SalaryPredictionEDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Data Scientist:__ Gail Wittich <br>__Email:__ gwittich@optusnet.com.au <br>__Website:__ www.linkedin.com/in/gail-wittich-7709b4a <br> __Copyright__ : 'Copyright 2020, Gail Wittich'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Salary Prediction Project - EDA\n",
    "The goal of this script is to build baseline predictive model for the Salary Prediction dataset. \n",
    "\n",
    "Before doing that it loads, examines, cleans, and validates the data. Then  data shows the distribution and correlation of each individual feature against the target feature, salary.\n",
    "Categoriacl features are then label encoded with the average salary for each category in each feature. The relationship between each features and the salary is shown on correlation matrix. \n",
    "A baseline model is created and a hypothisis defined. \n",
    "Cleaned data is saved for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "\n",
    "import gc\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read files and load dataframes\n",
    "train_feature_df = pd.read_csv('data/train_features.csv')\n",
    "train_target_df = pd.read_csv('data/train_salaries.csv')\n",
    "test_feature_df = pd.read_csv('data/test_features.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect length and dtypes of each dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JOB1362684407689</td>\n",
       "      <td>COMP52</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JOB1362684407690</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JOB1362684407691</td>\n",
       "      <td>COMP7</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId companyId         jobType       degree      major  \\\n",
       "0  JOB1362684407687    COMP37             CFO      MASTERS       MATH   \n",
       "1  JOB1362684407688    COMP19             CEO  HIGH_SCHOOL       NONE   \n",
       "2  JOB1362684407689    COMP52  VICE_PRESIDENT     DOCTORAL    PHYSICS   \n",
       "3  JOB1362684407690    COMP38         MANAGER     DOCTORAL  CHEMISTRY   \n",
       "4  JOB1362684407691     COMP7  VICE_PRESIDENT    BACHELORS    PHYSICS   \n",
       "\n",
       "  industry  yearsExperience  milesFromMetropolis  \n",
       "0   HEALTH               10                   83  \n",
       "1      WEB                3                   73  \n",
       "2   HEALTH               10                   38  \n",
       "3     AUTO                8                   17  \n",
       "4  FINANCE                8                   16  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000000, 8)\n"
     ]
    }
   ],
   "source": [
    "#train_feature_df.info()   df.head and df.shape give a nicer layout\n",
    "display(train_feature_df.head())\n",
    "print(train_feature_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JOB1362685407689</td>\n",
       "      <td>COMP10</td>\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>JOB1362685407690</td>\n",
       "      <td>COMP21</td>\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>JOB1362685407691</td>\n",
       "      <td>COMP36</td>\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": [
       "              jobId companyId  jobType       degree    major industry  \\\n",
       "0  JOB1362685407687    COMP33  MANAGER  HIGH_SCHOOL     NONE   HEALTH   \n",
       "1  JOB1362685407688    COMP13   JUNIOR         NONE     NONE     AUTO   \n",
       "2  JOB1362685407689    COMP10      CTO      MASTERS  BIOLOGY   HEALTH   \n",
       "3  JOB1362685407690    COMP21  MANAGER  HIGH_SCHOOL     NONE      OIL   \n",
       "4  JOB1362685407691    COMP36   JUNIOR     DOCTORAL  BIOLOGY      OIL   \n",
       "\n",
       "   yearsExperience  milesFromMetropolis  \n",
       "0               22                   73  \n",
       "1               20                   47  \n",
       "2               17                    9  \n",
       "3               14                   96  \n",
       "4               10                   44  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000000, 8)\n"
     ]
    }
   ],
   "source": [
    "display(test_feature_df.head())\n",
    "print(test_feature_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JOB1362684407689</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JOB1362684407690</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JOB1362684407691</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId  salary\n",
       "0  JOB1362684407687     130\n",
       "1  JOB1362684407688     101\n",
       "2  JOB1362684407689     137\n",
       "3  JOB1362684407690     142\n",
       "4  JOB1362684407691     163"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000000, 2)\n"
     ]
    }
   ],
   "source": [
    "display(train_target_df.head())\n",
    "print(train_target_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of data points in 'train_feature_df' and 'train_target_df' are the same. <br/>Both 'train_feature_df' and 'train_target_df' have 'jobId' so this will be used as the key when merging data.<br/>The format of 'test_feature_df' and train_feature_df' are identical. Therfore no manipulation of columns is required.<br/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "key = 'jobId'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge features and targets into single df (delete source to save memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.merge(train_feature_df, train_target_df, on=key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JOB1362684407689</td>\n",
       "      <td>COMP52</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JOB1362684407690</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JOB1362684407691</td>\n",
       "      <td>COMP7</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId companyId         jobType       degree      major  \\\n",
       "0  JOB1362684407687    COMP37             CFO      MASTERS       MATH   \n",
       "1  JOB1362684407688    COMP19             CEO  HIGH_SCHOOL       NONE   \n",
       "2  JOB1362684407689    COMP52  VICE_PRESIDENT     DOCTORAL    PHYSICS   \n",
       "3  JOB1362684407690    COMP38         MANAGER     DOCTORAL  CHEMISTRY   \n",
       "4  JOB1362684407691     COMP7  VICE_PRESIDENT    BACHELORS    PHYSICS   \n",
       "\n",
       "  industry  yearsExperience  milesFromMetropolis  salary  \n",
       "0   HEALTH               10                   83     130  \n",
       "1      WEB                3                   73     101  \n",
       "2   HEALTH               10                   38     137  \n",
       "3     AUTO                8                   17     142  \n",
