
+   },
+   "source": [
+    "## Feature Engineering\n",
+    "\n",
+    "Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.\n",
+    "\n",
+    "* There are mutliple ways of performing feature engineering.\n",
+    "* So many people in the Industry consider it the most important step to improve the Model Performance.\n",
+    "* We should always understand the columns well to make some new features using the old existing features.\n",
+    "* Let's discuss the ways how we can perform feature engineering\n",
+    "    * We can perform Feature Engineering by Removing Unnecassary Columns\n",
+    "    * We can do it by Extracting Features from the Date and Time Features.\n",
+    "    * We can do it by Extracting Features from the Categorcial Features.\n",
+    "    * We can do it by Binnning the Numerical and Categorical Features.\n",
+    "    * We can do it by Aggregating Multiple Features together by using simple Arithmetic operations\n",
+    "    \n",
+    "* Here, we are only going to perform Feature Engineering by Aggregating some features together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "sues7C7iKd8-",
+    "outputId": "6ee7ce9f-aaeb-48a1-9f83-e957fd125707"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    34147\n",
+       "1    18862\n",
+       "Name: KPIs_met >80%, dtype: int64"
+      ]
+     },
+     "execution_count": 322,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['KPIs_met >80%'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DD-RZqRZKjir",
+    "outputId": "547c5541-260b-46bf-bbfb-2b82612ed13f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0    22032\n",
+       "5.0    11359\n",
+       "4.0     9517\n",
+       "1.0     6020\n",
+       "2.0     4081\n",
+       "Name: previous_year_rating, dtype: int64"
+      ]
+     },
+     "execution_count": 323,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['previous_year_rating'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "qL36DV6BLKdH",
+    "outputId": "ac2c2923-0a5c-4637-d381-a4f3bd93e74a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50    2629\n",
+       "49    2602\n",
+       "48    2340\n",
+       "51    2274\n",
+       "60    2080\n",
+       "      ... \n",
+       "98      35\n",
+       "99      33\n",
+       "41      25\n",
+       "40       4\n",
+       "39       2\n",
+       "Name: avg_training_score, Length: 61, dtype: int64"
+      ]
+     },
+     "execution_count": 324,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['avg_training_score'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-vgJ54xGLLP8",
+    "outputId": "db16401c-22f0-4d78-a29c-3154c47dc389"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1     42806\n",
+       "2      7808\n",
+       "3      1744\n",
+       "4       459\n",
+       "5       124\n",
+       "6        42\n",
+       "7        11\n",
+       "8         5\n",
+       "9         5\n",
+       "10        5\n",
+       "Name: no_of_trainings, dtype: int64"
+      ]
+     },
+     "execution_count": 325,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['no_of_trainings'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {
+    "id": "YBoLaB3DHx93"
+   },
+   "outputs": [],
+   "source": [
+    "# lets create some extra features from existing features to improve our Model\n",
+    "\n",
+    "# creating a Metric of Sum\n",
+    "train['sum_metric'] = train['awards_won?']+train['KPIs_met >80%'] + train['previous_year_rating']\n",
+    "test['sum_metric'] = test['awards_won?']+test['KPIs_met >80%'] + test['previous_year_rating']\n",
+    "\n",
+    "# creating a total score column\n",
+    "train['total_score'] = train['avg_training_score'] * train['no_of_trainings']\n",
+    "test['total_score'] = test['avg_training_score'] * test['no_of_trainings']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JWdCV-fWL1Zz",
+    "outputId": "68660d64-f9e3-4837-e409-5b4f049035eb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['department', 'education', 'gender', 'no_of_trainings', 'age',\n",
+       "       'previous_year_rating', 'length_of_service', 'KPIs_met >80%',\n",
+       "       'awards_won?', 'avg_training_score', 'is_promoted', 'sum_metric',\n",
+       "       'total_score'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 327,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lets remove some of the columns which are not very useful for predicting the promotion.\n",
+    "\n",
+    "# we already know that the recruitment channel is very least related to promotion of an employee, so lets remove this column\n",
+    "# even the region seems to contribute very less, when it comes to promotion, so lets remove it too.\n",
+    "# also the employee id is not useful so lets remove it.\n",
+    "\n",
+    "train = train.drop(['recruitment_channel', 'region', 'employee_id'], axis = 1)\n",
+    "test = test.drop(['recruitment_channel', 'region', 'employee_id'], axis = 1)\n",
+    "\n",
+    "# lets check the columns in train and test data set after feature engineering\n",
+    "train.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 208
+    },
+    "id": "33M5FA7LL7ue",
+    "outputId": "17d9d19e-cb70-413f-837b-4b5554177769"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>department</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "      <th>sum_metric</th>\n",
+       "      <th>total_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>31860</th>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>58</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51374</th>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>58</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              department   education gender  no_of_trainings  age  \\\n",
+       "31860  Sales & Marketing  Bachelor's      m                1   27   \n",
+       "51374  Sales & Marketing  Bachelor's      m                1   31   \n",
+       "\n",
+       "       previous_year_rating  length_of_service  KPIs_met >80%  awards_won?  \\\n",
+       "31860                   1.0                  2              0            0   \n",
+       "51374                   1.0                  5              0            0   \n",
+       "\n",
+       "       avg_training_score  is_promoted  sum_metric  total_score  \n",
+       "31860                  58            1         1.0           58  \n",
+       "51374                  58            1         1.0           58  "
+      ]
+     },
+     "execution_count": 328,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "lets check the no. of employee who did not get an award, did not acheive 80+ KPI, previous_year_rating as 1\n",
+    "and avg_training score is less than 40\n",
+    "but, still got promotion.\n",
+    "''' \n",
+    "\n",
+    "train[(train['KPIs_met >80%'] == 0) & (train['previous_year_rating'] == 1.0) & \n",
+    "      (train['awards_won?'] == 0) & (train['avg_training_score'] < 60) & (train['is_promoted'] == 1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VEGSb20CMD0_",
+    "outputId": "2a59b083-5578-4626-8fe3-077669276c39"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before Deleting the above two rows : (53009, 13)\n",
+      "After Deletion of the above two rows : (53007, 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets remove the above two rows as they have a huge negative effect on our training data\n",
+    "\n",
+    "# lets check shape of the train data before deleting two rows\n",
+    "print(\"Before Deleting the above two rows :\", train.shape)\n",
+    "\n",
+    "train = train.drop(train[(train['KPIs_met >80%'] == 0) & (train['previous_year_rating'] == 1.0) & \n",
