salary-prediction-linear-regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salary Prediction\n",
    "## Description\n",
    "#### Your job as a data scientist is in this assignment is to examine a set of job postings with salaries and then predict salaries for a new set of job postings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The task\n",
    "#### You must build a model to predict the salaries for the job postings contained in test_features.csv."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, KFold\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JOB1362685407687</td>\n",
       "      <td>COMP33</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>22</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JOB1362685407688</td>\n",
       "      <td>COMP13</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>20</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId companyId  jobType       degree major industry  \\\n",
       "0  JOB1362685407687    COMP33  MANAGER  HIGH_SCHOOL  NONE   HEALTH   \n",
       "1  JOB1362685407688    COMP13   JUNIOR         NONE  NONE     AUTO   \n",
       "\n",
       "   yearsExperience  milesFromMetropolis  \n",
       "0               22                   73  \n",
       "1               20                   47  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import test data\n",
    "test_data = pd.read_csv(\"test_features.csv\")\n",
    "test_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data shape\n",
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JOB1362684407687</td>\n",
       "      <td>COMP37</td>\n",
       "      <td>CFO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JOB1362684407688</td>\n",
       "      <td>COMP19</td>\n",
       "      <td>CEO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>WEB</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
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      ],
      "text/plain": [
       "              jobId companyId jobType       degree major industry  \\\n",
       "0  JOB1362684407687    COMP37     CFO      MASTERS  MATH   HEALTH   \n",
       "1  JOB1362684407688    COMP19     CEO  HIGH_SCHOOL  NONE      WEB   \n",
       "\n",
       "   yearsExperience  milesFromMetropolis  \n",
       "0               10                   83  \n",
       "1                3                   73  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import train data\n",
    "training_data = pd.read_csv(\"train_features.csv\")\n",
    "training_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data shape\n",
    "training_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>jobId</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JOB1362684407687</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JOB1362684407688</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId  salary\n",
       "0  JOB1362684407687     130\n",
       "1  JOB1362684407688     101"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import salaries data\n",
    "training_salaries_data = pd.read_csv(\"train_salaries.csv\")\n",
    "training_salaries_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data shape\n",
    "training_salaries_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JOB1362684407687</td>\n",
       "      <td>COMP37</td>\n",
       "      <td>CFO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>83</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JOB1362684407688</td>\n",
       "      <td>COMP19</td>\n",
       "      <td>CEO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>WEB</td>\n",
       "      <td>3</td>\n",
       "      <td>73</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId companyId jobType       degree major industry  \\\n",
       "0  JOB1362684407687    COMP37     CFO      MASTERS  MATH   HEALTH   \n",
       "1  JOB1362684407688    COMP19     CEO  HIGH_SCHOOL  NONE      WEB   \n",
       "\n",
       "   yearsExperience  milesFromMetropolis  salary  \n",
       "0               10                   83     130  \n",
       "1                3                   73     101  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge features and target (salaries) into a single dataframe\n",
    "data_for_model = pd.merge(training_data, training_salaries_data, on='jobId')\n",
    "data_for_model.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop necessary columns\n",
    "# data_for_model.drop(\"jobId\", axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 9)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shuffle data\n",
    "data_for_model = shuffle(data_for_model, random_state = 42)\n",
    "data_for_model.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Check lengths and dtypes of dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000000 entries, 987231 to 121958\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count    Dtype \n",
      "---  ------               --------------    ----- \n",
      " 0   jobId                1000000 non-null  object\n",
      " 1   companyId            1000000 non-null  object\n",
      " 2   jobType              1000000 non-null  object\n",
      " 3   degree               1000000 non-null  object\n",
      " 4   major                1000000 non-null  object\n",
      " 5   industry             1000000 non-null  object\n",
      " 6   yearsExperience      1000000 non-null  int64 \n",
      " 7   milesFromMetropolis  1000000 non-null  int64 \n",
      " 8   salary               1000000 non-null  int64 \n",
      "dtypes: int64(3), object(6)\n",
      "memory usage: 76.3+ MB\n"
     ]
    }
   ],
   "source": [
    "data_for_model.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "jobId                  0\n",
       "companyId              0\n",
       "jobType                0\n",
       "degree                 0\n",
       "major                  0\n",
       "industry               0\n",
       "yearsExperience        0\n",
       "milesFromMetropolis    0\n",
       "salary                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_model.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Check duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_model.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Check invalid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30559</th>\n",
       "      <td>JOB1362684438246</td>\n",
       "      <td>COMP44</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>MATH</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652076</th>\n",
       "      <td>JOB1362685059763</td>\n",
       "      <td>COMP25</td>\n",
       "      <td>CTO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828156</th>\n",
       "      <td>JOB1362685235843</td>\n",
       "      <td>COMP40</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495984</th>\n",
       "      <td>JOB1362684903671</td>\n",
       "      <td>COMP34</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816129</th>\n",
       "      <td>JOB1362685223816</td>\n",
       "      <td>COMP42</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   jobId companyId         jobType       degree        major  \\\n",
       "30559   JOB1362684438246    COMP44          JUNIOR     DOCTORAL         MATH   \n",
       "652076  JOB1362685059763    COMP25             CTO  HIGH_SCHOOL         NONE   \n",
       "828156  JOB1362685235843    COMP40  VICE_PRESIDENT      MASTERS  ENGINEERING   \n",
