Randa_Banan_Classification_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1c8c5c66",
   "metadata": {},
   "source": [
    "# Classification Project : \n",
    "### Predicting eleigable employee for promotion using multinational corporation HR data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40ec6676",
   "metadata": {},
   "source": [
    "*** ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8e89de2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, VotingClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score , recall_score, f1_score \n",
    "from sklearn import metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "d2a10b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model, neighbors, ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "42f9eb63",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "378ef2af",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b57b8e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>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>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>60</td>\n",
       "      <td>0</td>\n",
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       "    <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>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>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>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>3030</td>\n",
       "      <td>Technology</td>\n",
       "      <td>region_14</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>74592</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_27</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>13918</td>\n",
       "      <td>Analytics</td>\n",
       "      <td>region_1</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>13614</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>51526</td>\n",
       "      <td>HR</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>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 13 columns</p>\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",
       "54803         3030         Technology  region_14        Bachelor's      m   \n",
       "54804        74592         Operations  region_27  Master's & above      f   \n",
       "54805        13918          Analytics   region_1        Bachelor's      m   \n",
       "54806        13614  Sales & Marketing   region_9               NaN      m   \n",
       "54807        51526                 HR  region_22        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",
       "54803            sourcing                1   48                   3.0   \n",
       "54804               other                1   37                   2.0   \n",
       "54805               other                1   27                   5.0   \n",
       "54806            sourcing                1   29                   1.0   \n",
       "54807               other                1   27                   1.0   \n",
       "\n",
       "       length_of_service  awards_won?  avg_training_score  is_promoted  \n",
       "0                      8            0                  49            0  \n",
       "1                      4            0                  60            0  \n",
       "2                      7            0                  50            0  \n",
       "3                     10            0                  50            0  \n",
       "4                      2            0                  73            0  \n",
       "...                  ...          ...                 ...          ...  \n",
       "54803                 17            0                  78            0  \n",
       "54804                  6            0                  56            0  \n",
       "54805                  3            0                  79            0  \n",
       "54806                  2            0                  45            0  \n",
       "54807                  5            0                  49            0  \n",
       "\n",
       "[54808 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('HR.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "894647ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>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",
       "    </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>0.023172</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>0.150450</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>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>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>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>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>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   awards_won?  avg_training_score   is_promoted  \n",
       "count       54808.000000  54808.000000        54808.000000  54808.000000  \n",
       "mean            5.865512      0.023172           63.386750      0.085170  \n",
       "std             4.265094      0.150450           13.371559      0.279137  \n",
       "min             1.000000      0.000000           39.000000      0.000000  \n",
       "25%             3.000000      0.000000           51.000000      0.000000  \n",
       "50%             5.000000      0.000000           60.000000      0.000000  \n",
       "75%             7.000000      0.000000           76.000000      0.000000  \n",
       "max            37.000000      1.000000           99.000000      1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a13ca316",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>Master's &amp; above</td>\n",
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       "      <th>2</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
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       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>Operations</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>Analytics</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54806</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>HR</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>54808 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              department         education  no_of_trainings  age  \\\n",
       "0      Sales & Marketing  Master's & above                1   35   \n",
       "1             Operations        Bachelor's                1   30   \n",
       "2      Sales & Marketing        Bachelor's                1   34   \n",
       "3      Sales & Marketing        Bachelor's                2   39   \n",
       "4             Technology        Bachelor's                1   45   \n",
       "...                  ...               ...              ...  ...   \n",
       "54803         Technology        Bachelor's                1   48   \n",
       "54804         Operations  Master's & above                1   37   \n",
       "54805          Analytics        Bachelor's                1   27   \n",
       "54806  Sales & Marketing               NaN                1   29   \n",
       "54807                 HR        Bachelor's                1   27   \n",
       "\n",
       "       previous_year_rating  length_of_service  awards_won?  \\\n",
       "0                       5.0                  8            0   \n",
       "1                       5.0                  4            0   \n",
       "2                       3.0                  7            0   \n",
       "3                       1.0                 10            0   \n",
       "4                       3.0                  2            0   \n",
       "...                     ...                ...          ...   \n",
       "54803                   3.0                 17            0   \n",
