stackreview-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# StackOverflow 2019 Review Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error, median_absolute_error\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "#pd.set_option('max_columns', None) # set pandas to display all columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1) Businss Understanding\n",
    "Software engineering is described as one of the hottest jobs of the 21st century. Because of its technical nature, unlike many other professions, a college degree and advanced degree is not always required to get a job in the field. In order to undersatnd how opportunity varies between having a post high shcool education and not having, I seek to answer the folowing questions using StackOverflow 2019 reiew survey responses:\n",
    "1. Are people with High School diploma as satisfied in their software engineering job/career as those with higher educational degrees?\n",
    "2. Are people with high school degrees paid at same rate as people with college education?\n",
    "3. How do they feel about becoming managers (growth opportunity)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2) Data Understanding\n",
    "\n",
    "**Get data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Respondent</th>\n",
       "      <th>MainBranch</th>\n",
       "      <th>Hobbyist</th>\n",
       "      <th>OpenSourcer</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Country</th>\n",
       "      <th>Student</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>UndergradMajor</th>\n",
       "      <th>...</th>\n",
       "      <th>WelcomeChange</th>\n",
       "      <th>SONewContent</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Sexuality</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>SurveyLength</th>\n",
       "      <th>SurveyEase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>I am a student who is learning to code</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Not employed, and not looking for work</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Primary/elementary school</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech articles written by other developers;Indu...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>I am a student who is learning to code</td>\n",
       "      <td>No</td>\n",
       "      <td>Less than once per year</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Not employed, but looking for work</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>Yes, full-time</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech articles written by other developers;Indu...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>I am not primarily a developer, but I write co...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Web development or web design</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech meetups or events in your area;Courses on...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Neither easy nor difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>No</td>\n",
       "      <td>Never</td>\n",
       "      <td>The quality of OSS and closed source software ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech articles written by other developers;Indu...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>I am a developer by profession</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Once a month or more often</td>\n",
       "      <td>OSS is, on average, of HIGHER quality than pro...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>No</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Computer science, computer engineering, or sof...</td>\n",
       "      <td>...</td>\n",
       "      <td>Just as welcome now as I felt last year</td>\n",
       "      <td>Tech meetups or events in your area;Courses on...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Man</td>\n",
       "      <td>No</td>\n",
       "      <td>Straight / Heterosexual</td>\n",
       "      <td>White or of European descent;Multiracial</td>\n",
       "      <td>No</td>\n",
       "      <td>Appropriate in length</td>\n",
       "      <td>Easy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Respondent                                         MainBranch Hobbyist  \\\n",
       "0           1             I am a student who is learning to code      Yes   \n",
       "1           2             I am a student who is learning to code       No   \n",
       "2           3  I am not primarily a developer, but I write co...      Yes   \n",
       "3           4                     I am a developer by profession       No   \n",
       "4           5                     I am a developer by profession      Yes   \n",
       "\n",
       "                  OpenSourcer  \\\n",
       "0                       Never   \n",
       "1     Less than once per year   \n",
       "2                       Never   \n",
       "3                       Never   \n",
       "4  Once a month or more often   \n",
       "\n",
       "                                          OpenSource  \\\n",
       "0  The quality of OSS and closed source software ...   \n",
       "1  The quality of OSS and closed source software ...   \n",
       "2  The quality of OSS and closed source software ...   \n",
       "3  The quality of OSS and closed source software ...   \n",
       "4  OSS is, on average, of HIGHER quality than pro...   \n",
       "\n",
       "                               Employment                 Country  \\\n",
       "0  Not employed, and not looking for work          United Kingdom   \n",
       "1      Not employed, but looking for work  Bosnia and Herzegovina   \n",
       "2                      Employed full-time                Thailand   \n",
       "3                      Employed full-time           United States   \n",
       "4                      Employed full-time                 Ukraine   \n",
       "\n",
       "          Student                                            EdLevel  \\\n",
       "0              No                          Primary/elementary school   \n",
       "1  Yes, full-time  Secondary school (e.g. American high school, G...   \n",
       "2              No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "3              No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "4              No           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "\n",
       "                                      UndergradMajor  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2                      Web development or web design   \n",
       "3  Computer science, computer engineering, or sof...   \n",
       "4  Computer science, computer engineering, or sof...   \n",
       "\n",
       "              ...                                        WelcomeChange  \\\n",
       "0             ...              Just as welcome now as I felt last year   \n",
       "1             ...              Just as welcome now as I felt last year   \n",
       "2             ...              Just as welcome now as I felt last year   \n",
       "3             ...              Just as welcome now as I felt last year   \n",
       "4             ...              Just as welcome now as I felt last year   \n",
       "\n",