+    "      (train['awards_won?'] == 0) & (train['avg_training_score'] < 60) & (train['is_promoted'] == 1)].index)\n",
+    "\n",
+    "# lets check the shape of the train data after deleting the two rows\n",
+    "print(\"After Deletion of the above two rows :\", train.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zfwVT0hzMbRV"
+   },
+   "source": [
+    "## Dealing with Categorical Columns\n",
+    "\n",
+    "Categorical variables are known to hide and mask lots of interesting information in a data set. It’s crucial to learn the methods of dealing with such variables. If you won’t, many a times, you’d miss out on finding the most important variables in a model. It has happened with me. Initially, I used to focus more on numerical variables. Hence, never actually got an accurate model. But, later I discovered my flaws and learnt the art of dealing with such variables.\n",
+    "\n",
+    "* There are various ways to encode categorical columns into Numerical columns\n",
+    "* This is an Essential Step, as we Machine Learning Models only works with Numerical Values.\n",
+    "* Here, we are going to use Business Logic to encode the education column\n",
+    "* Then we will use the Label Encoder, to Department and Gender Columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 330,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "w_3kl0JSMOrJ",
+    "outputId": "72950579-5b99-482d-ce34-d4c890f86b86"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>department</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>Master's &amp; above</td>\n",
+       "      <td>f</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Operations</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Sales &amp; Marketing</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Technology</td>\n",
+       "      <td>Bachelor's</td>\n",
+       "      <td>m</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          department         education gender\n",
+       "0  Sales & Marketing  Master's & above      f\n",
+       "1         Operations        Bachelor's      m\n",
+       "2  Sales & Marketing        Bachelor's      m\n",
+       "3  Sales & Marketing        Bachelor's      m\n",
+       "4         Technology        Bachelor's      m"
+      ]
+     },
+     "execution_count": 330,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## Lets check the categorical columns present in the data\n",
+    "train.select_dtypes('object').head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 331,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "qAIBc8ewMjF8",
+    "outputId": "15fbb0e8-8b63-47be-dfa8-5df150408f80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Bachelor's          38192\n",
+       "Master's & above    14010\n",
+       "Below Secondary       805\n",
+       "Name: education, dtype: int64"
+      ]
+     },
+     "execution_count": 331,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lets check the value counts for the education column\n",
+    "train['education'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 332,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0ur3VhvfMlZF",
+    "outputId": "7aa34817-f7e2-475e-cb02-4b2264ddf68c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index([], dtype='object')\n",
+      "Index([], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets start encoding these categorical columns to convert them into numerical columns\n",
+    "\n",
+    "# lets encode the education in their degree of importance \n",
+    "train['education'] = train['education'].replace((\"Master's & above\", \"Bachelor's\", \"Below Secondary\"),\n",
+    "                                                (3, 2, 1))\n",
+    "test['education'] = test['education'].replace((\"Master's & above\", \"Bachelor's\", \"Below Secondary\"),\n",
+    "                                                (3, 2, 1))\n",
+    "\n",
+    "# lets use Label Encoding for Gender and Department to convert them into Numerical\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "train['department'] = le.fit_transform(train['department'])\n",
+    "test['department'] = le.fit_transform(test['department'])\n",
+    "train['gender'] = le.fit_transform(train['gender'])\n",
+    "test['gender'] = le.fit_transform(test['gender'])\n",
+    "\n",
+    "# lets check whether we still have any categorical columns left after encoding\n",
+    "print(train.select_dtypes('object').columns)\n",
+    "print(test.select_dtypes('object').columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "A4-2EVf8NJqS"
+   },
+   "source": [
+    "In the above case, since the 'department' and 'gender' columns have a relatively small number of categories, using LabelEncoder is a simpler and more efficient way to convert the categorical data into numerical data. If the number of categories in a column is large, one hot encoding might be a better choice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 187
+    },
+    "id": "_15fLQ_-Mtqa",
+    "outputId": "5f068e5b-235c-42e4-825e-328e4d85d9db"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>department</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "      <th>sum_metric</th>\n",
+       "      <th>total_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>49</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   department  education  gender  no_of_trainings  age  previous_year_rating  \\\n",
+       "0           7          3       0                1   35                   5.0   \n",
+       "1           4          2       1                1   30                   5.0   \n",
+       "2           7          2       1                1   34                   3.0   \n",
+       "\n",
+       "   length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
+       "0                  8              1            0                  49   \n",
+       "1                  4              0            0                  60   \n",
+       "2                  7              0            0                  50   \n",
+       "\n",
+       "   is_promoted  sum_metric  total_score  \n",
+       "0            0         6.0           49  \n",
+       "1            0         5.0           60  \n",
+       "2            0         3.0           50  "
+      ]
+     },
+     "execution_count": 333,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# lets check the data after encoding\n",
+    "train.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8smpApdCNRuH"
+   },
+   "source": [
+    "## Splitting the Data\n",
+    "\n",
+    "This is one of the most Important step to perform Machine Learning Prediction on a Dataset,\n",
+    "We have to separate the Target and Independent Columns.\n",
+    "* We store the Target Variable in y, and then we store the rest of the columns in x, by deleting the target column from the data\n",
+    "* Also, we are changing the name of test dataset to x_test for ease of understanding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 334,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zO1xf974NOZD",
+    "outputId": "3d1efbb8-90f2-4800-f554-73843eacd7b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the x : (53007, 12)\n",
+      "Shape of the y : (53007,)\n",
+      "Shape of the x Test : (23490, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets split the target data from the train data\n",
+    "\n",
+    "y = train['is_promoted']\n",
+    "x = train.drop(['is_promoted'], axis = 1)\n",
+    "x_test = test\n",
+    "\n",
+    "# lets print the shapes of these newly formed data sets\n",