       "54804                   2.0                  6            0   \n",
       "54805                   5.0                  3            0   \n",
       "54806                   1.0                  2            0   \n",
       "54807                   1.0                  5            0   \n",
       "\n",
       "       avg_training_score  is_promoted  \n",
       "0                      49            0  \n",
       "1                      60            0  \n",
       "2                      50            0  \n",
       "3                      50            0  \n",
       "4                      73            0  \n",
       "...                   ...          ...  \n",
       "54803                  78            0  \n",
       "54804                  56            0  \n",
       "54805                  79            0  \n",
       "54806                  45            0  \n",
       "54807                  49            0  \n",
       "\n",
       "[54808 rows x 9 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['recruitment_channel','region','gender','employee_id'],inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb648f2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54808 entries, 0 to 54807\n",
      "Data columns (total 9 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   department            54808 non-null  object \n",
      " 1   education             52399 non-null  object \n",
      " 2   no_of_trainings       54808 non-null  int64  \n",
      " 3   age                   54808 non-null  int64  \n",
      " 4   previous_year_rating  50684 non-null  float64\n",
      " 5   length_of_service     54808 non-null  int64  \n",
      " 6   awards_won?           54808 non-null  int64  \n",
      " 7   avg_training_score    54808 non-null  int64  \n",
      " 8   is_promoted           54808 non-null  int64  \n",
      "dtypes: float64(1), int64(6), object(2)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8795bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "department              False\n",
       "education                True\n",
       "no_of_trainings         False\n",
       "age                     False\n",
       "previous_year_rating     True\n",
       "length_of_service       False\n",
       "awards_won?             False\n",
       "avg_training_score      False\n",
       "is_promoted             False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3dd275f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "department                 0\n",
       "education               2409\n",
       "no_of_trainings            0\n",
       "age                        0\n",
       "previous_year_rating    4124\n",
       "length_of_service          0\n",
       "awards_won?                0\n",
       "avg_training_score         0\n",
       "is_promoted                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e06b3585",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop null values\n",
    "df.dropna(subset=['education'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48a86612",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['previous_year_rating'].fillna(value=df['previous_year_rating'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49f98fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "department              0\n",
       "education               0\n",
       "no_of_trainings         0\n",
       "age                     0\n",
       "previous_year_rating    0\n",
       "length_of_service       0\n",
       "awards_won?             0\n",
       "avg_training_score      0\n",
       "is_promoted             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5224a74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9918"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identify duplicates records in the data\n",
    "dupes=df.duplicated()\n",
    "sum(dupes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fd552d19",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove Duplicate fro the dataset\n",
    "df.drop_duplicates(keep=False,inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "48b21767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identify duplicates records in the data\n",
    "dupes=df.duplicated()\n",
    "sum(dupes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3037e610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36219, 9)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "55af7f2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Not promoted  31871\n",
      "Number of promoted 4348\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Not promoted \",len(df[df[\"is_promoted\"]==0]))\n",
    "print(\"Number of promoted\",len(df[df[\"is_promoted\"]==1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c75ac4ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of is promoted')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the imbalance with a bar chart\n",
    "plt.title('Distribution of promoted', fontdict={'size' : 16, 'color':'brown'})\n",
    "sns.countplot(x='is_promoted', data=df)\n",
    "labels = ['Not promoted', 'promoted']   #to label the plot\n",
    "vals = [0, 1]   #to put the labels right\n",
    "plt.xticks(vals, labels)\n",
    "plt.xlabel('is_promoted', fontdict={'size' : 14, 'color' : 'green'})\n",
    "plt.ylabel('Number of is promoted', fontdict={'size' : 12, 'color':'green'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "975b5d63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not promoted is  0.8799525111129518 of data\n",
      "Promoted is 0.12004748888704823 of data\n"
     ]
    }
   ],
   "source": [
    "print(\"Not promoted is \",len(df[df[\"is_promoted\"]==0])/len(df),'of data')\n",
    "print(\"Promoted is\",len(df[df[\"is_promoted\"]==1])/len(df),'of data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "de92c405",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of the classes in the data:\n",
      "0    0.879953\n",
      "1    0.120047\n",
      "Name: is_promoted, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Proportion of the classes in the data:')\n",
    "print(df['is_promoted'].value_counts() / len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7da26146",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 36219 entries, 3 to 54807\n",
      "Data columns (total 9 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   department            36219 non-null  object \n",
      " 1   education             36219 non-null  object \n",
      " 2   no_of_trainings       36219 non-null  int64  \n",
      " 3   age                   36219 non-null  int64  \n",
      " 4   previous_year_rating  36219 non-null  float64\n",
      " 5   length_of_service     36219 non-null  int64  \n",
      " 6   awards_won?           36219 non-null  int64  \n",
      " 7   avg_training_score    36219 non-null  int64  \n",
      " 8   is_promoted           36219 non-null  int64  \n",
      "dtypes: float64(1), int64(6), object(2)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8832b87e",
   "metadata": {},
   "source": [
    "### Data Preproccessing:\n",
    "\n",
    "### 1) Feature Engineering\n",
    "###### Dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "393f77da",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>department</th>\n",
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       "      <th>Operations</th>\n",
       "      <th>Procurement</th>\n",
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       "      <th>Sales &amp; Marketing</th>\n",
       "      <th>Technology</th>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Analytics</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>1</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          department         education  no_of_trainings  age  \\\n",
