salary-prediction-random-forest.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.ensemble import RandomForestRegressor\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\n",
    "from sklearn.preprocessing import OneHotEncoder\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": [
    {
     "data": {
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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",
       "    </tr>\n",
       "  </tbody>\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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       "</style>\n",
       "<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": {
      "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",
       "      <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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       "  <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>jobType_CFO</th>\n",
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       "      <th>jobType_JANITOR</th>\n",
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       "      <th>major_PHYSICS</th>\n",
       "      <th>industry_EDUCATION</th>\n",
       "      <th>industry_FINANCE</th>\n",
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       "      <th>industry_OIL</th>\n",
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       "<p>5 rows × 25 columns</p>\n",
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      ],
      "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",
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       "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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       "      <th>jobType_CFO</th>\n",
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      ],
      "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": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = RandomForestRegressor()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict on the test set\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7036120006721602\n"
     ]
    }
   ],
   "source": [
    "# calculate r-squared\n",
    "r_squared = r2_score(y_test, y_pred)\n",
    "print(r_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7034184625750199"
      ]
     },
     "execution_count": 50,
     "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": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7035719824896145\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": "markdown",
   "metadata": {},
   "source": [
    "## Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply the model on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
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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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict = test_data\n",
    "to_predict.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</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",
       "    </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": 55,
     "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": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_vars = ['jobType', 'degree', 'major', 'industry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_vars_array = one_hot_encoder.transform(to_predict[categorical_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_feature_names = one_hot_encoder.get_feature_names(categorical_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 98.895     ,  85.71571429, 173.2       , ...,  53.15133333,\n",
       "       168.8575    , 124.3575    ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_predictions = regressor.predict(to_predict)\n",
    "salary_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_predict['salary'] = salary_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>73</td>\n",
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       "      <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",
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       "      <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>98.895000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>47</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <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>85.715714</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>173.200000</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>118.007278</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>97.590000</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   98.895000  \n",
       "1           0.0               0.0           0.0   85.715714  \n",
       "2           0.0               0.0           0.0  173.200000  \n",
       "3           1.0               0.0           0.0  118.007278  \n",
       "4           1.0               0.0           0.0   97.590000  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 28)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = to_predict.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18,15))\n",
    "sns.heatmap(corr, \n",
    "        xticklabels=corr.columns,\n",
    "        yticklabels=corr.columns, cmap=\"mako_r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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