LogisticRegrission(dummy_varibles).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Gouse MG\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (130,131,132,133,134,143,145,150,151,152) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(r'C:\\Users\\Gouse MG\\Desktop\\Machine Learning\\final3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128574, 191)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=df[['masked id','performance pay score','volatility index','leave quota', 'eps', 'esi',\n",
    "       'career score', 'alignment score', 'recognition score',\n",
    "       'empowerment score', 'strive score', 'rise score', 'crusade score',\n",
    "       'simplicity score', 'collaboration score', 'training (mcares) score',\n",
    "       'role change in 3 years', ' award counts in 3 years','reporting level','leave availed','generation','rating change','ctc til app'\n",
    "        ,'m&m tenure','talent category','ctc marketmedian','scaled travel', 'award count_x', 'ctc hike',\n",
    "       'current rating score', 'previous rating score','budget utilization percentage','time since last promotion','timeinlevel app','ctc internal median','train cnt attended beh',\n",
    "       'train cnt attended fun', 'train cnt beh fun','attrreportees new','ind','work home location','fresher flag','attrition','year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1=data[data['year'].isin([2018,2019,2020])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>masked id</th>\n",
       "      <th>performance pay score</th>\n",
       "      <th>volatility index</th>\n",
       "      <th>leave quota</th>\n",
       "      <th>eps</th>\n",
       "      <th>esi</th>\n",
       "      <th>career score</th>\n",
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       "      <th>empowerment score</th>\n",
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       "      <th>fresher flag</th>\n",
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       "      <th>year</th>\n",
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       "  </thead>\n",
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       "      <td>oyh1343205211hwl</td>\n",
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       "      <td>2020</td>\n",
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       "      <th>2</th>\n",
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       "      <td>111.23</td>\n",
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       "      <td>0.00</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.98</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2019</td>\n",
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       "      <th>3</th>\n",
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       "      <td>0.04</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.96</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>aff1092253544hkf</td>\n",
       "      <td>128.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.26</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.26</td>\n",
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       "      <td>2018</td>\n",
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       "      <td>0.11</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2020</td>\n",
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       "      <td>2020</td>\n",
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       "      <td>2019</td>\n",
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       "      <td>0.30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>4.000000</td>\n",
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       "      <td>0.84</td>\n",
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       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128572</th>\n",
       "      <td>kkj8920505711vot</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61836 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               masked id  performance pay score  volatility index  \\\n",
       "0       oyh1343205211hwl                 143.88              0.02   \n",
       "2       hvz9469205221evk                 111.23              0.10   \n",
       "3       hvz9469205221evk                 101.57              0.26   \n",
       "5       aff1092253544hkf                 128.32              0.00   \n",
       "6       aff1092253544hkf                 113.81              0.11   \n",
       "...                  ...                    ...               ...   \n",
       "128554  cot7754410001alz                   0.00              0.00   \n",
       "128556  tsi2962600021vpm                    NaN              0.06   \n",
       "128559  avd1207009182fyu                   0.00              0.33   \n",
       "128561  avd1207009182fyu                 111.84              0.30   \n",
       "128572  kkj8920505711vot                   0.00              0.25   \n",
       "\n",
       "        leave quota   eps  esi  career score  alignment score  \\\n",
       "0              0.25   NaN  NaN           NaN              NaN   \n",
       "2              0.00   NaN  NaN           NaN              NaN   \n",
       "3              0.04   NaN  NaN           NaN              NaN   \n",
       "5              0.26   8.0  5.0           5.0         3.666667   \n",
       "6              0.16   NaN  NaN           NaN              NaN   \n",
       "...             ...   ...  ...           ...              ...   \n",
       "128554          NaN   NaN  NaN           NaN              NaN   \n",
       "128556          NaN   NaN  NaN           NaN              NaN   \n",
       "128559         0.08   NaN  NaN           NaN              NaN   \n",
       "128561         0.92  10.0  4.0           4.0         4.200000   \n",
       "128572         0.09   NaN  NaN           NaN              NaN   \n",
       "\n",
       "        recognition score  empowerment score  ...  ctc internal median  \\\n",
       "0                     NaN                NaN  ...                 0.88   \n",
       "2                     NaN                NaN  ...                 0.98   \n",
       "3                     NaN                NaN  ...                 0.96   \n",
       "5                3.666667           3.666667  ...                 1.26   \n",
       "6                     NaN                NaN  ...                 1.27   \n",
       "...                   ...                ...  ...                  ...   \n",
       "128554                NaN                NaN  ...                 0.00   \n",
       "128556                NaN                NaN  ...                 0.48   \n",
       "128559                NaN                NaN  ...                 1.19   \n",
       "128561           4.000000           4.000000  ...                 0.84   \n",
       "128572                NaN                NaN  ...                 0.79   \n",
       "\n",
       "        train cnt attended beh  train cnt attended fun  train cnt beh fun  \\\n",
       "0                          0.0                     0.0                0.0   \n",
       "2                          0.0                     0.0                0.0   \n",
       "3                          0.0                     0.0                0.0   \n",
       "5                          0.0                     7.0                7.0   \n",
       "6                          0.0                     0.0                0.0   \n",
       "...                        ...                     ...                ...   \n",
       "128554                     0.0                     0.0                0.0   \n",
       "128556                     0.0                     0.0                0.0   \n",
       "128559                     0.0                     0.0                0.0   \n",