+    "print(\"Shape of the x :\", x.shape)\n",
+    "print(\"Shape of the y :\", y.shape)\n",
+    "print(\"Shape of the x Test :\", x_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "aGO_BTN4Nf-U"
+   },
+   "source": [
+    "## Resampling\n",
+    "\n",
+    "Resampling is the method that consists of drawing repeated samples from the original data samples. The method of Resampling is a nonparametric method of statistical inference.\n",
+    "\n",
+    "* Earlier, in this Problem we noticed that the Target column is Highly Imbalanced, we need to balance the data by using some Statistical Methods.\n",
+    "* There are many Statistical Methods we can use for Resampling the Data such as:\n",
+    "    * Over Samping\n",
+    "    * Cluster based Sampling\n",
+    "    * Under Sampling.\n",
+    "    \n",
+    "Oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set. These terms are used both in statistical sampling, survey design methodology and in machine learning. Oversampling and undersampling are opposite and roughly equivalent techniques\n",
+    "    \n",
+    "* We are going to use Over Sampling. \n",
+    "* We will not use Under Sampling to avoid data loss."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xyyc3HnfNWQ_",
+    "outputId": "54f6d7e3-8f1a-4e3a-8f6a-5a16a98da023"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    48462\n",
+       "1     4545\n",
+       "Name: is_promoted, dtype: int64"
+      ]
+     },
+     "execution_count": 335,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['is_promoted'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_KERkbTJNoUA"
+   },
+   "source": [
+    "There is quite a large difference between our both the classes so we have to do oversampling of our minority class in order to create a good model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 336,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "suLE1cCVNnG7",
+    "outputId": "6ad419e7-4621-4f92-a50f-ae458305abc5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(96924, 12)\n",
+      "(96924,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Here We are going to use Over Sampling Technique to resample the data.\n",
+    "# lets import the SMOTE algorithm to do the same.\n",
+    "\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "\n",
+    "x_resample, y_resample  = SMOTE().fit_resample(x, y.values.ravel())\n",
+    "\n",
+    "# lets print the shape of x and y after resampling it\n",
+    "print(x_resample.shape)\n",
+    "print(y_resample.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 337,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4TXhSYBANz38",
+    "outputId": "24966d61-d10c-4944-a46f-efe63546d089"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before Resampling :\n",
+      "0    48462\n",
+      "1     4545\n",
+      "Name: is_promoted, dtype: int64\n",
+      "After Resampling :\n",
+      "0    48462\n",
+      "1    48462\n",
+      "Name: 0, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets also check the value counts of our target variable4\n",
+    "\n",
+    "print(\"Before Resampling :\")\n",
+    "print(y.value_counts())\n",
+    "\n",
+    "print(\"After Resampling :\")\n",
+    "y_resample = pd.DataFrame(y_resample)\n",
+    "print(y_resample[0].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 338,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-zl044ocN_S2",
+    "outputId": "50de4e23-2edc-4ad2-e975-0b1b42fc63c3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the x Train : (77539, 12)\n",
+      "Shape of the y Train : (77539, 1)\n",
+      "Shape of the x Valid : (19385, 12)\n",
+      "Shape of the y Valid : (19385, 1)\n",
+      "Shape of the x Test : (23490, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lets create a validation set from the training data so that we can check whether the model that we have created is good enough\n",
+    "# lets import the train_test_split library from sklearn to do that\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "x_train, x_valid, y_train, y_valid = train_test_split(x_resample, y_resample, test_size = 0.2, random_state = 0)\n",
+    "\n",
+    "# lets print the shapes again \n",
+    "print(\"Shape of the x Train :\", x_train.shape)\n",
+    "print(\"Shape of the y Train :\", y_train.shape)\n",
+    "print(\"Shape of the x Valid :\", x_valid.shape)\n",
+    "print(\"Shape of the y Valid :\", y_valid.shape)\n",
+    "print(\"Shape of the x Test :\", x_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zxNiwALSOPQ8"
+   },
+   "source": [
+    "## Feature Scaling\n",
+    "\n",
+    "Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "metadata": {
+    "id": "lMUG3P01OILu"
+   },
+   "outputs": [],
+   "source": [
+    "# It is very import to scale all the features of the dataset into the same scale\n",
+    "# Here, we are going to use the standardization method, which is very commonly used.\n",
+    "\n",
+    "# lets import the standard scaler library from sklearn to do that\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "sc = StandardScaler()\n",
+    "x_train = sc.fit_transform(x_train)\n",
+    "x_valid = sc.transform(x_valid)\n",
+    "x_test = sc.transform(x_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zPEDc1Y6OX6H"
+   },
+   "source": [
+    "##  Machine Learning Predictive Modelling\n",
+    "\n",
+    "Predictive modeling is a process that uses data and statistics to predict outcomes with data models. These models can be used to predict anything from sports outcomes and TV ratings to technological advances and corporate earnings. Predictive modeling is also often referred to as: Predictive analytics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4A0xWsSpObKQ"
+   },
+   "source": [
+    "## Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 340,
+   "metadata": {
+    "id": "dAKuASiAOS4g"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 341,
+   "metadata": {
+    "id": "gnLlkjQrOS6-"
+   },
+   "outputs": [],
+   "source": [
+    "lg = LogisticRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "5eCWzdy6OS9s",
+    "outputId": "d19a1940-3b45-4a1c-c1d1-f93d993ec760"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 342,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lg.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {
+    "id": "7IoF8UcOOTAW"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_lg = lg.predict(x_valid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 344,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8lW8AciSOzGP",
+    "outputId": "2bd63719-eb10-4a8f-e0ad-854bddc109e2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 0, ..., 0, 1, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 344,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred_lg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 277
+    },
+    "id": "CyN3wH3lO4GW",
+    "outputId": "cadc2ee5-0599-40b3-88aa-27800d07c692"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy : 0.7617070119552741\n",
+      "Testing Accuracy : 0.7643022955893732\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 216x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, classification_report\n",
+    "\n",
+    "print(\"Training Accuracy :\", lg.score(x_train, y_train))\n",
+    "print(\"Testing Accuracy :\", lg.score(x_valid, y_valid))\n",
+    "\n",
+    "cm = confusion_matrix(y_valid, y_pred_lg)\n",
+    "plt.rcParams['figure.figsize'] = (3, 3)\n",
+    "sns.heatmap(cm, annot = True, cmap = 'Wistia', fmt = '.8g')\n",
+    "plt.xlabel('Predicted Values')\n",
+    "plt.ylabel('Actual Values')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HP8PKV3tQWAz",