       "3  Sales & Marketing        Bachelor's                2   39   \n",
       "4         Technology        Bachelor's                1   45   \n",
       "5          Analytics        Bachelor's                2   31   \n",
       "7         Operations  Master's & above                1   33   \n",
       "9  Sales & Marketing  Master's & above                1   32   \n",
       "\n",
       "   previous_year_rating  length_of_service  awards_won?  avg_training_score  \\\n",
       "3                   1.0                 10            0                  50   \n",
       "4                   3.0                  2            0                  73   \n",
       "5                   3.0                  7            0                  85   \n",
       "7                   3.0                  6            0                  63   \n",
       "9                   5.0                  5            0                  54   \n",
       "\n",
       "   is_promoted  Finance  HR  Legal  Operations  Procurement  R&D  \\\n",
       "3            0        0   0      0           0            0    0   \n",
       "4            0        0   0      0           0            0    0   \n",
       "5            0        0   0      0           0            0    0   \n",
       "7            0        0   0      0           1            0    0   \n",
       "9            0        0   0      0           0            0    0   \n",
       "\n",
       "   Sales & Marketing  Technology  \n",
       "3                  1           0  \n",
       "4                  0           1  \n",
       "5                  0           0  \n",
       "7                  0           0  \n",
       "9                  1           0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.copy()\n",
    "data_dep = pd.get_dummies(data['department'], drop_first=True)\n",
    "data_dummy = pd.concat([data, data_dep], axis=1)\n",
    "data_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb860f93",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          department         education  no_of_trainings  age  \\\n",
       "3  Sales & Marketing        Bachelor's                2   39   \n",
       "4         Technology        Bachelor's                1   45   \n",
       "5          Analytics        Bachelor's                2   31   \n",
       "7         Operations  Master's & above                1   33   \n",
       "9  Sales & Marketing  Master's & above                1   32   \n",
       "\n",
       "   previous_year_rating  length_of_service  awards_won?  avg_training_score  \\\n",
       "3                   1.0                 10            0                  50   \n",
       "4                   3.0                  2            0                  73   \n",
       "5                   3.0                  7            0                  85   \n",
       "7                   3.0                  6            0                  63   \n",
       "9                   5.0                  5            0                  54   \n",
       "\n",
       "   is_promoted  Finance  HR  Legal  Operations  Procurement  R&D  \\\n",
       "3            0        0   0      0           0            0    0   \n",
       "4            0        0   0      0           0            0    0   \n",
       "5            0        0   0      0           0            0    0   \n",
       "7            0        0   0      0           1            0    0   \n",
       "9            0        0   0      0           0            0    0   \n",
       "\n",
       "   Sales & Marketing  Technology  Below Secondary  Master's & above  \n",
       "3                  1           0                0                 0  \n",
       "4                  0           1                0                 0  \n",
       "5                  0           0                0                 0  \n",
       "7                  0           0                0                 1  \n",
       "9                  1           0                0                 1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edu = pd.get_dummies(data['education'], drop_first=True)\n",
    "df_dummy = pd.concat([data_dummy, data_edu], axis=1)\n",
    "df_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dcd2510",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9ec6c1d5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36219 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       no_of_trainings  age  previous_year_rating  length_of_service  \\\n",
       "3                    2   39                   1.0                 10   \n",
       "4                    1   45                   3.0                  2   \n",
       "5                    2   31                   3.0                  7   \n",
       "7                    1   33                   3.0                  6   \n",
       "9                    1   32                   5.0                  5   \n",
       "...                ...  ...                   ...                ...   \n",
       "54802                2   31                   1.0                  2   \n",
       "54803                1   48                   3.0                 17   \n",
       "54804                1   37                   2.0                  6   \n",
       "54805                1   27                   5.0                  3   \n",
       "54807                1   27                   1.0                  5   \n",
       "\n",
       "       awards_won?  avg_training_score  is_promoted  Finance  HR  Legal  \\\n",
       "3                0                  50            0        0   0      0   \n",
       "4                0                  73            0        0   0      0   \n",
       "5                0                  85            0        0   0      0   \n",
       "7                0                  63            0        0   0      0   \n",
       "9                0                  54            0        0   0      0   \n",
       "...            ...                 ...          ...      ...  ..    ...   \n",
       "54802            0                  49            0        0   0      0   \n",
       "54803            0                  78            0        0   0      0   \n",
       "54804            0                  56            0        0   0      0   \n",
       "54805            0                  79            0        0   0      0   \n",
       "54807            0                  49            0        0   1      0   \n",
       "\n",
       "       Operations  Procurement  R&D  Sales & Marketing  Technology  \\\n",
       "3               0            0    0                  1           0   \n",
       "4               0            0    0                  0           1   \n",
       "5               0            0    0                  0           0   \n",
       "7               1            0    0                  0           0   \n",
       "9               0            0    0                  1           0   \n",
       "...           ...          ...  ...                ...         ...   \n",
       "54802           0            0    0                  1           0   \n",
       "54803           0            0    0                  0           1   \n",
       "54804           1            0    0                  0           0   \n",
       "54805           0            0    0                  0           0   \n",
       "54807           0            0    0                  0           0   \n",
       "\n",
       "       Below Secondary  Master's & above  \n",
       "3                    0                 0  \n",