       "4  FINANCE                8                   16     163  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000000, 9)\n"
     ]
    }
   ],
   "source": [
    "display(train_df.head())\n",
    "print(train_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del train_feature_df\n",
    "del train_target_df\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "      <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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JOB1362684407689</td>\n",
       "      <td>COMP52</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JOB1362684407690</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JOB1362684407691</td>\n",
       "      <td>COMP7</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId companyId         jobType       degree      major  \\\n",
       "0  JOB1362684407687    COMP37             CFO      MASTERS       MATH   \n",
       "1  JOB1362684407688    COMP19             CEO  HIGH_SCHOOL       NONE   \n",
       "2  JOB1362684407689    COMP52  VICE_PRESIDENT     DOCTORAL    PHYSICS   \n",
       "3  JOB1362684407690    COMP38         MANAGER     DOCTORAL  CHEMISTRY   \n",
       "4  JOB1362684407691     COMP7  VICE_PRESIDENT    BACHELORS    PHYSICS   \n",
       "\n",
       "  industry  yearsExperience  milesFromMetropolis  salary  \n",
       "0   HEALTH               10                   83     130  \n",
       "1      WEB                3                   73     101  \n",
       "2   HEALTH               10                   38     137  \n",
       "3     AUTO                8                   17     142  \n",
       "4  FINANCE                8                   16     163  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000000, 9)\n"
     ]
    }
   ],
   "source": [
    "# train_df.info()\n",
    "display(train_df.head())\n",
    "print(train_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean the data up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_feature_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicates so no action required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features and number of missing values: \n",
      "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"
     ]
    }
   ],
   "source": [
    "print(\"Features and number of missing values: \")\n",
    "for col in train_df.columns:\n",
    "    print(col, train_df[col].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values so no action required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check validity of specific feature's data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following jobIds have invalid data for yearsExperience: \n"
     ]
    },
    {
     "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [jobId, companyId, jobType, degree, major, industry, yearsExperience, milesFromMetropolis, salary]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# yearsExperience should not be less than 0\n",
    "invalid_yearsExperience = train_df[train_df['yearsExperience'] < 0].copy()\n",
    "print(\"The following jobIds have invalid data for yearsExperience: \")\n",
    "display(invalid_yearsExperience)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no invalid yearsExperience values so no action required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following jobIds have invalid data for milesFromMetropolis: \n"
     ]
    },
    {
     "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [jobId, companyId, jobType, degree, major, industry, yearsExperience, milesFromMetropolis, salary]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# milesFromMetropolis should not be less than 0\n",
    "invalid_milesFromMetropolis = train_df[train_df['milesFromMetropolis'] < 0].copy()\n",
    "print(\"The following jobIds have invalid data for milesFromMetropolis: \")\n",
    "display(invalid_milesFromMetropolis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no invalid milesFromMetropolis values so no action required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following jobIds have invalid data for 'salary': \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Int64Index([30559, 495984, 652076, 816129, 828156], dtype='int64')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# salary should not be less than 1\n",
    "invalid_salary = train_df[train_df['salary'] < 1].index\n",
    "print(\"The following jobIds have invalid data for 'salary': \")\n",
    "display(invalid_salary)\n",
    "\n",
    "# **** this is a quick and risky way to delete - it is safe only because I have\n",
    "# done it all in one cell. Seperating it to two logioal cells runs the risk of \n",
    "# the second cell being run multiple times and deleting rows repeatedly based \n",
    "# new on indexes reducing the length of the df by 5 each time! ****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove records with invalid data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleting invalid entries from train_df.\n",
      "\n",
      "'train_df' shape after deletion and index reset:\n"
     ]
    },
    {
     "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>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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JOB1362684407689</td>\n",
       "      <td>COMP52</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JOB1362684407690</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>MANAGER</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>CHEMISTRY</td>\n",
       "      <td>AUTO</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JOB1362684407691</td>\n",
       "      <td>COMP7</td>\n",
       "      <td>VICE_PRESIDENT</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>PHYSICS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              jobId companyId         jobType       degree      major  \\\n",
       "0  JOB1362684407687    COMP37             CFO      MASTERS       MATH   \n",
       "1  JOB1362684407688    COMP19             CEO  HIGH_SCHOOL       NONE   \n",
       "2  JOB1362684407689    COMP52  VICE_PRESIDENT     DOCTORAL    PHYSICS   \n",
       "3  JOB1362684407690    COMP38         MANAGER     DOCTORAL  CHEMISTRY   \n",
       "4  JOB1362684407691     COMP7  VICE_PRESIDENT    BACHELORS    PHYSICS   \n",
       "\n",
       "  industry  yearsExperience  milesFromMetropolis  salary  \n",
       "0   HEALTH               10                   83     130  \n",
       "1      WEB                3                   73     101  \n",
       "2   HEALTH               10                   38     137  \n",
       "3     AUTO                8                   17     142  \n",