       "495984  JOB1362684903671    COMP34          JUNIOR         NONE         NONE   \n",
       "816129  JOB1362685223816    COMP42         MANAGER     DOCTORAL  ENGINEERING   \n",
       "\n",
       "       industry  yearsExperience  milesFromMetropolis  salary  \n",
       "30559      AUTO               11                    7       0  \n",
       "652076     AUTO                6                   60       0  \n",
       "828156      WEB                3                   29       0  \n",
       "495984      OIL                1                   25       0  \n",
       "816129  FINANCE               18                    6       0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data with salary == 0\n",
    "data_for_model[data_for_model[\"salary\"] <= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [jobId, companyId, jobType, degree, major, industry, yearsExperience, milesFromMetropolis, salary]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data with milesFromMetropolis < 0\n",
    "data_for_model[data_for_model[\"milesFromMetropolis\"] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>575347</th>\n",
       "      <td>JOB1362684983034</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>SENIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>NONE</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875277</th>\n",
       "      <td>JOB1362685282964</td>\n",
       "      <td>COMP61</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185498</th>\n",
       "      <td>JOB1362684593185</td>\n",
       "      <td>COMP56</td>\n",
       "      <td>JANITOR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186262</th>\n",
       "      <td>JOB1362684593949</td>\n",
       "      <td>COMP1</td>\n",
       "      <td>CFO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189080</th>\n",
       "      <td>JOB1362684596767</td>\n",
       "      <td>COMP11</td>\n",
       "      <td>CEO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724839</th>\n",
       "      <td>JOB1362685132526</td>\n",
       "      <td>COMP42</td>\n",
       "      <td>SENIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239629</th>\n",
       "      <td>JOB1362684647316</td>\n",
       "      <td>COMP51</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718315</th>\n",
       "      <td>JOB1362685126002</td>\n",
       "      <td>COMP51</td>\n",
       "      <td>CFO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>0</td>\n",
       "      <td>86</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787201</th>\n",
       "      <td>JOB1362685194888</td>\n",
       "      <td>COMP20</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121958</th>\n",
       "      <td>JOB1362684529645</td>\n",
       "      <td>COMP13</td>\n",
       "      <td>CTO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>EDUCATION</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39839 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   jobId companyId         jobType     degree      major  \\\n",
       "575347  JOB1362684983034    COMP38          SENIOR    MASTERS       NONE   \n",
       "875277  JOB1362685282964    COMP61         MANAGER  BACHELORS       MATH   \n",
       "185498  JOB1362684593185    COMP56         JANITOR       NONE       NONE   \n",
       "186262  JOB1362684593949     COMP1             CFO   DOCTORAL   BUSINESS   \n",
       "189080  JOB1362684596767    COMP11             CEO       NONE       NONE   \n",
       "...                  ...       ...             ...        ...        ...   \n",
       "724839  JOB1362685132526    COMP42          SENIOR   DOCTORAL    PHYSICS   \n",
       "239629  JOB1362684647316    COMP51          JUNIOR   DOCTORAL  CHEMISTRY   \n",
       "718315  JOB1362685126002    COMP51             CFO   DOCTORAL    BIOLOGY   \n",
       "787201  JOB1362685194888    COMP20  VICE_PRESIDENT    MASTERS   BUSINESS   \n",
       "121958  JOB1362684529645    COMP13             CTO    MASTERS    PHYSICS   \n",
       "\n",
       "         industry  yearsExperience  milesFromMetropolis  salary  \n",
       "575347     HEALTH                0                   71      90  \n",
       "875277     HEALTH                0                   43      93  \n",
       "185498        OIL                0                   98      52  \n",
       "186262       AUTO                0                   69      90  \n",
       "189080       AUTO                0                   92     112  \n",
       "...           ...              ...                  ...     ...  \n",
       "724839     HEALTH                0                   64      92  \n",
       "239629       AUTO                0                   51      65  \n",
       "718315  EDUCATION                0                   86     112  \n",
       "787201  EDUCATION                0                    0     115  \n",
       "121958  EDUCATION                0                   61      79  \n",
       "\n",
       "[39839 rows x 9 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data with yearsExperience <= 0\n",
    "data_for_model[data_for_model[\"yearsExperience\"] <= 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Deal with invalid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>987231</th>\n",
       "      <td>JOB1362685394918</td>\n",
       "      <td>COMP41</td>\n",
       "      <td>CFO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>WEB</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79954</th>\n",
       "      <td>JOB1362684487641</td>\n",
       "      <td>COMP44</td>\n",
       "      <td>JANITOR</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>15</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   jobId companyId  jobType       degree major industry  \\\n",
       "987231  JOB1362685394918    COMP41      CFO  HIGH_SCHOOL  NONE      WEB   \n",
       "79954   JOB1362684487641    COMP44  JANITOR  HIGH_SCHOOL  NONE   HEALTH   \n",
       "\n",
       "        yearsExperience  milesFromMetropolis  salary  \n",
       "987231               12                   21     174  \n",
       "79954                15                   64      58  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removed those 5 with zeros\n",
    "data_for_model = data_for_model[data_for_model.salary > 0]\n",
    "\n",
    "# reset the index values after dropping the invalid values \n",
    "data_for_model.reset_index(drop = True)\n",
    "data_for_model.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(999995, 9)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_model.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Drop necessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>987231</th>\n",
       "      <td>CFO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>WEB</td>\n",
       "      <td>12</td>\n",
       "      <td>21</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79954</th>\n",
       "      <td>JANITOR</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>15</td>\n",
       "      <td>64</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567130</th>\n",
       "      <td>CTO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500891</th>\n",
       "      <td>CTO</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>5</td>\n",
       "      <td>89</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55399</th>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>WEB</td>\n",