       "128561                     2.0                     0.0                2.0   \n",
       "128572                     0.0                     0.0                0.0   \n",
       "\n",
       "        attrreportees new  ind work home location  fresher flag attrition  \\\n",
       "0                     0.0  1.0                NaN           0.0       NaN   \n",
       "2                     0.0  1.0                NaN           0.0       NaN   \n",
       "3                     0.0  1.0                NaN           0.0       NaN   \n",
       "5                     0.0  1.0          different           0.0       NaN   \n",
       "6                     0.0  1.0                NaN           0.0       NaN   \n",
       "...                   ...  ...                ...           ...       ...   \n",
       "128554                0.0  1.0                NaN           0.0       NaN   \n",
       "128556                0.0  1.0                NaN           0.0       NaN   \n",
       "128559                0.0  1.0               same           0.0       NaN   \n",
       "128561                1.0  1.0               same           0.0       NaN   \n",
       "128572                0.0  1.0                NaN           0.0       NaN   \n",
       "\n",
       "        year  \n",
       "0       2020  \n",
       "2       2019  \n",
       "3       2020  \n",
       "5       2018  \n",
       "6       2020  \n",
       "...      ...  \n",
       "128554  2019  \n",
       "128556  2020  \n",
       "128559  2019  \n",
       "128561  2018  \n",
       "128572  2020  \n",
       "\n",
       "[61836 rows x 44 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked id                        0.000000\n",
       "performance pay score            0.060798\n",
       "volatility index                 0.000000\n",
       "leave quota                      0.047716\n",
       "eps                              0.238734\n",
       "esi                              0.238734\n",
       "career score                     0.238734\n",
       "alignment score                  0.238734\n",
       "recognition score                0.238734\n",
       "empowerment score                0.238734\n",
       "strive score                     0.238734\n",
       "rise score                       0.238734\n",
       "crusade score                    0.238734\n",
       "simplicity score                 0.238734\n",
       "collaboration score              0.238734\n",
       "training (mcares) score          0.238734\n",
       "role change in 3 years           0.437413\n",
       " award counts in 3 years         0.171567\n",
       "reporting level                  0.117730\n",
       "leave availed                    0.081533\n",
       "generation                       0.023146\n",
       "rating change                    0.172305\n",
       "ctc til app                      0.000000\n",
       "m&m tenure                       0.000000\n",
       "talent category                  0.338334\n",
       "ctc marketmedian                 0.401512\n",
       "scaled travel                    0.000000\n",
       "award count_x                    0.236727\n",
       "ctc hike                         0.083757\n",
       "current rating score             0.132943\n",
       "previous rating score            0.124318\n",
       "budget utilization percentage    0.351090\n",
       "time since last promotion        0.222106\n",
       "timeinlevel app                  0.222183\n",
       "ctc internal median              0.000000\n",
       "train cnt attended beh           0.000000\n",
       "train cnt attended fun           0.000000\n",
       "train cnt beh fun                0.000000\n",
       "attrreportees new                0.000000\n",
       "ind                              0.000000\n",
       "work home location               0.168028\n",
       "fresher flag                     0.000000\n",
       "attrition                        0.435975\n",
       "year                             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.isnull().sum()/data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          NaN\n",
       "2          NaN\n",
       "3         1.13\n",
       "5          NaN\n",
       "6          NaN\n",
       "          ... \n",
       "128554     NaN\n",
       "128556     NaN\n",
       "128559    1.54\n",
       "128561     NaN\n",
       "128572     NaN\n",
       "Name: ctc marketmedian, Length: 61836, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['ctc marketmedian']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fresher=data1['fresher flag']\n",
    "fresherdummies=pd.get_dummies(fresher)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-479147af423a>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['attrition']=data1['attrition'].fillna(0)\n",
      "<ipython-input-10-479147af423a>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['performance pay score']=data1['performance pay score'].fillna(data1['performance pay score'].mean())\n",
      "<ipython-input-10-479147af423a>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['leave quota']=data1['leave quota'].fillna(data1['leave quota'].mean())\n",
      "<ipython-input-10-479147af423a>:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['eps']=data1['eps'].fillna(data1['eps'].mean())\n",
      "<ipython-input-10-479147af423a>:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['esi']=data1['esi'].fillna(data1['esi'].mean())\n",
      "<ipython-input-10-479147af423a>:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['alignment score']=data1['alignment score'].fillna(data1['alignment score'].mean())\n",
      "<ipython-input-10-479147af423a>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['career score']=data1['career score'].fillna(data1['career score'].mean())\n",
      "<ipython-input-10-479147af423a>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['recognition score']=data1['recognition score'].fillna(data1['recognition score'].mean())\n",
      "<ipython-input-10-479147af423a>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['empowerment score']=data1['empowerment score'].fillna(data1['empowerment score'].mean())\n",
      "<ipython-input-10-479147af423a>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['strive score']=data1['strive score'].fillna(data1['strive score'].mean())\n",
      "<ipython-input-10-479147af423a>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['rise score']=data1['rise score'].fillna(data1['rise score'].mean())\n",
      "<ipython-input-10-479147af423a>:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['crusade score']=data1['crusade score'].fillna(data1['crusade score'].mean())\n",
      "<ipython-input-10-479147af423a>:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['simplicity score']=data1['simplicity score'].fillna(data1['simplicity score'].mean())\n",
      "<ipython-input-10-479147af423a>:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['collaboration score']=data1['collaboration score'].fillna(data1['collaboration score'].mean())\n",
      "<ipython-input-10-479147af423a>:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['training (mcares) score']=data1['training (mcares) score'].fillna(data1['training (mcares) score'].mean())\n",
      "<ipython-input-10-479147af423a>:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['role change in 3 years']=data1['role change in 3 years'].fillna('Yes')\n",
      "<ipython-input-10-479147af423a>:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1[' award counts in 3 years']=data1[' award counts in 3 years'].fillna(data1[' award counts in 3 years'].mean())\n",