+    "outputId": "988f2217-f1ae-4d0d-b4f2-eeee2f3ac3b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.76      0.77      0.77      9658\n",
+      "           1       0.77      0.75      0.76      9727\n",
+      "\n",
+      "    accuracy                           0.76     19385\n",
+      "   macro avg       0.76      0.76      0.76     19385\n",
+      "weighted avg       0.76      0.76      0.76     19385\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "print(classification_report(y_valid, y_pred_lg))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {
+    "id": "n5wOsY2FZVNW"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_lg = list(y_pred_lg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "y0w2PdxQZX3U",
+    "outputId": "2693288e-2c31-48e9-cc5e-16f69521cb07"
+   },
+   "outputs": [
+    {
+     "data": {
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+      ]
+     },
+     "execution_count": 348,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_valid_lg = []\n",
+    "for i in y_valid[0]:\n",
+    "  y_valid_lg.append(i)\n",
+    "y_valid_lg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 349,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 423
+    },
+    "id": "BUwYQcFwZX6Z",
+    "outputId": "05676c7e-84fb-4a80-9f3e-abd68173d132"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual Values</th>\n",
+       "      <th>Predicted Values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "    </tr>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19380</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19381</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19382</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19383</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19384</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19385 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Actual Values  Predicted Values\n",
+       "0                  1                 1\n",
+       "1                  1                 1\n",
+       "2                  1                 0\n",
+       "3                  0                 0\n",
+       "4                  0                 0\n",
+       "...              ...               ...\n",
+       "19380              1                 1\n",
+       "19381              1                 1\n",
+       "19382              0                 0\n",
+       "19383              1                 1\n",
+       "19384              1                 1\n",
+       "\n",
+       "[19385 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 349,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_lg = pd.DataFrame({'Actual Values': y_valid_lg, 'Predicted Values': y_pred_lg})\n",
+    "\n",
+    "compare_lg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 423
+    },
+    "id": "ipGc1MZ-ZX9U",
+    "outputId": "a713afca-10e3-49e7-c089-948075a016c0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual Values</th>\n",
+       "      <th>Predicted Values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19360</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19366</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19367</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19375</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19376</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4569 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Actual Values  Predicted Values\n",
+       "2                  1                 0\n",
+       "7                  0                 1\n",
+       "13                 0                 1\n",
+       "20                 0                 1\n",
+       "23                 0                 1\n",
+       "...              ...               ...\n",
+       "19360              0                 1\n",
+       "19366              1                 0\n",
+       "19367              0                 1\n",
+       "19375              0                 1\n",
+       "19376              0                 1\n",
+       "\n",
+       "[4569 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 350,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_lg[compare_lg['Actual Values'] != compare_lg['Predicted Values']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "V5IC40_qPtQW"
+   },
+   "source": [
+    "## KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 351,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "2z24CBILPNCL",
+    "outputId": "1f4e961d-8ede-4f7d-a694-d579efdb1838"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 351,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
+    "knn.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 352,
+   "metadata": {
+    "id": "2Dyfr1BEP_rz"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_knn = knn.predict(x_valid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "R01FaGtZQIJs",
+    "outputId": "87ade259-095a-403b-f01a-0d138755144f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, ..., 0, 1, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 353,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred_knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 277
+    },
+    "id": "NC2DP2b3QOmS",
+    "outputId": "c36339c7-6e13-491f-a8a0-a63fd7068549"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy : 0.9254439701311598\n",
+      "Testing Accuracy : 0.8937322672169203\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 216x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Training Accuracy :\", knn.score(x_train, y_train))\n",
+    "print(\"Testing Accuracy :\", knn.score(x_valid, y_valid))\n",
+    "\n",
+    "cm = confusion_matrix(y_valid, y_pred_knn)\n",
+    "plt.rcParams['figure.figsize'] = (3, 3)\n",
+    "sns.heatmap(cm, annot = True, cmap = 'Wistia', fmt = '.8g')\n",
+    "plt.xlabel('Predicted Values')\n",
+    "plt.ylabel('Actual Values')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AwHceIK8Qyau",
+    "outputId": "eac2c3e3-4d0a-44ba-c1b4-d5daa3096f90"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.92      0.86      0.89      9658\n",
+      "           1       0.87      0.93      0.90      9727\n",
+      "\n",
+      "    accuracy                           0.89     19385\n",
+      "   macro avg       0.90      0.89      0.89     19385\n",
+      "weighted avg       0.90      0.89      0.89     19385\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "print(classification_report(y_valid, y_pred_knn))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 356,
+   "metadata": {
+    "id": "6XhXOSWAYI7a"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_knn = list(y_pred_knn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mWw34sBSYI-O",
+    "outputId": "33ac6c52-95b6-4ec6-ffb4-4ae8a7f56544"
+   },
+   "outputs": [
+    {
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+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 357,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_valid_knn = []\n",
+    "for i in y_valid[0]:\n",
+    "  y_valid_knn.append(i)\n",
+    "y_valid_knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 358,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 423
+    },
+    "id": "md7qFbHIYJBJ",
+    "outputId": "2661fa43-63e5-4d99-a49f-ab5185544d48"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual Values</th>\n",
+       "      <th>Predicted Values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