       "4  FINANCE                8                   16     163  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(999995, 9)\n"
     ]
    }
   ],
   "source": [
    "# Delete rows with invalid salary from train_target_df\n",
    "print(\"Deleting invalid entries from train_df.\")\n",
    "print()\n",
    "train_df.drop(invalid_salary, inplace=True)\n",
    "\n",
    "# Ensures that re-running this cell does not delete the new entries at \n",
    "# the old indexes with invalid salaries.\n",
    "invalid_salary = 'NaN'\n",
    "\n",
    "# reset index, because we dropped rows\n",
    "train_df.reset_index(drop=True, inplace=True)\n",
    "print(\"'train_df' shape after deletion and index reset:\")\n",
    "display(train_df.head())\n",
    "print(train_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify numeric and categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "jobId                  object\n",
       "companyId              object\n",
       "jobType                object\n",
       "degree                 object\n",
       "major                  object\n",
       "industry               object\n",
       "yearsExperience         int64\n",
       "milesFromMetropolis     int64\n",
       "salary                  int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarize numeric and categorical variables separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['yearsExperience', 'milesFromMetropolis', 'salary']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cols = train_df.select_dtypes(include=np.number).columns.tolist()\n",
    "numeric_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove target variable (salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['yearsExperience', 'milesFromMetropolis']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_cols.remove('salary')\n",
    "numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['jobId', 'companyId', 'jobType', 'degree', 'major', 'industry']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols = train_df.select_dtypes(include=object).columns.tolist()\n",
    "categorical_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove less helpful variable, jobId, i.e. the key.\n",
    "The key is discarded because it is unique to individual entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['companyId', 'jobType', 'degree', 'major', 'industry']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols.remove('jobId')\n",
    "categorical_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   companyId\n",
      "COMP39    16193\n",
      "COMP35    16114\n",
      "COMP59    16066\n",
      "COMP30    16041\n",
      "COMP3     16028\n",
      "          ...  \n",
      "COMP17    15715\n",
      "COMP0     15666\n",
      "COMP2     15641\n",
      "COMP8     15638\n",
      "COMP57    15635\n",
      "Name: companyId, Length: 63, dtype: int64\n",
      "\n",
      "   jobType\n",
      "SENIOR            125886\n",
      "VICE_PRESIDENT    125234\n",
      "MANAGER           125120\n",
      "CTO               125045\n",
      "JANITOR           124971\n",
      "CEO               124778\n",
      "JUNIOR            124592\n",
      "CFO               124369\n",
      "Name: jobType, dtype: int64\n",
      "\n",
      "   degree\n",
      "HIGH_SCHOOL    236975\n",
      "NONE           236853\n",
      "BACHELORS      175495\n",
      "DOCTORAL       175362\n",
      "MASTERS        175310\n",
      "Name: degree, dtype: int64\n",
      "\n",
      "   major\n",
      "NONE           532353\n",
      "CHEMISTRY       58875\n",
      "LITERATURE      58684\n",
      "ENGINEERING     58594\n",
      "BUSINESS        58518\n",
      "PHYSICS         58410\n",
      "COMPSCI         58382\n",
      "BIOLOGY         58379\n",
      "MATH            57800\n",
      "Name: major, dtype: int64\n",
      "\n",
      "   industry\n",
      "WEB          143205\n",
      "AUTO         142941\n",
      "FINANCE      142866\n",
      "EDUCATION    142819\n",
      "OIL          142770\n",
      "HEALTH       142755\n",
      "SERVICE      142639\n",
      "Name: industry, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in categorical_cols: \n",
    "    print(\"  \", col)\n",
    "    print(train_df[col].value_counts())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   salary\n",
      "108    10467\n",
      "114    10407\n",
      "107    10371\n",
      "112    10361\n",
      "104    10297\n",
      "       ...  \n",
      "293        2\n",
      "294        2\n",
      "298        1\n",
      "290        1\n",
      "17         1\n",
      "Name: salary, Length: 279, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"  \", 'salary')\n",
    "print(train_df.salary.value_counts())\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   yearsExperience\n",
      "15    40312\n",
      "1     40264\n",
      "9     40235\n",
      "3     40190\n",
      "22    40187\n",
      "8     40118\n",
      "6     40094\n",
      "18    40088\n",
      "17    40072\n",
      "7     40047\n",
      "2     40045\n",
      "4     40036\n",
      "10    40032\n",
      "23    40015\n",
      "5     40013\n",
      "20    39988\n",
      "11    39948\n",
      "16    39873\n",
      "19    39860\n",
      "24    39860\n",
      "0     39839\n",
      "14    39827\n",
      "13    39826\n",
      "21    39759\n",
      "12    39467\n",
      "Name: yearsExperience, dtype: int64\n",
      "\n",
      "   milesFromMetropolis\n",
      "99    10180\n",
      "62    10175\n",
      "63    10159\n",
      "97    10155\n",
      "41    10147\n",
      "      ...  \n",
      "26     9845\n",
      "6      9839\n",
      "18     9824\n",
      "87     9815\n",
      "20     9807\n",
      "Name: milesFromMetropolis, Length: 100, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for col in numeric_cols:\n",
    "    print(\"  \", col)\n",
