Predict the employee attrition rate in organizations (elasticnet implementation).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict the employee attrition rate in organizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn import preprocessing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Dataset/Train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Employee_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Relationship_Status</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Decision_skill_possess</th>\n",
       "      <th>Time_of_service</th>\n",
       "      <th>Time_since_promotion</th>\n",
       "      <th>...</th>\n",
       "      <th>Compensation_and_Benefits</th>\n",
       "      <th>Work_Life_balance</th>\n",
       "      <th>VAR1</th>\n",
       "      <th>VAR2</th>\n",
       "      <th>VAR3</th>\n",
       "      <th>VAR4</th>\n",
       "      <th>VAR5</th>\n",
       "      <th>VAR6</th>\n",
       "      <th>VAR7</th>\n",
       "      <th>Attrition_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EID_23371</td>\n",
       "      <td>F</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>IT</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>type2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.7516</td>\n",
       "      <td>1.8688</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EID_18000</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Logistics</td>\n",
       "      <td>Analytical</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>type2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.9612</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EID_3891</td>\n",
       "      <td>F</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>Clinton</td>\n",
       "      <td>Quality</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>type2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.9612</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EID_17492</td>\n",
       "      <td>F</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>Human Resource Management</td>\n",
       "      <td>Behavioral</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>type2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.8176</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EID_22534</td>\n",
       "      <td>F</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Logistics</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>type3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7516</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Employee_ID Gender   Age  Education_Level Relationship_Status     Hometown  \\\n",
       "0   EID_23371      F  42.0                4             Married     Franklin   \n",
       "1   EID_18000      M  24.0                3              Single  Springfield   \n",
       "2    EID_3891      F  58.0                3             Married      Clinton   \n",
       "3   EID_17492      F  26.0                3              Single      Lebanon   \n",
       "4   EID_22534      F  31.0                1             Married  Springfield   \n",
       "\n",
       "                        Unit Decision_skill_possess  Time_of_service  \\\n",
       "0                         IT             Conceptual              4.0   \n",
       "1                  Logistics             Analytical              5.0   \n",
       "2                    Quality             Conceptual             27.0   \n",
       "3  Human Resource Management             Behavioral              4.0   \n",
       "4                  Logistics             Conceptual              5.0   \n",
       "\n",
       "   Time_since_promotion  ...  Compensation_and_Benefits  Work_Life_balance  \\\n",
       "0                     4  ...                      type2                3.0   \n",
       "1                     4  ...                      type2                4.0   \n",
       "2                     3  ...                      type2                1.0   \n",
       "3                     3  ...                      type2                1.0   \n",
       "4                     4  ...                      type3                3.0   \n",
       "\n",
       "   VAR1    VAR2    VAR3  VAR4  VAR5  VAR6  VAR7  Attrition_rate  \n",
       "0     4  0.7516  1.8688   2.0     4     5     3          0.1841  \n",
       "1     3 -0.9612 -0.4537   2.0     3     5     3          0.0670  \n",
       "2     4 -0.9612 -0.4537   3.0     3     8     3          0.0851  \n",
       "3     3 -1.8176 -0.4537   NaN     3     7     3          0.0668  \n",
       "4     1  0.7516 -0.4537   2.0     2     8     2          0.1827  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7000, 24)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Time_of_service</th>\n",
       "      <th>Time_since_promotion</th>\n",
       "      <th>growth_rate</th>\n",
       "      <th>Travel_Rate</th>\n",
       "      <th>Post_Level</th>\n",
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       "      <th>Work_Life_balance</th>\n",
       "      <th>VAR1</th>\n",
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       "      <th>VAR3</th>\n",
       "      <th>VAR4</th>\n",