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+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19380</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19381</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19382</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19383</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19384</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19385 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Actual Values  Predicted Values\n",
+       "0                  1                 1\n",
+       "1                  1                 1\n",
+       "2                  1                 1\n",
+       "3                  0                 0\n",
+       "4                  0                 0\n",
+       "...              ...               ...\n",
+       "19380              1                 1\n",
+       "19381              1                 1\n",
+       "19382              0                 0\n",
+       "19383              1                 1\n",
+       "19384              1                 1\n",
+       "\n",
+       "[19385 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 358,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_knn = pd.DataFrame({'Actual Values': y_valid_knn, 'Predicted Values': y_pred_knn})\n",
+    "\n",
+    "compare_knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 359,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 423
+    },
+    "id": "oH68cE2SZFrt",
+    "outputId": "ccc9ea88-38d0-4983-d4a1-be178dfc3d75"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual Values</th>\n",
+       "      <th>Predicted Values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19320</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19341</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19353</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19375</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19376</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2060 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Actual Values  Predicted Values\n",
+       "7                  0                 1\n",
+       "9                  1                 0\n",
+       "13                 0                 1\n",
+       "19                 1                 0\n",
+       "20                 0                 1\n",
+       "...              ...               ...\n",
+       "19320              0                 1\n",
+       "19341              1                 0\n",
+       "19353              0                 1\n",
+       "19375              0                 1\n",
+       "19376              0                 1\n",
+       "\n",
+       "[2060 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 359,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_knn[compare_knn['Actual Values'] != compare_knn['Predicted Values']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qQUuNH71VDCL"
+   },
+   "source": [
+    "## Random Forest Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 360,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 74
+    },
+    "id": "dnyooP7gVBVZ",
+    "outputId": "429279d4-e60d-4aa1-fa5c-e6045a25c13d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(random_state=0)"
+      ]
+     },
+     "execution_count": 360,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "rf = RandomForestClassifier(random_state = 0)\n",
+    "rf.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 361,
+   "metadata": {
+    "id": "xQGWGDk8VUtL"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_rf = rf.predict(x_valid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 362,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "YOPdihaYVZO8",
+    "outputId": "953d08bf-be44-49d6-a083-422fab2ec415"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, ..., 0, 1, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 362,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred_rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 363,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 277
+    },
+    "id": "_um_Hj2BVagp",
+    "outputId": "103344f7-95f4-445c-c91b-8652026de4c4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy : 0.9902629644436993\n",
+      "Testing Accuracy : 0.9496517926231622\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 216x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Training Accuracy :\", rf.score(x_train, y_train))\n",
+    "print(\"Testing Accuracy :\", rf.score(x_valid, y_valid))\n",
+    "\n",
+    "cm = confusion_matrix(y_valid, y_pred_rf)\n",
+    "plt.rcParams['figure.figsize'] = (3, 3)\n",
+    "sns.heatmap(cm, annot = True, cmap = 'Wistia', fmt = '.8g')\n",
+    "plt.xlabel('Predicted Values')\n",
+    "plt.ylabel('Actual Values')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 364,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "yvF_OtmyVhwD",
+    "outputId": "a1583a4b-edb8-4df3-d397-fa0c87971aeb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.96      0.94      0.95      9658\n",
+      "           1       0.94      0.96      0.95      9727\n",
+      "\n",
+      "    accuracy                           0.95     19385\n",
+      "   macro avg       0.95      0.95      0.95     19385\n",
+      "weighted avg       0.95      0.95      0.95     19385\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_valid, y_pred_rf))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 365,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "7ELpqSkkVofb",
+    "outputId": "95a4a2b3-4607-4d95-d320-4b85c3f9ebca"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, ..., 0, 1, 1], dtype=int64)"
+      ]
+     },
+     "execution_count": 365,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred_rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 366,
+   "metadata": {
+    "id": "LFHQB3j3W1cw"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred_rf = list(y_pred_rf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jrUwSH6AWIiQ",
+    "outputId": "1fc0b099-0bca-40d0-e27d-267e94737a60"
+   },
+   "outputs": [
+    {
+     "data": {
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+      ]
+     },
+     "execution_count": 367,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_valid_rf = []\n",
+    "for i in y_valid[0]:\n",
+    "  y_valid_rf.append(i)\n",
+    "y_valid_rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 423
+    },
+    "id": "TbTzzfm-WKbr",
+    "outputId": "aa939d0c-5ec7-4bf6-fee1-5d7e26dcf6f4"
+   },
+   "outputs": [
+    {
+     "data": {
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+       "<div>\n",
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th>Predicted Values</th>\n",
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19385 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Actual Values  Predicted Values\n",
+       "0                  1                 1\n",
+       "1                  1                 1\n",
+       "2                  1                 1\n",
+       "3                  0                 0\n",
+       "4                  0                 0\n",
+       "...              ...               ...\n",
+       "19380              1                 1\n",
+       "19381              1                 1\n",
+       "19382              0                 0\n",
+       "19383              1                 1\n",
+       "19384              1                 1\n",
+       "\n",
+       "[19385 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 368,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_rf = pd.DataFrame({'Actual Values': y_valid_rf, 'Predicted Values': y_pred_rf})\n",
+    "\n",
+    "compare_rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 423
+    },
+    "id": "WNhu7lArXQzu",