       "4                    0                 0  \n",
       "5                    0                 0  \n",
       "7                    0                 1  \n",
       "9                    0                 1  \n",
       "...                ...               ...  \n",
       "54802                0                 0  \n",
       "54803                0                 0  \n",
       "54804                0                 1  \n",
       "54805                0                 0  \n",
       "54807                0                 0  \n",
       "\n",
       "[36219 rows x 17 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy.drop(columns=['department','education'],inplace=True)\n",
    "df_dummy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9626a9",
   "metadata": {},
   "source": [
    "### EDA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d488d8ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(df.corr(),cbar=False, annot=True, cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dc8254cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#barplots showing the frequency of each category separated by label\n",
    "plt.figure(figsize=[15,20])\n",
    "\n",
    "fft=[\"age\"]                   \n",
    "n=1\n",
    "for f in fft:\n",
    "    plt.subplot(12,1,n)\n",
    "    sns.countplot(x=f, hue='is_promoted',edgecolor=\"black\", palette = \"Set2\", data=df)\n",
    "    sns.despine()\n",
    "    plt.title(\"Countplot of {}  by is_promoted\".format(f))\n",
    "    n=n+1\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb6a5a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1ba84a9e430>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"education\", y=\"is_promoted\", kind=\"bar\", data=df, height=6, aspect=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b6ecba30",
   "metadata": {
    "scrolled": true
   },
   "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>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</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>3</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Analytics</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Operations</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54802</th>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54803</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54804</th>\n",
       "      <td>Operations</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54805</th>\n",
       "      <td>Analytics</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54807</th>\n",
       "      <td>HR</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36219 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              department         education  no_of_trainings  age  \\\n",
       "3      Sales & Marketing        Bachelor's                2   39   \n",
       "4             Technology        Bachelor's                1   45   \n",
       "5              Analytics        Bachelor's                2   31   \n",
       "7             Operations  Master's & above                1   33   \n",
       "9      Sales & Marketing  Master's & above                1   32   \n",
       "...                  ...               ...              ...  ...   \n",
       "54802  Sales & Marketing        Bachelor's                2   31   \n",
       "54803         Technology        Bachelor's                1   48   \n",
       "54804         Operations  Master's & above                1   37   \n",
       "54805          Analytics        Bachelor's                1   27   \n",
       "54807                 HR        Bachelor's                1   27   \n",
       "\n",
       "       previous_year_rating  length_of_service  awards_won?  \\\n",
       "3                       1.0                 10            0   \n",
       "4                       3.0                  2            0   \n",
       "5                       3.0                  7            0   \n",
       "7                       3.0                  6            0   \n",
       "9                       5.0                  5            0   \n",
       "...                     ...                ...          ...   \n",
       "54802                   1.0                  2            0   \n",
       "54803                   3.0                 17            0   \n",
       "54804                   2.0                  6            0   \n",
       "54805                   5.0                  3            0   \n",
       "54807                   1.0                  5            0   \n",
       "\n",
       "       avg_training_score  is_promoted  \n",
       "3                      50            0  \n",
       "4                      73            0  \n",
       "5                      85            0  \n",
       "7                      63            0  \n",
       "9                      54            0  \n",
       "...                   ...          ...  \n",
       "54802                  49            0  \n",
       "54803                  78            0  \n",
       "54804                  56            0  \n",
       "54805                  79            0  \n",
       "54807                  49            0  \n",
       "\n",
       "[36219 rows x 9 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "433f9fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,4))\n",
    "ax = sns.countplot(x=\"department\",data=df, palette = \"Set2\",hue=\"is_promoted\", order = df['department'].value_counts().index)\n",
    "ax.grid(False)\n",
    "plt.suptitle(\"Department\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ee6c849a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#barplots showing the frequency of each category separated by label\n",
    "plt.figure(figsize=[15,20])\n",
    "\n",
    "fft=[\"no_of_trainings\"]                   \n",
    "n=1\n",
    "for f in fft:\n",
    "    plt.subplot(12,1,n)\n",
    "    sns.countplot(x=f, hue='is_promoted', edgecolor=\"black\",palette = \"Set2\", data=df)\n",
    "    sns.despine()\n",
    "    plt.title(\"Countplot of {}  by is_promoted\".format(f))\n",
    "    n=n+1\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c41eb9b",
   "metadata": {},
   "source": [
    "# Exploring algorithms:\n",
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6a30c6f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_dummy.drop('is_promoted',axis = 1)\n",
    "y = df_dummy.is_promoted\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.30, random_state=42,stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "76d2c7df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "91e4b3dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9256  306]\n",
      " [1001  303]]\n",
      "KNN Accuracy =  87.9716547027425\n",
      "KNN Precision =  49.75369458128079\n",
      "KNN Recall =  23.236196319018404\n",
      "KNN F1 score=  31.677992681651858\n"
     ]
    }
   ],
   "source": [
    "y_pred_knn = knn.predict(X_test)\n",
    "#confusion matrix on KNN\n",
    "cf_matrix = metrics.confusion_matrix(y_test, y_pred_knn)\n",
    "print(cf_matrix)\n",
    "#accuracy on KNN\n",
    "accuracy = metrics.accuracy_score(y_test,y_pred_knn)\n",
    "print ('KNN Accuracy = ', accuracy * 100)\n",
    "#precision on KNN\n",
    "Precision = metrics.precision_score(y_test,y_pred_knn)\n",
    "print ('KNN Precision = ', Precision*100)\n",
    "#Recall on KNN\n",
    "Recall = metrics.recall_score(y_test,y_pred_knn)\n",
    "print ('KNN Recall = ', Recall*100)\n",
    "#F1 score on KNN\n",
    "f1_score = metrics.f1_score(y_test,y_pred_knn)\n",
    "print ('KNN F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5ad6552e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The scores are: [0.8816952  0.87727775 0.87838211 0.87838211 0.87905564]\n",