      "<ipython-input-10-479147af423a>:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['ctc hike']=data1['ctc hike'].fillna(data1['ctc hike'].mean())\n",
      "<ipython-input-10-479147af423a>:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['reporting level']=data1['reporting level'].fillna('nan')\n",
      "<ipython-input-10-479147af423a>:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['leave availed']=data1['leave availed'].fillna(data1['leave availed'].mean())\n",
      "<ipython-input-10-479147af423a>:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['current rating score']=data1['current rating score'].fillna(3)\n",
      "<ipython-input-10-479147af423a>:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['previous rating score']=data1['previous rating score'].fillna(3)\n",
      "<ipython-input-10-479147af423a>:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['reporting level']=data1['reporting level'].fillna('nan')\n",
      "<ipython-input-10-479147af423a>:24: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['leave availed']=data1['leave availed'].fillna(data1['leave availed'].mean())\n",
      "<ipython-input-10-479147af423a>:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['current rating score']=data1['current rating score'].fillna(3)\n",
      "<ipython-input-10-479147af423a>:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['previous rating score']=data1['previous rating score'].fillna(3)\n",
      "<ipython-input-10-479147af423a>:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['time since last promotion']=data1['time since last promotion'].fillna(data1['time since last promotion'].mean())\n",
      "<ipython-input-10-479147af423a>:28: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['timeinlevel app']=data1['timeinlevel app'].fillna(3)\n",
      "<ipython-input-10-479147af423a>:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['work home location']=data1['work home location'].fillna('na')\n",
      "<ipython-input-10-479147af423a>:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['generation']=data1['generation'].fillna('nan')\n",
      "<ipython-input-10-479147af423a>:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['rating change']=data1['rating change'].fillna('nan')\n",
      "<ipython-input-10-479147af423a>:32: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['ctc marketmedian']=data1['ctc marketmedian'].fillna('')\n"
     ]
    }
   ],
   "source": [
    "data1['attrition']=data1['attrition'].fillna(0)\n",
    "data1['performance pay score']=data1['performance pay score'].fillna(data1['performance pay score'].mean())\n",
    "data1['leave quota']=data1['leave quota'].fillna(data1['leave quota'].mean())\n",
    "data1['eps']=data1['eps'].fillna(data1['eps'].mean())\n",
    "data1['esi']=data1['esi'].fillna(data1['esi'].mean())\n",
    "data1['alignment score']=data1['alignment score'].fillna(data1['alignment score'].mean())\n",
    "data1['career score']=data1['career score'].fillna(data1['career score'].mean())\n",
    "data1['recognition score']=data1['recognition score'].fillna(data1['recognition score'].mean())\n",
    "data1['empowerment score']=data1['empowerment score'].fillna(data1['empowerment score'].mean())\n",
    "data1['strive score']=data1['strive score'].fillna(data1['strive score'].mean())\n",
    "data1['rise score']=data1['rise score'].fillna(data1['rise score'].mean())\n",
    "data1['crusade score']=data1['crusade score'].fillna(data1['crusade score'].mean())\n",
    "data1['simplicity score']=data1['simplicity score'].fillna(data1['simplicity score'].mean())\n",
    "data1['collaboration score']=data1['collaboration score'].fillna(data1['collaboration score'].mean())\n",
    "data1['training (mcares) score']=data1['training (mcares) score'].fillna(data1['training (mcares) score'].mean())\n",
    "data1['role change in 3 years']=data1['role change in 3 years'].fillna('Yes')\n",
    "data1[' award counts in 3 years']=data1[' award counts in 3 years'].fillna(data1[' award counts in 3 years'].mean())\n",
    "data1['ctc hike']=data1['ctc hike'].fillna(data1['ctc hike'].mean())\n",
    "data1['reporting level']=data1['reporting level'].fillna('nan')\n",
    "data1['leave availed']=data1['leave availed'].fillna(data1['leave availed'].mean())\n",
    "data1['current rating score']=data1['current rating score'].fillna(3)\n",
    "data1['previous rating score']=data1['previous rating score'].fillna(3)\n",
    "data1['reporting level']=data1['reporting level'].fillna('nan')\n",
    "data1['leave availed']=data1['leave availed'].fillna(data1['leave availed'].mean())\n",
    "data1['current rating score']=data1['current rating score'].fillna(3)\n",
    "data1['previous rating score']=data1['previous rating score'].fillna(3)\n",
    "data1['time since last promotion']=data1['time since last promotion'].fillna(data1['time since last promotion'].mean())\n",
    "data1['timeinlevel app']=data1['timeinlevel app'].fillna(3)\n",
    "data1['work home location']=data1['work home location'].fillna('na')\n",
    "data1['generation']=data1['generation'].fillna('nan')\n",
    "data1['rating change']=data1['rating change'].fillna('nan')\n",
    "data1['ctc marketmedian']=data1['ctc marketmedian'].fillna('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying one hot encoding to categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical=data1[['role change in 3 years','reporting level','generation','rating change','work home location']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummie=pd.get_dummies(categorical, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <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>61831</th>\n",
       "      <td>128554</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61832</th>\n",
       "      <td>128556</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61833</th>\n",
       "      <td>128559</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61834</th>\n",
       "      <td>128561</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61835</th>\n",
       "      <td>128572</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61836 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index  role change in 3 years_Yes  reporting level_2 above  \\\n",
       "0           0                           1                        1   \n",
       "1           2                           1                        0   \n",
       "2           3                           1                        0   \n",
       "3           5                           1                        1   \n",
       "4           6                           1                        1   \n",
       "...       ...                         ...                      ...   \n",
       "61831  128554                           1                        0   \n",
       "61832  128556                           1                        1   \n",
       "61833  128559                           1                        0   \n",
       "61834  128561                           1                        0   \n",
       "61835  128572                           1                        1   \n",
       "\n",
       "       reporting level_nan  reporting level_same level  \\\n",
       "0                        0                           0   \n",
       "1                        0                           1   \n",
       "2                        0                           1   \n",
       "3                        0                           0   \n",
       "4                        0                           0   \n",
       "...                    ...                         ...   \n",
       "61831                    1                           0   \n",
       "61832                    0                           0   \n",