       "      <td>23</td>\n",
       "      <td>64</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        jobType       degree    major industry  yearsExperience  \\\n",
       "987231      CFO  HIGH_SCHOOL     NONE      WEB               12   \n",
       "79954   JANITOR  HIGH_SCHOOL     NONE   HEALTH               15   \n",
       "567130      CTO  HIGH_SCHOOL     NONE      OIL               21   \n",
       "500891      CTO  HIGH_SCHOOL     NONE  FINANCE                5   \n",
       "55399    JUNIOR     DOCTORAL  BIOLOGY      WEB               23   \n",
       "\n",
       "        milesFromMetropolis  salary  \n",
       "987231                   21     174  \n",
       "79954                    64      58  \n",
       "567130                    4     168  \n",
       "500891                   89      85  \n",
       "55399                    64     145  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_model.drop([\"jobId\", \"companyId\"], axis=1, inplace=True)\n",
    "data_for_model.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(999995, 7)\n"
     ]
    }
   ],
   "source": [
    "# check the final shape\n",
    "print(data_for_model.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### View descriptive statistics of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>999995.000000</td>\n",
       "      <td>999995.000000</td>\n",
       "      <td>999995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.992407</td>\n",
       "      <td>49.529381</td>\n",
       "      <td>116.062398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.212390</td>\n",
       "      <td>28.877721</td>\n",
       "      <td>38.717163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>114.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>141.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>301.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       yearsExperience  milesFromMetropolis         salary\n",
       "count    999995.000000        999995.000000  999995.000000\n",
       "mean         11.992407            49.529381     116.062398\n",
       "std           7.212390            28.877721      38.717163\n",
       "min           0.000000             0.000000      17.000000\n",
       "25%           6.000000            25.000000      88.000000\n",
       "50%          12.000000            50.000000     114.000000\n",
       "75%          18.000000            75.000000     141.000000\n",
       "max          24.000000            99.000000     301.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_model.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    999995.000000\n",
       "mean        116.062398\n",
       "std          38.717163\n",
       "min          17.000000\n",
       "25%          88.000000\n",
       "50%         114.000000\n",
       "75%         141.000000\n",
       "max         301.000000\n",
       "Name: salary, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary', ylabel='Density'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show salary statistics\n",
    "display(data_for_model.salary.describe())\n",
    "\n",
    "# plot boxplot and histogram\n",
    "plt.figure(figsize = (10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data_for_model.salary)\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.distplot(data_for_model.salary, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "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>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>330797</th>\n",
       "      <td>CTO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>LITERATURE</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274636</th>\n",
       "      <td>CEO</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765403</th>\n",
       "      <td>CEO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836405</th>\n",
       "      <td>CTO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>OIL</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340741</th>\n",
       "      <td>CFO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960536</th>\n",
       "      <td>CEO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>OIL</td>\n",
       "      <td>18</td>\n",
       "      <td>17</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600785</th>\n",
       "      <td>CEO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>MATH</td>\n",
       "      <td>WEB</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908756</th>\n",
       "      <td>CEO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>19</td>\n",
       "      <td>27</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>826637</th>\n",
       "      <td>CEO</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>WEB</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357671</th>\n",
       "      <td>CTO</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>MATH</td>\n",
       "      <td>OIL</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       jobType     degree        major industry  yearsExperience  \\\n",
       "330797     CTO   DOCTORAL   LITERATURE  FINANCE               23   \n",
       "274636     CEO       NONE         NONE      OIL               17   \n",
       "765403     CEO   DOCTORAL  ENGINEERING      WEB               17   \n",
       "836405     CTO  BACHELORS         MATH      OIL               21   \n",
       "340741     CFO   DOCTORAL  ENGINEERING      OIL               11   \n",
       "960536     CEO    MASTERS    CHEMISTRY      OIL               18   \n",
       "600785     CEO  BACHELORS         MATH      WEB               15   \n",
       "908756     CEO   DOCTORAL         NONE      OIL               19   \n",
       "826637     CEO  BACHELORS      COMPSCI      WEB               19   \n",
       "357671     CTO   DOCTORAL         MATH      OIL               14   \n",
       "\n",
       "        milesFromMetropolis  salary  \n",
       "330797                   15     222  \n",
       "274636                   30     222  \n",
       "765403                   25     235  \n",
       "836405                   12     231  \n",
       "340741                    1     232  \n",
       "960536                   17     242  \n",
       "600785                    6     229  \n",
       "908756                   27     224  \n",
       "826637                    7     243  \n",
       "357671                    7     228  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate Q1, Q3 and upper bound of salary\n",
    "Q1 = data_for_model.salary.quantile(0.25)\n",
    "Q3 = data_for_model.salary.quantile(0.75)\n",
    "upper_bound = Q3 + 1.5 * (Q3 - Q1)\n",
    "\n",
    "# show first 10 jobs that has salary more than upper bound\n",
    "display(data_for_model[data_for_model.salary.gt(upper_bound)].head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define function for Stat and Boxplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_statistics_and_boxplot(feature):\n",
    "    # statistics\n",
    "    display(data_for_model.groupby([feature]).salary.describe().sort_values(by='mean', ascending=False))\n",
    "    \n",
    "    # boxplot\n",
    "    data_for_model[[feature, 'salary']].boxplot(by=feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Type of Job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jobType</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CEO</th>\n",
       "      <td>124778.0</td>\n",
