salary-prediction-regression-tree.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.tree import DecisionTreeRegressor, plot_tree\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, KFold\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\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",
       "      <th>industry_SERVICE</th>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   jobType_CFO  jobType_CTO  jobType_JANITOR  jobType_JUNIOR  jobType_MANAGER  \\\n",
       "0          0.0          0.0              0.0             0.0              0.0   \n",
       "1          0.0          0.0              0.0             0.0              0.0   \n",
       "2          0.0          1.0              0.0             0.0              0.0   \n",
       "3          0.0          0.0              0.0             1.0              0.0   \n",
       "4          0.0          0.0              0.0             1.0              0.0   \n",
       "\n",
       "   jobType_SENIOR  jobType_VICE_PRESIDENT  degree_DOCTORAL  \\\n",
       "0             0.0                     1.0              0.0   \n",
       "1             1.0                     0.0              0.0   \n",
       "2             0.0                     0.0              0.0   \n",
       "3             0.0                     0.0              0.0   \n",
       "4             0.0                     0.0              0.0   \n",
       "\n",
       "   degree_HIGH_SCHOOL  degree_MASTERS  ...  major_LITERATURE  major_MATH  \\\n",
       "0                 0.0             0.0  ...               0.0         0.0   \n",
       "1                 0.0             0.0  ...               0.0         0.0   \n",
       "2                 0.0             1.0  ...               0.0         0.0   \n",
       "3                 0.0             0.0  ...               0.0         0.0   \n",
       "4                 0.0             0.0  ...               0.0         0.0   \n",
       "\n",
       "   major_NONE  major_PHYSICS  industry_EDUCATION  industry_FINANCE  \\\n",
       "0         1.0            0.0                 1.0               0.0   \n",
       "1         0.0            0.0                 0.0               0.0   \n",
       "2         0.0            0.0                 0.0               1.0   \n",
       "3         1.0            0.0                 1.0               0.0   \n",
       "4         0.0            0.0                 0.0               0.0   \n",
       "\n",
       "   industry_HEALTH  industry_OIL  industry_SERVICE  industry_WEB  \n",
       "0              0.0           0.0               0.0           0.0  \n",
       "1              1.0           0.0               0.0           0.0  \n",
       "2              0.0           0.0               0.0           0.0  \n",