    "    print(train_df[col].value_counts())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize target variable (salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z0ryOElW3XFauwfEpvXy02+soQEKifAAAFmu22cUz78Q21z6zbjezQk1fFamTtFxSjpl9eNaDm33MzLab2fbOTsbieyEcdpHJ5sk73+OMa1eVKJiRpp/tZ+gVcDEoHwCAxWqVVHXO1yv01iFSZ/eJDKPKl9Rznte+S1KTc67TOTcp6QeSrp3t4M65B51zm5xzm0pLS5fgdLBQx7qGNDg2lZT395gpM+DXO9aU6mf7TykU5m7nwEIl97VRAEAsvCapwczqJLVpemL4HTP2eULSnZJ+LekDkp51zjkze0LSI2b2d5q+wtEg6VVJYUlbzCxb0qikmyVtj8XJYOF2tkxPNr8qia58PLKtZc7nghlp6h6e0BeeOqi6kpy3PH/H5upoRgMSGuUDALAozrkpM/uEpGc0vSrVt5xz+83s85K2O+eekPSQpO+aWaOmr3jcHnntfjN7TNIbkqYkfdw5F5K0zcwel7Qzsn2XpAdjfW6Yn10tfcrLTNPKWX4QT0ZrynPlN9PBjoFZyweAuVE+AACL5px7UtKTM7Z95pzHY5Jum+O190u6f5btn5X02aVNimjY1dKrK6sL5fMl580FZ8oI+LWiMEvHu4a9jgIkHOZ8AACAizYwNqnDpwaT+v4es6kryVFb3yhL7gILRPkAAAAXbfvxHoWdtLmuyOsoMVVbkqOwk1p6RryOAiQUygcAALho2471KOC3lFhm91w1RdnymRh6BSwQ5QMAAFy0V5p6tH5FgbLS/V5HiamMgF/LC7LURPkAFoTyAQAALsrQ+JT2tfVr88rUGnJ1Rl1xjk70jmoyFPY6CpAwKB8AAOCi7GjuVSjstLmu2OsonqgtyVEo7HSil3kfwHzFrHw88MADeuCBB2J1OADzxJ9NABfr1aZu+X2mjTWpNd/jjNriHJmY9wEsRMzu89HY2BirQwFYAP5sArhY24716PLKfOVkpOZtw7LS/arIz2TeB7AADLsCAAALNjoR0p7WvpSd73FGbXGOWnpGNBVm3gcwH5QPAACwYLtaejUZctqSovM9zqgrydFkyKm9d9TrKEBCoHwAAIAF29bUI59JG2tTc77HGbUlOZKkpm4mnQPzQfkAAAALtretXw1lucrLDHgdxVPBjDSV5mYw6RyYJ8oHAABYsH1t/bp0eZ7XMeJCXXGOjncPK+yc11GAuJeay1MAAIB5e2Rby5u+Hhyb1OnBcY1Phd/yXCqqK8nRq8d7dLJ/TJUFWV7HAeIaVz4AAMCCtPeNSZKWFWR6nCQ+nJ33wdAr4IIoHwAAYEFO9k+v7LQ8n3/ll6T8rICKctKZ9wHMA+UDAAAsSFvfqIpy0pUZ8HsdJW7UFeeoqYt5H8CFUD4AAMCCnOwf03LmNrxJXUmORidDOj047nUUIK5RPgAAwLyNToTUMzyh5fnM9zgX8z6A+aF8AACAeWs/M9+DKx9vUpgdUH5WQE2dQ15HAeIa5QMAAMzbyT7Kx2zMTKtKc3Ssa1jhMPM+gLlQPgAAwLy1948pLzNNwQxuFTbTqtKgRiZCOtAx4HUUIG5RPgAAwLy1941y1WMOK0uDkqSXG7s9TgLEL8oHAACYl4mpsDoHxykfc8jPCqgkmKGXj3Z5HQWIW5QPAAAwLyf7R+XEzQXPZ1Vpjl5t6tFkKOx1FCAuUT4AAMC8tPZOTzZfUUj5mMuq0qCGJ0Lac6LP6yhAXKJ8AACAeWnrG1VuZprysgJeR4lbK0tyZCa9fJR5H8BsKB8AAGBeWntHtYL5HueVnZGmdcvy9FIj8z6A2VA+AADABY1NhtQ1NK5Khlxd0HX1JdrV0qfRiZDXUYC4Q/kAAAAX1NZ3Zr5HtsdJ4t/muiJNhMJ6vZV5H8BMlA8AAHBBbZHJ5pUMu7qgK6sKJEm7mXQOvAXlAwAAXFBb36gKswPK4c7mF1QczFB1UbZ2tVA+gJkoHwAA4IJae0e46rEAV1YVcOUDmAXlAwAAnNfI+JR6RyZVyXyPedtQXaCOgTGd7B/1OgoQVygfAADgvFr7uLngQp2d98HQK+BNKB8AAOC8zqx0tTyf8jFf65bnKd3vY+gVMAPlAwAAnFdrz4hKgunKSvd7HSVhZKT5tW55nnZRPoA3oXwAAIA5OefU3DOi6iLmeyzUlVUF2tvar6lQ2OsoQNygfAAAgDk1d49oZCKkKsrHgm2oLtDoZEiHTg16HQWIG5QPAAAwp50tvZKkmqIcj5Mkng1VhZLE/T6Ac1A+AADAnHY09yojzaeyvAyvoyScqqIsFeekUz6Ac1A+AADAnHa29KmqKFs+M6+jJBwz08aaQr12vMfrKEDcoHwAAIBZDY1P6VDHAJPNF2HLymK19IyovY+bDQIS5QMAAMxhz4k+hZ0oH4uweWWRJGlbU7fHSYD4QPkAAACz2tk8Pdm8qpDycbHWVuQpLzNNrxxl6BUgUT4AAMAcdrT0qqEsyM0FF8HvM11TV8yVDyCC8gEAAN4iHHba1dKnq6oLvY6S8LasLNLx7hF19I95HQXwHOUDAAC8xbGuYfWPTuqqmgKvoyS8zXXFkpj3AUiUDwAAMIvXW6fvTXFlFVc+Fmvd8jzlZqTplWPM+wAoHwAA4C1eb+1XVsCvVaXc2Xyx/D7T1XVF2naMKx8A5QMAALzF3rZ+XVaZpzQ/PyoshS0ri3Ssa1inB5j3gdTG3ygAgEUzs61mdsjMGs3sU7M8n2Fm3488v83Mas957tOR7YfM7JZztheY2eNmdtDMDpjZ22JzNpgKhbW/vV+XVzLfY6lcu6pEkvTCkS6PkwDeonwAABbFzPySvibpPZLWSfqQma2bsdtdknqdc/WSviTpi5HXrpN0u6RLJW2V9PXI+0nSlyU97ZxbK2m9pAPRPhdMO3J6SGOTYV2xIt/rKElj3bI8lQQz9KvDnV5HATxF+QAALNY1khqdc8eccxOSHpV064x9bpX0cOTx45JuNjOLbH/UOTfunGuS1CjpGjPLk3SjpIckyTk34Zzri8G5QNLe1n5JonwsIZ/P9I41pXr+cKemQmGv4wCeoXwAABarUtKJc75ujWybdR/n3JSkfknF53ntSkmdkv7JzHaZ2TfNjJnPMfJ6W59yM9JUW8z/8qX0zjVl6h+d1O4T9GikLsoHAGCxbJZtbp77zLU9TdJVkv7BObdB0rCkt8wlkSQz+5iZbTez7Z2dDGlZCq+39uuyynz5fLN9e3Cxrm8okd9n+tUhfp8idaV5HQAAkPBaJVWd8/UKSe1z7NNqZmmS8iX1nOe1rZJanXPbItsf1xzlwzn3oKQHJWnTpk0zSw8WaHwqpAMnB/T719d5HSVhPbKtZc7nqgqz9IOdrVpekDXr83dsro5WLCAucOUDALBYr0lqMLM6M0vX9ATyJ2bs84SkOyOPPyDpWeeci2y/PbIaVp2kBkmvOuc6JJ0wszWR19ws6Y1onwikwx1Dmgw5XcFKV1GxpjxX7f1jGhib9DoK4AnKBwBgUSJzOD4h6RlNr0j1mHNuv5l93szeF9ntIUnFZtYo6ZOKXMVwzu2X9Jimi8XTkj7unAtFXnO3pP9tZq9LulLSX8XqnFLZ623T8xGYbB4dqytyJUlHTg16nATwBsOuAACL5px7UtKTM7Z95pzHY5Jum+O190u6f5btuyVtWtqkuJDXT/SrMDugFYWzDwvC4lTkZSovM02HOga1sabI6zhAzHHlAwAAnLX7RJ+uWFGg6ZWQsdTMTA1luTraOaywY4oSUg/lAwAASJIGxiZ1+PSgrqou9DpKUltZmqPRyZA6+se8jgLEHOUDAABIkna39Mk5aWMN5SOaVpYGJUnHuoY9TgLEHuUDAABIkna29MpMWl/FZPNoys8KqCSYrmOdQ15HAWKO8gEAACRJO1v6tKY8V7mZAa+jJL2VJUE1dQ0rFGbeB1IL5QMAACgcdtrV0qsNzPeIiZWlORqfCqu9b9TrKEBMUT4AAIAaO4c0ODalq6q5uWAsnJ33wdArpBjKBwAA0M7mXklMNo+VYEaayvMymHSOlEP5AAAA2tnSq8LsgOpKcryOkjJWlgZ1vHtYU+Gw11GAmOEO5wC0Z88eveMd7/A6RlT5/X6FQqE5n1+3bp3eeOON877Hhz/8YX3ve99Tenq6nHOamppSbW2tPv7xj+uzn/2s/uzP/kx//dd/LeecvvrVr6q+vl7d3d363Oc+p89+9rMqLi5WY2Oj7rnnHn35y19WfX39Up8mcNF2tvRpQ3UhNxeMoVUlQf36aLdO9IxS+pAyuPIBICWcr3hIumDxkKTvfe97kqSJiQlNTk7KOaempibde++9Gh4e1v3336/R0VGNjY3pvvvukyQ9/PDD2rt3r77zne9Iku677z4NDw+ffR6IB30jE2o8PcR8jxirK8mRSTrWxbwPpA7KB5Di9uzZ43WEhDc0NP2Dw9TU1Nltx48f144dO/T000/LOaenn35a27dv1/Hjx88+39jY6EVc4C12neiTJO5sHmNZ6X4tL8jSsU7mfSB1xGzYVVtbm0ZHR3XPPffE6pBJp7GxUb6J1FkP3Dc2oMbGQX7PIGF99rOfVTgyljsUCunee+990/P33Xefvv3tb8c+GDDDruZe+UxaX8WVj1hbWZKjl491azIUVsDPvwkj+V3wd7mZfczMtpvZ9s7OzlhkAoCkMDQ0dPZqyNTU1NkrJGecuQoCeG1nS5/WVuQpJ4OpoLG2sjSoUNipuXvE6yhATFzwbxnn3IOSHpSkTZs2XfQ/u1dWVkqSvvzlL1/sW6S8e+65RzuOnfI6RsyEM/NUv7Kc3zNRluwTzb0UDAY1NjamqakppaWlKTMz800FpLa21rtwQEQocnPB919V6XWUlFRbnC2fTd/vo74s6HUcIOq4vgcAUfK5z31OPt/0X7N+v/8tw67+8i//0oNUwJsdPjWo4YkQ8z08khHwa0Vhto5ys0GkCMoHkOLWr1/vdYSEFwxO/2tlWtp/XEyura3Vxo0btXXrVpmZtm7dqk2bNp292lFbW8tSu4gLO1umby5I+fDOytIctfWNanzy/KvyAcmA8gEgJfj9/vM+v27dugu+x4c//GFJUnp6ugKBgMxMdXV1uvfee5WTk6O/+Iu/UFZWljIzM89e1bjzzjt1+eWX66Mf/aik6asdOTk5XPVA3NjZ3KfinHTVFGd7HSVlrSoNKuyk492seoXkx8wyAFq/fj1za+bpD/7gD2bd/tOf/lSS9M53vvNN24uLi/WVr3zl7Nf19fVn9wXiwc6WXm4u6LHqomz5faajLLmLFMCVDwAAUlTP8ISauoZ1VQ1L7Hop4Pepuihbx5j3gRRA+QAAIEXtYr5H3FhVGtTJ/jH1DE94HQWIKsoHAAApamdLr/w+0/oVXPnwWn1ZUE7Sr492ex0FiCrmfAAAkOQe2dYy6/an9naoIi9TP9zVFuNEmKmyIEsZaT692Nil37himddxgKjhygcAACko7Jxa+0ZVVZTldRRI8vtMK0uDeqmxy+soQFRRPgAASEGdg+OamAprRSFL7MaL+tIctfSMqKV7xOsoQNRQPgAASEEneqZ/wF1RyJWPeLGqbPqGpS8d5eoHkhflAwCAFNTaO6rMgE8lwQyvoyCiNJihirxMvcjQKyQxygcAACmotXdEKwqy5ePmgnHDzHRdfYlebuxSOOy8jgNEBeUDAIAUMxkKq2NgjCFXcej6hmL1jkzqjZMDXkcBooLyAQBAimnvG1XYSVVFTDaPN9etKpEkVr1C0qJ8AACQYk70jkqSKrnyEXfK8jK1ujzIvA8kLcoHAAApprV3RPlZAeVlBryOgllcV1+i1473aGwy5HUUYMlRPgAASDGtvaPM94hj19eXaGwyrJ0tvV5HAZYc5QMAgBQyND6lnuEJVXFzwbi1eWWx/D5j3geSEuUDAIAU0tbLzQXjXTAjTRuqCvRiY7fXUYAlR/kAACCFNPeMyGfSCq58xLXr6ku0t7VP/SOTXkcBlhTlAwCAFNLcPaJl+VlKT+NHgHh2fUOJwk769TGufiC58DcPAAApIhR2au0dUXUxVz3i3ZVVBcpJ9zPvA0mH8gEAQIo42T+qyZBTDTcXjHsBv0+bVxZzvw8kHcoHAAAporl7erJ5TXGOx0kwHzc2lKipa1jHu3u8cnwAACAASURBVIa9jgIsGcoHAAAporlnRAVZAeVncXPBRHDT2nJJ0rMHT3ucBFg6lA8AAFKAc04t3cPM90gg1cXZaigLUj6QVCgfAACkgL6RSQ2MTTHkKsHcdEmZtjV1a3CMJXeRHCgfAACkgOaeyHwPJpsnlJvWlGky5PTiESaeIzmkeR0AAABEX3P3sNLTfCrPy/Q6Cs7jkW0tb/o6FHbKDPj0jy80qXceNxy8Y3N1tKIBS4IrHwAApICWnhFVF2bL7zOvo2AB/D7T6vJcHeoYUNg5r+MAi0b5AAAgyY1NhtTRP8Zk8wS1tiJPwxMhtfWOeh0FWDTKBwAASe5E74icpBrKR0JaXR6USTrYMeh1FGDRKB8AACS55u4RmaSqQspHIspOT1NNcbYOdQx4HQVYNMoHAABJrqV7RBX5mcoM+L2Ogou0tiJP7f1j6h9lyV0kNsoHAABJbCoUVkvviKpZYjehranIlSQdYugVEhzlAwCAJHbo1KAmpsLcXDDBleVmqDA7oIMMvUKCo3wAABbNzLaa2SEzazSzT83yfIaZfT/y/DYzqz3nuU9Hth8ys1tmvM5vZrvM7CfRP4vktKO5VxI3F0x0ZqY1FXk62jmkyVDY6zjARaN8AAAWxcz8kr4m6T2S1kn6kJmtm7HbXZJ6nXP1kr4k6YuR166TdLukSyVtlfT1yPudcY+kA9E9g+S2/Xiv8jLTVJAd8DoKFmltRa4mQ07HOoe9jgJcNMoHAGCxrpHU6Jw75pybkPSopFtn7HOrpIcjjx+XdLOZWWT7o865cedck6TGyPvJzFZI+g1J34zBOSStHc29qi7O0fT/biSyupIcpft9DL1CQqN8AAAWq1LSiXO+bo1sm3Uf59yUpH5JxRd47d9L+nNJ5x1jYmYfM7PtZra9s7PzYs8hKZ3sH1Vb3yhDrpJEwO9TfVlQhzoG5bjbORIU5QMAsFiz/ZP6zJ+M5tpn1u1m9puSTjvndlzo4M65B51zm5xzm0pLSy+cNoWcne/BzQWTxtqKXPWNTurUwLjXUYCLQvkAACxWq6Sqc75eIal9rn3MLE1SvqSe87z2OknvM7Pjmh7GdZOZfS8a4ZPZ9uO9ygr4tSw/y+soWCKrI0vuMvQKiYryAQBYrNckNZhZnZmla3oC+RMz9nlC0p2Rxx+Q9KybHjfyhKTbI6th1UlqkPSqc+7TzrkVzrnayPs965