       "      <td>145.311425</td>\n",
       "      <td>34.423846</td>\n",
       "      <td>55.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTO</th>\n",
       "      <td>125045.0</td>\n",
       "      <td>135.481067</td>\n",
       "      <td>33.090947</td>\n",
       "      <td>51.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CFO</th>\n",
       "      <td>124369.0</td>\n",
       "      <td>135.458547</td>\n",
       "      <td>33.069203</td>\n",
       "      <td>51.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VICE_PRESIDENT</th>\n",
       "      <td>125234.0</td>\n",
       "      <td>125.368630</td>\n",
       "      <td>31.956295</td>\n",
       "      <td>44.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANAGER</th>\n",
       "      <td>125120.0</td>\n",
       "      <td>115.368518</td>\n",
       "      <td>30.833865</td>\n",
       "      <td>37.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SENIOR</th>\n",
       "      <td>125886.0</td>\n",
       "      <td>105.487775</td>\n",
       "      <td>30.069722</td>\n",
       "      <td>31.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JUNIOR</th>\n",
       "      <td>124592.0</td>\n",
       "      <td>95.333087</td>\n",
       "      <td>29.246759</td>\n",
       "      <td>23.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JANITOR</th>\n",
       "      <td>124971.0</td>\n",
       "      <td>70.813045</td>\n",
       "      <td>24.643021</td>\n",
       "      <td>17.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count        mean        std   min    25%    50%    75%  \\\n",
       "jobType                                                                      \n",
       "CEO             124778.0  145.311425  34.423846  55.0  120.0  142.0  167.0   \n",
       "CTO             125045.0  135.481067  33.090947  51.0  111.0  132.0  156.0   \n",
       "CFO             124369.0  135.458547  33.069203  51.0  111.0  132.0  156.0   \n",
       "VICE_PRESIDENT  125234.0  125.368630  31.956295  44.0  102.0  122.0  146.0   \n",
       "MANAGER         125120.0  115.368518  30.833865  37.0   93.0  112.0  135.0   \n",
       "SENIOR          125886.0  105.487775  30.069722  31.0   83.0  102.0  124.0   \n",
       "JUNIOR          124592.0   95.333087  29.246759  23.0   74.0   92.0  114.0   \n",
       "JANITOR         124971.0   70.813045  24.643021  17.0   52.0   68.0   86.0   \n",
       "\n",
       "                  max  \n",
       "jobType                \n",
       "CEO             298.0  \n",
       "CTO             301.0  \n",
       "CFO             301.0  \n",
       "VICE_PRESIDENT  272.0  \n",
       "MANAGER         270.0  \n",
       "SENIOR          258.0  \n",
       "JUNIOR          248.0  \n",
       "JANITOR         189.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "show_statistics_and_boxplot('jobType')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# jobType or the Type of Job has a strong correlation with salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Type of Degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degree</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DOCTORAL</th>\n",
       "      <td>175362.0</td>\n",
       "      <td>135.490979</td>\n",
       "      <td>35.790555</td>\n",
       "      <td>45.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MASTERS</th>\n",
       "      <td>175310.0</td>\n",
       "      <td>130.505647</td>\n",
       "      <td>35.399830</td>\n",
       "      <td>38.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BACHELORS</th>\n",
       "      <td>175495.0</td>\n",
       "      <td>125.454663</td>\n",
       "      <td>35.042720</td>\n",
       "      <td>35.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HIGH_SCHOOL</th>\n",
       "      <td>236975.0</td>\n",
       "      <td>101.921085</td>\n",
       "      <td>36.112684</td>\n",
       "      <td>18.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONE</th>\n",
       "      <td>236853.0</td>\n",
       "      <td>98.176882</td>\n",
       "      <td>34.915141</td>\n",
       "      <td>17.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>254.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count        mean        std   min    25%    50%    75%    max\n",
       "degree                                                                        \n",
       "DOCTORAL     175362.0  135.490979  35.790555  45.0  109.0  132.0  158.0  301.0\n",
       "MASTERS      175310.0  130.505647  35.399830  38.0  105.0  127.0  153.0  301.0\n",
       "BACHELORS    175495.0  125.454663  35.042720  35.0  100.0  122.0  148.0  294.0\n",
       "HIGH_SCHOOL  236975.0  101.921085  36.112684  18.0   75.0   99.0  125.0  260.0\n",
       "NONE         236853.0   98.176882  34.915141  17.0   72.0   95.0  121.0  254.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "show_statistics_and_boxplot('degree')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# degree or the Type of Degree one takes has a strong correlation with salary\n",
    "# Bachelor's, Master's and Doctoral degrees have much higher salary compared to those who don't have and just High School\n",
    "# there are less job openings looking for/with Bachelor's, Master's and Doctoral\n",
    "# there are more job openings for those without degrees and with high school attainment only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Type of Major"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENGINEERING</th>\n",
       "      <td>58594.0</td>\n",
       "      <td>138.441342</td>\n",
       "      <td>38.178934</td>\n",
       "      <td>41.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUSINESS</th>\n",
       "      <td>58518.0</td>\n",
       "      <td>135.648980</td>\n",
       "      <td>37.006526</td>\n",
       "      <td>40.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MATH</th>\n",
       "      <td>57800.0</td>\n",
       "      <td>133.322042</td>\n",
       "      <td>35.539246</td>\n",
       "      <td>39.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMPSCI</th>\n",
       "      <td>58382.0</td>\n",
       "      <td>132.075605</td>\n",
       "      <td>34.776640</td>\n",
       "      <td>41.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>286.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHYSICS</th>\n",
       "      <td>58410.0</td>\n",
       "      <td>130.372436</td>\n",
       "      <td>34.956981</td>\n",
       "      <td>38.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>293.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMISTRY</th>\n",
       "      <td>58875.0</td>\n",
       "      <td>129.072085</td>\n",
       "      <td>34.832396</td>\n",
       "      <td>40.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>282.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BIOLOGY</th>\n",
       "      <td>58379.0</td>\n",
       "      <td>127.932664</td>\n",
       "      <td>34.820141</td>\n",
       "      <td>35.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LITERATURE</th>\n",
       "      <td>58684.0</td>\n",
       "      <td>124.423097</td>\n",
       "      <td>33.576413</td>\n",
       "      <td>37.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>271.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONE</th>\n",
       "      <td>532353.0</td>\n",
       "      <td>102.583864</td>\n",
