F_Pro.ipynb

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   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Importing the libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "996e0164-58e7-4515-8415-e360df2f7715",
   "metadata": {
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "638a9aba-70e8-411a-8ce6-d81c0e059a2a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Loading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a6efedd-c3bd-45fc-82e9-b60f6f254471",
   "metadata": {
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_data=pd.read_csv(r\"C:\\Users\\soleh\\Downloads\\MS-EXCEL Sheets\\PP11.csv\")\n",
    "test_data= pd.read_csv(r\"C:\\Users\\soleh\\Downloads\\MS-EXCEL Sheets\\hr_test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3dce681d-3118-435b-bb46-34393db0c937",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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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>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65438</td>\n",
       "      <td>Sales &amp; Marketing</td>\n",
       "      <td>region_7</td>\n",
       "      <td>Master's &amp; above</td>\n",
       "      <td>f</td>\n",
       "      <td>sourcing</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65141</td>\n",
       "      <td>Operations</td>\n",
       "      <td>region_22</td>\n",
       "      <td>Bachelor's</td>\n",
       "      <td>m</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "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",
       "\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",
       "\n",
       "   length_of_service  KPIs_met >80%  awards_won?  avg_training_score  \\\n",
       "0                  8              1            0                  49   \n",
       "1                  4              0            0                  60   \n",
       "\n",
       "   is_promoted  \n",
       "0            0  \n",
       "1            0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f467a57-10fd-4960-981c-0bb0c2227cd4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54808, 14)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e83c5bc1-5294-45d4-976d-8a4ba74bcb53",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23490, 13)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "874b17b2-bc33-49b9-81b1-abe2e8fa5bbb",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a684bb4-a9ab-43e7-b214-b94b891f8d76",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "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>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>50684.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "      <td>54808.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39195.830627</td>\n",
       "      <td>1.253011</td>\n",
       "      <td>34.803915</td>\n",
       "      <td>3.329256</td>\n",
       "      <td>5.865512</td>\n",
       "      <td>0.351974</td>\n",
       "      <td>0.023172</td>\n",
       "      <td>63.386750</td>\n",
       "      <td>0.085170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22586.581449</td>\n",
       "      <td>0.609264</td>\n",
       "      <td>7.660169</td>\n",
       "      <td>1.259993</td>\n",
       "      <td>4.265094</td>\n",
       "      <td>0.477590</td>\n",
       "      <td>0.150450</td>\n",
       "      <td>13.371559</td>\n",
       "      <td>0.279137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19669.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39225.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58730.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78298.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        employee_id  no_of_trainings           age  previous_year_rating  \\\n",
       "count  54808.000000     54808.000000  54808.000000          50684.000000   \n",
       "mean   39195.830627         1.253011     34.803915              3.329256   \n",
       "std    22586.581449         0.609264      7.660169              1.259993   \n",
       "min        1.000000         1.000000     20.000000              1.000000   \n",
       "25%    19669.750000         1.000000     29.000000              3.000000   \n",
       "50%    39225.500000         1.000000     33.000000              3.000000   \n",
       "75%    58730.500000         1.000000     39.000000              4.000000   \n",
       "max    78298.000000        10.000000     60.000000              5.000000   \n",
       "\n",
       "       length_of_service  KPIs_met >80%   awards_won?  avg_training_score  \\\n",
       "count       54808.000000   54808.000000  54808.000000        54808.000000   \n",
       "mean            5.865512       0.351974      0.023172           63.386750   \n",
       "std             4.265094       0.477590      0.150450           13.371559   \n",
       "min             1.000000       0.000000      0.000000           39.000000   \n",
       "25%             3.000000       0.000000      0.000000           51.000000   \n",
       "50%             5.000000       0.000000      0.000000           60.000000   \n",
       "75%             7.000000       1.000000      0.000000           76.000000   \n",
       "max            37.000000       1.000000      1.000000           99.000000   \n",
       "\n",
       "        is_promoted  \n",
       "count  54808.000000  \n",
       "mean       0.085170  \n",
       "std        0.279137  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f89a2209-a723-4e2b-acd8-866dc4d85a3e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
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   "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>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>23490.000000</td>\n",
       "      <td>23490.000000</td>\n",
       "      <td>23490.000000</td>\n",
       "      <td>21678.000000</td>\n",
       "      <td>23490.000000</td>\n",