       "      <th>VAR5</th>\n",
       "      <th>VAR6</th>\n",
       "      <th>VAR7</th>\n",
       "      <th>Attrition_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6588.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>6856.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>6991.000000</td>\n",
       "      <td>6989.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>6423.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>6344.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.622799</td>\n",
       "      <td>3.187857</td>\n",
       "      <td>13.385064</td>\n",
       "      <td>2.367143</td>\n",
       "      <td>47.064286</td>\n",
       "      <td>0.817857</td>\n",
       "      <td>2.798000</td>\n",
       "      <td>6.006294</td>\n",
       "      <td>2.387895</td>\n",
       "      <td>3.098571</td>\n",
       "      <td>-0.008126</td>\n",
       "      <td>-0.013606</td>\n",
       "      <td>1.891078</td>\n",
       "      <td>2.834143</td>\n",
       "      <td>7.101286</td>\n",
       "      <td>3.257000</td>\n",
       "      <td>0.189376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.606920</td>\n",
       "      <td>1.065102</td>\n",
       "      <td>10.364188</td>\n",
       "      <td>1.149395</td>\n",
       "      <td>15.761406</td>\n",
       "      <td>0.648205</td>\n",
       "      <td>1.163721</td>\n",
       "      <td>2.058435</td>\n",
       "      <td>1.122786</td>\n",
       "      <td>0.836377</td>\n",
       "      <td>0.989850</td>\n",
       "      <td>0.986933</td>\n",
       "      <td>0.529403</td>\n",
       "      <td>0.938945</td>\n",
       "      <td>1.164262</td>\n",
       "      <td>0.925319</td>\n",
       "      <td>0.185753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.817600</td>\n",
       "      <td>-2.776200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.961200</td>\n",
       "      <td>-0.453700</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.070400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.104800</td>\n",
       "      <td>-0.453700</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.142650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.751600</td>\n",
       "      <td>0.707500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.608100</td>\n",
       "      <td>1.868800</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.995900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  Education_Level  Time_of_service  Time_since_promotion  \\\n",
       "count  6588.000000      7000.000000      6856.000000           7000.000000   \n",
       "mean     39.622799         3.187857        13.385064              2.367143   \n",
       "std      13.606920         1.065102        10.364188              1.149395   \n",
       "min      19.000000         1.000000         0.000000              0.000000   \n",
       "25%      27.000000         3.000000         5.000000              1.000000   \n",
       "50%      37.000000         3.000000        10.000000              2.000000   \n",
       "75%      52.000000         4.000000        21.000000              3.000000   \n",
       "max      65.000000         5.000000        43.000000              4.000000   \n",
       "\n",
       "       growth_rate  Travel_Rate   Post_Level    Pay_Scale  Work_Life_balance  \\\n",
       "count  7000.000000  7000.000000  7000.000000  6991.000000        6989.000000   \n",
       "mean     47.064286     0.817857     2.798000     6.006294           2.387895   \n",
       "std      15.761406     0.648205     1.163721     2.058435           1.122786   \n",
       "min      20.000000     0.000000     1.000000     1.000000           1.000000   \n",
       "25%      33.000000     0.000000     2.000000     5.000000           1.000000   \n",
       "50%      47.000000     1.000000     3.000000     6.000000           2.000000   \n",
       "75%      61.000000     1.000000     3.000000     8.000000           3.000000   \n",
       "max      74.000000     2.000000     5.000000    10.000000           5.000000   \n",
       "\n",
       "              VAR1         VAR2         VAR3         VAR4         VAR5  \\\n",
       "count  7000.000000  6423.000000  7000.000000  6344.000000  7000.000000   \n",
       "mean      3.098571    -0.008126    -0.013606     1.891078     2.834143   \n",
       "std       0.836377     0.989850     0.986933     0.529403     0.938945   \n",
       "min       1.000000    -1.817600    -2.776200     1.000000     1.000000   \n",
       "25%       3.000000    -0.961200    -0.453700     2.000000     2.000000   \n",
       "50%       3.000000    -0.104800    -0.453700     2.000000     3.000000   \n",
       "75%       3.000000     0.751600     0.707500     2.000000     3.000000   \n",
       "max       5.000000     1.608100     1.868800     3.000000     5.000000   \n",
       "\n",
       "              VAR6         VAR7  Attrition_rate  \n",
       "count  7000.000000  7000.000000     7000.000000  \n",
       "mean      7.101286     3.257000        0.189376  \n",
       "std       1.164262     0.925319        0.185753  \n",
       "min       5.000000     1.000000        0.000000  \n",