+    "outputId": "2b637706-3783-466f-95e8-003d386ca0c0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "      <th>19364</th>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>976 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Actual Values  Predicted Values\n",
+       "13                 0                 1\n",
+       "20                 0                 1\n",
+       "50                 1                 0\n",
+       "57                 0                 1\n",
+       "123                0                 1\n",
+       "...              ...               ...\n",
+       "19316              1                 0\n",
+       "19317              0                 1\n",
+       "19341              1                 0\n",
+       "19345              0                 1\n",
+       "19364              1                 0\n",
+       "\n",
+       "[976 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 369,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "compare_rf[compare_rf['Actual Values'] != compare_rf['Predicted Values']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "A3z1A39Of-a0"
+   },
+   "source": [
+    "## Predicting on unknown data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "94fECOIDhGzX"
+   },
+   "source": [
+    "Now we will perform Real Time Predictions using the Model which we have created.\n",
+    "\n",
+    "So, lets get started, \n",
+    "* First, we we will check the descriptive summary of the data again, so that we can analyze the columns and values which we can provide to the Model as Input and expect the Model to return Output whether the Employee should get a promotion or not.\n",
+    "\n",
+    "* Then we will define the value for which we want the predction, and then finally we will predict the values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 381
+    },
+    "id": "McS2kot8hWYS",
+    "outputId": "2ec69127-2c97-4252-b7d1-32c84303b66a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>department</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "      <th>sum_metric</th>\n",
+       "      <th>total_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>53007.000000</td>\n",
+       "      <td>53007.000000</td>\n",
+       "      <td>53007.000000</td>\n",
+       "      <td>53007.000000</td>\n",
+       "      <td>53007.000000</td>\n",
+       "      <td>53007.000000</td>\n",
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+       "      <td>53007.000000</td>\n",
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+       "      <td>53007.000000</td>\n",
+       "      <td>53007.000000</td>\n",
+       "      <td>53007.000000</td>\n",
+       "      <td>53007.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.958685</td>\n",
+       "      <td>2.249118</td>\n",
+       "      <td>0.702888</td>\n",
+       "      <td>1.255910</td>\n",
+       "      <td>34.254514</td>\n",
+       "      <td>3.304073</td>\n",
+       "      <td>5.382516</td>\n",
+       "      <td>0.355840</td>\n",
+       "      <td>0.023638</td>\n",
+       "      <td>63.427887</td>\n",
+       "      <td>0.085743</td>\n",
+       "      <td>3.683551</td>\n",
+       "      <td>80.015224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.516142</td>\n",
+       "      <td>0.466300</td>\n",
+       "      <td>0.456990</td>\n",
+       "      <td>0.611854</td>\n",
+       "      <td>7.120903</td>\n",
+       "      <td>1.214660</td>\n",
+       "      <td>3.352680</td>\n",
+       "      <td>0.478772</td>\n",
+       "      <td>0.151921</td>\n",
+       "      <td>13.404032</td>\n",
+       "      <td>0.279987</td>\n",
+       "      <td>1.463719</td>\n",
+       "      <td>44.121742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>53.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>64.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>710.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         department     education        gender  no_of_trainings  \\\n",
+       "count  53007.000000  53007.000000  53007.000000     53007.000000   \n",
+       "mean       4.958685      2.249118      0.702888         1.255910   \n",
+       "std        2.516142      0.466300      0.456990         0.611854   \n",
+       "min        0.000000      1.000000      0.000000         1.000000   \n",
+       "25%        4.000000      2.000000      0.000000         1.000000   \n",
+       "50%        5.000000      2.000000      1.000000         1.000000   \n",
+       "75%        7.000000      3.000000      1.000000         1.000000   \n",
+       "max        8.000000      3.000000      1.000000        10.000000   \n",
+       "\n",
+       "                age  previous_year_rating  length_of_service  KPIs_met >80%  \\\n",
+       "count  53007.000000          53007.000000       53007.000000   53007.000000   \n",
+       "mean      34.254514              3.304073           5.382516       0.355840   \n",
+       "std        7.120903              1.214660           3.352680       0.478772   \n",
+       "min       20.000000              1.000000           1.000000       0.000000   \n",
+       "25%       29.000000              3.000000           3.000000       0.000000   \n",
+       "50%       33.000000              3.000000           5.000000       0.000000   \n",
+       "75%       38.000000              4.000000           7.000000       1.000000   \n",
+       "max       60.000000              5.000000          16.000000       1.000000   \n",
+       "\n",
+       "        awards_won?  avg_training_score   is_promoted    sum_metric  \\\n",
+       "count  53007.000000        53007.000000  53007.000000  53007.000000   \n",
+       "mean       0.023638           63.427887      0.085743      3.683551   \n",
+       "std        0.151921           13.404032      0.279987      1.463719   \n",
+       "min        0.000000           39.000000      0.000000      1.000000   \n",
+       "25%        0.000000           51.000000      0.000000      3.000000   \n",
+       "50%        0.000000           60.000000      0.000000      4.000000   \n",
+       "75%        0.000000           76.000000      0.000000      5.000000   \n",
+       "max        1.000000           99.000000      1.000000      7.000000   \n",
+       "\n",
+       "        total_score  \n",
+       "count  53007.000000  \n",
+       "mean      80.015224  \n",
+       "std       44.121742  \n",
+       "min       39.000000  \n",
+       "25%       53.000000  \n",
+       "50%       64.000000  \n",
+       "75%       84.000000  \n",
+       "max      710.000000  "
+      ]
+     },
+     "execution_count": 370,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 386,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0    22032\n",
+       "5.0    11359\n",
+       "4.0     9517\n",
+       "1.0     6018\n",
+       "2.0     4081\n",
+       "Name: previous_year_rating, dtype: int64"
+      ]
+     },
+     "execution_count": 386,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['previous_year_rating'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 390,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>department</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "      <th>sum_metric</th>\n",
+       "      <th>total_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>133</th>\n",
+       "      <td>0</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>89</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54624</th>\n",
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+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24</td>\n",