       "      <td>23490.000000</td>\n",
       "      <td>23490.000000</td>\n",
       "      <td>23490.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39041.399149</td>\n",
       "      <td>1.254236</td>\n",
       "      <td>34.782929</td>\n",
       "      <td>3.339146</td>\n",
       "      <td>5.810387</td>\n",
       "      <td>0.358834</td>\n",
       "      <td>0.022776</td>\n",
       "      <td>63.263133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22640.809201</td>\n",
       "      <td>0.600910</td>\n",
       "      <td>7.679492</td>\n",
       "      <td>1.263294</td>\n",
       "      <td>4.207917</td>\n",
       "      <td>0.479668</td>\n",
       "      <td>0.149191</td>\n",
       "      <td>13.411750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19370.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>51.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38963.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58690.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78295.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        employee_id  no_of_trainings           age  previous_year_rating  \\\n",
       "count  23490.000000     23490.000000  23490.000000          21678.000000   \n",
       "mean   39041.399149         1.254236     34.782929              3.339146   \n",
       "std    22640.809201         0.600910      7.679492              1.263294   \n",
       "min        3.000000         1.000000     20.000000              1.000000   \n",
       "25%    19370.250000         1.000000     29.000000              3.000000   \n",
       "50%    38963.500000         1.000000     33.000000              3.000000   \n",
       "75%    58690.000000         1.000000     39.000000              4.000000   \n",
       "max    78295.000000         9.000000     60.000000              5.000000   \n",
       "\n",
       "       length_of_service  KPIs_met >80%   awards_won?  avg_training_score  \n",
       "count       23490.000000   23490.000000  23490.000000        23490.000000  \n",
       "mean            5.810387       0.358834      0.022776           63.263133  \n",
       "std             4.207917       0.479668      0.149191           13.411750  \n",
       "min             1.000000       0.000000      0.000000           39.000000  \n",
       "25%             3.000000       0.000000      0.000000           51.000000  \n",
       "50%             5.000000       0.000000      0.000000           60.000000  \n",
       "75%             7.000000       1.000000      0.000000           76.000000  \n",
       "max            34.000000       1.000000      1.000000           99.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1bb89164-2aa1-4222-a912-ed7ff338d5e3",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id                0\n",
       "department                 0\n",
       "region                     0\n",
       "education               2409\n",
       "gender                     0\n",
       "recruitment_channel        0\n",
       "no_of_trainings            0\n",
       "age                        0\n",
       "previous_year_rating    4124\n",
       "length_of_service          0\n",
       "KPIs_met >80%              0\n",
       "awards_won?                0\n",
       "avg_training_score         0\n",
       "is_promoted                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for null values:\n",
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "08f35461-5735-42fd-893e-bc652a8afcc1",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id                0\n",
       "department                 0\n",
       "region                     0\n",
       "education               1034\n",
       "gender                     0\n",
       "recruitment_channel        0\n",
       "no_of_trainings            0\n",
       "age                        0\n",
       "previous_year_rating    1812\n",
       "length_of_service          0\n",
       "KPIs_met >80%              0\n",
       "awards_won?                0\n",
       "avg_training_score         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "98aee8bd-28ec-4e3d-9210-fda694f85d3f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 54808 entries, 0 to 54807\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   employee_id           54808 non-null  int64  \n",
      " 1   department            54808 non-null  object \n",
      " 2   region                54808 non-null  object \n",
      " 3   education             52399 non-null  object \n",
      " 4   gender                54808 non-null  object \n",
      " 5   recruitment_channel   54808 non-null  object \n",
      " 6   no_of_trainings       54808 non-null  int64  \n",
      " 7   age                   54808 non-null  int64  \n",
      " 8   previous_year_rating  50684 non-null  float64\n",
      " 9   length_of_service     54808 non-null  int64  \n",
      " 10  KPIs_met >80%         54808 non-null  int64  \n",
      " 11  awards_won?           54808 non-null  int64  \n",
      " 12  avg_training_score    54808 non-null  int64  \n",
      " 13  is_promoted           54808 non-null  int64  \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 5.9+ MB\n"
     ]
    }
   ],
   "source": [
    "train_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853794c3-05e6-4c0b-867d-be4a73e74a36",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8af9e82-1056-4384-a0eb-09b5d62a140f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_promoted\n",
       "0    50140\n",