      "The mean score is 0.8789585603434548\n",
      "The F1 score is 0.31677992681651856\n"
     ]
    }
   ],
   "source": [
    "#Importing\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#create a new KNN model\n",
    "KNN_CV = KNeighborsClassifier(n_neighbors=3)\n",
    "#train model with CV of 5 \n",
    "cv_scores = cross_val_score(KNN_CV, X, y, cv=5)\n",
    "#print each CV score (accuracy) and find the average\n",
    "print('The scores are:', cv_scores)\n",
    "print(f'The mean score is {np.mean(cv_scores)}')\n",
    "print(f'The F1 score is {f1_score}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "744641e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Create a new KNN model\n",
    "KNN_2 = KNeighborsClassifier()\n",
    "# Create a dictionary of all values we want to test for n_neighbors\n",
    "param_grid = {'n_neighbors': np.arange(1, 20)}\n",
    "# use gridsearch to test all values for n_neighbors\n",
    "KNN_gscv = GridSearchCV(KNN_2, param_grid, cv=5)\n",
    "\n",
    "#fit model to data\n",
    "KNN_gscv.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "73f16f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 11}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_gscv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e7e4caaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8932604743451531"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_gscv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f4e7db9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9533   29]\n",
      " [1055  249]]\n",
      "KNN F1 score=  31.479140328697852\n"
     ]
    }
   ],
   "source": [
    "y_pred_knn2 = KNN_gscv.predict(X_test)\n",
    "#confusion matrix on grid KNN\n",
    "cf_matrix = metrics.confusion_matrix(y_test, y_pred_knn2)\n",
    "print(cf_matrix)\n",
    "#F1 score on grid KNN\n",
    "f1_score = metrics.f1_score(y_test,y_pred_knn2)\n",
    "print ('KNN F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "93f4ea65",
   "metadata": {},
   "outputs": [],
   "source": [
    "#KNN_gscv.scoring(y,y_pred_knn2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "38bac25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_pred_knn2 = KNN_gscv.predict(X)\n",
    "#f1_score = metrics.f1_score(y_test,y_pred_knn2)\n",
    "#print ('KNN F1 score= ',f1_score * 100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58b768a0",
   "metadata": {},
   "source": [
    "\n",
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "768979f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\banon\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Log_Reg = LogisticRegression()\n",
    "Log_Reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b0e665b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9472   90]\n",
      " [1110  194]]\n",
      "Logistic Regression Accuracy =  88.95637769188293\n",
      "Logistic Regression Precision =  68.30985915492957\n",
      "Logistic Regression Recall =  14.877300613496933\n",
      "Logistic Regression F1 score=  24.43324937027708\n"
     ]
    }
   ],
   "source": [
    "y_pred_Log_Reg = Log_Reg.predict(X_test)\n",
    "#confusion matrix on LR\n",
    "cf_matrix = metrics.confusion_matrix(y_test, y_pred_Log_Reg)\n",
    "print(cf_matrix)\n",
    "#accuracy on LR\n",
    "accuracy = metrics.accuracy_score(y_test,y_pred_Log_Reg)\n",
    "print ('Logistic Regression Accuracy = ', accuracy * 100)\n",
    "#precision on LR\n",
    "Precision = metrics.precision_score(y_test,y_pred_Log_Reg)\n",
    "print ('Logistic Regression Precision = ', Precision*100)\n",
    "#Recall on LR\n",
    "Recall = metrics.recall_score(y_test,y_pred_Log_Reg)\n",
    "print ('Logistic Regression Recall = ', Recall*100)\n",
    "#F1 score on LR\n",
    "f1_score = metrics.f1_score(y_test,y_pred_Log_Reg)\n",
    "print ('Logistic Regression F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "605cc3a3",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "79c302b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = DecisionTreeClassifier()\n",
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5a07f8df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8623  939]\n",
      " [ 771  533]]\n",
      "Decision Tree Accuracy =  84.26283821093318\n",
      "Decision Tree Precision =  36.20923913043478\n",
      "Decision Tree Recall =  40.87423312883436\n",
      "Decision Tree F1 score=  38.400576368876074\n"
     ]
    }
   ],
   "source": [
    "y_pred_dtree = dtree.predict(X_test)\n",
    "#confusion matrix on DT\n",
    "cf_matrix = metrics.confusion_matrix(y_test, y_pred_dtree)\n",
    "print(cf_matrix)\n",
    "#accuracy on DT\n",
    "accuracy = metrics.accuracy_score(y_test,y_pred_dtree)\n",
    "print ('Decision Tree Accuracy = ', accuracy * 100)\n",
    "#precision on DT\n",
    "Precision = metrics.precision_score(y_test,y_pred_dtree)\n",
    "print ('Decision Tree Precision = ', Precision*100)\n",
    "#Recall on DT\n",
    "Recall = metrics.recall_score(y_test,y_pred_dtree)\n",
    "print ('Decision Tree Recall = ', Recall*100)\n",
    "#F1 score on DT\n",
    "f1_score = metrics.f1_score(y_test,y_pred_dtree)\n",
    "print ('Decision Tree F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5caa06a5",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2ba00162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=500)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators= 500)\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "462d5810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9309  253]\n",
      " [ 831  473]]\n",
      "Random Forest Accuracy =  90.02392784833425\n",
      "Random Forest Precision =  65.15151515151516\n",
      "Random Forest Recall =  36.27300613496933\n",
      "Random Forest F1 score=  46.60098522167488\n"
     ]
    }
   ],
   "source": [
    "y_pred_rf = rf.predict(X_test)\n",
    "#confusion matrix on RF\n",
    "cf_matrix = metrics.confusion_matrix(y_test, y_pred_rf)\n",
    "print(cf_matrix)\n",
    "#accuracy on RF\n",
    "accuracy = metrics.accuracy_score(y_test,y_pred_rf)\n",
    "print ('Random Forest Accuracy = ', accuracy * 100)\n",
    "#precision on RF\n",
    "Precision = metrics.precision_score(y_test,y_pred_rf)\n",
    "print ('Random Forest Precision = ', Precision*100)\n",
    "#Recall on RF\n",
    "Recall = metrics.recall_score(y_test,y_pred_rf)\n",
    "print ('Random Forest Recall = ', Recall*100)\n",
    "#F1 score on RF\n",
    "f1_score = metrics.f1_score(y_test,y_pred_rf)\n",
    "print ('Random Forest F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "611d8fa0",
   "metadata": {},
   "source": [
    "## Data Imbalance technique:\n",
    "### Standardize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "af785006",
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardize the data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d4604f44",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 2, shuffle = True, stratify = y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a087b3",
   "metadata": {},
   "source": [
    "### SMOTE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5cdfca53",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "sm = SMOTE(random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9971268f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_new, y_train_new = sm.fit_resample(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7acbc584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42706, 42706)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_new), len(y_train_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8a7e2aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(y_train_new).value_counts().plot.bar();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5613ba98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of is promoted')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize the imbalance with a bar chart\n",