z7cCxOJpnsaO7V+qp8+X3M90gWeZkBVRZk6SD3+0CConwAABYlMofjE5Ke0fTKVI855/ab2efN7H2R3R6SVGxmjZI+KelTkdful/SYpDckPS3p4865UKzPIRmNTEzpjZMD2lRT5HUULLG1Fbk60TOi4fEpr6MAC5bmdQAAQOJzzj0p6ckZ2z5zzuMxSbfN8dr7Jd1/nvf+laRfLUXOVLL7RJ9CYaeNNYU62T/mdRwsobUVefrFwdM6fGpQG6oLvY4DLAhXPgAASEI7jk9PNr+KH06TzrKCTOVmpDH0CgmJ8gEAQBLa3tyr1eVB5XNzwaTjM9OailwdOT2oUJgld5FYKB8AACSZcNhpZ0uvNjLfI2mtrcjV2GRYzd3c7RyJhfIBAECSOXJ6SINjU9pYw5CrZLWqLKg0n+mNkyy5i8RC+QAAIMlsb+6RJG2ifCStjDS/1lTkak9rP0OvkFAoHwAAJJkdx3tVEkznzuZJbkNVoYbHp9R4monnSByUDwAAksz25l5trCmUGTcXTGarK4LKTvdr14k+r6MA80b5AAAgibT3jaqlZ0RX1zLZPNml+Xy6YkW+3mgf0Ngk9+ZEYqB8AACQRF5q7JIkXd9Q4nESxMKGqkJNhZ32tfV7HQWYF8oHAABJ5KXGLpUE07WmPNfrKIiBFYVZKgmmM/QKCYPyAQBAknDO6cXGbl1XX8J8jxRhZrqyqlBNXcPqG5nwOg5wQZQPAACSxOFTQ+oaGtd19Qy5SiVXrMiXJO1v554fiH+UDwAAksSZ+R6Uj9RSEszQsvxM7WXeBxIA5QMAgCTxUmOX6kpyVFmQ5XUUxNily/PV0jOijv4xr6MA50X5AAAgCUyGwnrlWLeuqy/2Ogo8cHnl9NCrp/ad9DgJcH6UDwAAksCeE30angjpeoZcpaTS3AxV5GXqqb0dXkcBzovyAQBAEnipsVtm0paVXPlIVZdW5um15h6dHmDoFeJXzMpHfX296uvrY3U4APPEn00gObxyrFuXLs9TQXa611HgkcuX58s56en9XP1A/EqL1YHuvvvuWB0KwALwZxNIfGOTIe1s6dVHttR4HQUeKsvL1OryoH6y56Q++rZar+MAs2LYFQAACW7PiT6NT4UZcgX91hXL9erxHrX3jXodBZgV5QMAgAT3yrEemUlX1xV5HQUee9+VyyVJP3m93eMkwOwoHwAAJLgz8z3yswJeR4HHaopztL6qQD/eTflAfKJ8AACQwM7M99hSx5ArTHvf+uXa3z6go51DXkcB3oLyAQBAAjsz32Mz8z0Q8ZtXLJOZ9ARXPxCHYrbaFQAAuHiPbGuZdfsvDp6SSWrpHplzH6SW8rxMbakr1hN72vXf3tUgM/M6EnAWVz4AAEhgTZ3DWpafqax0v9dREEduvXK5mrqG9Xprv9dRgDehfAAAkKAmQ2G19IyoriTH6yiIM++9Ypky0nx6fEer11GAN6F8AACQoFp6RjQVdlpZGvQ6CuJMXmZAWy+r0I93t2lsMuR1HOAsygcAAAnq6Okh+Uxc+cCsPrBxhQbGpvTvB055HQU4i/IBAECCOto5pBWF2coMMN8Db3XtqhItz8/Uv2xn6BXiB+UDAIAENDYZUmvvqFaVctUDs/P7TL9z1Qq9cKRTHf1jXscBJFE+AABISE1dw3KSVjHfA+fxgY0rFHbSD3Zx9QPxgfIBAEACauwcUsBvqi7K9joK4lhtSY6uri3UD3e2eR0FkET5AAAgIR09PaTa4hyl+fkox/m9b/1yHTk9pEMdg15HASgfAAAkmoGxSZ0eHGfIFeblPZcvk8+kf9vT7nUUgPIBAECiOdY5JIn5HpifkmCGrqsv0b+93i7nnNdxkOIoHwAAJJjG00PKCvi1rCDT6yhIEL91xXI1d49ob1u/11GQ4igfAAAkkLBzOnRqSA3lQfnMvI6DBHHLpRUK+I2hV/BcmtcBAADA/LX3jWp4fEprK3K9joI49Mi2ljmfW1Ua1GPbW1VTnDNrcb1jc3U0owGSuPIBAEBCOdgxKJPUUEb5wMJcsaJA/aOTau4e8ToKUhjlAwCABHKoY1BVRdnKyWDwAhZm3bI8paf5tLO51+soSGGUDwAAEsTg2KTa+ka1hiFXuAjpaT5dUZmvvW39Gp8KeR0HKYryAQBAgjh8anqJ3TXllA9cnI01hZoIhbWPVa/gEcoHAAAJ4lDHgPIy07QsnyV2cXGqi7JVEszQDoZewSOUDwAAEkAo7HTk9JDWVOTKWGIXF8nMtLG6QMe7R9Q1NO51HKQgygcAAAmguWdY41NhrWbIFRZpQ3WhTGLiOTxB+QAAIAEcOTUkn03fqwFYjLysgFaX52pHc6+mQmGv4yDFUD4AAEgAR04NqrooR5kBv9dRkASurS/W4PiUXm9l4jlii/IBAECc6xwcV3v/mFaXc9UDS6O+NKjyvAy9dLRLzjmv4yCFUD4AAIhzLxzplCQ1MN8DS8TMdN2qEp3sH9OxrmGv4yCFUD4AAIhzzx/uVE66nyV2saTWVxUoJyNNLzV2eR0FKYTyAQBYNDPbamaHzKzRzD41y/MZZvb9yPPbzKz2nOc+Hdl+yMxuiWyrMrNfmtkBM9tvZvfE7mziSzjs9PyRLjWU58rHErtYQgG/T1vqinSwY1CnB8e8joMUQfkAACyKmfklfU3SeyStk/QhM1s3Y7e7JPU65+olfUnSFyOvXSfpdkmXStoq6euR95uS9H875y6RtEXSx2d5z5Swv31APcMTaihjvgeW3uaVxQr4TS8c5uoHYoPyAQBYrGskNTrnjjnnJiQ9KunWGfvcKunhyOPHJd1s03fKu1XSo865cedck6RGSdc4504653ZKknNuUNIBSZUxOJe489zh05KY74HoCGakaVNtkXad6FVr74jXcZACKB8AgMWqlHTinK9b9daicHYf59yUpH5JxfN5bWSI1gZJ22Y7uJl9zMy2m9n2zs7Oiz6JePWrQ526rDJPwYw0r6MgSd3YUCqT6f9/7pjXUZACKB8AgMWabSLCzLU759rnvK81s6Ckf5X035xzA7Md3Dn3oHNuk3NuU2lp6TwjJ4ae4QntbOnVTWvLvY6CJJafFdBVNQX6/vYTOj3A3A9EF+UDALBYrZKqzvl6haT2ufYxszRJ+ZJ6zvdaMwtounj8b+fcD6KSPM49d/i0wk66eW2Z11GQ5G5sKNVUKKx/fIGrH4guygcAYLFek9RgZnVmlq7pCeRPzNjnCUl3Rh5/QNKzbvrOZk9Iuj2yGladpAZJr0bmgzwk6YBz7u9ichZx6BcHTqs0N0OXV+Z7HQVJrjiYoVuvrNR3X2lWRz9XPxA9lA8AwKJE5nB8QtIzmp4Y/phzbr+Zfd7M3hfZ7SFJxWbWKOmTkj4Vee1+SY9JekPS05I+7pwLSbpO0kck3WRmuyO/3hvTE/PYZCis5w536qY1ZfL5WGIX0ffJd69WOCz93c8PeR0FSYzZawCARXPOPSnpyRnbPnPO4zFJt83x2vsl3T9j24uafT5Iyth+vFeDY1O66RKGXCE2qoqydee1Nfrmi036vevqdMmyPK8jIQlx5QMAgDj07MFTSvf7dH19iddRkEI+8c4G5WUG9FdPHvA6CpIU5QMAgDj0i4OntWVVsXJYYhcxlJ8d0N031euFI1167nDyLV0N71E+AACIM01dwzrWOcwqV/DER95Wo+qibP2/Tx5QKDxz1WxgcSgfAADEmWf2d0iSbma+BzyQkebX/9i6Vgc7BvWvO1u9joMkQ/kAACDOPLO/Q5dX5mtFYbbXUZCi3nt5hTZUF+j/+9khjUxMeR0HSYTyAQBAHOnoH9Oulj5tvazC6yhIYWamv3jvJTo1MK6HXmjyOg6SCOUDAIA48rM3podc3XIp5QPe2lRbpK2XVugbzx1V5+C413GQJCgfAADEkaf3dai+LKj6sqDXUQD9+dY1GpsK64Fnj3gdBUmC8gEAQJzoGZ7QtqYebeWqB+LEytKgPnRNlR7Z1qKmrmGv4yAJUD4AAIgT/37glEJhx3wPxJU/vblB6Wk+/e0zh7yOgiTAnYsAAIgTz+zrUGVBli5dnud1FKSgR7a1zPnclpXF+unek6p+6qCqit66Ctsdm6ujGQ1JhCsfAADEgaHxKb1wpEu3XFohM/M6DvAmN9SXKJiRpif2tHPjQSwK5QMAgDjwq0OnNREKM+QKcSkj4NdvrV+utr5RvXy0y+s4SGCUDwAA4sDT+zpUEkzXxppCr6MAs7pseZ4uqcjVvx84pZ7hCa/jIEFRPgAA8NjYZEi/PHha715XIb+PIVeIT2am911ZKZ+ZfrSrTc4x/AoLR/kAAMBjLzV2aXgixJArxL38rIBuubRCjZ1D2tnS53UcJCBWuwIAIEbmWk3oX3e0KjPgU3P3sB7ZNhrjVMDCXFNXpD0n+vTk3pNaXR5UbmbA60hIIFz5AADAQ6Gw0xsnB7S2Ik9pPj6WEf98Znr/hkpNhML6yesnvY6DBMPfcgAAeKipa1ijkyGtW8a9PZA4yvIy9c41pdrb1q8DJwe8joMEQvkAAMBDr7f2KT3NpzUVuV5HARbkxtWlqsjL1I92tbH6FeaN8gEAgEemQmHta+/XpcvyFPDzkYzEkubz6bZNKzQyEdJf/mgvq19hXphwnmD8Iz3KOvhkDI/XLUkxPeZ/HLtHUnnMjwsAsXL41KDGJsNaX1XgdRTgoizLz9K7LinTk3s79KPdbXr/hhVeR0Kco3wkkPr6+pgfs61tSpJUWelFCSj35JwBIFZ2t/YrJ92vVaVBr6MAF+2G1aXqHp7QZ368X1dVF6qmOMfrSIhjlI8Ecvfdd3sdAQCwRMYmQzp4ckCbaou4sSASms9MX/rglfqtr76oux7erh/+ybUsv4s5McAUAAAPvHFyQFNhpytX5HsdBVi0qqJsff13r1JT17DueXS3QmHmf2B2lA8AADyw50SfCrMDqirK9joKsCSuXVWie39rnZ49eFp/88whr+MgTlE+AACIsf7RSTWeHtKVVQUyY8gVksdH3lar391crW88d1Q/3NXqdRzEIcoHAAAxtrOlV07Sxpoir6MAS+7e912qLSuL9D/+da92n+jzOg7iDOUDAIAYCjunHc29WlmSo6KcdK/jAEsu4Pfp67+7UeV5GfrYd7artXfE60iII5QPAABi6HjXsHqGJ7SpttDrKEDUFOWk65sfvVpjkyHd8Y/bdLJ/1OtIiBOUDwAAYmh7c68yAz5dupxVrpDc1lTk6rt3bVbv8ITu+MdtOj0w5nUkxAHu8wEAQIyMToS0r61fG2sKFfDz739IHo9sa5nzuTs2V+ufXjqu33jgRf3hDSsVzHjrj593bK6OZjzEEf7mAwAgRnaf6NVU2GljDUOukDpqinN057W16huZ0LdebNLI+JTXkeAhygcAADEQDju9fLRbVYVZWlHIvT2QWupKcvSRLbXqGhrXt15q0uhEyOtI8AjlAwCAGHj24Gl1D0/ouvoSr6MAnqgvC+rDW2p0anBc//Ryk8YmKSCpiPIBAEAMPPRik/KzAkw0R0pbXZ6rO66pVnvfqL798nGNT1FAUg3lAwCAKNvf3q9fH+vW21YWy+/jjuZIbZcsy9PtV1ertXdE//TScQ0xBySlUD4AAIiyf3rpuLICfl1dyx3NAUm6rDJfH7x6+grI13/ZqP3t/V5HQoxQPgAAiKITPSP68e423bZphbLS/V7HAeLG5ZX5+qMbV8lJ+k//8LK+++vjCoed17EQZZQPAACi6Mu/OCIz0x+/Y5XXUYC4U1mYpT95xypdXVuk//Xj/fpP33hZBzsGvI6FKKJ8AAAQJY2nB/WDna366JYaLcvP8joOEJdyMwP6zu9foy99cL2au0f0m195UV98+iCrYSUpygcAAFHypZ8fUVbAz1UP4ALMTO/fsEK/+OTb9f4NlfqHXx3V//Wl5/XzN07JOYZiJRPKBwAAUbCvrV8/3XtSd11fp+JghtdxgIRQmJOuv7ltvf75D7cozW/6w+9s1+0PvqI9J/q8joYlkuZ1AAAAko1zTp//tzdUmB3QH9y40us4QNx7ZFvLW7b93rV1eu14j35x4JRu/dpLWluRq5vXlquy8M1DGO/YXB2rmFgClA8AAJbYj3a36dXjPfrC71yuvMyA13GAhOT3mbasLNaVVQX69bFuvXikS1/7VaPWVuTqprVlWlGY7XVEXATKBwAAS2hgbFL3//Sgrqwq0H/eVOV1HCDhZQb8eueaMr1tZfHZEvL1Xx3VmvJc3XxJmdfxsECUDwAAltCXfn5Y3cPj+vbvXS0fdzMHlsy5JeSVY916IVJCDpwc0H99+ypdU1ckM/7MxTvKBwAAS+TFI116+OXj+vDmGl1Wme91HCApZQb8esc5V0JePd6jDz74ilaV5uhD11Tr3evKVVOc43VMzIHyAQDAPM02KfaM3pEJfe2XjSrNzdCq0uB59wWweBmREvL3t1+pn7x+Uv/8aovu++kB3ffTA6otztbbV5fq7WtKtWVlsbLT+ZE3XvCdAABgkSZDYT2yrUWhsNPvbq5Rehor2QOx8qNd7ZKk2zZW6aY1ZTp8alCHTw3pkVdb9PCvm+X3meqKc9RQHtTq8lyV5WacHZ7FSlmxR/kAAGARxiZD+v5rJ9TWN6qPbKlRCff0ADxTHMzQ24IZetuqEk2GwmruHomUkUE9ta9DT+3rUH5WQA1lQa0ozNYly3K1piKXKyMxxP9pAMCimdlWSV+W5Jf0TefcF2Y8nyHpO5I2SuqW9EHn3PHIc5+WdJekkKQ/dc49M5/3jAfdQ+P67ivN6hoa161XLtcly/K8jgQgIuD3qb4sqPqyoN57+TL1jUzoyOkhHT41qH3t/dre3Ksf7W6TmVRbnKNLluXqkoo8XbIsT2sqcrW8IEt+Fo1YcpQPAMCimJlf0tckvVtSq6TXzOwJ59wb5+x2l6Re51y9md0u6YuSPmhm6yTdLulSScsl/buZrY685kLv6ZnRiZBePtallxq7ZDL93nV1WlUa9DoWgPMoyE7X1bVFurq2SM459Y5MqqE8qAMnB3Tg5ID2tw/oyb0dZ/dP9/tUVZSl2uIc1RTnqKooS6W5GSoNZqgkN0MlwQzlZaaxwtYCUT4AAIt1jaRG59wxSTKzRyXdKunconCrpHsjjx+X9FWb/sS+VdKjzrlxSU1m1hh5P83jPWNifCqk/pFJnR4c16tNPWrpGdb+9gGNT4W1tiJXv3H5MhUz1ApIKGamopx0dQ9NqCw3U2W5mXr76jKNT4bUMTCm0wPj6h6eUPfwuPa3D+j5I52aDLm3vE/Ab8rNDCg3M03BjOlfZx9npimYMf1cTrpf6Wl+pflNAb8p4PcpzedTwG9K8/vO2Rb5b+TrgG/6cZrflO73ye8z+cxkJpki/z3nsc9Mpsi2OC1FlA8AwGJVSjpxztetkjbPtY9zbsrM+iUVR7a/MuO1lZHHF3rPJfWBf3hZ+9sHFHZOzklh5yK/3rxfdrpflyzL0w0NJVqWnxXNSABiLCPgV03kSse5nHManghpaGxKQ+NTGhqf1ODYlIbHQxqfCml8KqyxyZBODYyrpWfk7Nfjk2GF3FtLS6xMF5PpIuKLlBSd3RbZR/am/SXpv759lf705oaoZFpQ+dixY0eXmTVHJYl3SiR1eR0iyjjH5MA5JoelOMeapQiyhGb757WZn7Zz7TPX9tmWi5r1E9zMPibpY5Evh8zs0Cy7LenvrQOSfrxUb7Y4yfpnhvNKLMl4Xsl4TtI8z+ue/0e6Z3HHmfNzakHlwzlXurgc8cfMtjvnNnmdI5o4x+TAOSaHJD3HVklV53y9QlL7HPu0mlmapHxJPRd47YXeU5LknHtQ0oPnC5ik/985rwTDeSWOZDwnKT7Oi4XIAQCL9ZqkBjOrM7N0TU8gf2LGPk9IujPy+AOSnnXOucj2280sw8zqJDVIenWe7wkASDDM+QAALEpkDscnJD2j6WVxv+Wc229mn5e03Tn3hKSHJH03MqG8R9NlQpH9HtP0RPIpSR93zoUkabb3jPW5AQCWFuXjApfqkwTnmBw4x+SQlOfonHtS0pMztn3mnMdjkm6b47X3S7p/Pu+5CEn5/12cV6LhvBJHMp6TFAfnZc7DGfgAAAAAUgdzPgAAAADERMqVDzM7bmZ7zWy3mW2PbCsys5+b2ZHIfwu9zrkQZvYtMzttZvvO2TbrOdm0r5hZo5m9bmZXeZd8/uY4x3vNrC3yvdxtZu8957lPR87xkJnd4k3q+TOzKjP7pZkdMLP9ZnZPZHvSfB/Pc47J9H3MNLNXzWxP5Bw/F9leZ2bbIt/H70cmUCsyyfr7kXPcZma1XuZPRma2NfL7p9HMPuV1nsVIls+vZP3MStbPqWT9fErWz6SE+BxyzqXUL0nHJZXM2PbXkj4VefwpSV/0OucCz+lGSVdJ2nehc5L0XklPaXpt/S2StnmdfxHneK+kP5tl33WS9kjKkFQn6agkv9fncIHzWybpqsjjXEmHI+eRNN/H85xjMn0fTVIw8jggaVvk+/OYpNsj278h6Y8jj/9E0jcij2+X9H2vzyGZfml6ovpRSSslpUd+P63zOtcizicpPr+S9TMrWT+nkvXzKVk/kxLhcyjlrnzM4VZJD0cePyzptz3MsmDOuec1vXrMueY6p1slfcdNe0VSgZkti03SizfHOc7lVkmPOufGnXNNkholXRO1cEvAOXfSObcz8nhQ0/cwq1QSfR/Pc45zScTvo3PODUW+DER+OUk3SXo8sn3m9/HM9/dxSTeb2Ww33cPFuUZSo3Pu2P9p715CrarCAI7/P6S60kuKisrACovoZWYRWEkhkVZU4MBJWTSIsEFzQYqCiB6TXgN7UYk9qMhRGJWTgpLI1IisQaAYSgMNatAtvwZ7XTsczr1687bP3ev+f3A4++y9zmV9d+2zPtZZ6+ydmX8Cb9H8z2vSufxVa86qNU/Vmp9qzUldyEMzcfCRwKaI+Dqau+ICnJGZv0BzMgKnD612U2e8mM4GdvWU283EH7bp7sEyrftKz3KDTsdYpjyvoPm2osp27IsRKmrHiJgVEVuBfcDHNN+O7c/Mv0qR3jgOxViOHwBObbfGVevkOTSBmvNXlX1dUVP/No8K81NtOWm656GZOPhYnJkLgWXA6oi4ftgVatmg0WxXL3n2InA+sAD4BXi67O9sjBFxAvAe8FBm/jZR0QH7uhpjVe2YmX9n5gKaO3JfDVw0qFh57mSMHVLb/3cm5q+ut2E1/Vut+anGnDTd89CMG3xk5p7yvA/4gKZR9o5NCZbnfcOr4ZQZL6bdwDk95eYCe1qu25TIzL3lA3YQWMe/05+djDEijqHpANdn5vtld1XtOCjG2tpxTGbuBzbTrLWdExFj91XqjeNQjOX4yRz5sg0dXqfPoX6V56+q+roxtfRvtean2nPSdM1DM2rwERHHR8SJY9vATcAOYCOwqhRbBXw4nBpOqfFi2gjcXa5GcQ1wYGzatGv61pDeSdOW0MS4slzB4VxgPvBV2/WbjLK+8mXg+8x8pudQNe04XoyVteNpETGnbM8GltKsI/4MWFGK9bfjWPuuAD7NzGn5TVpHbQHml6u8HEvzY8qNQ67TfzID8lc1fV2vGvq3WvNTrTmpE3losr9Q7/KD5oon35bHd8Casv9U4BPgx/J8yrDrOsm4NtBMDY7SjGDvGy8mmum152nW/20HFg27/kcR4xslhm00H54ze8qvKTH+/ig98gAAAlVJREFUACwbdv2PIL5raaY5twFby2N5Te04QYw1teNlwDcllh3A2rL/PJok9RPwLnBc2T9SXv9Ujp837Bhqe5RzbGc5j9YMuz5HEUc1+avWnFVrnqo1P9Wak7qQh7zDuSRJkqRWzKhlV5IkSZKGx8GHJEmSpFY4+JAkSZLUCgcfkiRJklrh4EOSJElSKxx8qHoR8VpErDh8SUmS2mee0kzi4EPq03MHUEmSph3zlLrMk1edVO7w+w4wF5gFPApcCNwGzAa+AO7PvhvZRMTaQWUiYnN5vRj4NCLuAS7IzNGIOInmZj3zM3O0hfAkSR1nnpIGc+ZDXXUzsCczL8/MS4CPgOcy86ryejZw64D3TVRmTmYuycxHgM3ALWX/SuA9O3RJ0iSYp6QBHHyoq7YDSyPiiYi4LjMPADdExJcRsR24Ebh4wPsmKvN2z/ZLwL1l+17g1akPQZJUMfOUNIDLrtRJmbkzIq4ElgOPR8QmYDWwKDN3RcTDwEjveyJiBHhhgjK/9/z9zyNiXkQsAWZl5o7/NyJJUk3MU9JgznyokyLiLOCPzHwTeApYWA79GhEnAIOuGjJyBGV6vQ5swG+TJEmTZJ6SBnPmQ111KfBkRBwERoEHgDtoprl/Brb0vyEz90fEuonK9FkPPEbTsUuSNBnmKWmA6LvIgqSiXHP99sy8a9h1kSSpn3lKXeTMhzRARDwLLKNZqytJ0rRinlJXOfMhSZIkqRX+4FySJElSKxx8SJIkSWqFgw9JkiRJrXDwIUmSJKkVDj4kSZIktcLBhyRJkqRW/AM+XRaVf92LGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (14, 6))\n",
    "plt.subplot(1,2,1)\n",
    "sns.boxplot(train_df.salary)\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(train_df.salary, bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use IQR rule to identify potential outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n",
      "\n",
      "The suspected outliers Upper Bound is 220.5 and the Lower Bound is 8.5.\n"
     ]
    }
   ],
   "source": [
    "stat = train_df.salary.describe()\n",
    "print(stat)\n",
    "IQR = stat['75%'] - stat['25%']\n",
    "upper = stat['75%'] + 1.5 * IQR\n",
    "lower = stat['25%'] - 1.5 * IQR\n",
    "print()\n",
    "print('The suspected outliers Upper Bound is {} and the Lower Bound is {}.'\n",
    "      .format(upper, lower))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine potential outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CEO               2893\n",
       "CFO               1308\n",
       "CTO               1298\n",
       "VICE_PRESIDENT     520\n",
       "MANAGER            188\n",
       "SENIOR              50\n",
       "JUNIOR              16\n",
       "Name: jobType, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check potential outlier below lower bound\n",
    "train_df[train_df.salary < 8.5]\n",
    "\n",
    "train_df.loc[train_df.salary > 222.5, 'jobType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature           Count\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CEO               2893\n",
       "CFO               1308\n",
       "CTO               1298\n",
       "VICE_PRESIDENT     520\n",
       "MANAGER            188\n",
       "SENIOR              50\n",
       "JUNIOR              16\n",
       "Name: jobType, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summarise potential outlier above upper bound\n",
    "print(\"Feature           Count\")\n",
    "train_df.loc[train_df.salary > 222.5, 'jobType'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Management type roles would be amoung those you would expect to earn salaries above the upper bound. However, it seem unusual for a Junior role to have a salary above the upper bound."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>1222</th>\n",
       "      <td>JOB1362684408909</td>\n",
       "      <td>COMP40</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27710</th>\n",
       "      <td>JOB1362684435397</td>\n",
       "      <td>COMP21</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31354</th>\n",
       "      <td>JOB1362684439042</td>\n",
       "      <td>COMP45</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>COMPSCI</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100041</th>\n",
       "      <td>JOB1362684507729</td>\n",
       "      <td>COMP17</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160332</th>\n",