       "1     4668\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['is_promoted'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4946773-a893-4e61-ab34-e09c4e71bf3b",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Data is imbalanced as most of the emoloyee's not promoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9068411-f1f1-4cf2-bc28-f32603eb3132",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='is_promoted', ylabel='count'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'is_promoted', data = train_data ,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "746fea28-1b39-4bc2-a504-dd88194bf3e9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education\n",
       "Bachelor's          36669\n",
       "Master's & above    14925\n",
       "Below Secondary       805\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df928ce1-4d3b-4ad9-a3ef-403052d4e388",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='education', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'education',data=train_data,hue='is_promoted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f6220fd3-5d8c-4495-a1e4-256e7f8bab65",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'gender',data=train_data,hue='is_promoted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9dff7cdf-2bc7-4eff-90c7-c46b1e405a3e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2139a82ef90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(train_data['age'],kde=True)#kernal density estimation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022e6660-4ac5-4feb-a644-f746585d9a8f",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "plot is rightly_skewed positive graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eec7df11-bbda-4816-8379-0fa2fddd5c6d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='previous_year_rating', ylabel='count'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=train_data['previous_year_rating'],data=train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2c6604f9-c8cf-4d83-9c00-5491758e5dcb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='department', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x = 'department',data= train_data,hue='is_promoted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6bbf776e-607f-4a9b-a628-d743aaa24332",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='recruitment_channel', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'recruitment_channel',data=train_data,hue= 'is_promoted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b75f96e-3732-4dc3-945a-291535cc291e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2139aee1c90>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(6,4))\n",
    "plt.hist([train_data[train_data['is_promoted']==1]['age'],\n",
    "          train_data[train_data['is_promoted']==0]['age']],\n",
    "         color=['g','b'],\n",
    "         bins = 10,label=['Promoted','Not Promoted'])\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('No. of Employee')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e0dac438-1612-4092-9464-cb5493872f13",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2139b34c950>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(6,4))\n",
    "plt.hist([train_data[train_data['is_promoted']==1]['length_of_service'],\n",
    "          train_data[train_data['is_promoted']==0]['length_of_service']],\n",
    "         color=['g','r'],\n",
    "         bins = 10,label=['Promoted','Not Promoted'])\n",
    "plt.xlabel('Length of service')\n",
    "plt.ylabel('No. of Employee')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "146afa3d-4030-4487-bea4-f956739ccad7",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='length_of_service'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data.plot.scatter('age','length_of_service',c = 'is_promoted',\n",
    "                        colormap='jet',alpha=0.8,figsize=(8,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80d873e3-3d13-47bc-a2be-8cf41c0c7748",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='awards_won?', ylabel='count'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'awards_won?', data = train_data,hue = 'is_promoted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5b4e6b87-4302-413e-9402-e01b4bd82d6d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2139d4e82d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,5))\n",
    "sns.set_style('darkgrid')\n",
    "sns.boxplot(x='education',y='length_of_service',data=train_data,hue='is_promoted')\n",
    "plt.legend(loc=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "436816f8-636b-49d9-9f24-db5536fdc510",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='education', ylabel='length_of_service'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.stripplot(x='education',y = 'length_of_service',data= train_data,jitter=True,hue='is_promoted',dodge=True)\n",
    "           "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e8373b6-d97a-454a-99cd-93000832a824",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "67dffe81-e5cd-4cce-8c35-90bce5d044fc",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\soleh\\AppData\\Local\\Temp\\ipykernel_15536\\2255178454.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  train_data['education'].fillna(\"Bachelor's\",inplace=True)\n",
      "C:\\Users\\soleh\\AppData\\Local\\Temp\\ipykernel_15536\\2255178454.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  test_data['education'].fillna(\"Bachelor's\",inplace=True)\n"
     ]
    }
   ],
   "source": [
    "train_data['education'].fillna(\"Bachelor's\",inplace=True)\n",
    "test_data['education'].fillna(\"Bachelor's\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d64364eb-5e1b-45c5-a6df-1d9323611f24",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3106378431385327"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['previous_year_rating'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b0544fb-5a73-4ecb-817a-edf23b603d0a",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Numerical data so checked skewness which is negative hence using medain for NA value imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "24a255c7-be35-430c-8b40-a785340b474c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\soleh\\AppData\\Local\\Temp\\ipykernel_15536\\368697610.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  train_data['previous_year_rating'].fillna(train_data['previous_year_rating'].median(),inplace=True)\n"