       "61833                    1                           0   \n",
       "61834                    1                           0   \n",
       "61835                    0                           0   \n",
       "\n",
       "       generation_generation x  generation_millenials  generation_nan  \\\n",
       "0                            0                      1               0   \n",
       "1                            0                      1               0   \n",
       "2                            0                      1               0   \n",
       "3                            0                      1               0   \n",
       "4                            0                      1               0   \n",
       "...                        ...                    ...             ...   \n",
       "61831                        0                      1               0   \n",
       "61832                        0                      0               1   \n",
       "61833                        0                      0               1   \n",
       "61834                        0                      0               1   \n",
       "61835                        0                      1               0   \n",
       "\n",
       "       rating change_improved  rating change_nan  rating change_static  \\\n",
       "0                           0                  1                     0   \n",
       "1                           0                  1                     0   \n",
       "2                           0                  1                     0   \n",
       "3                           0                  0                     1   \n",
       "4                           0                  0                     0   \n",
       "...                       ...                ...                   ...   \n",
       "61831                       0                  1                     0   \n",
       "61832                       0                  1                     0   \n",
       "61833                       0                  1                     0   \n",
       "61834                       0                  0                     1   \n",
       "61835                       0                  1                     0   \n",
       "\n",
       "       work home location_na  work home location_same  0.0  1.0  \n",
       "0                          1                        0    1    0  \n",
       "1                          1                        0    1    0  \n",
       "2                          1                        0    1    0  \n",
       "3                          0                        0    1    0  \n",
       "4                          1                        0    1    0  \n",
       "...                      ...                      ...  ...  ...  \n",
       "61831                      1                        0    1    0  \n",
       "61832                      1                        0    1    0  \n",
       "61833                      0                        1    1    0  \n",
       "61834                      0                        1    1    0  \n",
       "61835                      1                        0    1    0  \n",
       "\n",
       "[61836 rows x 15 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummie=pd.concat([dummie,fresherdummies],axis=1)\n",
    "dummie=dummie.reset_index()\n",
    "dummie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "continues =[features for features in data1.columns if data1[features].dtype!='O']\n",
    "continues =data1[continues]\n",
    "continues=continues.drop(['year'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>performance pay score</th>\n",
       "      <th>volatility index</th>\n",
       "      <th>leave quota</th>\n",
       "      <th>eps</th>\n",
       "      <th>esi</th>\n",
       "      <th>career score</th>\n",
       "      <th>alignment score</th>\n",
       "      <th>recognition score</th>\n",
       "      <th>empowerment score</th>\n",
       "      <th>strive score</th>\n",
       "      <th>...</th>\n",
       "      <th>time since last promotion</th>\n",
       "      <th>timeinlevel app</th>\n",
       "      <th>ctc internal median</th>\n",
       "      <th>train cnt attended beh</th>\n",
       "      <th>train cnt attended fun</th>\n",
       "      <th>train cnt beh fun</th>\n",
       "      <th>attrreportees new</th>\n",
       "      <th>ind</th>\n",
       "      <th>fresher flag</th>\n",
       "      <th>attrition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>143.880000</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>8.579397</td>\n",
       "      <td>4.316234</td>\n",
       "      <td>4.166443</td>\n",
       "      <td>4.417203</td>\n",
       "      <td>4.233572</td>\n",
       "      <td>4.221121</td>\n",
       "      <td>4.094722</td>\n",
       "      <td>...</td>\n",
       "      <td>3.102149</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.88</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111.230000</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.579397</td>\n",
       "      <td>4.316234</td>\n",
       "      <td>4.166443</td>\n",
       "      <td>4.417203</td>\n",
       "      <td>4.233572</td>\n",
       "      <td>4.221121</td>\n",
       "      <td>4.094722</td>\n",
       "      <td>...</td>\n",
       "      <td>3.102149</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101.570000</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>8.579397</td>\n",
       "      <td>4.316234</td>\n",
       "      <td>4.166443</td>\n",
       "      <td>4.417203</td>\n",
       "      <td>4.233572</td>\n",
       "      <td>4.221121</td>\n",
       "      <td>4.094722</td>\n",
       "      <td>...</td>\n",
       "      <td>3.102149</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>128.320000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>113.810000</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>8.579397</td>\n",
       "      <td>4.316234</td>\n",
       "      <td>4.166443</td>\n",
       "      <td>4.417203</td>\n",
       "      <td>4.233572</td>\n",
       "      <td>4.221121</td>\n",
       "      <td>4.094722</td>\n",
       "      <td>...</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128554</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.527978</td>\n",
       "      <td>8.579397</td>\n",
       "      <td>4.316234</td>\n",
       "      <td>4.166443</td>\n",
       "      <td>4.417203</td>\n",
       "      <td>4.233572</td>\n",
       "      <td>4.221121</td>\n",
       "      <td>4.094722</td>\n",
       "      <td>...</td>\n",
       "      <td>3.102149</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128556</th>\n",
       "      <td>109.511091</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.527978</td>\n",
       "      <td>8.579397</td>\n",
       "      <td>4.316234</td>\n",
       "      <td>4.166443</td>\n",
       "      <td>4.417203</td>\n",
       "      <td>4.233572</td>\n",
       "      <td>4.221121</td>\n",
       "      <td>4.094722</td>\n",
       "      <td>...</td>\n",
       "      <td>3.102149</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128559</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>8.579397</td>\n",
       "      <td>4.316234</td>\n",
       "      <td>4.166443</td>\n",
       "      <td>4.417203</td>\n",
       "      <td>4.233572</td>\n",
       "      <td>4.221121</td>\n",
       "      <td>4.094722</td>\n",
       "      <td>...</td>\n",
       "      <td>3.102149</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128561</th>\n",
       "      <td>111.840000</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.102149</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128572</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>8.579397</td>\n",