+       "      <td>3.0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54730</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>58</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54757</th>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>54</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54792</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>59</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54796</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1133 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       department  education  gender  no_of_trainings  age  \\\n",
+       "67              7          2       1                1   27   \n",
+       "79              7          2       1                1   30   \n",
+       "84              3          2       1                1   29   \n",
+       "116             8          2       1                1   27   \n",
+       "133             0          2       1                1   32   \n",
+       "...           ...        ...     ...              ...  ...   \n",
+       "54624           1          2       1                1   24   \n",
+       "54730           7          2       1                1   29   \n",
+       "54757           8          3       1                1   54   \n",
+       "54792           7          2       1                1   59   \n",
+       "54796           7          3       1                1   34   \n",
+       "\n",
+       "       previous_year_rating  length_of_service  KPIs_met >80%  awards_won?  \\\n",
+       "67                      3.0                  1              0            0   \n",
+       "79                      3.0                  3              0            0   \n",
+       "84                      3.0                  1              0            0   \n",
+       "116                     5.0                  2              0            0   \n",
+       "133                     3.0                  8              0            0   \n",
+       "...                     ...                ...            ...          ...   \n",
+       "54624                   3.0                  1              0            0   \n",
+       "54730                   5.0                  4              0            0   \n",
+       "54757                   4.0                  7              0            0   \n",
+       "54792                   3.0                 11              0            0   \n",
+       "54796                   3.0                  7              0            0   \n",
+       "\n",
+       "       avg_training_score  is_promoted  sum_metric  total_score  \n",
+       "67                     61            1         3.0           61  \n",
+       "79                     58            1         3.0           58  \n",
+       "84                     65            1         3.0           65  \n",
+       "116                    86            1         5.0           86  \n",
+       "133                    89            1         3.0           89  \n",
+       "...                   ...          ...         ...          ...  \n",
+       "54624                  71            1         3.0           71  \n",
+       "54730                  58            1         5.0           58  \n",
+       "54757                  81            1         4.0           81  \n",
+       "54792                  65            1         3.0           65  \n",
+       "54796                  60            1         3.0           60  \n",
+       "\n",
+       "[1133 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 390,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train[(train['awards_won?']!=1) & (train['is_promoted']==1) & (train['KPIs_met >80%']!=1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 372,
+   "metadata": {
+    "id": "gBjIxdERgB4g"
+   },
+   "outputs": [],
+   "source": [
+    "new_data = np.array([(2, #department code\n",
+    "                      3, #masters degree\n",
+    "                      1, #male\n",
+    "                      1, #1 training\n",
+    "                      30, #30 years old\n",
+    "                      5, #previous year rating\n",
+    "                      10, #length of service\n",
+    "                      1, #KPIs met >80%\n",
+    "                      1, #awards won\n",
+    "                      95, #avg training score\n",
+    "                      7, #sum of metric \n",
+    "                      700 #total score\n",
+    "                     )])\n",
+    "new_data = sc.transform(new_data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zDShgB6ogwMl",
+    "outputId": "6a7ee6b7-8b2d-4e8d-e913-c36a85dc5fab"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Whether the Employee should get a Promotion : 1-> Promotion, and 0-> No Promotion : [1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "final_prediction = rf.predict(new_data)\n",
+    "print(\"Whether the Employee should get a Promotion : 1-> Promotion, and 0-> No Promotion :\", final_prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ww4cyuQloMhH"
+   },
+   "source": [
+    "## Predicting for our test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ptnv2JiOmrSZ",
+    "outputId": "a0d9d719-55c0-427d-ace5-f87bfb99c1e5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Whether the Employee should get a Promotion : 1-> Promotion, and 0-> No Promotion : [0 0 0 ... 0 0 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_data_final_prediction = rf.predict(x_test)\n",
+    "print(\"Whether the Employee should get a Promotion : 1-> Promotion, and 0-> No Promotion :\", test_data_final_prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "d7Qz2wKrmyS9",
+    "outputId": "82ee33e2-eb42-4487-8669-41929abab66c"
+   },
+   "outputs": [
+    {
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+      ]
+     },
+     "execution_count": 375,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data_final_prediction = list(test_data_final_prediction)\n",
+    "test_data_final_prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "B_K0LVfcnRN7",
+    "outputId": "25f0c7d1-c8fd-4fd1-e678-5a7f71f22176"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23490"
+      ]
+     },
+     "execution_count": 376,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(test_data_final_prediction)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "quovkFc2nF-j",
+    "outputId": "c24a709c-7b76-4052-bba9-0c0f352df3f9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23490"
+      ]
+     },
+     "execution_count": 377,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(x_test)"
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+  },
+  {
+   "cell_type": "code",
+   "execution_count": 378,
+   "metadata": {
+    "id": "Yli6T0HTnWc8"
+   },
+   "outputs": [],
+   "source": [
+    "test['is_promoted'] = test_data_final_prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 379,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 250
+    },
+    "id": "VLCjrx5unqJg",
+    "outputId": "b2cad386-ada2-452b-81d5-3991985be929"
+   },
+   "outputs": [
+    {
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>department</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>sum_metric</th>\n",
+       "      <th>total_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>77</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>77</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>51</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>51</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>47</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>31</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>195</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>61</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   department  education  gender  no_of_trainings  age  previous_year_rating  \\\n",