     ]
    }
   ],
   "source": [
    "train_data['previous_year_rating'].fillna(train_data['previous_year_rating'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7b10f319-8a8e-43ee-95f3-6edb77480318",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\soleh\\AppData\\Local\\Temp\\ipykernel_15536\\3055590629.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  test_data['previous_year_rating'].fillna(test_data['previous_year_rating'].median(),inplace=True)\n"
     ]
    }
   ],
   "source": [
    "test_data['previous_year_rating'].fillna(test_data['previous_year_rating'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a32eac81-35f5-4dfd-bcf0-fc0001edae74",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id             0\n",
       "department              0\n",
       "region                  0\n",
       "education               0\n",
       "gender                  0\n",
       "recruitment_channel     0\n",
       "no_of_trainings         0\n",
       "age                     0\n",
       "previous_year_rating    0\n",
       "length_of_service       0\n",
       "KPIs_met >80%           0\n",
       "awards_won?             0\n",
       "avg_training_score      0\n",
       "is_promoted             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ca706fb6-a7aa-4f81-a92f-12876de16c00",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee_id             0\n",
       "department              0\n",
       "region                  0\n",
       "education               0\n",
       "gender                  0\n",
       "recruitment_channel     0\n",
       "no_of_trainings         0\n",
       "age                     0\n",
       "previous_year_rating    0\n",
       "length_of_service       0\n",
       "KPIs_met >80%           0\n",
       "awards_won?             0\n",
       "avg_training_score      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d993a592-4f04-4f2c-915f-e0d11fee90c7",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "We have five attributes which are categorical or string type,label conversion is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "60841f66-aa53-4a50-b3d3-00370f600a6b",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder    #for gender:M=1,F=0\n",
    "lbl_enco = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f6e077e0-092e-4e24-8c10-3d9e080ace61",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_data['department'] = lbl_enco.fit_transform(train_data['department'])\n",
    "test_data['department'] = lbl_enco.fit_transform(test_data['department'])\n",
    "train_data['gender'] = lbl_enco.fit_transform(train_data['gender'])\n",
    "test_data['gender'] = lbl_enco.fit_transform(test_data['gender'])\n",
    "train_data['education'] = lbl_enco.fit_transform(train_data['education'])\n",
    "test_data['education'] = lbl_enco.fit_transform(test_data['education'])\n",
    "train_data['recruitment_channel'] = lbl_enco.fit_transform(train_data['recruitment_channel'])\n",
    "test_data['recruitment_channel'] = lbl_enco.fit_transform(test_data['recruitment_channel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7cd3671a-c455-43aa-9067-24ca0b2fc83f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_data.drop( labels = 'employee_id',axis=1,inplace=True)\n",
    "train_data.drop(labels='region',axis=1,inplace=True)\n",
    "test_data.drop(labels='region',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a653d950-34d1-42cb-867b-2c380e9909fd",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>recruitment_channel</th>\n",
       "      <th>no_of_trainings</th>\n",
       "      <th>age</th>\n",
       "      <th>previous_year_rating</th>\n",
       "      <th>length_of_service</th>\n",
       "      <th>KPIs_met &gt;80%</th>\n",
       "      <th>awards_won?</th>\n",
       "      <th>avg_training_score</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   department  education  gender  recruitment_channel  no_of_trainings  age  \\\n",
       "0           7          2       0                    2                1   35   \n",
       "1           4          0       1                    0                1   30   \n",
       "2           7          0       1                    2                1   34   \n",
       "\n",
       "   previous_year_rating  length_of_service  KPIs_met >80%  awards_won?  \\\n",
       "0                   5.0                  8              1            0   \n",
       "1                   5.0                  4              0            0   \n",
       "2                   3.0                  7              0            0   \n",
       "\n",
       "   avg_training_score  is_promoted  \n",
       "0                  49            0  \n",
       "1                  60            0  \n",
       "2                  50            0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a4b0274-5776-4c7d-a384-ef0e1dbccd28",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Correlation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "71045c0b-b624-4f90-9eb7-e5627e96a479",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['department', 'education', 'gender', 'recruitment_channel',\n",
       "       'no_of_trainings', 'age', 'previous_year_rating', 'length_of_service',\n",
       "       'KPIs_met >80%', 'awards_won?', 'avg_training_score', 'is_promoted'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ac387067-0409-4304-b94e-fc12c60da4a2",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rel_features =['department', 'education', 'gender', 'recruitment_channel',\n",
    "       'no_of_trainings', 'age', 'previous_year_rating', 'length_of_service',\n",