       "      <td>36.136268</td>\n",
       "      <td>17.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count        mean        std   min    25%    50%    75%    max\n",
       "major                                                                         \n",
       "ENGINEERING   58594.0  138.441342  38.178934  41.0  111.0  135.0  163.0  301.0\n",
       "BUSINESS      58518.0  135.648980  37.006526  40.0  109.0  132.0  159.0  294.0\n",
       "MATH          57800.0  133.322042  35.539246  39.0  107.0  130.0  156.0  280.0\n",
       "COMPSCI       58382.0  132.075605  34.776640  41.0  107.0  129.0  154.0  286.0\n",
       "PHYSICS       58410.0  130.372436  34.956981  38.0  105.0  127.0  153.0  293.0\n",
       "CHEMISTRY     58875.0  129.072085  34.832396  40.0  104.0  126.0  151.0  282.0\n",
       "BIOLOGY       58379.0  127.932664  34.820141  35.0  103.0  125.0  150.0  285.0\n",
       "LITERATURE    58684.0  124.423097  33.576413  37.0  100.0  122.0  146.0  271.0\n",
       "NONE         532353.0  102.583864  36.136268  17.0   76.0  100.0  126.0  270.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_statistics_and_boxplot('major')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# those who majored in engineering and business have high salary offers\n",
    "# those who majored in literature have low salary offers among those with degrees\n",
    "# those with no degrees have the lowest salary offers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Type of Industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industry</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OIL</th>\n",
       "      <td>142770.0</td>\n",
       "      <td>130.953863</td>\n",
       "      <td>38.528558</td>\n",
       "      <td>37.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FINANCE</th>\n",
       "      <td>142866.0</td>\n",
       "      <td>130.747659</td>\n",
       "      <td>38.319820</td>\n",
       "      <td>36.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEB</th>\n",
       "      <td>143205.0</td>\n",
       "      <td>121.645362</td>\n",
       "      <td>38.165659</td>\n",
       "      <td>31.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HEALTH</th>\n",
       "      <td>142755.0</td>\n",
       "      <td>115.735540</td>\n",
       "      <td>36.865179</td>\n",
       "      <td>28.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUTO</th>\n",
       "      <td>142941.0</td>\n",
       "      <td>109.435222</td>\n",
       "      <td>36.091590</td>\n",
       "      <td>25.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>264.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SERVICE</th>\n",
       "      <td>142639.0</td>\n",
       "      <td>104.446820</td>\n",
       "      <td>35.577606</td>\n",
       "      <td>22.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>256.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDUCATION</th>\n",
       "      <td>142819.0</td>\n",
       "      <td>99.448386</td>\n",
       "      <td>35.110257</td>\n",
       "      <td>17.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>253.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count        mean        std   min    25%    50%    75%    max\n",
       "industry                                                                    \n",
       "OIL        142770.0  130.953863  38.528558  37.0  103.0  128.0  156.0  301.0\n",
       "FINANCE    142866.0  130.747659  38.319820  36.0  103.0  128.0  156.0  294.0\n",
       "WEB        143205.0  121.645362  38.165659  31.0   94.0  119.0  146.0  290.0\n",
       "HEALTH     142755.0  115.735540  36.865179  28.0   89.0  114.0  140.0  275.0\n",
       "AUTO       142941.0  109.435222  36.091590  25.0   84.0  108.0  133.0  264.0\n",
       "SERVICE    142639.0  104.446820  35.577606  22.0   79.0  103.0  128.0  256.0\n",
       "EDUCATION  142819.0   99.448386  35.110257  17.0   75.0   98.0  122.0  253.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": [
    "show_statistics_and_boxplot('industry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# oil and finance industries have the highest salaries or offer the most"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Years Experience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yearsExperience</th>\n",
       "      <td>999995.0</td>\n",
       "      <td>11.992407</td>\n",
       "      <td>7.212390</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>999995.0</td>\n",
       "      <td>116.062398</td>\n",
       "      <td>38.717163</td>\n",
       "      <td>17.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count        mean        std   min   25%    50%    75%  \\\n",
       "yearsExperience  999995.0   11.992407   7.212390   0.0   6.0   12.0   18.0   \n",
       "salary           999995.0  116.062398  38.717163  17.0  88.0  114.0  141.0   \n",
       "\n",
       "                   max  \n",
       "yearsExperience   24.0  \n",
       "salary           301.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_model[['yearsExperience','salary']].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='yearsExperience'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_for_model.groupby(['yearsExperience']).salary.mean().plot(x='yearsExperience', y='salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the more number of years of experience one has, the higher the salary is offered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Miles from metropolis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <td>999995.0</td>\n",
       "      <td>49.529381</td>\n",
       "      <td>28.877721</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>999995.0</td>\n",
       "      <td>116.062398</td>\n",
       "      <td>38.717163</td>\n",
       "      <td>17.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>301.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count        mean        std   min   25%    50%  \\\n",
       "milesFromMetropolis  999995.0   49.529381  28.877721   0.0  25.0   50.0   \n",
       "salary               999995.0  116.062398  38.717163  17.0  88.0  114.0   \n",
       "\n",
       "                       75%    max  \n",
       "milesFromMetropolis   75.0   99.0  \n",
       "salary               141.0  301.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_for_model[['milesFromMetropolis','salary']].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='milesFromMetropolis'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_for_model.groupby(['milesFromMetropolis']).salary.mean().plot(x='milesFromMetropolis', y='salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the closer you are to the metropolis, the higher the salary is offered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Split input and output variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data_for_model.drop([\"salary\"], axis = 1)\n",
    "y = data_for_model[\"salary\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Split out training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Deal with categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_vars = ['jobType', 'degree', 'major', 'industry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoder = OneHotEncoder(sparse=False, drop = \"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_encoded = one_hot_encoder.fit_transform(X_train[categorical_vars])\n",