       "25%       6.000000     3.000000        0.070400  \n",
       "50%       7.000000     3.000000        0.142650  \n",
       "75%       8.000000     4.000000        0.235000  \n",
       "max       9.000000     5.000000        0.995900  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.Decision_skill_possess.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_gender(data):\n",
    "    if data['Gender'] == 'F':\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "df['Gender'] = df.apply(convert_gender, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_relationship_status(data):\n",
    "    if data['Relationship_Status'] == 'Married':\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "df['Relationship_Status'] = df.apply(convert_relationship_status, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_VAR23(data):\n",
    "    return data['VAR2'] * data['VAR3']\n",
    "\n",
    "df['VAR23'] = df.apply(generate_VAR23, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_age_growthRate(data):\n",
    "    return data['Age'] * data['growth_rate']\n",
    "\n",
    "df['AGE_GROWTH'] = df.apply(generate_age_growthRate, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_age_var23(data):\n",
    "    return data['VAR23'] * data['Age']\n",
    "\n",
    "df['AGE_VAR23'] = df.apply(generate_age_var23, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_Time_of_service_var23(data):\n",
    "    return data['VAR23'] * data['Time_of_service']\n",
    "\n",
    "df['TIME_OF_SERVICE_VAR23'] = df.apply(generate_Time_of_service_var23, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_growth_rate_var23(data):\n",
    "    return data['VAR23'] * data['growth_rate']\n",
    "\n",
    "df['GROWTH_RATE_VAR23'] = df.apply(generate_growth_rate_var23, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_age_var4567(data):\n",
    "    return data['VAR4'] * data['VAR5'] * data['VAR6'] * data['VAR7']\n",
    "\n",
    "df['VAR4567'] = df.apply(generate_age_var4567, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_age_time_of_service(data):\n",
    "    return data['Age'] * data['Time_of_service']\n",
    "\n",
    "df['AGE_TIME_OF_SERVICE'] = df.apply(generate_age_time_of_service, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def generate_new(data):\n",
    "#     return data['Age'] * data['VAR23']\n",
    "\n",
    "# df['new'] = df.apply(generate_new, axis=1)\n",
    "\n",
    "# plt.scatter(df.Age, df.new)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import seaborn as sns\n",
    "# # Create the default pairplot\n",
    "# my_plt = sns.pairplot(df)\n",
    "# my_plt.savefig(\"output.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.AGE_GROWTH = np.log1p(df.AGE_GROWTH) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.Time_of_service = np.log1p(df.Time_of_service) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.corr()\n",
    "# f, ax = plt.subplots(figsize=(16, 16))\n",
    "# sns.heatmap(df.corr(), annot=True, linewidths=0.5, square=True, vmax=0.3, center=0, cmap=sns.cubehelix_palette())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr><hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Filling missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_missing_values(df):\n",
    "    missing = df.isnull().sum()\n",
    "    missing = missing[missing > 0]\n",
    "    print(missing)\n",
    "    missing.sort_values(inplace=True)\n",
    "    try:\n",
    "        missing.plot.bar()\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age                      412\n",
      "Time_of_service          144\n",
      "Pay_Scale                  9\n",
      "Work_Life_balance         11\n",
      "VAR2                     577\n",
      "VAR4                     656\n",
      "VAR23                    577\n",
      "AGE_GROWTH               412\n",
      "AGE_VAR23                952\n",
      "TIME_OF_SERVICE_VAR23    706\n",
      "GROWTH_RATE_VAR23        577\n",
      "VAR4567                  656\n",
      "AGE_TIME_OF_SERVICE      549\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_missing_values(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr><hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dealing with clean Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_X = clean_df.drop([\"Employee_ID\", \"Attrition_rate\"], axis=1)\n",
    "df_y = df.loc[:, \"Attrition_rate\"]\n",
    "# print(df_X.info())\n",
    "# df_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7000, 51)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_X = pd.get_dummies(df_X,drop_first=False)\n",
    "df_X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler = MinMaxScaler()\n",
    "# df_X_scaled = scaler.fit_transform(df_X)\n",
    "# print(df_X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler = preprocessing.QuantileTransformer(output_distribution='normal', random_state=0)\n",
    "# df_X_scaled = scaler.fit_transform(df_X)\n",
    "# print(df_X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.          0.10335656  1.         ...  0.          0.\n",
      "   0.        ]\n",
      " [-1.         -0.67925213  0.         ...  0.          0.\n",
      "   0.        ]\n",