    "       'KPIs_met >80%', 'awards_won?', 'avg_training_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0e003f90-6310-445d-b5ab-f680f48baea9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rel_features_corr = train_data.corr()['is_promoted'][['department', 'education', 'gender', 'recruitment_channel',\n",
    "       'no_of_trainings', 'age', 'previous_year_rating', 'length_of_service',\n",
    "       'KPIs_met >80%', 'awards_won?', 'avg_training_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "912bf932-e5eb-4a36-ac23-6135f962bb92",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "department              0.000130\n",
       "education               0.029257\n",
       "gender                 -0.011109\n",
       "recruitment_channel     0.002229\n",
       "no_of_trainings        -0.024896\n",
       "age                    -0.017166\n",
       "previous_year_rating    0.153230\n",
       "length_of_service      -0.010670\n",
       "KPIs_met >80%           0.221582\n",
       "awards_won?             0.195871\n",
       "avg_training_score      0.181147\n",
       "Name: is_promoted, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel_features_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a80a3499-873f-4f84-b7c8-2ceb2d2d2f91",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Here KPI's, award's won & avg_training_score attributes have positive correlation thus having high impact on target variable('is_promoted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "aa1d3bd7-a45a-4b97-b01e-54c90e6cf750",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "sns.heatmap(train_data[rel_features].corr(),annot = True, linewidths = 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879bbc3a-6427-4d22-8c10-9ea7fb5f846f",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c23f8f6c-df7e-458c-b649-b0357611435c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "x= train_data[rel_features]\n",
    "y = train_data['is_promoted']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "175a38c0-6ad9-41a2-ac01-e662209be9b4",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e6e8339-28d7-456c-8985-c7576901cf51",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "57c4d81c-7362-4ada-ad1c-b0b5b6d59f6e",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\soleh\\anaconda3\\envs\\notebook\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [21:37:05] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-06abd128ca6c1688d-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"nthreads\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(alpha=5, base_score=0.15, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
       "              min_child_weight=5, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=200, n_jobs=None, nthreads=5, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(alpha=5, base_score=0.15, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
       "              min_child_weight=5, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=200, n_jobs=None, nthreads=5, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(alpha=5, base_score=0.15, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=0, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=10, max_leaves=None,\n",
       "              min_child_weight=5, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=200, n_jobs=None, nthreads=5, ...)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "clf_xgb = XGBClassifier(n_estimators=200,\n",
    "max_depth=10,\n",
    "min_child_weight=5,\n",
    "gamma=0,\n",
    "subsample=0.5,\n",
    "objective='binary:logistic',\n",
    "nthreads = 5,\n",
    "scale_pos_weight =13,\n",
    "reg_lambda=5,\n",
    "alpha=5,\n",
    "base_score=0.15,\n",
    "random_state=45)\n",
    "clf_xgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "027b93ea-4532-42e5-81d9-b01af5b1d118",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#Predicting test set results:\n",
    "xg_pred = clf_xgb.predict(x_test).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9f7b1059-bc84-4727-8202-6eda5a620b3a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function confusion_matrix at 0x000002139736B600> [[8961 1080]\n",
      " [ 383  538]]\n",
      "<function classification_report at 0x00000213973849A0>               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.89      0.92     10041\n",
      "           1       0.33      0.58      0.42       921\n",
      "\n",
      "    accuracy                           0.87     10962\n",
      "   macro avg       0.65      0.74      0.67     10962\n",
      "weighted avg       0.91      0.87      0.88     10962\n",
      "\n",
      "Accuracy: 0.8665389527458492\n"
     ]
    }
   ],
   "source": [
    "#Evaluate Predictions:\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
    "print(confusion_matrix,confusion_matrix(y_test,xg_pred))\n",
    "print(classification_report,classification_report(y_test,xg_pred))\n",
    "print(\"Accuracy:\",accuracy_score(y_test,xg_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61c631c-fee9-42f5-a90d-70a18fcbd593",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c851df8e-2882-4c4e-88e2-affd83cea748",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#The problem is classification one :Importing Random Forest model:\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "Parameters = {'bootstrap':False,\n",
    "              'min_samples_leaf':3,\n",
    "              'n_estimators':500,\n",
    "              'min_samples_split':10,\n",
    "              'max_features':'sqrt',\n",
    "              'max_depth':10\n",
    "             }\n",
    "#Create Random forest classifier with 100 trees\n",
    "clf_rf = RandomForestClassifier(**Parameters)\n",
    "#Training the model\n",
    "clf_rf.fit(x_train,y_train)\n",
    "\n",