       "3              0.0           0.0               0.0           0.0  \n",
       "4              0.0           0.0               0.0           0.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_encoded = pd.DataFrame(X_train_encoded, columns = encoder_feature_names)\n",
    "X_train = pd.concat([X_train.reset_index(drop=True), X_train_encoded.reset_index(drop=True)], axis = 1)\n",
    "X_train.drop(categorical_vars, axis = 1, inplace = True)\n",
    "X_train_encoded.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .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": [
       "DecisionTreeRegressor(max_depth=4, random_state=42)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = DecisionTreeRegressor(random_state = 42, max_depth = 4)\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": [
      "0.463314655624843\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": [
       "0.4630829955994768"
      ]
     },
     "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.46324219259621313\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": [
    "## Finding the Max Depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_depth_list = list(range(1,9))\n",
    "accuracy_scores = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "for depth in max_depth_list:\n",
    "    \n",
    "    regressor = DecisionTreeRegressor(max_depth = depth, random_state = 42)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    y_pred = regressor.predict(X_test)\n",
    "    accuracy = r2_score(y_test, y_pred)\n",
    "    accuracy_scores.append(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6271575321564593"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_accuracy = max(accuracy_scores)\n",
    "max_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_accuracy_idx = accuracy_scores.index(max_accuracy)\n",
    "max_accuracy_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimal_depth = max_depth_list[max_accuracy_idx]\n",
    "optimal_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e86FeUsBLSIW4RhRBjZ3lfVaWA3/2/n+X4oaRL/HaEC7pXyoifb2S5X24quAD3vRbuB9C47X07qmuA6YWLx2JyB9wCfpc7weGKYUlqMh3B258pFdx1Rgf436xjS623mu4azT7cRefA5sWPwC87FVfXXmS8UzFVXHtANbiGmtUKHUXun8KXI0rBe7GVXEV3ft1vKEfihsqIodxLbHm4qp+BqrqKu9423G/9u/FJZztuIYCgZ+fV3DXgnbjqqd+5W2bhdcC0Xt9hxzv/MTdR/NRGatchrtGlu6dZz5usMcfUdVcL66ihgXXATPVjeMUOMzJROBCcR3gljjEiLrGODfgGk1k4rUG9fZ7Hy7xZeAS+2vHOc3jvU9+i7u2sxj3nn2MH77eU3ENQI41nxeRSSJS1rhWVwOJXoyPAperarq37ZneewDvXOfg/t8feK9BR1xSAddw40Lce/CAfH+/05kBsbTE3e5R0jhhj+BK2RsDtr23jLirLOuLrwoQkSm4Rg9/8juWykhcZ6+vqupkv2Mpidfa8GugnxYbGj5Sibu5dYKqnuF3LCZ0bLgNYyo5r5TQ9bgrRgivGvV23A3LphKzKj5jTMTwriWm466bHq8a0UQ4q+IzxhgTlqwEZYwxJixF3DWo+Ph4bdu2rd9hGGOMqSBLly7dq6o/upk+4hJU27ZtWbJkid9hGGOMqSAisq2k+VbFZ4wxJixZgjLGGBOWLEEZY4wJS5agjDHGhCVLUMYYY8qv+D20Ibin1hKUMcaY8nngAbjrru+TkqqbfuCBCj2MJShjjDHBU4UDB2DiRDLvupusnDyXnCZOdPMrsCQVcfdBGWOM8UdWbj5Ltmbw7U9vYX6NQaymDk/0O5dR676EO++EJ54AOdmxSb9nCcoYY0yJcvMLWZF6gHnJ+/h2016+S8kgr0CJjhL6tW/Lr159mh57NrmVKzg5gSUoY4wxnsJCZe2ug8zbtJdvk/exeOt+snILEIGeLepz4xntOK1DPAPbxBF7z93w7evfb3zXXVaCMsYYUzFUlc17jzAv2SWkBVv2cSArD4COTepw+YBWnNYhnqHtG1E/Nrpoo++vORVV6xVNQ4UmKUtQxhhThew8cJRvk/cyf5OrtttzMAeAlnG1OLdbU07vGM/QDo1oWi+m5B2IQFzcD685PfGEWxYXV6ElqIgbDyoxMVGts1hjjAnOvsM5zN+8j3mb9jEveS9b92UB0Kh2DYZ2aMTpHeM5rUMjEhrGIuVJLqo/TEbFp8tBRJaqamLx+VaCMsaYSuRQdh6Ltuxn3qZ9fJu8l6TdhwCoU7M6Q9o3ZNzQtpzesRFdmtYtX0Iqrvi2FdxAAixBGWNMRMvOK2BZSsaxlnYrUzMpKFRqVK/GwLYNuPtnXTitQyN6taxP9ajIuvXVEpQxxkSQ/IJCVu3IdFV2m/ayZGsGOfmFRFUTereqz21nd+C0Do3o36YBMdFRfod7UkKaoERkJDARiAImq+qjJawzDHgSiAb2qurZoYzJGGMiiaqyfs8h5iW7hLRw834O5eQD0LVZXcYOacNpHRoxqF1D6sZE+xxtxQpZghKRKOAZ4FwgFVgsIrNUdW3AOnHAs8BIVU0RkSahiscYYyKBqpKyP+vYNaT5m/ax70guAG0bxXJhnxac3rERQ9s3olGdmj5HG1qhLEENApJVdTOAiEwHRgFrA9YZA7ytqikAqpoWwniMMSYspR3MPpaQ5m3ax44DRwFoUrcmZ3VuzGkdGnFax3haxtXyOdJTK5QJqiWwPWA6FRhcbJ3OQLSIzAXqAhNVdWrxHYnIBGACQEJCQkiCNcaYU+VobgHfJu/lq43pzNu0j+S0wwDUrxXN0PaNuPXs9gztEE+HxrVPrqVdhAtlgirpVS1+01V1YAAwAqgFzBeRBaq64QcbqT4PPA/uPqgQxGqMMSG188BR5iSlMScpjW+T95KTX0it6CgGtWvIFQNacXrHeLo1r0dUtaqbkIoLZYJKBVoHTLcCdpawzl5VPQIcEZGvgD7ABowxJoIVFCorUg8wZ10as5PSWLfrIAAJDWMZMziBEV2bMrBdA2pWj+yWdqEUygS1GOgkIu2AHcDVuGtOgWYCT4tIdaAGrgrwiRDGZIwxIXMoO4+vN+5l9ro05q5PY9+RXKKqCYltGnDv+V0Z3rVpla+2K4+QJShVzReRO4BPcM3MX1TVNSJyq7d8kqquE5GPgZVAIa4p+upQxWSMMRVt694jzE5KY07SHhZt2U9egVK/VjTDujRmRLemnN2p8fcdrZpysb74jDGmHPIKClm6LYPZ6/YwOymNzelHAOjUpA7DuzVhRNem9E+Ii7heG/xkffEZY8wJyjiSy5cb0pmdlMaX69M4mJ1PjahqDG7fkPFD2jC8a1MSGsX6HWalYwnKGGOKUVU2ph1m9jpXdbd0WwaFCvF1ajKyZzOGd23KGZ3iqVPTvkJDyV5dY4wBcvILWLB5P3O8qrvUDHezbI8W9bjjJx0Z3q0pvVvWp5o1Az9lLEEZY6qstEPZfJGUxux1aXyTvJes3AJioqtxRsd4bh/WkeFdm9CsfikD95mQswRljKkyVJU1Ow8eq7pbkZoJQIv6MVzWvyUjujZlaIdGEd8LeGVhCcoYU6ll5ebzbfI+5iTtYU5SGnsO5iAC/VrHcffPujC8axO6NjvJwftMSFiCMsZUOqkZWa7qLimNeZv2kZtfSJ2a1TmrczzDuzZlWJfGxFfynsArA0tQxpiIV1CoLN+e4VXdpR0b5rxNo1jGDm7DiG5NGNi2ITWq271JkcQSlDEmIh3MzuPrDXuZnbSHuevT2e91KzSwbQP+eH43hndrQvt461YoklmCMsZEDFVl4Zb9vLYwhY9X7ya3oJC42GiGdW7McOtWqNKxBGWMCXsHsnJ5a2kqry9KYVP6EerGVOeaQa25sE8L+rW2boUqK0tQxpiwpKos3prB64tS+GDVLnLzC+mXEMc/Lu/Nhb1bUKuGNQWv7CxBGWPCSmZWHjOWudLSxrTD1K1ZnasSW3PNoAS6t6jnd3jmFLIEZYzxnaqyLCWDaQtT+GDlLnLyC+nTOo6/j+7NhX2aE1vDvqqqIvuvG2N8k3k0j3eWpfL6ou2s33OIOjWrc/mAVowZnECPFvX9Ds/4zBKUMeaUUlW+236A1xam8P7KnWTnFdKrZX3+dlkvLu7TgtrWQ7jx2DvBGHNKHMzOY+Z3O5i2MIWk3YeIrRHFpf1aMWZQAr1aWWnJ/JglKGNMyKgqK1IzeW3hNt5bsYujeQX0aFGPv17ak1F9W9p4SqZM9u4wxlS4Q9l5zFy+k9cWprB210FqRUdxcZ8WjBmcQO9W9a13BxMUS1DGmAqzMvUAry9KYebynWTlFtC1WV0eHtWDUf1aUi/Gengw5WMJyhhzUg7n5DNr+U5eW7SN1TsOEhNdjYt6u9JS39ZxVloyJ8wSlDHmhKzekclri1KY+d0OjuQW0KVpXR68uAeX9GtJ/VpWWjInzxKUMSZoR3LyeW/FTl5flMKK1ExqVq/GhV5pqX+ClZZMxbIEZYw5rrU7D/Laom28+91ODufk06lJHf58UXcu69fKeg83IWMJyhhToqzcfN5fuYvXFqawfPsBalSvxgW9mjNmcAKJbRpYacmEnCUoY8wPJO0+yGsLU3jnux0cys6nQ+Pa3Hdhdy7r15IGtWv4HZ6pQixBGWPIzivwSkvbWJZygBpR1TivVzPGDEpgULuGVloyvrAEZUwVtnHPIaYtTOHtZakczM6nfXxt/nh+N0YPaEVDKy0Zn1mCMqaKKSxUPlq9mynztrB4awbRUcLIns0ZMyiBIe2ttGTChyUoY6qI/IJC3l+5i6e/SCY57TBtGsXyh/O6cvmAVjSqU9Pv8Iz5EUtQxlRyeQWFvPvdDp75Ipmt+7Lo3LQO/76mHxf0ak5UNSstmfBlCcqYSionv4AZS3fw7NxkUjOO0r15PSaN7c9PuzejmiUmEwEsQRlTyWTnFfDG4u1M+nITuzKz6dOqPg9c1IMR3ZrY9SUTUSxBGVNJHM0tYNrCbTz31WbSD+WQ2KYBj47uzVmd4i0xmYgU0gQlIiOBiUAUMFlVHy22fBgwE9jizXpbVR8KZUzGVDaHc/J5Zf42Jn+9mX1HchnSviETr+7L0PaNLDGZiBayBCUiUcAzwLlAKrBYRGap6tpiq36tqheGKg5jKqvMo3m8PG8rL367hQNZeZzZKZ5fjejEwLYN/Q7NmAoRyhLUICBZVTcDiMh0YBRQPEEZY8rhQFYuL36zhZfmbeVQdj4jujbhlyM60bd1nN+hGVOhQpmgWgLbA6ZTgcElrDdURFYAO4Hfquqa4iuIyARgAkBCQkIIQjUm/O09nMPkr7fwyvytHMktYGSPZtwxvCM9W9b3OzRjQiKUCaqkym8tNr0MaKOqh0XkfOBdoNOPNlJ9HngeIDExsfg+jKnU0g5m89xXm5m2cBs5+YVc0Ks5dwzvSNdm9fwOzZiQCmWCSgVaB0y3wpWSjlHVgwHPPxSRZ0UkXlX3hjAuYyLCzgNHmfTlJqYv3k5BoTKqTwtu/0lHOjap43doxpwSoUxQi4FOItIO2AFcDYwJXEFEmgF7VFVFZBBQDdgXwpiMCXvb92fx7NxNvLV0O6owun8rbv9JB9o0qu13aMacUiFLUKqaLyJ3AJ/gmpm/qKprRORWb/kk4HLgNhHJB44CV6uqVeGZKmnL3iM880Uy73y3gygRrkxszW3DOtCqQazfoRnjC4m0fJCYmKhLlizxOwxjKkxy2iGenpPMrBU7iY6qxjWDErjl7PY0r1/L79CMOSVEZKmqJhafbz1JGOOTdbsO8vScZD5cvYuY6lHcdGZ7bjqzHU3qxvgdmjFhwRKUMafYqtRM/j1nI5+t3UOdmtW57ewO/PyMdjbkhTHFWIIy5hRZlpLBU7M38sX6dOrFVOfOEZ244fS2xMXayLXGlMQSlDEhtnDzPp6ak8w3yXtpEBvN3T/rwrihbagXE+13aMaENUtQxoSAqjJv0z4mzt7Ioi37ia9Tgz+c15WxQ9pQu6Z97IwJhn1SjKlAqsrcDek8NXsjy1IO0LReTe6/sDvXDEqgVo0ov8MzJqJYgjKmAqgqn63dw9NfJLMyNZOWcbV4+JKeXDGgFTHRlpiMORGWoIw5CYWFykerd/PUnI0k7T5EQsNYHr2sF5f1b0WN6tX8Ds+YiGYJypgTUFCovL9yJ0/PSWZj2mHax9fmn1f0YVTfFlSPssRkTEWwBGVMOc1dn8ZD769lc/oROjetw7+v6ccFvZoTVc1GrzWmIlmCMiZIqRlZPPz+Wj5Zs4f28bX5z7X9+VmPZlSzxGRMSFiCMuY4cvILmPz1Fp6asxGAu3/WhZvObEfN6tb4wZhQOm6CEpELgQ9VtfAUxGNMWPl6Yzp/nrmGzXuPMLJHM+67qDst46wTV2NOhWBKUFcDE0VkBvCSqq4LcUzG+G5X5lH+8v46Pli1i7aNYplyw0CGdWnid1jGVCnHTVCqOlZE6gHXAC+JiAIvAa+r6qFQB2jMqZSbX8iL327h37M3UlCo/N+5nbn5rPZ2L5MxPgjqGpSqHvRKULWAXwOXAneLyL9V9akQxmfMKTMveS/3zVzNpvQjnNOtKX++qDutG9pggcb4JZhrUBcBNwIdgFeAQaqaJiKxwDrAEpSJaLszs/nrh+t4b8VOWjesxQvXJTKiW1O/wzKmygumBHUF8ISqfhU4U1WzROTG0IRlTOjlFRTy8rytPPHZBvIKlV+f04lbz+5g1XnGhIlgEtSfgV1FEyJSC2iqqltVdXbIIjMmhBZs3sf9M1ezYc9hftKlMQ9c3IM2jWr7HZYxJkAwCepN4LSA6QJv3sCQRGRMCKUdyuaRD9bx7vKdtIyrxX/HJ3JOtyaI2M22xoSbYBJUdVXNLZpQ1VwRsSFATUTJLyhk6vxtPPHZBnLyC/nl8I7cPqyjDYFhTBgLJkGli8jFqjoLQERGAXtDG5YxFWfx1v3c9+5qknYf4qzOjXnw4h60i7fqPGPCXTAJ6lZgmog8DQiwHRgf0qiMqQDph3J49KMkZixLpUX9GCaNdX3nWXWeMZEhmBt1NwFDRKQOIHZzrgl3BYXKqwu28fin68nOK+D2YR24Y3hHYmtY15PGRJKgPrEicgHQA4gp+vWpqg+FMC5jTsjSbRncP3M1a3Ye5IyO8Tw4qgcdGtfxOyxjzAkI5kbdSUAs8BNgMnA5sCjEcRlTLvsO5/DYx0n8b0kqzerF8MyY/pzfy6rzjIlkwZSgTlPV3iKyUlUfFJF/Am+HOjBjglFQqLy+KIV/fLKeIzn53HJWe341ohO1a1p1njGRLphPcbb3N0tEWgD7gHahC8mY4KzYfoD7Zq5mZWomQ9s