       "      <td>4.316234</td>\n",
       "      <td>4.166443</td>\n",
       "      <td>4.417203</td>\n",
       "      <td>4.233572</td>\n",
       "      <td>4.221121</td>\n",
       "      <td>4.094722</td>\n",
       "      <td>...</td>\n",
       "      <td>3.102149</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61836 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        performance pay score  volatility index  leave quota        eps  \\\n",
       "0                  143.880000              0.02     0.250000   8.579397   \n",
       "2                  111.230000              0.10     0.000000   8.579397   \n",
       "3                  101.570000              0.26     0.040000   8.579397   \n",
       "5                  128.320000              0.00     0.260000   8.000000   \n",
       "6                  113.810000              0.11     0.160000   8.579397   \n",
       "...                       ...               ...          ...        ...   \n",
       "128554               0.000000              0.00     0.527978   8.579397   \n",
       "128556             109.511091              0.06     0.527978   8.579397   \n",
       "128559               0.000000              0.33     0.080000   8.579397   \n",
       "128561             111.840000              0.30     0.920000  10.000000   \n",
       "128572               0.000000              0.25     0.090000   8.579397   \n",
       "\n",
       "             esi  career score  alignment score  recognition score  \\\n",
       "0       4.316234      4.166443         4.417203           4.233572   \n",
       "2       4.316234      4.166443         4.417203           4.233572   \n",
       "3       4.316234      4.166443         4.417203           4.233572   \n",
       "5       5.000000      5.000000         3.666667           3.666667   \n",
       "6       4.316234      4.166443         4.417203           4.233572   \n",
       "...          ...           ...              ...                ...   \n",
       "128554  4.316234      4.166443         4.417203           4.233572   \n",
       "128556  4.316234      4.166443         4.417203           4.233572   \n",
       "128559  4.316234      4.166443         4.417203           4.233572   \n",
       "128561  4.000000      4.000000         4.200000           4.000000   \n",
       "128572  4.316234      4.166443         4.417203           4.233572   \n",
       "\n",
       "        empowerment score  strive score  ...  time since last promotion  \\\n",
       "0                4.221121      4.094722  ...                   3.102149   \n",
       "2                4.221121      4.094722  ...                   3.102149   \n",
       "3                4.221121      4.094722  ...                   3.102149   \n",
       "5                3.666667      1.000000  ...                   2.600000   \n",
       "6                4.221121      4.094722  ...                   0.600000   \n",
       "...                   ...           ...  ...                        ...   \n",
       "128554           4.221121      4.094722  ...                   3.102149   \n",
       "128556           4.221121      4.094722  ...                   3.102149   \n",
       "128559           4.221121      4.094722  ...                   3.102149   \n",
       "128561           4.000000      4.250000  ...                   3.102149   \n",
       "128572           4.221121      4.094722  ...                   3.102149   \n",
       "\n",
       "        timeinlevel app  ctc internal median  train cnt attended beh  \\\n",
       "0                   3.0                 0.88                     0.0   \n",
       "2                   3.0                 0.98                     0.0   \n",
       "3                   3.0                 0.96                     0.0   \n",
       "5                   0.6                 1.26                     0.0   \n",
       "6                   0.1                 1.27                     0.0   \n",
       "...                 ...                  ...                     ...   \n",
       "128554              3.0                 0.00                     0.0   \n",
       "128556              3.0                 0.48                     0.0   \n",
       "128559              3.0                 1.19                     0.0   \n",
       "128561              3.0                 0.84                     2.0   \n",
       "128572              3.0                 0.79                     0.0   \n",
       "\n",
       "        train cnt attended fun  train cnt beh fun  attrreportees new  ind  \\\n",
       "0                          0.0                0.0                0.0  1.0   \n",
       "2                          0.0                0.0                0.0  1.0   \n",
       "3                          0.0                0.0                0.0  1.0   \n",
       "5                          7.0                7.0                0.0  1.0   \n",
       "6                          0.0                0.0                0.0  1.0   \n",
       "...                        ...                ...                ...  ...   \n",
       "128554                     0.0                0.0                0.0  1.0   \n",
       "128556                     0.0                0.0                0.0  1.0   \n",
       "128559                     0.0                0.0                0.0  1.0   \n",
       "128561                     0.0                2.0                1.0  1.0   \n",
       "128572                     0.0                0.0                0.0  1.0   \n",
       "\n",
       "        fresher flag  attrition  \n",
       "0                0.0        0.0  \n",
       "2                0.0        0.0  \n",
       "3                0.0        0.0  \n",
       "5                0.0        0.0  \n",
       "6                0.0        0.0  \n",
       "...              ...        ...  \n",
       "128554           0.0        0.0  \n",
       "128556           0.0        0.0  \n",
       "128559           0.0        0.0  \n",
       "128561           0.0        0.0  \n",
       "128572           0.0        0.0  \n",
       "\n",
       "[61836 rows x 35 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# applying one MinMaxScaling for continues variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaling=MinMaxScaler()\n",
    "minscaling=scaling.fit_transform(continues)\n",
    "type(minscaling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.086667</td>\n",
       "      <td>0.001818</td>\n",
       "      <td>0.85794</td>\n",
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       "      <td>0.833289</td>\n",
       "      <td>0.883441</td>\n",
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       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
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       "      <td>0.308739</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.011818</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.200000</td>\n",
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       "      <td>0.255319</td>\n",
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       "      <td>0.366279</td>\n",
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       "      <td>0.669471</td>\n",
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       "      <th>61831</th>\n",
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       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
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       "      <td>0.308739</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>61832</th>\n",
       "      <td>0.644183</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.023999</td>\n",