    "plt.title('Distribution of promoted', fontdict={'size' : 16, 'color':'brown'})\n",
    "sns.countplot(x=y_train_new, data=df)\n",
    "labels = ['Not promoted', 'promoted']   #to label the plot\n",
    "vals = [0, 1]   #to put the labels right\n",
    "plt.xticks(vals, labels)\n",
    "plt.xlabel('is_promoted', fontdict={'size' : 14, 'color' : 'green'})\n",
    "plt.ylabel('Number of is promoted', fontdict={'size' : 12, 'color':'green'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f585df",
   "metadata": {},
   "source": [
    "### 1. Logistic Regression after SMOTE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6083b39f",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(solver = 'lbfgs')\n",
    "# fit the model\n",
    "clf.fit(X_train_new, y_train_new)\n",
    "\n",
    "# prediction for Training data\n",
    "train_pred_sm = clf.predict(X_train_new)\n",
    "\n",
    "# prediction for Testing data\n",
    "test_pred_sm = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fe88815f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score for Training Dataset =  0.7287968903666932\n",
      "Accuracy score for Testing Dataset =  0.7498535932401907\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy score for Training Dataset = ', accuracy_score(train_pred_sm, y_train_new))\n",
    "print('Accuracy score for Testing Dataset = ', accuracy_score(test_pred_sm, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9aba7f6b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Training Dataset\n",
      "Predicted      0      1    All\n",
      "True                          \n",
      "0          16268   5085  21353\n",
      "1           6497  14856  21353\n",
      "All        22765  19941  42706\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix - Training Dataset')\n",
    "#lr_matrix =pd.crosstab(y_train_new, train_pred_sm, rownames = ['True'], colnames = ['Predicted'], margins = False)\n",
    "#print(lr_matrix)                    \n",
    "print(pd.crosstab(y_train_new, train_pred_sm, rownames = ['True'], colnames = ['Predicted'], margins = True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f874eda2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression F1 score=  38.729508196721305\n"
     ]
    }
   ],
   "source": [
    "#F1 score on LR after SMOTE\n",
    "f1_score = metrics.f1_score(y_test,test_pred_sm)\n",
    "print ('Logistic regression F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4a67c811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7104244060124572"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUC_lr=roc_auc_score(y_test,test_pred_sm)\n",
    "AUC_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2b0148c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal Logistic Regression Test F1: 0.387, Test AUC: 0.801\n",
      "Balanced class weights Logistic Regression Test F1: 0.387, Test AUC: 0.801\n",
      "9:1 class weights Logistic Regression Test F1: 0.235, Test AUC: 0.803\n"
     ]
    }
   ],
   "source": [
    "#LR with resampling data using class weight\n",
    "\n",
    "lr_weight = LogisticRegression(solver='liblinear') \n",
    "lr_balanced = LogisticRegression(class_weight='balanced', solver='liblinear')\n",
    "lr_4x = LogisticRegression(class_weight={0 : 1, 1 : 9}, solver='liblinear')\n",
    "\n",
    "lr_weight.fit(X_train_new, y_train_new)\n",
    "print('Normal Logistic Regression Test F1: %.3f, Test AUC: %.3f' % \\\n",
    "      (f1_score(y_test, lr_weight.predict(X_test)), roc_auc_score(y_test, lr_weight.predict_proba(X_test)[:,1])))\n",
    "\n",
    "lr_balanced.fit(X_train_new, y_train_new)\n",
    "print('Balanced class weights Logistic Regression Test F1: %.3f, Test AUC: %.3f' % \\\n",
    "      (f1_score(y_test, lr_balanced.predict(X_test)), roc_auc_score(y_test, lr_balanced.predict_proba(X_test)[:,1])))\n",
    "\n",
    "lr_4x.fit(X_train_new, y_train_new)\n",
    "print('9:1 class weights Logistic Regression Test F1: %.3f, Test AUC: %.3f' % \\\n",
    "      (f1_score(y_test, lr_4x.predict(X_test)), roc_auc_score(y_test, lr_4x.predict_proba(X_test)[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fb149af3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Model best F1 score 0.489 at prob decision threshold >= 0.771\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import imblearn.over_sampling\n",
    "\n",
    "from sklearn.metrics import precision_score, recall_score, accuracy_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "X_val, y_val = X_test, y_test\n",
    "\n",
    "thresh_ps = np.linspace(.10,.80,1000)\n",
    "model_val_probs = lr_weight.predict_proba(X_val)[:,1] # positive class probabilities \n",
    "\n",
    "f1_scores, prec_scores, rec_scores, acc_scores = [], [], [], []\n",
    "for p in thresh_ps:\n",
    "    model_val_labels = model_val_probs >= p\n",
    "    f1_scores.append(f1_score(y_val, model_val_labels))    \n",
    "    prec_scores.append(precision_score(y_val, model_val_labels))\n",
    "    rec_scores.append(recall_score(y_val, model_val_labels))\n",
    "    acc_scores.append(accuracy_score(y_val, model_val_labels))\n",
    "    \n",
    "plt.plot(thresh_ps, f1_scores)\n",
    "plt.plot(thresh_ps, prec_scores)\n",
    "plt.plot(thresh_ps, rec_scores)\n",
    "plt.plot(thresh_ps, acc_scores)\n",
    "\n",
    "plt.title('Metric Scores vs. Positive Class Decision Probability Threshold')\n",
    "plt.legend(['F1','Precision','Recall','Accuracy'], bbox_to_anchor=(1.05, 0), loc='lower left')\n",
    "plt.xlabel('P threshold')\n",
    "plt.ylabel('Metric score')\n",
    "\n",
    "best_f1_score = np.max(f1_scores) \n",
    "best_thresh_p = thresh_ps[np.argmax(f1_scores)]\n",
    "\n",
    "print('Logistic Regression Model best F1 score %.3f at prob decision threshold >= %.3f' \n",
    "      % (best_f1_score, best_thresh_p))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d66dcc1",
   "metadata": {},
   "source": [
    "### 2. KNN after SMOTE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "22630f8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Training Dataset\n",
      "Predicted      0      1    All\n",
      "True                          \n",
      "0          19090   2263  21353\n",
      "1            455  20898  21353\n",
      "All        19545  23161  42706\n",
      "Accuracy score for Training Dataset =  0.9363555472298974\n",
      "Accuracy score for Testing Dataset =  0.7746172508993558\n",
      "KNN F1 score=  36.87910028116213\n"
     ]
    }
   ],
   "source": [
    "knn_SMOTE = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn_SMOTE.fit(X_train_new, y_train_new)\n",
    "train_pred_knn = knn_SMOTE.predict(X_train_new)\n",
    "test_pred_knn = knn_SMOTE.predict(X_test)\n",
    "print('Confusion Matrix - Training Dataset')\n",
    "print(pd.crosstab(y_train_new, train_pred_knn, rownames = ['True'], colnames = ['Predicted'], margins = True))\n",
    "print('Accuracy score for Training Dataset = ', accuracy_score(train_pred_knn, y_train_new))\n",
    "print('Accuracy score for Testing Dataset = ', accuracy_score(test_pred_knn, y_test))\n",
    "f1_score = metrics.f1_score(y_test,test_pred_knn)\n",
    "print ('KNN F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7c51b674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6769541910234521"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUC_knn=roc_auc_score(y_test,test_pred_knn)\n",