       "      <td>JOB1362684568020</td>\n",
       "      <td>COMP18</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303777</th>\n",
       "      <td>JOB1362684711465</td>\n",
       "      <td>COMP51</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>WEB</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348353</th>\n",
       "      <td>JOB1362684756041</td>\n",
       "      <td>COMP56</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500737</th>\n",
       "      <td>JOB1362684908426</td>\n",
       "      <td>COMP40</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627532</th>\n",
       "      <td>JOB1362685035221</td>\n",
       "      <td>COMP5</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645553</th>\n",
       "      <td>JOB1362685053242</td>\n",
       "      <td>COMP36</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685772</th>\n",
       "      <td>JOB1362685093462</td>\n",
       "      <td>COMP38</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>BACHELORS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743323</th>\n",
       "      <td>JOB1362685151013</td>\n",
       "      <td>COMP14</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787671</th>\n",
       "      <td>JOB1362685195361</td>\n",
       "      <td>COMP43</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>FINANCE</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796953</th>\n",
       "      <td>JOB1362685204643</td>\n",
       "      <td>COMP30</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855214</th>\n",
       "      <td>JOB1362685262906</td>\n",
       "      <td>COMP13</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>MASTERS</td>\n",
       "      <td>ENGINEERING</td>\n",
       "      <td>OIL</td>\n",
       "      <td>22</td>\n",
       "      <td>26</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>954363</th>\n",
       "      <td>JOB1362685362055</td>\n",
       "      <td>COMP11</td>\n",
       "      <td>JUNIOR</td>\n",
       "      <td>DOCTORAL</td>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>OIL</td>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   jobId companyId jobType     degree        major industry  \\\n",
       "1222    JOB1362684408909    COMP40  JUNIOR    MASTERS      COMPSCI      OIL   \n",
       "27710   JOB1362684435397    COMP21  JUNIOR   DOCTORAL  ENGINEERING      OIL   \n",
       "31354   JOB1362684439042    COMP45  JUNIOR   DOCTORAL      COMPSCI  FINANCE   \n",
       "100041  JOB1362684507729    COMP17  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "160332  JOB1362684568020    COMP18  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "303777  JOB1362684711465    COMP51  JUNIOR    MASTERS  ENGINEERING      WEB   \n",
       "348353  JOB1362684756041    COMP56  JUNIOR   DOCTORAL  ENGINEERING      OIL   \n",
       "500737  JOB1362684908426    COMP40  JUNIOR   DOCTORAL  ENGINEERING      OIL   \n",
       "627532  JOB1362685035221     COMP5  JUNIOR   DOCTORAL  ENGINEERING      OIL   \n",
       "645553  JOB1362685053242    COMP36  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "685772  JOB1362685093462    COMP38  JUNIOR  BACHELORS  ENGINEERING      OIL   \n",
       "743323  JOB1362685151013    COMP14  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "787671  JOB1362685195361    COMP43  JUNIOR   DOCTORAL     BUSINESS  FINANCE   \n",
       "796953  JOB1362685204643    COMP30  JUNIOR    MASTERS     BUSINESS      OIL   \n",
       "855214  JOB1362685262906    COMP13  JUNIOR    MASTERS  ENGINEERING      OIL   \n",
       "954363  JOB1362685362055    COMP11  JUNIOR   DOCTORAL     BUSINESS      OIL   \n",
       "\n",
       "        yearsExperience  milesFromMetropolis  salary  \n",
       "1222                 24                    5     225  \n",
       "27710                24                    3     246  \n",
       "31354                24                    0     225  \n",
       "100041               23                    8     248  \n",
       "160332               22                    3     223  \n",
       "303777               24                    2     226  \n",
       "348353               23                   25     226  \n",
       "500737               21                    0     227  \n",
       "627532               24                   29     230  \n",
       "645553               24                    1     225  \n",
       "685772               24                   13     225  \n",
       "743323               19                    0     236  \n",
       "787671               18                   15     232  \n",
       "796953               24                    2     228  \n",
       "855214               22                   26     225  \n",
       "954363               24                   26     223  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# investigate most suspicious potential outliers above upper bound\n",
    "train_df[(train_df.salary > 222.5) & (train_df.jobType == 'JUNIOR')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further investigation reveals that the junior roles with salaries above the upper bound are within industries that are well known for high salaries (oil, finance). It is likely that these entries are valid and they will remain in the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Plot module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generic Feature Plotting module\n",
    "def plot_feature(df, col, target):\n",
    "    # Make plot for each feature\n",
    "    #     distribution of samples of the feature - on the left\n",
    "    #     dependance of TARGET on the feature - on the right\n",
    "    plt.figure(figsize = (14, 6))\n",
    "    plt.subplot(1, 2, 1)\n",
    "\n",
    "    if df[col].dtype == 'int64':\n",
    "        df[col].value_counts().sort_index().plot()\n",
    "    else:\n",
    "        # change the categorical variable to category type and order their level by the mean salary\n",
    "        # in each category\n",
    "        mean = df.groupby(col)[target].mean()\n",
    "        df[col] = df[col].astype('category')\n",
    "        levels = mean.sort_values().index.tolist()\n",
    "        df[col].cat.reorder_categories(levels, inplace=True)\n",
    "        df[col].value_counts().plot()\n",
    "    \n",
    "    plt.xticks(rotation=45)\n",
    "    plt.xlabel(col)\n",
    "    plt.ylabel('Counts')\n",
    "    plt.subplot(1, 2, 2)\n",
    "\n",
    "    if df[col].dtype == 'int64' or col == 'companyId':\n",
    "        # plot the mean for the target for each category and fill between the (mean - std, mean + std)\n",
    "        mean = df.groupby(col)[target].mean()\n",