       "      <td>0.85794</td>\n",
       "      <td>0.863247</td>\n",
       "      <td>0.833289</td>\n",
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       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308739</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.139535</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>61833</th>\n",
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       "      <td>0.85794</td>\n",
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       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.345930</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>61834</th>\n",
       "      <td>0.657882</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.041818</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308739</td>\n",
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       "      <td>0.285714</td>\n",
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       "      <td>0.10</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.846714</td>\n",
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       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308739</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.229651</td>\n",
       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>61836 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2        3         4         5         6   \\\n",
       "0      0.846353  0.006667  0.011364  0.85794  0.863247  0.833289  0.883441   \n",
       "1      0.654294  0.033333  0.000000  0.85794  0.863247  0.833289  0.883441   \n",
       "2      0.597471  0.086667  0.001818  0.85794  0.863247  0.833289  0.883441   \n",
       "3      0.754824  0.000000  0.011818  0.80000  1.000000  1.000000  0.733333   \n",
       "4      0.669471  0.036667  0.007273  0.85794  0.863247  0.833289  0.883441   \n",
       "...         ...       ...       ...      ...       ...       ...       ...   \n",
       "61831  0.000000  0.000000  0.023999  0.85794  0.863247  0.833289  0.883441   \n",
       "61832  0.644183  0.020000  0.023999  0.85794  0.863247  0.833289  0.883441   \n",
       "61833  0.000000  0.110000  0.003636  0.85794  0.863247  0.833289  0.883441   \n",
       "61834  0.657882  0.100000  0.041818  1.00000  0.800000  0.800000  0.840000   \n",
       "61835  0.000000  0.083333  0.004091  0.85794  0.863247  0.833289  0.883441   \n",
       "\n",
       "             7         8         9   ...        25        26        27  \\\n",
       "0      0.846714  0.844224  0.818944  ...  0.308739  1.000000  0.255814   \n",
       "1      0.846714  0.844224  0.818944  ...  0.308739  1.000000  0.284884   \n",
       "2      0.846714  0.844224  0.818944  ...  0.308739  1.000000  0.279070   \n",
       "3      0.733333  0.733333  0.200000  ...  0.255319  0.200000  0.366279   \n",
       "4      0.846714  0.844224  0.818944  ...  0.042553  0.033333  0.369186   \n",
       "...         ...       ...       ...  ...       ...       ...       ...   \n",
       "61831  0.846714  0.844224  0.818944  ...  0.308739  1.000000  0.000000   \n",
       "61832  0.846714  0.844224  0.818944  ...  0.308739  1.000000  0.139535   \n",
       "61833  0.846714  0.844224  0.818944  ...  0.308739  1.000000  0.345930   \n",
       "61834  0.800000  0.800000  0.850000  ...  0.308739  1.000000  0.244186   \n",
       "61835  0.846714  0.844224  0.818944  ...  0.308739  1.000000  0.229651   \n",
       "\n",
       "             28        29    30        31   32   33   34  \n",
       "0      0.000000  0.000000  0.00  0.000000  0.0  0.0  0.0  \n",
       "1      0.000000  0.000000  0.00  0.000000  0.0  0.0  0.0  \n",
       "2      0.000000  0.000000  0.00  0.000000  0.0  0.0  0.0  \n",
       "3      0.000000  0.411765  0.35  0.000000  0.0  0.0  0.0  \n",
       "4      0.000000  0.000000  0.00  0.000000  0.0  0.0  0.0  \n",
       "...         ...       ...   ...       ...  ...  ...  ...  \n",
       "61831  0.000000  0.000000  0.00  0.000000  0.0  0.0  0.0  \n",
       "61832  0.000000  0.000000  0.00  0.000000  0.0  0.0  0.0  \n",
       "61833  0.000000  0.000000  0.00  0.000000  0.0  0.0  0.0  \n",
       "61834  0.285714  0.000000  0.10  0.027027  0.0  0.0  0.0  \n",
       "61835  0.000000  0.000000  0.00  0.000000  0.0  0.0  0.0  \n",
       "\n",
       "[61836 rows x 35 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continous=pd.DataFrame(minscaling)\n",
    "continous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "continous.columns=['performance pay score', 'volatility index', 'leave quota', 'eps',\n",
    "       'esi', 'career score', 'alignment score', 'recognition score',\n",
    "       'empowerment score', 'strive score', 'rise score', 'crusade score',\n",
    "       'simplicity score', 'collaboration score', 'training (mcares) score',\n",
    "       ' award counts in 3 years', 'leave availed', 'ctc til app',\n",
    "       'm&m tenure', 'scaled travel', 'award count_x', 'ctc hike',\n",
    "       'current rating score', 'previous rating score',\n",
    "       'budget utilization percentage', 'time since last promotion',\n",
    "       'timeinlevel app', 'ctc internal median', 'train cnt attended beh',\n",
    "       'train cnt attended fun', 'train cnt beh fun', 'attrreportees new',\n",
    "       'ind', 'fresher flag','attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>performance pay score</th>\n",
       "      <th>volatility index</th>\n",
       "      <th>leave quota</th>\n",
       "      <th>eps</th>\n",
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       "      <th>attrreportees new</th>\n",
       "      <th>ind</th>\n",
       "      <th>fresher flag</th>\n",
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       "      <td>0.00</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>61836 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       performance pay score  volatility index  leave quota      eps  \\\n",
       "0                   0.846353          0.006667     0.011364  0.85794   \n",
       "1                   0.654294          0.033333     0.000000  0.85794   \n",
       "2                   0.597471          0.086667     0.001818  0.85794   \n",
       "3                   0.754824          0.000000     0.011818  0.80000   \n",
       "4                   0.669471          0.036667     0.007273  0.85794   \n",
       "...                      ...               ...          ...      ...   \n",
       "61831               0.000000          0.000000     0.023999  0.85794   \n",
       "61832               0.644183          0.020000     0.023999  0.85794   \n",
       "61833               0.000000          0.110000     0.003636  0.85794   \n",
       "61834               0.657882          0.100000     0.041818  1.00000   \n",
       "61835               0.000000          0.083333     0.004091  0.85794   \n",
       "\n",
       "            esi  career score  alignment score  recognition score  \\\n",
       "0      0.863247      0.833289         0.883441           0.846714   \n",
       "1      0.863247      0.833289         0.883441           0.846714   \n",
       "2      0.863247      0.833289         0.883441           0.846714   \n",
       "3      1.000000      1.000000         0.733333           0.733333   \n",
       "4      0.863247      0.833289         0.883441           0.846714   \n",
       "...         ...           ...              ...                ...   \n",
       "61831  0.863247      0.833289         0.883441           0.846714   \n",
       "61832  0.863247      0.833289         0.883441           0.846714   \n",
       "61833  0.863247      0.833289         0.883441           0.846714   \n",
       "61834  0.800000      0.800000         0.840000           0.800000   \n",