    "X_test_encoded = one_hot_encoder.transform(X_test[categorical_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_feature_names = one_hot_encoder.get_feature_names(categorical_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>jobType_CFO</th>\n",
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       "      <th>jobType_JANITOR</th>\n",
       "      <th>jobType_JUNIOR</th>\n",
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       "      <th>degree_MASTERS</th>\n",
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       "      <th>major_NONE</th>\n",
       "      <th>major_PHYSICS</th>\n",
       "      <th>industry_EDUCATION</th>\n",
       "      <th>industry_FINANCE</th>\n",
       "      <th>industry_HEALTH</th>\n",
       "      <th>industry_OIL</th>\n",
       "      <th>industry_SERVICE</th>\n",
       "      <th>industry_WEB</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   jobType_CFO  jobType_CTO  jobType_JANITOR  jobType_JUNIOR  jobType_MANAGER  \\\n",
       "0          0.0          0.0              0.0             0.0              0.0   \n",
       "1          0.0          0.0              0.0             0.0              0.0   \n",
       "2          0.0          1.0              0.0             0.0              0.0   \n",
       "3          0.0          0.0              0.0             1.0              0.0   \n",
       "4          0.0          0.0              0.0             1.0              0.0   \n",
       "\n",
       "   jobType_SENIOR  jobType_VICE_PRESIDENT  degree_DOCTORAL  \\\n",
       "0             0.0                     1.0              0.0   \n",
       "1             1.0                     0.0              0.0   \n",
       "2             0.0                     0.0              0.0   \n",
       "3             0.0                     0.0              0.0   \n",
       "4             0.0                     0.0              0.0   \n",
       "\n",
       "   degree_HIGH_SCHOOL  degree_MASTERS  ...  major_LITERATURE  major_MATH  \\\n",
       "0                 0.0             0.0  ...               0.0         0.0   \n",
       "1                 0.0             0.0  ...               0.0         0.0   \n",
       "2                 0.0             1.0  ...               0.0         0.0   \n",
       "3                 0.0             0.0  ...               0.0         0.0   \n",
       "4                 0.0             0.0  ...               0.0         0.0   \n",
       "\n",
       "   major_NONE  major_PHYSICS  industry_EDUCATION  industry_FINANCE  \\\n",
       "0         1.0            0.0                 1.0               0.0   \n",
       "1         0.0            0.0                 0.0               0.0   \n",
       "2         0.0            0.0                 0.0               1.0   \n",
       "3         1.0            0.0                 1.0               0.0   \n",
       "4         0.0            0.0                 0.0               0.0   \n",
       "\n",
       "   industry_HEALTH  industry_OIL  industry_SERVICE  industry_WEB  \n",
       "0              0.0           0.0               0.0           0.0  \n",
       "1              1.0           0.0               0.0           0.0  \n",
       "2              0.0           0.0               0.0           0.0  \n",
       "3              0.0           0.0               0.0           0.0  \n",
       "4              0.0           0.0               0.0           0.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_encoded = pd.DataFrame(X_train_encoded, columns = encoder_feature_names)\n",
    "X_train = pd.concat([X_train.reset_index(drop=True), X_train_encoded.reset_index(drop=True)], axis = 1)\n",
    "X_train.drop(categorical_vars, axis = 1, inplace = True)\n",
    "X_train_encoded.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>jobType_CFO</th>\n",
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       "      <td>...</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   jobType_CFO  jobType_CTO  jobType_JANITOR  jobType_JUNIOR  jobType_MANAGER  \\\n",
       "0          0.0          0.0              0.0             0.0              1.0   \n",
       "1          0.0          0.0              0.0             0.0              1.0   \n",
       "2          1.0          0.0              0.0             0.0              0.0   \n",
       "3          0.0          0.0              0.0             0.0              0.0   \n",
       "4          0.0          0.0              1.0             0.0              0.0   \n",
       "\n",
       "   jobType_SENIOR  jobType_VICE_PRESIDENT  degree_DOCTORAL  \\\n",
       "0             0.0                     0.0              1.0   \n",
       "1             0.0                     0.0              0.0   \n",
       "2             0.0                     0.0              0.0   \n",
       "3             1.0                     0.0              0.0   \n",
       "4             0.0                     0.0              0.0   \n",
       "\n",
       "   degree_HIGH_SCHOOL  degree_MASTERS  ...  major_LITERATURE  major_MATH  \\\n",
       "0                 0.0             0.0  ...               1.0         0.0   \n",
       "1                 0.0             1.0  ...               0.0         0.0   \n",
       "2                 0.0             0.0  ...               0.0         0.0   \n",
       "3                 1.0             0.0  ...               0.0         0.0   \n",
       "4                 1.0             0.0  ...               0.0         0.0   \n",
       "\n",
       "   major_NONE  major_PHYSICS  industry_EDUCATION  industry_FINANCE  \\\n",
       "0         0.0            0.0                 0.0               0.0   \n",
       "1         0.0            1.0                 1.0               0.0   \n",
       "2         1.0            0.0                 0.0               0.0   \n",
       "3         1.0            0.0                 0.0               0.0   \n",
       "4         1.0            0.0                 0.0               0.0   \n",
       "\n",
       "   industry_HEALTH  industry_OIL  industry_SERVICE  industry_WEB  \n",
       "0              1.0           0.0               0.0           0.0  \n",
       "1              0.0           0.0               0.0           0.0  \n",
       "2              0.0           0.0               0.0           1.0  \n",
       "3              1.0           0.0               0.0           0.0  \n",
       "4              1.0           0.0               0.0           0.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_encoded = pd.DataFrame(X_test_encoded, columns = encoder_feature_names)\n",
    "X_test = pd.concat([X_test.reset_index(drop=True), X_test_encoded.reset_index(drop=True)], axis = 1)\n",
    "X_test.drop(categorical_vars, axis = 1, inplace = True)\n",
    "X_test_encoded.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = LinearRegression()\n",
    "feature_selector = RFECV(regressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit = feature_selector.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of features: 27\n"
     ]
    }
   ],
   "source": [
    "optimal_feature_count = feature_selector.n_features_\n",
    "print(f\"Optimal number of features: {optimal_feature_count}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.loc[:, feature_selector.get_support()]\n",
    "X_test = X_test.loc[:, feature_selector.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(fit.grid_scores_) + 1), fit.grid_scores_, marker = \"o\")\n",