+       "0           8          2       1                1   24                   3.0   \n",
+       "1           2          2       0                1   31                   3.0   \n",
+       "2           7          2       1                1   31                   1.0   \n",
+       "3           5          2       0                3   31                   2.0   \n",
+       "4           1          2       1                1   30                   4.0   \n",
+       "\n",
+       "   length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
+       "0                  1              1            0                  77   \n",
+       "1                  5              0            0                  51   \n",
+       "2                  4              0            0                  47   \n",
+       "3                  9              0            0                  65   \n",
+       "4                  7              0            0                  61   \n",
+       "\n",
+       "   sum_metric  total_score  is_promoted  \n",
+       "0         4.0           77            0  \n",
+       "1         3.0           51            0  \n",
+       "2         1.0           47            0  \n",
+       "3         2.0          195            0  \n",
+       "4         4.0           61            0  "
+      ]
+     },
+     "execution_count": 379,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 380,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 485
+    },
+    "id": "3HGgbV5jnr0-",
+    "outputId": "09e7b4e4-c304-4480-d079-d5eb84b9a978"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>department</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>no_of_trainings</th>\n",
+       "      <th>age</th>\n",
+       "      <th>previous_year_rating</th>\n",
+       "      <th>length_of_service</th>\n",
+       "      <th>KPIs_met &gt;80%</th>\n",
+       "      <th>awards_won?</th>\n",
+       "      <th>avg_training_score</th>\n",
+       "      <th>sum_metric</th>\n",
+       "      <th>total_score</th>\n",
+       "      <th>is_promoted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>76</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>85</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>85</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>90</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>90</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>21</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23425</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>53</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23431</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>89</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>89</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23449</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>83</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>83</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23454</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>32</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>59</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>59</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23489</th>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>40</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>89</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>267</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2125 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       department  education  gender  no_of_trainings  age  \\\n",
+       "9               8          2       1                1   29   \n",
+       "32              8          2       1                1   29   \n",
+       "40              0          2       1                1   26   \n",
+       "53              4          1       1                1   21   \n",
+       "59              4          2       0                1   29   \n",
+       "...           ...        ...     ...              ...  ...   \n",
+       "23425           7          2       1                1   38   \n",
+       "23431           6          3       1                1   34   \n",
+       "23449           8          2       1                1   32   \n",
+       "23454           7          3       1                1   32   \n",
+       "23489           8          3       1                3   40   \n",
+       "\n",
+       "       previous_year_rating  length_of_service  KPIs_met >80%  awards_won?  \\\n",
+       "9                       5.0                  2              1            0   \n",
+       "32                      3.0                  1              1            0   \n",
+       "40                      5.0                  4              1            0   \n",
+       "53                      3.0                  1              1            0   \n",
+       "59                      5.0                  4              1            0   \n",
+       "...                     ...                ...            ...          ...   \n",
+       "23425                   5.0                  5              1            0   \n",
+       "23431                   5.0                  2              1            0   \n",
+       "23449                   4.0                  2              1            0   \n",
+       "23454                   2.0                  6              0            1   \n",
+       "23489                   5.0                  5              1            0   \n",
+       "\n",
+       "       avg_training_score  sum_metric  total_score  is_promoted  \n",
+       "9                      76         6.0           76            1  \n",
+       "32                     85         4.0           85            1  \n",
+       "40                     90         6.0           90            1  \n",
+       "53                     56         4.0           56            1  \n",
+       "59                     56         6.0           56            1  \n",
+       "...                   ...         ...          ...          ...  \n",
+       "23425                  53         6.0           53            1  \n",
+       "23431                  89         6.0           89            1  \n",
+       "23449                  83         5.0           83            1  \n",
+       "23454                  59         3.0           59            1  \n",
+       "23489                  89         6.0          267            1  \n",
+       "\n",
+       "[2125 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 380,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test[test['is_promoted']==1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qM7Mrf2Cny9v"
+   },
+   "source": [
+    "### There are 2180 people who will get promotion according to our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 381,
+   "metadata": {
+    "id": "gjDT5oKinxpb"
+   },
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# Save the model to a file using pickle\n",
+    "with open('rf.pkl', 'wb') as file:\n",
+    "    pickle.dump(rf, file)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 382,
+   "metadata": {
+    "id": "24fAvRKPpyae"
+   },
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# Save the standard scaler object to a file using pickle\n",
+    "with open('sc.pkl', 'wb') as file:\n",
+    "    pickle.dump(sc, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "qJoj2-ZnqPQy"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}