    "        std = df.groupby(col)[target].std()\n",
    "        mean.plot()\n",
    "        plt.fill_between(range(len(std.index)), mean.values-std.values, mean.values + std.values, \\\n",
    "                         alpha = 0.1)\n",
    "    else:\n",
    "        sns.boxplot(x = col, y = target, data=df)\n",
    "    \n",
    "    plt.xticks(rotation=45)\n",
    "    plt.ylabel(target)\n",
    "    plt.show()\n",
    "    return(target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine the distribution and then the correlation of each feature with salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target = 'salary'\n",
    "for col in categorical_cols:\n",
    "    plot_feature(train_df, col, target)\n",
    "\n",
    "for col in numeric_cols:\n",
    "    plot_feature(train_df, col, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "COMPANY - Salary is weakly associated with companies.<br/>JOB TYPE - There is a clear positive correlation between job type and salary.<br/>DEGREE - More advanced degrees tend to correspond to higher salaries.<br/>MAJOR - People with majors of engineering, business and math generally have higher salaries.<br/>INDUSTRY - Oil, finance and web industries generally pay better.<br/>YEARS OF EXPERIENCE - In general, there is a clear correlation between salary and years of experience.<br/>DISTANCE TO METRO - In general, salaries decrease with greater distance from the metropolis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode categories using average salary for each category to replace label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_label(df, col):\n",
    "    #encode the categories using average salary for each category to replace label\n",
    "    categories_dict = {}\n",
    "    cats = df[col].cat.categories.tolist()\n",
    "    for cat in cats:\n",
    "        categories_dict[cat] = train_df[train_df[col] == cat]['salary'].mean()   \n",
    "    df[col] = df[col].map(categories_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "jobId                    object\n",
       "companyId              category\n",
       "jobType                category\n",
       "degree                 category\n",
       "major                  category\n",
       "industry               category\n",
       "yearsExperience           int64\n",
       "milesFromMetropolis       int64\n",
       "salary                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = train_df.copy()\n",
    "for col in df.columns:\n",
    "    if df[col].dtype.name == \"category\":\n",
    "        encode_label(df, col)\n",
    "df.dtypes       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in categorical_cols:\n",
    "    df[col] = df[col].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlations between features (excluding jobId) and response (salary)\n",
    "fig = plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(df[categorical_cols + numeric_cols + ['salary']].corr(), cmap='Blues', annot=True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(df[categorical_cols + ['salary']].corr(), cmap='Blues', annot=True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 10))\n",
    "sns.heatmap(df[numeric_cols + ['salary']].corr(), cmap='Blues', annot=True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that jobType is most strongly correlated with salary, followed by degree, major, and yearsExperience.\n",
    "\n",
    "Among the features, we see that degree and major have a strong degree of correlation.\n",
    "Also, yearsExperience has a moderate degree of correlation with both degree and major.\n",
    "The dataset is sufficently large, and the features are not 100% correlated, for both degree and major to remain in the data set without causing a collinearity issue, ie impacting their influence over other features on the Target. \n",
    "The same is true for yearsExperience."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.6 Establish a baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create an extremely simple model and measure its efficacy using MSE\n",
    "#Run Baseline Model\n",
    "regression = LinearRegression().fit(df[categorical_cols + numeric_cols], df['salary'])\n",
    "\n",
    "#then measure MSE during 5-fold cross-validation\n",
    "regression.score = cross_val_score(regression, df[categorical_cols + numeric_cols], df['salary'], scoring=\"neg_mean_squared_error\")\n",
    "regression_mse = -1.0 * regression.score.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display Baseline Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression - MSE: 399.7641175068985\n"
     ]
    }
   ],
   "source": [
    "print(\"LinearRegression - MSE:\", regression_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.7 Hypothesize solution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models: \n",
    "The baseline model predicts the salary based on the data provided without fitting, feature generation or tunning.\n",
    "\n",
    "Supervised Machine Learning algorithms will be used to improve the prediction results. Regression and Ensembles of Regression Algorithms suit our data and goal. 3 models that may improve results over the baseline model given the above EDA:\n",
    " - LinearRegression                - Sometimes simple is best\n",
    " - RandomForestRegressor           - Improved accuracy and control over-fittings\n",
    " - GradientBoostingRegressor       - Can optimise on Least Squares regression. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Generation\n",
    "The strength of the relationships between Salary and each of the following features: \n",
    "- jobType\n",
    "- degree\n",
    "- major\n",
    "- industry\\\n",
    "indicates a need to account for categories within them in some way in future models.\n",
    "\n",
    "New features that could be beneficial to future models based on EDA:\n",
    " - summary statistics for each group, for each of the above positively correlated features. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.to_csv('data/salaryPredictEDA.csv')\n",
    "\n",
    "with open('data/categorical_cols.csv', 'w') as f:\n",
    "    for item in categorical_cols:\n",
    "        f.write(\"%s\\n\" % item)\n",
    "\n",
    "with open('data/numeric_cols.csv', 'w') as f:\n",
    "    for item in numeric_cols:\n",
    "        f.write(\"%s\\n\" % item)\n",
    "        \n",
    "with open('data/target.csv', 'w') as f:\n",
    "        f.write(\"%s\\n\" % target)\n",
    "    \n",
    "with open('data/key.csv', 'w') as f:\n",
    "        f.write(\"%s\\n\" % key)"
   ]
  }
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