       "61835  0.863247      0.833289         0.883441           0.846714   \n",
       "\n",
       "       empowerment score  strive score  ...  time since last promotion  \\\n",
       "0               0.844224      0.818944  ...                   0.308739   \n",
       "1               0.844224      0.818944  ...                   0.308739   \n",
       "2               0.844224      0.818944  ...                   0.308739   \n",
       "3               0.733333      0.200000  ...                   0.255319   \n",
       "4               0.844224      0.818944  ...                   0.042553   \n",
       "...                  ...           ...  ...                        ...   \n",
       "61831           0.844224      0.818944  ...                   0.308739   \n",
       "61832           0.844224      0.818944  ...                   0.308739   \n",
       "61833           0.844224      0.818944  ...                   0.308739   \n",
       "61834           0.800000      0.850000  ...                   0.308739   \n",
       "61835           0.844224      0.818944  ...                   0.308739   \n",
       "\n",
       "       timeinlevel app  ctc internal median  train cnt attended beh  \\\n",
       "0             1.000000             0.255814                0.000000   \n",
       "1             1.000000             0.284884                0.000000   \n",
       "2             1.000000             0.279070                0.000000   \n",
       "3             0.200000             0.366279                0.000000   \n",
       "4             0.033333             0.369186                0.000000   \n",
       "...                ...                  ...                     ...   \n",
       "61831         1.000000             0.000000                0.000000   \n",
       "61832         1.000000             0.139535                0.000000   \n",
       "61833         1.000000             0.345930                0.000000   \n",
       "61834         1.000000             0.244186                0.285714   \n",
       "61835         1.000000             0.229651                0.000000   \n",
       "\n",
       "       train cnt attended fun  train cnt beh fun  attrreportees new  ind  \\\n",
       "0                    0.000000               0.00           0.000000  0.0   \n",
       "1                    0.000000               0.00           0.000000  0.0   \n",
       "2                    0.000000               0.00           0.000000  0.0   \n",
       "3                    0.411765               0.35           0.000000  0.0   \n",
       "4                    0.000000               0.00           0.000000  0.0   \n",
       "...                       ...                ...                ...  ...   \n",
       "61831                0.000000               0.00           0.000000  0.0   \n",
       "61832                0.000000               0.00           0.000000  0.0   \n",
       "61833                0.000000               0.00           0.000000  0.0   \n",
       "61834                0.000000               0.10           0.027027  0.0   \n",
       "61835                0.000000               0.00           0.000000  0.0   \n",
       "\n",
       "       fresher flag  attrition  \n",
       "0               0.0        0.0  \n",
       "1               0.0        0.0  \n",
       "2               0.0        0.0  \n",
       "3               0.0        0.0  \n",
       "4               0.0        0.0  \n",
       "...             ...        ...  \n",
       "61831           0.0        0.0  \n",
       "61832           0.0        0.0  \n",
       "61833           0.0        0.0  \n",
       "61834           0.0        0.0  \n",
       "61835           0.0        0.0  \n",
       "\n",
       "[61836 rows x 35 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    56055\n",
       "1.0     5781\n",
       "Name: attrition, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continous['attrition'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "finalDataset=pd.concat([continous,dummie],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>performance pay score</th>\n",
       "      <th>volatility index</th>\n",
       "      <th>leave quota</th>\n",
       "      <th>eps</th>\n",
       "      <th>esi</th>\n",
       "      <th>career score</th>\n",
       "      <th>alignment score</th>\n",
       "      <th>recognition score</th>\n",
       "      <th>empowerment score</th>\n",
       "      <th>strive score</th>\n",
       "      <th>...</th>\n",
       "      <th>generation_generation x</th>\n",
       "      <th>generation_millenials</th>\n",
       "      <th>generation_nan</th>\n",
       "      <th>rating change_improved</th>\n",
       "      <th>rating change_nan</th>\n",
       "      <th>rating change_static</th>\n",
       "      <th>work home location_na</th>\n",
       "      <th>work home location_same</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.846353</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.011364</td>\n",
       "      <td>0.85794</td>\n",
       "      <td>0.863247</td>\n",
       "      <td>0.833289</td>\n",
       "      <td>0.883441</td>\n",
       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.654294</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.85794</td>\n",
       "      <td>0.863247</td>\n",
       "      <td>0.833289</td>\n",
       "      <td>0.883441</td>\n",
       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.597471</td>\n",
       "      <td>0.086667</td>\n",
       "      <td>0.001818</td>\n",
       "      <td>0.85794</td>\n",
       "      <td>0.863247</td>\n",
       "      <td>0.833289</td>\n",
       "      <td>0.883441</td>\n",
       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011818</td>\n",
       "      <td>0.80000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.669471</td>\n",
       "      <td>0.036667</td>\n",
       "      <td>0.007273</td>\n",
       "      <td>0.85794</td>\n",
       "      <td>0.863247</td>\n",
       "      <td>0.833289</td>\n",
       "      <td>0.883441</td>\n",
       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61831</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023999</td>\n",
       "      <td>0.85794</td>\n",
       "      <td>0.863247</td>\n",
       "      <td>0.833289</td>\n",
       "      <td>0.883441</td>\n",
       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61832</th>\n",
       "      <td>0.644183</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.023999</td>\n",
       "      <td>0.85794</td>\n",
       "      <td>0.863247</td>\n",
       "      <td>0.833289</td>\n",
       "      <td>0.883441</td>\n",
       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61833</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.003636</td>\n",
       "      <td>0.85794</td>\n",
       "      <td>0.863247</td>\n",
       "      <td>0.833289</td>\n",
       "      <td>0.883441</td>\n",
       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61834</th>\n",
       "      <td>0.657882</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.041818</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.850000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61835</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.004091</td>\n",
       "      <td>0.85794</td>\n",
       "      <td>0.863247</td>\n",
       "      <td>0.833289</td>\n",
       "      <td>0.883441</td>\n",
       "      <td>0.846714</td>\n",
       "      <td>0.844224</td>\n",
       "      <td>0.818944</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61836 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       performance pay score  volatility index  leave quota      eps  \\\n",
       "0                   0.846353          0.006667     0.011364  0.85794   \n",