    "AUC_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de7d2445",
   "metadata": {},
   "source": [
    "### Best KNN parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "3faace6c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Training Dataset\n",
      "Predicted      0      1    All\n",
      "True                          \n",
      "0          19090   2263  21353\n",
      "1            455  20898  21353\n",
      "All        19545  23161  42706\n",
      "Accuracy score for Training Dataset =  88.7978270032314\n",
      "Accuracy score for Testing Dataset =  74.66744750271899\n",
      "KNN F1 score=  36.67921371810958\n"
     ]
    }
   ],
   "source": [
    "knn_SMOTE2 = KNeighborsClassifier(n_neighbors = 7)\n",
    "knn_SMOTE2.fit(X_train_new, y_train_new)\n",
    "train_pred_knn2 = knn_SMOTE2.predict(X_train_new)\n",
    "test_pred_knn2 = knn_SMOTE2.predict(X_test)\n",
    "print('Confusion Matrix - Training Dataset')\n",
    "print(pd.crosstab(y_train_new, train_pred_knn, rownames = ['True'], colnames = ['Predicted'], margins = True))\n",
    "print('Accuracy score for Training Dataset = ', accuracy_score(train_pred_knn2, y_train_new)*100)\n",
    "print('Accuracy score for Testing Dataset = ', accuracy_score(test_pred_knn2, y_test)*100)\n",
    "f1_score = metrics.f1_score(y_test,test_pred_knn2)\n",
    "print ('KNN F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "610b3263",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.688157152861562"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUC_knn2=roc_auc_score(y_test,test_pred_knn2)\n",
    "AUC_knn2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d68f00f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree_SMOTE = DecisionTreeClassifier()\n",
    "dtree_SMOTE.fit(X_train_new, y_train_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "28411b2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9341 1177]\n",
      " [ 871  564]]\n",
      "Decision Tree Accuracy =  82.86622605203715\n",
      "Decision Tree Precision =  32.39517518667432\n",
      "Decision Tree Recall =  39.30313588850174\n",
      "Decision Tree F1 score=  35.51637279596977\n"
     ]
    }
   ],
   "source": [
    "y_pred_dtree = dtree_SMOTE.predict(X_test)\n",
    "#confusion matrix on DT\n",
    "cf_matrix = metrics.confusion_matrix(y_test, y_pred_dtree)\n",
    "print(cf_matrix)\n",
    "#accuracy on DT\n",
    "accuracy = metrics.accuracy_score(y_test,y_pred_dtree)\n",
    "print ('Decision Tree Accuracy = ', accuracy * 100)\n",
    "#precision on DT\n",
    "Precision = metrics.precision_score(y_test,y_pred_dtree)\n",
    "print ('Decision Tree Precision = ', Precision*100)\n",
    "#Recall on DT\n",
    "Recall = metrics.recall_score(y_test,y_pred_dtree)\n",
    "print ('Decision Tree Recall = ', Recall*100)\n",
    "#F1 score on DT\n",
    "f1_score = metrics.f1_score(y_test,y_pred_dtree)\n",
    "print ('Decision Tree F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8a669c72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6405639775980515"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUC_dtree_SMOTE=roc_auc_score(y_test,y_pred_dtree)\n",
    "AUC_dtree_SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9d8a3010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=500)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_SMOTE = RandomForestClassifier(n_estimators= 500)\n",
    "rf_SMOTE.fit(X_train_new, y_train_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6ab393fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9953  565]\n",
      " [ 830  605]]\n",
      "Random Forest Accuracy =  88.32928971806241\n",
      "Random Forest Precision =  51.70940170940172\n",
      "Random Forest Recall =  42.1602787456446\n",
      "Random Forest F1 score=  46.449136276391556\n"
     ]
    }
   ],
   "source": [
    "y_pred_rf = rf_SMOTE.predict(X_test)\n",
    "#confusion matrix on RF\n",
    "cf_matrix = metrics.confusion_matrix(y_test, y_pred_rf)\n",
    "print(cf_matrix)\n",
    "#accuracy on RF\n",
    "accuracy = metrics.accuracy_score(y_test,y_pred_rf)\n",
    "print ('Random Forest Accuracy = ', accuracy * 100)\n",
    "#precision on RF\n",
    "Precision = metrics.precision_score(y_test,y_pred_rf)\n",
    "print ('Random Forest Precision = ', Precision*100)\n",
    "#Recall on RF\n",
    "Recall = metrics.recall_score(y_test,y_pred_rf)\n",
    "print ('Random Forest Recall = ', Recall*100)\n",
    "#F1 score on RF\n",
    "f1_score = metrics.f1_score(y_test,y_pred_rf)\n",
    "print ('Random Forest F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b0ba844c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6839426753406969"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUC_rf_SMOTE=roc_auc_score(y_test,y_pred_rf)\n",
    "AUC_rf_SMOTE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "828b5290",
   "metadata": {},
   "source": [
    "### Ensemble Method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5569c17e",
   "metadata": {},
   "source": [
    "### 3. Adaboosting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "63c22b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier()"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import ensemble\n",
    "\n",
    "ada_SMOTE= ensemble.AdaBoostClassifier()\n",
    "ada_SMOTE.fit(X_train_new, y_train_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a00d38c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix - Training Dataset\n",
      "Predicted      0      1    All\n",
      "True                          \n",
      "0          15896   5457  21353\n",
      "1           4183  17170  21353\n",
      "All        20079  22627  42706\n",
      "Accuracy score for Training Dataset =  77.42705942958835\n",
      "Accuracy score for Testing Dataset =  73.5714883292897\n",
      "AdaBoost F1 score=  38.14372429998041\n"
     ]
    }
   ],
   "source": [
    "train_pred_ada = ada_SMOTE.predict(X_train_new)\n",
    "test_pred_ada = ada_SMOTE.predict(X_test)\n",
    "print('Confusion Matrix - Training Dataset')\n",
    "print(pd.crosstab(y_train_new, train_pred_ada, rownames = ['True'], colnames = ['Predicted'], margins = True))\n",
    "print('Accuracy score for Training Dataset = ', accuracy_score(train_pred_ada, y_train_new)*100)\n",
    "print('Accuracy score for Testing Dataset = ', accuracy_score(test_pred_ada, y_test)*100)\n",
    "f1_score = metrics.f1_score(y_test,test_pred_ada)\n",
    "print ('AdaBoost F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d4640b83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7111164998048806"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUC_ada=roc_auc_score(y_test,test_pred_ada)\n",
    "AUC_ada"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9964d83c",
   "metadata": {},
   "source": [
    "### 4. Experimenting Stacking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "e392d5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a18066d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate 4 models\n",
    "lr_model = linear_model.LogisticRegression(solver=\"lbfgs\", random_state=1)\n",
    "knn_model = neighbors.KNeighborsClassifier()\n",
    "rf_model = ensemble.RandomForestClassifier(n_estimators=100, random_state=1)\n",
    "et_model = ensemble.ExtraTreesClassifier(n_estimators=100, random_state=1)\n",
    "\n",
    "models = [\"lr_model\", \"knn_model\", \"rf_model\", \"et_model\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c70adacd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.mkdir('models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "aa51804d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in models:\n",
    "    \n",
    "    curr_model = eval(model_name)\n",