      " [ 0.          0.79900874  0.         ...  0.          0.\n",
      "   0.        ]\n",
      " ...\n",
      " [ 0.          0.40770439  0.         ...  0.          0.\n",
      "   0.        ]\n",
      " [ 0.          0.32074787  0.         ... -1.          0.\n",
      "   0.        ]\n",
      " [ 0.          0.79900874  0.         ...  0.          0.\n",
      "   0.        ]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "df_X_scaled = scaler.fit_transform(df_X)\n",
    "print(df_X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_X_scaled, df_y, test_size=0.22, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics as skmetrics\n",
    "\n",
    "def calculate_regression_metrics(y_test, predictions):\n",
    "    mean_squared_error = skmetrics.mean_squared_error(y_test, predictions)\n",
    "    mean_absolute_error = skmetrics.mean_absolute_error(y_test, predictions)\n",
    "    r2_error = skmetrics.r2_score(y_test, predictions)\n",
    "    result = {'mean_squared_error': mean_squared_error, 'mean_absolute_error': mean_absolute_error, 'r2_score': r2_error}\n",
    "    return result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: scikit-learn in c:\\users\\paras\\anaconda3\\lib\\site-packages (0.23.1)\n",
      "Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in c:\\users\\paras\\appdata\\roaming\\python\\python37\\site-packages (from scikit-learn) (1.18.4)\n",
      "Requirement already satisfied, skipping upgrade: scipy>=0.19.1 in c:\\users\\paras\\appdata\\roaming\\python\\python37\\site-packages (from scikit-learn) (1.4.1)\n",
      "Requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in c:\\users\\paras\\anaconda3\\lib\\site-packages (from scikit-learn) (2.1.0)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.11 in c:\\users\\paras\\anaconda3\\lib\\site-packages (from scikit-learn) (0.15.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade --user scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import xgboost as xgb\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "\n",
    "\n",
    "# reg1 = GradientBoostingRegressor(random_state=1, n_estimators=10)\n",
    "# reg2 = RandomForestRegressor(n_estimators=12, max_depth=9)\n",
    "# reg3 = LinearRegression()\n",
    "# reg4 = ElasticNet(normalize=True)\n",
    "# reg5 = xgb.XGBRegressor()\n",
    "\n",
    "# ensemble = VotingRegressor(estimators=[\n",
    "#                                        ('gb', reg4), \n",
    "#                                        ('rf', reg2), \n",
    "#                                        ('lr', reg3), \n",
    "#                                        ('enet', reg1), \n",
    "#                                        ('xgbr', reg5)\n",
    "#                                       ])\n",
    "# ensemble.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ensemble = ElasticNet(normalize=True)\n",
    "ensemble.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import RidgeCV, LassoCV\n",
    "# from sklearn.svm import SVR\n",
    "# estimators = [('ridge', RidgeCV()),\n",
    "#               ('lasso', LassoCV(random_state=42)),\n",
    "#               ('svr', SVR(C=1, gamma=1e-6))]\n",
    "\n",
    "# from sklearn.ensemble import GradientBoostingRegressor\n",
    "# from sklearn.ensemble import StackingRegressor\n",
    "# ensemble = StackingRegressor(\n",
    "#     estimators=estimators,\n",
    "#     final_estimator=GradientBoostingRegressor(random_state=42))\n",
    "\n",
    "# ensemble.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_squared_error': 0.03509606185165778,\n",
       " 'mean_absolute_error': 0.12689836049463843,\n",
       " 'r2_score': -0.013201642790835688}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = ensemble.predict(X_test)\n",
    "pred = pred.reshape(-1, 1)\n",
    "\n",
    "calculate_regression_metrics(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working on Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.read_csv(\"Dataset/Test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employee_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Relationship_Status</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Decision_skill_possess</th>\n",
       "      <th>Time_of_service</th>\n",
       "      <th>Time_since_promotion</th>\n",
       "      <th>...</th>\n",
       "      <th>Pay_Scale</th>\n",
       "      <th>Compensation_and_Benefits</th>\n",
       "      <th>Work_Life_balance</th>\n",
       "      <th>VAR1</th>\n",
       "      <th>VAR2</th>\n",
       "      <th>VAR3</th>\n",
       "      <th>VAR4</th>\n",
       "      <th>VAR5</th>\n",
       "      <th>VAR6</th>\n",
       "      <th>VAR7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EID_22713</td>\n",
       "      <td>F</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Single</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>type2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.9612</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EID_9658</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>IT</td>\n",
       "      <td>Directive</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>type2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.9612</td>\n",