    "plt.ylabel(\"Model Score\")\n",
    "plt.xlabel(\"Number of Features\")\n",
    "plt.title(f\"Feature Selection using RFE \\n Optimal number of features is {optimal_feature_count} (at score of {round(max(fit.grid_scores_), 4)})\")\n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict on the test set\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.84519640288732"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate MAE\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "383.8305810819356"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate MSE\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.591594653879902"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate RMSE\n",
    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7440750193426415\n"
     ]
    }
   ],
   "source": [
    "# calculate r-squared\n",
    "r_squared = r2_score(y_test, y_pred)\n",
    "print(r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7434274624312392"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cross validation (CV)\n",
    "cv = KFold(n_splits = 5, shuffle = True, random_state = 42)\n",
    "cv_scores = cross_val_score(regressor, X_train, y_train, cv = cv, scoring = \"r2\")\n",
    "cv_scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7440404644597948\n"
     ]
    }
   ],
   "source": [
    "# calculate adjusted r-squared\n",
    "num_data_points, num_input_vars = X_test.shape\n",
    "adjusted_r_squared = 1 - (1 - r_squared) * (num_data_points - 1) / (num_data_points - num_input_vars - 1)\n",
    "print(adjusted_r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract model coefficients\n",
    "coefficients = pd.DataFrame(regressor.coef_)\n",
    "input_variable_names = pd.DataFrame(X_train.columns)\n",
    "summary_stats = pd.concat([input_variable_names, coefficients], axis = 1)\n",
    "summary_stats.columns = [\"input_variable\", \"coefficient\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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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>input_variable</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>industry_OIL</td>\n",
       "      <td>21.300578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>industry_FINANCE</td>\n",
       "      <td>21.134726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>industry_WEB</td>\n",
       "      <td>12.124698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>major_ENGINEERING</td>\n",
       "      <td>10.636198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>degree_DOCTORAL</td>\n",
       "      <td>9.997025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>major_BUSINESS</td>\n",
       "      <td>7.734929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>industry_HEALTH</td>\n",
       "      <td>6.238519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>major_MATH</td>\n",
       "      <td>5.127258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>degree_MASTERS</td>\n",
       "      <td>4.987941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>major_COMPSCI</td>\n",
       "      <td>4.021274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>major_PHYSICS</td>\n",
       "      <td>2.389132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yearsExperience</td>\n",
       "      <td>2.010736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>major_CHEMISTRY</td>\n",
       "      <td>1.178764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>milesFromMetropolis</td>\n",
       "      <td>-0.400264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>major_LITERATURE</td>\n",
       "      <td>-3.594837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>major_NONE</td>\n",
       "      <td>-4.889698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>industry_SERVICE</td>\n",
       "      <td>-4.998180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>degree_HIGH_SCHOOL</td>\n",
       "      <td>-5.739772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>degree_NONE</td>\n",
       "      <td>-9.469887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jobType_CFO</td>\n",
       "      <td>-9.762521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jobType_CTO</td>\n",
       "      <td>-9.772673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>industry_EDUCATION</td>\n",
       "      <td>-9.994662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>jobType_VICE_PRESIDENT</td>\n",
       "      <td>-19.890922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>jobType_MANAGER</td>\n",
       "      <td>-29.862386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>jobType_SENIOR</td>\n",
       "      <td>-39.780405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>jobType_JUNIOR</td>\n",
       "      <td>-49.725663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>jobType_JANITOR</td>\n",
       "      <td>-62.343419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            input_variable  coefficient\n",
       "24            industry_OIL    21.300578\n",
       "22        industry_FINANCE    21.134726\n",
       "26            industry_WEB    12.124698\n",
       "16       major_ENGINEERING    10.636198\n",
       "9          degree_DOCTORAL     9.997025\n",
       "13          major_BUSINESS     7.734929\n",
       "23         industry_HEALTH     6.238519\n",
       "18              major_MATH     5.127258\n",
       "11          degree_MASTERS     4.987941\n",
       "15           major_COMPSCI     4.021274\n",
       "20           major_PHYSICS     2.389132\n",
       "0          yearsExperience     2.010736\n",
       "14         major_CHEMISTRY     1.178764\n",
       "1      milesFromMetropolis    -0.400264\n",
       "17        major_LITERATURE    -3.594837\n",
       "19              major_NONE    -4.889698\n",
       "25        industry_SERVICE    -4.998180\n",
       "10      degree_HIGH_SCHOOL    -5.739772\n",
       "12             degree_NONE    -9.469887\n",
       "2              jobType_CFO    -9.762521\n",
       "3              jobType_CTO    -9.772673\n",
       "21      industry_EDUCATION    -9.994662\n",
       "8   jobType_VICE_PRESIDENT   -19.890922\n",
       "6          jobType_MANAGER   -29.862386\n",
       "7           jobType_SENIOR   -39.780405\n",
       "5           jobType_JUNIOR   -49.725663\n",
       "4          jobType_JANITOR   -62.343419"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_stats.sort_values(by=['coefficient'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134.8623785352131"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract model intercept\n",
    "regressor.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply the model on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>jobId</th>\n",
       "      <th>companyId</th>\n",
       "      <th>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JOB1362685407687</td>\n",
       "      <td>COMP33</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>22</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JOB1362685407688</td>\n",
       "      <td>COMP13</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>20</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId companyId  jobType       degree major industry  \\\n",
       "0  JOB1362685407687    COMP33  MANAGER  HIGH_SCHOOL  NONE   HEALTH   \n",