    "rf_pred=clf_rf.predict(x_test).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2c170795-0f62-4348-a5e8-0aeab043ddf5",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function confusion_matrix at 0x000002139736B600> [[10025    16]\n",
      " [  750   171]]\n",
      "<function classification_report at 0x00000213973849A0>               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      1.00      0.96     10041\n",
      "           1       0.91      0.19      0.31       921\n",
      "\n",
      "    accuracy                           0.93     10962\n",
      "   macro avg       0.92      0.59      0.64     10962\n",
      "weighted avg       0.93      0.93      0.91     10962\n",
      "\n",
      "Accuracy: 0.930122240467068\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix,confusion_matrix(y_test,rf_pred))\n",
    "print(classification_report,classification_report(y_test,rf_pred))\n",
    "print(\"Accuracy:\",accuracy_score(y_test,rf_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fc96e04-de78-4749-af97-626549a11a94",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Accuracy is not a good parameter for classification models, here focus is on recall or f1-score to make it close to 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a37b30b6-dd06-4eda-b6a8-d8596df6319c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18566775244299674"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "recall_score(y_test,rf_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "99f43201-ce28-4481-a8c8-ff7c327fc60c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "rf_pred = clf_rf.predict(test_data[rel_features]).astype(int)\n",
    "sub=pd.DataFrame()\n",
    "sub ['employee_id'] = test_data['employee_id']\n",
    "sub['is_promoted'] =rf_pred\n",
    "# sub[['employee_id','is_promoted']].to_csv('submission_rf.csv',index=False)|| To save prediction in a csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "26377e12-e4d2-4cbd-a686-0547cd682bcb",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>is_promoted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>74430</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72255</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38562</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64486</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id  is_promoted\n",
       "0         8724            0\n",
       "1        74430            0\n",
       "2        72255            0\n",
       "3        38562            0\n",
       "4        64486            0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "78d24973-b867-40d8-8b67-c9f69b562dcc",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23490, 2)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "baf78e83-ef04-41db-868a-120075f0ba9c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_promoted\n",
       "0    23156\n",
       "1      334\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub.is_promoted.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468b3518-48c1-4d39-b527-3f3c6870fea7",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Count for 1 is too low w.r.t total observations which implies model is negatively trained."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5496cab-dba3-49ae-ac8a-d190f9a07b4c",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## GBM(GradientBoostingModel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36e82e05-d9fa-4a84-97e3-8c583c0f4a2f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import ensemble\n",
    "gbm = ensemble.GradientBoostingClassifier(n_estimators =500,max_depth =10,\n",
    "                                         learning_rate=0.1)\n",
    "gbm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ae456bb-3c45-476c-a35f-87e2f352783d",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "gbm_prediction=gbm.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a1a745d-0ec9-4365-8946-216a4b9682d9",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "#Evaluate Predictions:\n",
    "gbm_prediction = gbm.predict(test_data[rel_features]).astype(int)\n",
    "sub=pd.DataFrame()\n",
    "sub ['employee_id'] = test_data['employee_id']\n",
    "sub['is_promoted'] =gbm_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc1b753e-dd4e-4323-a82d-f0cb1c5e8040",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "sub.is_promoted.value_counts()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "44c8299c-fbb3-463f-a212-dadbb80e73d2",
   "metadata": {
    "pycharm": {
     "name": "#%% raw\n"
    }
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f1eca09b-dedf-43d7-b674-8737e7ab6f41",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Different Model Predictions for 0's and 1's :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a269f751-4763-491e-a7cc-08e94af6076c",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "##   XGBooster(XGB) --------------- RandomForest(RF) --------------GradientBoosting(GBM)\n",
    "\n",
    "###    0-______1______________________________0_____________1 ____________________________________0________________1\n",
    "\n",
    "#### 10041______921________________________________23154__________336___________________________________________22269_______________1221"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cc3cb2b-bbb2-4d23-815f-30362271817f",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99167326-1e94-4fa8-8cc9-0d0912f9a54c",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6273d2f-1ceb-4bd9-808a-8e84f57784bc",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e46de5c4-e48e-4462-821c-a7c4abcb308a",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6524d1a8-e74e-4923-9b08-f61dd10ac551",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9446360-2c0e-47e1-8d0d-368205fd7d46",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
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