       "      <td>0.7075</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EID_22203</td>\n",
       "      <td>M</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Directive</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>type3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.1048</td>\n",
       "      <td>0.7075</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EID_7652</td>\n",
       "      <td>M</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Single</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Analytical</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>type0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.1048</td>\n",
       "      <td>0.7075</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EID_6516</td>\n",
       "      <td>F</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>type2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.6081</td>\n",
       "      <td>0.7075</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Employee_ID Gender   Age  Education_Level Relationship_Status     Hometown  \\\n",
       "0   EID_22713      F  32.0                5              Single  Springfield   \n",
       "1    EID_9658      M  65.0                2              Single      Lebanon   \n",
       "2   EID_22203      M  52.0                3             Married  Springfield   \n",
       "3    EID_7652      M  50.0                5              Single   Washington   \n",
       "4    EID_6516      F  44.0                3             Married     Franklin   \n",
       "\n",
       "        Unit Decision_skill_possess  Time_of_service  Time_since_promotion  \\\n",
       "0        R&D             Conceptual              7.0                     4   \n",
       "1         IT              Directive             41.0                     2   \n",
       "2      Sales              Directive             21.0                     3   \n",
       "3  Marketing             Analytical             11.0                     4   \n",
       "4        R&D             Conceptual             12.0                     4   \n",
       "\n",
       "   ...  Pay_Scale  Compensation_and_Benefits  Work_Life_balance  VAR1    VAR2  \\\n",
       "0  ...        4.0                      type2                1.0     3 -0.9612   \n",
       "1  ...        1.0                      type2                1.0     4 -0.9612   \n",
       "2  ...        8.0                      type3                1.0     4 -0.1048   \n",
       "3  ...        2.0                      type0                4.0     3 -0.1048   \n",
       "4  ...        2.0                      type2                4.0     4  1.6081   \n",
       "\n",
       "     VAR3  VAR4  VAR5  VAR6  VAR7  \n",
       "0 -0.4537   2.0     1     8     4  \n",
       "1  0.7075   1.0     2     8     2  \n",
       "2  0.7075   2.0     1     9     3  \n",
       "3  0.7075   2.0     2     8     3  \n",
       "4  0.7075   2.0     2     7     4  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['Gender'] = df_test.apply(convert_gender, axis=1)\n",
    "df_test['Relationship_Status'] = df_test.apply(convert_relationship_status, axis=1)\n",
    "df_test['VAR23'] = df_test.apply(generate_VAR23, axis=1)\n",
    "df_test['AGE_GROWTH'] = df_test.apply(generate_age_growthRate, axis=1)\n",
    "df_test['AGE_VAR23'] = df_test.apply(generate_age_var23, axis=1)\n",
    "df_test['TIME_OF_SERVICE_VAR23'] = df_test.apply(generate_Time_of_service_var23, axis=1)\n",
    "df_test['GROWTH_RATE_VAR23'] = df_test.apply(generate_growth_rate_var23, axis=1)\n",
    "df_test['VAR4567'] = df_test.apply(generate_age_var4567, axis=1)\n",
    "df_test['AGE_TIME_OF_SERVICE'] = df_test.apply(generate_age_time_of_service, axis=1)\n",
    "\n",
    "# df_test['AGE_VAR1'] = df_test.apply(generate_age_VAR1, axis=1)\n",
    "# df_test['AGE_VAR2'] = df_test.apply(generate_age_VAR2, axis=1)\n",
    "# df_test['AGE_VAR3'] = df_test.apply(generate_age_VAR3, axis=1)\n",
    "# df_test['AGE_VAR4'] = df_test.apply(generate_age_VAR4, axis=1)\n",
    "# df_test['AGE_VAR5'] = df_test.apply(generate_age_VAR5, axis=1)\n",
    "# df_test['AGE_VAR6'] = df_test.apply(generate_age_VAR6, axis=1)\n",
    "# df_test['AGE_VAR7'] = df_test.apply(generate_age_VAR7, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "check_missing_values(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_X = df_test.drop([\"Employee_ID\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_X = pd.get_dummies(df_test_X,drop_first=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 51)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_test_X.head().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test_X = scaler.transform(df_test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 51)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "predn = ensemble.predict(df_test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"Employee_ID\": df_test[\"Employee_ID\"],\n",
    "        \"Attrition_rate\": predn\n",
    "    })\n",
    "\n",
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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