       "1  JOB1362685407688    COMP13   JUNIOR         NONE  NONE     AUTO   \n",
       "\n",
       "   yearsExperience  milesFromMetropolis  \n",
       "0               22                   73  \n",
       "1               20                   47  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict = test_data\n",
    "to_predict.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>jobType</th>\n",
       "      <th>degree</th>\n",
       "      <th>major</th>\n",
       "      <th>industry</th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MANAGER</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>22</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>20</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CTO</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MANAGER</td>\n",
       "      <td>HIGH_SCHOOL</td>\n",
       "      <td>NONE</td>\n",
       "      <td>OIL</td>\n",
       "      <td>14</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BIOLOGY</td>\n",
       "      <td>OIL</td>\n",
       "      <td>10</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   jobType       degree    major industry  yearsExperience  \\\n",
       "0  MANAGER  HIGH_SCHOOL     NONE   HEALTH               22   \n",
       "1   JUNIOR         NONE     NONE     AUTO               20   \n",
       "2      CTO      MASTERS  BIOLOGY   HEALTH               17   \n",
       "3  MANAGER  HIGH_SCHOOL     NONE      OIL               14   \n",
       "4   JUNIOR     DOCTORAL  BIOLOGY      OIL               10   \n",
       "\n",
       "   milesFromMetropolis  \n",
       "0                   73  \n",
       "1                   47  \n",
       "2                    9  \n",
       "3                   96  \n",
       "4                   44  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict.drop([\"jobId\", \"companyId\"], axis=1, inplace=True)\n",
    "to_predict.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_vars = ['jobType', 'degree', 'major', 'industry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_vars_array = one_hot_encoder.transform(to_predict[categorical_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_feature_names = one_hot_encoder.get_feature_names(categorical_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_vars_df = pd.DataFrame(encoder_vars_array, columns = encoder_feature_names)\n",
    "to_predict = pd.concat([to_predict.reset_index(drop=True), encoder_vars_df.reset_index(drop=True)], axis = 1)\n",
    "to_predict.drop(categorical_vars, axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([115.62594615,  92.17943148, 166.89629584, ...,  45.04665135,\n",
       "       164.44822683, 111.84129216])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_predictions = regressor.predict(to_predict)\n",
    "salary_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_predict['salary'] = salary_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yearsExperience</th>\n",
       "      <th>milesFromMetropolis</th>\n",
       "      <th>jobType_CFO</th>\n",
       "      <th>jobType_CTO</th>\n",
       "      <th>jobType_JANITOR</th>\n",
       "      <th>jobType_JUNIOR</th>\n",
       "      <th>jobType_MANAGER</th>\n",
       "      <th>jobType_SENIOR</th>\n",
       "      <th>jobType_VICE_PRESIDENT</th>\n",
       "      <th>degree_DOCTORAL</th>\n",
       "      <th>...</th>\n",
       "      <th>major_MATH</th>\n",
       "      <th>major_NONE</th>\n",
       "      <th>major_PHYSICS</th>\n",
       "      <th>industry_EDUCATION</th>\n",
       "      <th>industry_FINANCE</th>\n",
       "      <th>industry_HEALTH</th>\n",
       "      <th>industry_OIL</th>\n",
       "      <th>industry_SERVICE</th>\n",
       "      <th>industry_WEB</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>47</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.179431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</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",
       "      <td>0.0</td>\n",
       "      <td>166.896296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>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",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "      <td>105.396044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>44</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",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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",
       "      <td>118.930054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yearsExperience  milesFromMetropolis  jobType_CFO  jobType_CTO  \\\n",
       "0               22                   73          0.0          0.0   \n",
       "1               20                   47          0.0          0.0   \n",
       "2               17                    9          0.0          1.0   \n",
       "3               14                   96          0.0          0.0   \n",
       "4               10                   44          0.0          0.0   \n",
       "\n",
       "   jobType_JANITOR  jobType_JUNIOR  jobType_MANAGER  jobType_SENIOR  \\\n",
       "0              0.0             0.0              1.0             0.0   \n",
       "1              0.0             1.0              0.0             0.0   \n",
       "2              0.0             0.0              0.0             0.0   \n",
       "3              0.0             0.0              1.0             0.0   \n",
       "4              0.0             1.0              0.0             0.0   \n",
       "\n",
       "   jobType_VICE_PRESIDENT  degree_DOCTORAL  ...  major_MATH  major_NONE  \\\n",
       "0                     0.0              0.0  ...         0.0         1.0   \n",
       "1                     0.0              0.0  ...         0.0         1.0   \n",
       "2                     0.0              0.0  ...         0.0         0.0   \n",
       "3                     0.0              0.0  ...         0.0         1.0   \n",
       "4                     0.0              1.0  ...         0.0         0.0   \n",
       "\n",
       "   major_PHYSICS  industry_EDUCATION  industry_FINANCE  industry_HEALTH  \\\n",
       "0            0.0                 0.0               0.0              1.0   \n",
       "1            0.0                 0.0               0.0              0.0   \n",
       "2            0.0                 0.0               0.0              1.0   \n",
       "3            0.0                 0.0               0.0              0.0   \n",
       "4            0.0                 0.0               0.0              0.0   \n",
       "\n",
       "   industry_OIL  industry_SERVICE  industry_WEB      salary  \n",
       "0           0.0               0.0           0.0  115.625946  \n",
       "1           0.0               0.0           0.0   92.179431  \n",
       "2           0.0               0.0           0.0  166.896296  \n",
       "3           1.0               0.0           0.0  105.396044  \n",
       "4           1.0               0.0           0.0  118.930054  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 28)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = to_predict.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1656 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25,23))\n",
    "sns.heatmap(corr, \n",
    "        xticklabels=corr.columns,\n",
    "        yticklabels=corr.columns, cmap=\"mako_r\")"
   ]
  },
  {
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