    "    \n",
    "    curr_model.fit(X_train_new, y_train_new)\n",
    "    \n",
    "    with open(f\"models/{model_name}.pickle\", \"wb\") as pfile:\n",
    "        pickle.dump(curr_model, pfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2964c9ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install mlxtend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "db43685a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.classifier import StackingClassifier # <-- note: this is not from sklearn!\n",
    "\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "bea880fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = [\"lr_model\", \"knn_model\", \"rf_model\", \"et_model\"]\n",
    "\n",
    "for model_name in model_names:\n",
    "    with open(f\"models/{model_name}.pickle\", \"rb\") as pfile:\n",
    "        exec(f\"{model_name} = pickle.load(pfile)\")\n",
    "\n",
    "model_vars = [eval(n) for n in model_names]\n",
    "model_list = list(zip(model_names, model_vars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a01e3fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[LogisticRegression(random_state=1),\n",
       " KNeighborsClassifier(),\n",
       " RandomForestClassifier(random_state=1),\n",
       " ExtraTreesClassifier(random_state=1)]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b85876ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('lr_model', LogisticRegression(random_state=1)),\n",
       " ('knn_model', KNeighborsClassifier()),\n",
       " ('rf_model', RandomForestClassifier(random_state=1)),\n",
       " ('et_model', ExtraTreesClassifier(random_state=1))]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "39b3d799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr_model score: 74.98535932401907\n",
      "F1 score:  38.729508196721305\n",
      "AUC:  71.04244060124572\n",
      "knn_model score: 75.6044507654982\n",
      "F1 score:  36.442894507410635\n",
      "AUC:  68.11446844400805\n",
      "rf_model score: 87.98627959508073\n",
      "F1 score:  45.48215641609719\n",
      "AUC:  68.01882685928156\n",
      "et_model score: 87.23333054463315\n",
      "F1 score:  45.028818443804035\n",
      "AUC:  68.3733145700783\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for model_name in model_names:\n",
    "    curr_model = eval(model_name)\n",
    "    print(f'{model_name} score: {curr_model.score(X_test, y_test)*100}')\n",
    "    y_pred_ensample = curr_model.predict(X_test)\n",
    "    f1_score = metrics.f1_score(y_test,y_pred_ensample)\n",
    "    print('F1 score: ',f1_score * 100)\n",
    "    AUC_stac=roc_auc_score(y_test,y_pred_ensample)\n",
    "    print('AUC: ',AUC_stac * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1095388c",
   "metadata": {},
   "source": [
    "### 5. Gradiant Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "db518afa",
   "metadata": {},
   "outputs": [],
   "source": [
    "gb = GradientBoostingClassifier(n_estimators=24,\n",
    "                               max_depth=3,\n",
    "                               learning_rate=.71)\n",
    "gb.fit(X_train_new, y_train_new)\n",
    "y_pred = gb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "adf4b876",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9885  633]\n",
      " [ 804  631]]\n",
      "0.46758058540200076\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6897693550727375"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
    "print(cf_matrix)\n",
    "print(metrics.f1_score(y_test, y_pred))\n",
    "AUC_gb=roc_auc_score(y_test, y_pred)\n",
    "AUC_gb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1ee0b43",
   "metadata": {},
   "source": [
    "### 6. Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e7b97d66",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "52f5ab34",
   "metadata": {},
   "outputs": [],
   "source": [
    "Gauss= GaussianNB()\n",
    "Gauss.fit(X_train_new, y_train_new)\n",
    "Gauss_pred = Gauss.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "0b3964c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix \n",
      "Predicted     0     1    All\n",
      "True                        \n",
      "0          6210  4308  10518\n",
      "1           399  1036   1435\n",
      "All        6609  5344  11953\n",
      "Accuracy score  =  60.62076466159123\n",
      "AdaBoost F1 score=  30.564980085558346\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix ')\n",
    "print(pd.crosstab(y_test, Gauss_pred, rownames = ['True'], colnames = ['Predicted'], margins = True))\n",
    "print('Accuracy score  = ', accuracy_score(y_test,Gauss_pred)*100)\n",
    "f1_score = metrics.f1_score(y_test,Gauss_pred)\n",
    "print ('AdaBoost F1 score= ',f1_score * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "43907969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6561838242455442"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AUC_Gauss=roc_auc_score(y_test,Gauss_pred)\n",
    "AUC_Gauss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33d0390f",
   "metadata": {},
   "source": [
    "### Comparing performance of all modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "72893147",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ee5f290a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "# KNN\n",
    "knn_probs = knn_SMOTE.predict_proba(X_test)[:, 1]\n",
    "# LR\n",
    "lr_probs = lr_4x.predict_proba(X_test)[:, 1]\n",
    "# tree\n",
    "dtree_probs = dtree_SMOTE.predict_proba(X_test)[:, 1]\n",
    "# RF\n",
    "rf_probs = rf_SMOTE.predict_proba(X_test)[:, 1]\n",
    "# Adaboosting\n",
    "ada_probs = ada_SMOTE.predict_proba(X_test)[:, 1]\n",
    "# GB\n",
    "Gb_probs = gb.predict_proba(X_test)[:, 1]\n",
    "# NB\n",
    "NB_probs = Gauss.predict_proba(X_test)[:, 1]\n",
    "# plot baseline roc curve\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', label='Baseline')\n",
    "# calculate roc curve for model\n",
    "fpr1, tpr1, _ = roc_curve(y_test, knn_probs)\n",
    "fpr2, tpr2, _ = roc_curve(y_test, lr_probs )\n",
    "fpr3, tpr3, _ = roc_curve(y_test, dtree_probs)\n",
    "fpr4, tpr4, _ = roc_curve(y_test, rf_probs )\n",
    "fpr5, tpr5, _ = roc_curve(y_test, ada_probs)\n",
    "fpr6, tpr6, _ = roc_curve(y_test, Gb_probs)\n",
    "fpr7, tpr7, _ = roc_curve(y_test, NB_probs)\n",
    "auc1 = round(100*(roc_auc_score(y_test, knn_probs)), 3)\n",
    "auc2 = round(100*(roc_auc_score(y_test,lr_probs)), 3)\n",
    "auc3 = round(100*(roc_auc_score(y_test,dtree_probs)), 3)\n",
    "auc4 = round(100*(roc_auc_score(y_test, rf_probs)), 3)\n",
    "auc5 = round(100*(roc_auc_score(y_test, ada_probs)), 3)\n",
    "auc6 = round(100*(roc_auc_score(y_test, Gb_probs)), 3)\n",
    "auc7 = round(100*(roc_auc_score(y_test, NB_probs)), 3)\n",
    "# plot model roc curve\n",
    "plt.plot(fpr1, tpr1, linestyle='-', label=f'KNN, AUC: {auc1}')\n",
    "plt.plot(fpr2, tpr2, linestyle='-', label=f'Logistic Regression, AUC: {auc2}')\n",
    "plt.plot(fpr3, tpr3, linestyle='-', label=f'Decision Tree, AUC: {auc3}')\n",
    "plt.plot(fpr4, tpr4, linestyle='-', label=f'Random Forest, AUC: {auc4}')\n",
    "plt.plot(fpr5, tpr5, linestyle='-', label=f'Adaboosting, AUC: {auc5}')\n",
    "plt.plot(fpr6, tpr6, linestyle='-', label=f'Gradient Boosting, AUC: {auc6}')\n",
    "plt.plot(fpr7, tpr7, linestyle='-', label=f'GaussianNB, AUC: {auc7}')\n",
    "# axis labels\n",
    "plt.xlabel('‘False Positive Rate’')\n",
    "plt.ylabel('‘True Positive Rate’')\n",
    "# show the legend\n",
    "plt.legend()\n",
    "# show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fae5252",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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