34qFRPejUtK7fYRljKkgwCeo9EYkD/gEsAxT4byiDMqYsGUdy+fsn65m+OIXGdWoy8eq+XNynhVXnGVPJlJmgRKQaMFtVDwAzROR9IEZVM09FcMYEKixU/rdkO499nMTB7Hx+fno77jynE3Vjov0OzRgTAmUmKFUt9K45DfWmc4CcUxGYMYFWpWZy38zVLN9+gEHtGvLwqJ50aWbVecZUZsFU8X0qIqOBt1VVQx2QMYEys/J4/NP1vLpwG41q1+SJq/pwSd+WVp1nTBUQTIL6DVAbyBeRbFxvEqqq9UIamanSCguVt5al8uhHSRzIyuX609py17mdqWfVecZUGcH0JGH1KOaUWrMzk/tnrmHptgwGtGnAw6MG072F/R4ypqoJ5kbds0qaX3wAw1K2HQlMBKKAyar6aCnrDQQWAFep6lvH26+pnDKP5vHEZxuYOn8rDWJr8PgVfbisX0uqVbPqPGOqomCq+O4OeB4DDAKWAsPL2khEooBngHOBVGCxiMxS1bUlrPcY8Ek54jaViKryznc7eOTDJPYfyWHskDb837ldqB9r1XnGVGXBVPFdFDgtIq2Bvwex70FAsqpu9rabDowC1hZb75fADGwAxCopafdB7nt3NYu3ZtAvIY4pNwykZ8v6fodljAkDJ9IfTCrQM4j1WuKG5gjcbnDgCiLSErgUVxorNUGJyARgAkBCQkI5wzXh6FB2Hk9+vpEp87ZSL6Y6j43uxRUDWlt1njHmmGCuQT2F6z0CoBrQF1gRxL5L+qYp3kz9SeAeVS0oq9mwqj4PPA+QmJhoTd0j3Odr9/CHd1ax93AOYwYlcPfPuhAXa4M0G2N+KJgS1JKA5/nA66r6bRDbpQKtA6ZbATuLrZMITPeSUzxwvojkq+q7QezfRBhV5dm5m/jHJ+vp0aIek8cn0qd1nN9hGWPCVDAJ6i0gW1ULwDVqEJFYVc06znaLgU4i0g7YAVwNjAlcQVWPdTorIlOA9y05VU7ZeQX87q2VzFqxk0v6tuDR0b2JiY7yOyxjTBgLJkHNBs4BDnvTtYBPgdPK2khV80XkDlzrvCjgRVVdIyK3essnnXDUJqLszsxmwitLWLUjk9+N7MJtZ3ewniCMMccVTIKKUdWi5ISqHhaR2GB2rqofAh8Wm1diYlLV64PZp4ksy7cfYMLUJRzJyee/4xI5p3tTv0MyxkSIakGsc0RE+hdNiMgA4GjoQjKVxbvf7eDK5+ZTM7oab99+uiUnY0y5BFOC+jXwpogUNXBoDlwVsohMxCsoVP7xyXomfbmJIe0b8uy1A2hY21rpGWPKJ5gbdReLSFegC67peJKq5oU8MhORDmXn8evpy5mdlMa1gxN44OIeREcFU1A3xpgfOu43h4j8AqitqqtVdRVQR0RuD31oJtKk7Mti9H/mMXdDOg+P6sFfL+1lyckYc8KC+fa42RtRFwBVzQBuDllEJiLN37SPUc98w56DObxy4yDGDW3rd0jGmAgXzDWoaiIiRYMVep272gUFc8yrC7bxwKw1tI2vzeTxibSNr+13SMaYSiCYBPUJ8D8RmYTrquhW4KOQRmUiQl5BIQ+9t5ZXFmzjJ10aM/GafjagoDGmwgSToO7BddR6G66RxHe4lnymCss4ksvt05Yxf/M+bjmrPb8b2ZUo6+jVGFOBgmnFVygiC4D2uOblDXHDY5gqauOeQ9w0dQm7DmTzzyv6MHpAK79DMsZUQqUmKBHpjOs/7xpgH/AGgKr+5NSEZsLRnKQ9/Or15cRERzH9liH0T2jgd0jGmEqqrBJUEvA1cJGqJgOIyF2nJCoTdlSV577azGMfJ9GjRT3+Oz6R5vVr+R2WMaYSKytBjcaVoL4QkY+B6ZQ8xpOp5LLzCrj37VW8/d0OLujdnMcv70OtGtYTuTEmtEpNUKr6DvCOiNQGLgHuApqKyH+Ad1T101MTovFT2sFsJryylOXbD/B/53bmjuEdrSdyY8wpEUwjiSPANGCaiDQErgB+jxtyw1Riq1IzuXnqEjKP5jFpbH9G9rTGm8aYU6dc/dCo6n5VfU5Vh4cqIBMe3luxkyuem0dUNWHGbadZcjLGnHLB3AdlqpDCQuWJzzfw1JxkBrZtwH/GDiC+Tk2/wzLGVEGWoMwxR3Ly+c3/lvPJmj1cldiahy/pSY3q1tmrMcYflqAMANv3Z3Hz1CVs2HOI+y/szg2nt7XGEMYYX1mCMizasp9bX11KXkEhU24YxFmdG/sdkjHGWIKq6t5YnMKf3l1N6waxTL4ukfaN6/gdkjHGAJagqqz8gkL++uE6Xvp2K2d1bsxT1/Sjfi3ridwYEz4sQVVBmVl53PH6Mr7euJcbT2/Hved3pbqNfGuMCTOWoKqYTemHufnlJWzPyOLvo3tz5cDWfodkjDElsgRVhXy5IZ07XltGjahqvHbzEAa2beh3SMYYUypLUFWAqvLCN1t45MN1dGlWj/+OH0CrBrF+h2WMMWWyBFXJ5eQX8Kd3VvPm0lRG9mjGP6/sQ+2a9m83xoQ/+6aqxNIP5XDbq0tZsi2DX43oxK9HdKKaDctujIkQlqAqqTU7M7n55SXsz8rlmTH9uaC3dfZqjIkslqAqoY9X7+KuN1YQFxvNW7eeRs+W9f0OyRhjys0SVCWiqvx7djJPfL6BfglxPDduAE3qxvgdljHGnBBLUJXE0dwCfvvmCj5YtYvL+rfkkUt7ERNtw7IbYyKXJahKYOeBo9w8dQlrdx3k3vO7cvOZ7a0ncmNMxLMEFeGWbsvglleWkpNXwIvXDeQnXZv4HZIxxlQIS1AR7K2lqdz79iqax8UwfcJgOjap63dIxhhTYULaQ6iIjBSR9SKSLCK/L2H5KBFZKSLLRWSJiJwRyngqi4JC5a8frOW3b65gYLsGzPzF6ZacjDGVTshKUCISBTwDnAukAotFZJaqrg1YbTYwS1VVRHoD/wO6hiqmyiArN59fTFvGF+vTuW5oG/50YXeirSdyY0wlFMoqvkFAsqpuBhCR6cAo4FiCUtXDAevXBjSE8US8g9l53PjSYpalZPCXS3oydkgbv0MyxpiQCeVP75bA9oDpVG/eD4jIpSKSBHwA3FjSjkRkglcFuCQ9PT0kwYa7jCO5jJ28kOXbD/D0mP6WnIwxlV4oE1RJ7Zx/VEJS1XdUtStwCfBwSTtS1edVNVFVExs3blyxUUaA9EM5XP38ApJ2H+L58QM4v5d1W2SMqfxCmaBSgcDR8FoBO0tbWVW/AjqISHwIY4o4uzKPctVz80nZn8VL1w9keNemfodkjDGnRCgT1GKgk4i0E5EawNXArMAVRKSjeHeUikh/oAawL4QxRZSUfVlcMWk+6YdyeOXngzi9o+VuY0zVEbJGEqqaLyJ3AJ8AUcCLqrpGRG71lk8CRgPjRSQPOApcparWUAJITjvMtZMXkJNfyGs3D6FXK+vw1RhTtUik5YPExERdsmSJ32GE1LpdBxk7eSEiwqs3DaJrs3p+h2SMMSEjIktVNbH4fOtJIsws336A615cRGyNKKbdNJj2jev4HZIxxvjCElQYWbRlPzdOWUzD2jWYdtNgWjeM9TskY4zxjSWoMPH1xnRunrqElnG1mHbTEJrVt3GcjDFVmyWoMPDZ2j38YtoyOjSpwys/H0R8nZp+h2SMMb6zBOWz91bs5K43ltOjZX1evmEgcbE1/A7JGGPCgvUy6qP/LdnOndO/o39CA179+SBLTsYYE8BKUD6ZOn8r989cw5md4nl+XCK1atjw7MYYE8gSlA+e+3ITf/soiXO7N+XpMf2oWd2SkzHGFGcJ6hRSVZ78fCMTZ2/koj4t+NeVfWwsJ2OMKYUlqFNEVfnbR0k8/9VmrhjQikdH9yaqWkkdvhtjjAFLUKdEYaFy/6zVvLogheuGtuHPF/WgmiUnY4wpkyWoEMsvKOSeGauYsSyVW85uz+9HdsXrwN0YY0wZLEGFUG5+IXe9sZwPVu3iN+d25pfDO1pyMsaYIFmCCpHsvAJ+MW0Zs5PS+NMF3bjpzPZ+h2SMMRHFElQIZOXmM2HqUr5J3stfLunJ2CFt/A7JGGMijiWoCnYwO48bX1rMspQM/nlFH0YPaOV3SMYYE5EsQVWgA1m5jH9xEWt3HuSpa/pzQe/mfodkjDERyxJUBUk/lMO4Fxayee8Rnhs3gBHdmvodkjHGRDRLUBVgV+ZRrv3vQnZlZvPS9QM5vWO83yEZY0zEswR1klL2ZTFm8gIys/KY+vNBDGzb0O+QjDGmUrAEdRKS0w4zdvJCsvMLmHbzYHq3ivM7JGOMqTQsQZ2gdbsOMnbyQkRg+oQhdG1Wz++QjDGmUrEEdQJWbD/A+BcXEVsjimk3DaZ94zp+h2SMMZWOJahyWrRlPzdOWUyD2tG8dtMQWjeM9TskY4yplCxBlcPXG9O5eeoSWsbVYtpNQ2hWP8bvkIwxptKyBBWkz9fu4fZpy2jfuDav3jSY+Do1/Q7JGGMqNUtQQXhvxU7uemM5PVrU4+UbBxEXW8PvkIwxptKzBHUcby7Zzj0zVpLYpiEvXJ9I3Zhov0MyxpgqwRJUGV6Zv5X7Zq7hzE7xPD8ukVo1ovwOyRhjqgxLUKV47stN/O2jJM7p1pSnx/QjJtqSkzHGnEqWoIpRVZ78fCMTZ2/kwt7NeeKqvkRHVfM7LGOMqXIsQQVQVf72URLPf7WZywe04rHRvYmqZkO0G2OMHyxBeQoLlftnrebVBSmMH9qGBy7qQTVLTsYY4xtLUEB+QSH3zFjFjGWp3HJ2e34/sisilpyMMcZPIb24IiIjRWS9iCSLyO9LWH6tiKz0HvNEpE8o4ylJXkEhd76xnBnLUrnrnM6WnIwxJkyErAQlIlHAM8C5QCqwWERmqeragNW2AGeraoaInAc8DwwOVUzFZecVcMdry/h8XRp/PL8bN5/V/lQd2hhjzHGEsopvEJCsqpsBRGQ6MAo4lqBUdV7A+guAViGM5weycvOZMHUp3yTv5eFLejJuSJtTdWhjjDFBCGUVX0tge8B0qjevND8HPippgYhMEJElIrIkPT39pAM7lJ3HdS8uYt6mvTx+RR9LTsYYE4ZCWYIq6UKOlriiyE9wCeqMkpar6vO46j8SExNL3EewDmTlMv7FRazdeZCnrunPBb2bn8zujDHGhEgoE1Qq0DpguhWws/hKItIbmAycp6r7QhgPAA/MWkPS7kM8N24AI7o1DfXhjDHGnKBQJqjFQCcRaQfsAK4GxgSuICIJwNvAOFXdEMJYjvnThd25elACQ9o3OhWHM8YYc4JClqBUNV9E7gA+AaKAF1V1jYjc6i2fBNwPNAKe9Zp256tqYqhiAoivU9PGcjLGmAggqid1SeeUS0xM1CVLlvgdhjHGmAoiIktLKpxYL6jGGGPCkiUoY4wxYckSlDHGmLBkCcoYY0xYsgRljDEmLFmCMsYYE5Yirpm5iKQD205yN/HA3goIx0+Rfg6RHj9E/jlEevwQ+ecQ6fFDxZxDG1VtXHxmxCWoiiAiS0J9Q3CoRfo5RHr8EPnnEOnxQ+SfQ6THD6E9B6viM8YYE5YsQRljjAlLVTVBPe93ABUg0s8h0uOHyD+HSI8fIv8cIj1+COE5VMlrUMYYY8JfVS1BGWOMCXOWoIwxxoSlKpWgRORFEUkTkdV+x3IiRKS1iHwhIutEZI2I3Ol3TOUlIjEiskhEVnjn8KDfMZ0IEYkSke9E5H2/YzkRIrJVRFaJyHIRibjxa0QkTkTeEpEk7/Mw1O+YykNEunivfdHjoIj82u+4ykNE7vI+w6tF5HURianwY1Sla1AichZwGJiqqj39jqe8RKQ50FxVl4lIXWApcImqrvU5tKCJG5mytqoeFpFo4BvgTlVd4HNo5SIivwESgXqqeqHf8ZSXiGwFElU1Im8SFZGXga9VdbKI1ABiVfWAz2GdEBGJwo06PlhVT7YTglNCRFriPrvdVfWoiPwP+FBVp1TkcapUCUpVvwL2+x3HiVLVXaq6zHt+CFgHtPQ3qvJR57A3Ge09IupXkoi0Ai4AJvsdS1UkIvWAs4AXAFQ1N1KTk2cEsClSklOA6kAtEakOxAI7K/oAVSpBVSYi0hboByz0OZRy86rHlgNpwGeqGmnn8CTwO6DQ5zhOhgKfishSEZngdzDl1B5IB17yqlkni0htv4M6CVcDr/sdRHmo6g7gcSAF2AVkquqnFX0cS1ARSETqADOAX6vqQb/jKS9VLVDVvkArYJCIREx1q4hcCKSp6lK/YzlJp6tqf+A84Bde9XekqA70B/6jqv2AI8Dv/Q3pxHjVkxcDb/odS3mISANgFNAOaAHUFpGxFX0cS1ARxrtuMwOYpqpv+x3PyfCqZeYCI/2NpFxOBy72ruFMB4aLyKv+hlR+qrrT+5sGvAMM8jeickkFUgNK3m/hElYkOg9Ypqp7/A6knM4BtqhquqrmAW8Dp1X0QSxBRRCvgcELwDpV/Zff8ZwIEWksInHe81q4N3qSr0GVg6r+QVVbqWpbXNXMHFWt8F+OoSQitb1GNnhVYz8FIqZlq6ruBraLSBdv1gggYhoKFXMNEVa950kBhohIrPe9NAJ3TbxCVakEJSKvA/OBLiKSKiI/9zumcjodGIf71V7UPPV8v4Mqp+bAFyKyEliMuwYVkU21I1hT4BsRWQEsAj5Q1Y99jqm8fglM895HfYFH/A2n/EQkFjgXV/qIKF7p9S1gGbAKl0sqvMujKtXM3BhjTOSoUiUoY4wxkcMSlDHGmLBkCcoYY0xYsgRljDEmLFmCMsYYE5YsQZmwIiIqIq8ETFcXkfSK6DVcRIaJSKbXPc56EfnK6xniRPfXVkTGBExfLyJPn8T+zvR6h17u3SMWuKzAm7/G6wn+NyJyQp9fEXlIRM4pY/mtIjL+RPYdsI9eAbdC7BeRLd7zz09mv6Zqqe53AMYUcwToKSK1VPUo7j6RHRW4/6+Leh8Xkb7AuyJyVFVnn8C+2gJjgNcqKLZrgcdV9aUSlh31uodCRJp4x6wP/Lm8B1HV+4+zfFJ591nCPlbh7k9CRKYA76vqW4HriEh1Vc0/2WOZystKUCYcfYTrLRyK3WkvIoNEZJ5XCppX1JuAV6J40XveyxujJrasg6jqcuAh4A5vu8YiMkNEFnuP0735D4jIKyIyR0Q2isjN3i4eBc70SgZ3efNaiMjH3np/L+m4IjLCi3+VuDHKaorITcCVwP0iMu04cacBE4A7xIkSkX94Ma8UkVsCjvU77zgrRORRb94UEbnce/6oiKz1tns84Hx/6z3vKyILvOXviOuDDRGZKyKPiRvba4OInFlWzAHxzBWRR0TkS+BOERkgIl+K67T2E3FDyiAiHbzXcamIfC0iXYPZv6lkVNUe9gibB268rt64u9RjgOXAMNwvcIB6QHXv+TnADO95NeAr4FJgCa4z1OL7PrafgHl9cV1HgSuVnOE9TwiY/wCwAqgFxAPbcR1k/mB/wPXAZlzJJgbYBrQudrwYb/vO3vRUXKe/AFOAy0t7XUqYl4HrFWIC8CdvXk3v/Nvh+nmbhxsrCaBh4HGAhsB6vr9hPy7gfH/rPV8JnO09fwh40ns+F/in9/x84PMy/qfHzsvb7lnvebQXX2Nv+irgRe/5bKCT93wwrksp39+f9ji1D6viM2FHVVeKG07kGuDDYovrAy+LSCfckBHR3jaFInI97gv1OVX9NsjDScDzc4DuIsdm1ROvzzpgproqx6Mi8gWuc9UDJexvtqpmAojIWqANLiEV6YLrZHODN/0y8AvcEB7lVRToT4HeRaUi3GvUyTufl1Q1C0BVi4+FdhDIBiaLyAfAD67ziUh9XNL6MiDWwF63i7roWYqr7gzWG97fLkBP4DPvNY8Cdonrrf804M2A/0XNcuzfVBKWoEy4moUbb2YY0Chg/sPAF6p6qZfE5gYs64QrgbUox3H68X0nl9WAoV4iOsb7kizeJ1hpfYTlBDwv4MefMaECiEh7b/9p3j5/qaqfFFtnZBlxoqr5IjII19Hn1biqzuHlCKPoXEs6z7IcKQoRWKOqPxiuXdyAhAfUu+Zmqi67BmXC1YvAQ+outgeqz/eNJq4vmun92p+IG2m1UUBpolQi0hu4D3jGm/Up3vUob3nfgNVHiUiMiDTCJc3FwCGgLuWTBLQVkY7e9DjgyzLWLynuxsAk4GlVVeAT4DZxQ7EgIp3F9VL+KXBj0bU4EWlYbD91gPqq+iHwa7xGDUW8kmBGwPWlcsd6HOuBxiIy1IsnWkR6qBvjbIuIXOHNFxHpU4HHNRHCSlAmLKlqKi7hFPd3XBXfb4A5AfOfwF3b2CCul/ovROQrdQ0KAp0pIt/hhqhOA36l37fg+xXwjLgesqvjrmnd6i1bBHyAuzb1sKruFJF0IF9cr+BTcNeEjnde2SJyA676qjou0QXTaq6WuFGIo4F84BWgaMiVybgqtmXiinvpwCWq+rGXZJeISC6uuvTegH3WBWaKSAyuNHMXP3YdMMlLcpuBG4KINSiqmuv9kPi39wOjOq6qcw2uReN/RORP3jlPx10HNFWI9WZuzHGIyAO4RgqP+x2LMVWJVfEZY4wJS1aCMsYYE5asBGWMMSYsWYIyxhgTlixBGWOMCUuWoIwxxoQlS1DGGGPC0v8DX8e0G4AZD48AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of max depths\n",