       "1                   0.654294          0.033333     0.000000  0.85794   \n",
       "2                   0.597471          0.086667     0.001818  0.85794   \n",
       "3                   0.754824          0.000000     0.011818  0.80000   \n",
       "4                   0.669471          0.036667     0.007273  0.85794   \n",
       "...                      ...               ...          ...      ...   \n",
       "61831               0.000000          0.000000     0.023999  0.85794   \n",
       "61832               0.644183          0.020000     0.023999  0.85794   \n",
       "61833               0.000000          0.110000     0.003636  0.85794   \n",
       "61834               0.657882          0.100000     0.041818  1.00000   \n",
       "61835               0.000000          0.083333     0.004091  0.85794   \n",
       "\n",
       "            esi  career score  alignment score  recognition score  \\\n",
       "0      0.863247      0.833289         0.883441           0.846714   \n",
       "1      0.863247      0.833289         0.883441           0.846714   \n",
       "2      0.863247      0.833289         0.883441           0.846714   \n",
       "3      1.000000      1.000000         0.733333           0.733333   \n",
       "4      0.863247      0.833289         0.883441           0.846714   \n",
       "...         ...           ...              ...                ...   \n",
       "61831  0.863247      0.833289         0.883441           0.846714   \n",
       "61832  0.863247      0.833289         0.883441           0.846714   \n",
       "61833  0.863247      0.833289         0.883441           0.846714   \n",
       "61834  0.800000      0.800000         0.840000           0.800000   \n",
       "61835  0.863247      0.833289         0.883441           0.846714   \n",
       "\n",
       "       empowerment score  strive score  ...  generation_generation x  \\\n",
       "0               0.844224      0.818944  ...                        0   \n",
       "1               0.844224      0.818944  ...                        0   \n",
       "2               0.844224      0.818944  ...                        0   \n",
       "3               0.733333      0.200000  ...                        0   \n",
       "4               0.844224      0.818944  ...                        0   \n",
       "...                  ...           ...  ...                      ...   \n",
       "61831           0.844224      0.818944  ...                        0   \n",
       "61832           0.844224      0.818944  ...                        0   \n",
       "61833           0.844224      0.818944  ...                        0   \n",
       "61834           0.800000      0.850000  ...                        0   \n",
       "61835           0.844224      0.818944  ...                        0   \n",
       "\n",
       "       generation_millenials  generation_nan  rating change_improved  \\\n",
       "0                          1               0                       0   \n",
       "1                          1               0                       0   \n",
       "2                          1               0                       0   \n",
       "3                          1               0                       0   \n",
       "4                          1               0                       0   \n",
       "...                      ...             ...                     ...   \n",
       "61831                      1               0                       0   \n",
       "61832                      0               1                       0   \n",
       "61833                      0               1                       0   \n",
       "61834                      0               1                       0   \n",
       "61835                      1               0                       0   \n",
       "\n",
       "       rating change_nan  rating change_static  work home location_na  \\\n",
       "0                      1                     0                      1   \n",
       "1                      1                     0                      1   \n",
       "2                      1                     0                      1   \n",
       "3                      0                     1                      0   \n",
       "4                      0                     0                      1   \n",
       "...                  ...                   ...                    ...   \n",
       "61831                  1                     0                      1   \n",
       "61832                  1                     0                      1   \n",
       "61833                  1                     0                      0   \n",
       "61834                  0                     1                      0   \n",
       "61835                  1                     0                      1   \n",
       "\n",
       "       work home location_same  0.0  1.0  \n",
       "0                            0    1    0  \n",
       "1                            0    1    0  \n",
       "2                            0    1    0  \n",
       "3                            0    1    0  \n",
       "4                            0    1    0  \n",
       "...                        ...  ...  ...  \n",
       "61831                        0    1    0  \n",
       "61832                        0    1    0  \n",
       "61833                        1    1    0  \n",
       "61834                        1    1    0  \n",
       "61835                        0    1    0  \n",
       "\n",
       "[61836 rows x 50 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing over sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from imblearn.under_sampling import RandomUnderSampler \n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=finalDataset.drop(['attrition','award count_x','budget utilization percentage','fresher flag','index',],axis=1)\n",
    "y=finalDataset['attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    " #Apply the random under-sampling \n",
    "rus = RandomUnderSampler()\n",
    "X_RUS, y_RUS = rus.fit_resample(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr1 = LogisticRegression(solver='liblinear')\n",
    "lr1.fit(X_RUS, y_RUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1 = lr1.coef_.tolist()\n",
    "len(list1[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight4=pd.DataFrame({'varibles':X_RUS.columns,'coeff':list1[0]})\n",
    "weight4.sort_values(by='coeff',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight4.to_csv('finalvariable.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(X_RUS, y_RUS,test_size = 0.3,random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import model_selection\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr1 = LogisticRegression(solver='liblinear')\n",
    "lr1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = lr1.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n         0.0       0.95      0.87      0.91      1782\\n         1.0       0.87      0.95      0.91      1687\\n\\n    accuracy                           0.91      3469\\n   macro avg       0.91      0.91      0.91      3469\\nweighted avg       0.91      0.91      0.91      3469\\n'"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "classification_report(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1545,  237],\n",
       "       [  87, 1600]], dtype=int64)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9066013260305563"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
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   "source": []
  },
  {
   "cell_type": "code",
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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