    "plt.plot(max_depth_list, accuracy_scores)\n",
    "plt.scatter(optimal_depth, max_accuracy, marker = \"x\", color = \"red\")\n",
    "plt.title(f\"Accuracy by Max Depth \\n Optimal Tree Depth: {optimal_depth} (Accuracy: {round(max_accuracy, 4)}\")\n",
    "plt.xlabel(\"Max Depth of Decision Tree\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply the model on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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",
       "    </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": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict = test_data\n",
    "to_predict.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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": 59,
     "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": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_vars = ['jobType', 'degree', 'major', 'industry']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_vars_array = one_hot_encoder.transform(to_predict[categorical_vars])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_feature_names = one_hot_encoder.get_feature_names(categorical_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([110.98001873, 113.58646876, 160.84377377, ...,  58.55395683,\n",
       "       146.42921469, 106.67695841])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_predictions = regressor.predict(to_predict)\n",
    "salary_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_predict['salary'] = salary_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>industry_SERVICE</th>\n",
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       "      <td>160.843774</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "    </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  110.980019  \n",
       "1           0.0               0.0           0.0  113.586469  \n",
       "2           0.0               0.0           0.0  160.843774  \n",
       "3           1.0               0.0           0.0   97.489139  \n",
       "4           1.0               0.0           0.0  110.652342  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 28)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = to_predict.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 69,
     "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": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:my_env] *",
   "language": "python",
   "name": "conda-env-my_env-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}