       "                                        SONewContent   Age Gender Trans  \\\n",
       "0  Tech articles written by other developers;Indu...  14.0    Man    No   \n",
       "1  Tech articles written by other developers;Indu...  19.0    Man    No   \n",
       "2  Tech meetups or events in your area;Courses on...  28.0    Man    No   \n",
       "3  Tech articles written by other developers;Indu...  22.0    Man    No   \n",
       "4  Tech meetups or events in your area;Courses on...  30.0    Man    No   \n",
       "\n",
       "                 Sexuality                                 Ethnicity  \\\n",
       "0  Straight / Heterosexual                                       NaN   \n",
       "1  Straight / Heterosexual                                       NaN   \n",
       "2  Straight / Heterosexual                                       NaN   \n",
       "3  Straight / Heterosexual              White or of European descent   \n",
       "4  Straight / Heterosexual  White or of European descent;Multiracial   \n",
       "\n",
       "  Dependents           SurveyLength                  SurveyEase  \n",
       "0         No  Appropriate in length  Neither easy nor difficult  \n",
       "1         No  Appropriate in length  Neither easy nor difficult  \n",
       "2        Yes  Appropriate in length  Neither easy nor difficult  \n",
       "3         No  Appropriate in length                        Easy  \n",
       "4         No  Appropriate in length                        Easy  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main = pd.read_csv(\"./data/survey_results_public.csv\")# read the dataset and store in 'df'\n",
    "df_main.head(5) #display first five rows of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Access**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 88883 reponses and 85 attributes\n"
     ]
    }
   ],
   "source": [
    "num_rows = df_main.shape[0] # number of rows is equivalent to number of players \n",
    "num_columns = df_main.shape[1]\n",
    "print('There are {} reponses and {} attributes'.format(num_rows,num_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Respondent', 'MainBranch', 'Hobbyist', 'OpenSourcer', 'OpenSource',\n",
       "       'Employment', 'Country', 'Student', 'EdLevel', 'UndergradMajor',\n",
       "       'EduOther', 'OrgSize', 'DevType', 'YearsCode', 'Age1stCode',\n",
       "       'YearsCodePro', 'CareerSat', 'JobSat', 'MgrIdiot', 'MgrMoney',\n",
       "       'MgrWant', 'JobSeek', 'LastHireDate', 'LastInt', 'FizzBuzz',\n",
       "       'JobFactors', 'ResumeUpdate', 'CurrencySymbol', 'CurrencyDesc',\n",
       "       'CompTotal', 'CompFreq', 'ConvertedComp', 'WorkWeekHrs', 'WorkPlan',\n",
       "       'WorkChallenge', 'WorkRemote', 'WorkLoc', 'ImpSyn', 'CodeRev',\n",
       "       'CodeRevHrs', 'UnitTests', 'PurchaseHow', 'PurchaseWhat',\n",
       "       'LanguageWorkedWith', 'LanguageDesireNextYear', 'DatabaseWorkedWith',\n",
       "       'DatabaseDesireNextYear', 'PlatformWorkedWith',\n",
       "       'PlatformDesireNextYear', 'WebFrameWorkedWith',\n",
       "       'WebFrameDesireNextYear', 'MiscTechWorkedWith',\n",
       "       'MiscTechDesireNextYear', 'DevEnviron', 'OpSys', 'Containers',\n",
       "       'BlockchainOrg', 'BlockchainIs', 'BetterLife', 'ITperson', 'OffOn',\n",
       "       'SocialMedia', 'Extraversion', 'ScreenName', 'SOVisit1st',\n",
       "       'SOVisitFreq', 'SOVisitTo', 'SOFindAnswer', 'SOTimeSaved',\n",
       "       'SOHowMuchTime', 'SOAccount', 'SOPartFreq', 'SOJobs', 'EntTeams',\n",
       "       'SOComm', 'WelcomeChange', 'SONewContent', 'Age', 'Gender', 'Trans',\n",
       "       'Sexuality', 'Ethnicity', 'Dependents', 'SurveyLength', 'SurveyEase'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# attributes for each response\n",
    "df_main.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For better comparison, the analysis is limited to only the United States else additional factors such as work conditions and economy will need to be factored into the analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main = df_main[df_main[\"Country\"]==\"United States\"]\n",
    "df_main[['Country']].nunique() # Get list of countries in the Country Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Clean**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Respondent</th>\n",
       "      <th>Employment</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>EduOther</th>\n",
       "      <th>OrgSize</th>\n",
       "      <th>DevType</th>\n",
       "      <th>YearsCode</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>Age1stCode</th>\n",
       "      <th>CareerSat</th>\n",
       "      <th>JobSat</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>MgrIdiot</th>\n",
       "      <th>MgrMoney</th>\n",
       "      <th>MgrWant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>Taken an online course in programming or softw...</td>\n",
       "      <td>100 to 499 employees</td>\n",
       "      <td>Developer, full-stack</td>\n",
       "      <td>3</td>\n",
       "      <td>Less than 1 year</td>\n",
       "      <td>16</td>\n",
       "      <td>Very satisfied</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>Very confident</td>\n",
       "      <td>No</td>\n",
       "      <td>Not sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>Taken an online course in programming or softw...</td>\n",
       "      <td>10 to 19 employees</td>\n",
       "      <td>Data or business analyst;Database administrato...</td>\n",
       "      <td>17</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>Very satisfied</td>\n",
       "      <td>Very satisfied</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Respondent          Employment  \\\n",
       "3            4  Employed full-time   \n",
       "12          13  Employed full-time   \n",
       "\n",
       "                                        EdLevel  \\\n",
       "3      Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
       "12  Master’s degree (MA, MS, M.Eng., MBA, etc.)   \n",
       "\n",
       "                                             EduOther               OrgSize  \\\n",
       "3   Taken an online course in programming or softw...  100 to 499 employees   \n",
       "12  Taken an online course in programming or softw...    10 to 19 employees   \n",
       "\n",
       "                                              DevType YearsCode  \\\n",
       "3                               Developer, full-stack         3   \n",
       "12  Data or business analyst;Database administrato...        17   \n",
       "\n",
       "        YearsCodePro Age1stCode       CareerSat              JobSat  \\\n",
       "3   Less than 1 year         16  Very satisfied  Slightly satisfied   \n",
       "12                 8         11  Very satisfied      Very satisfied   \n",
       "\n",
       "    CompTotal  ConvertedComp        MgrIdiot MgrMoney   MgrWant  \n",
       "3     61000.0        61000.0  Very confident       No  Not sure  \n",
       "12    90000.0        90000.0             NaN      NaN       NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove unused columns\n",
    "df_main = df_main[[\"Respondent\", \"Employment\", \"EdLevel\", \"EduOther\", \"OrgSize\", \"DevType\", \n",
    "     \"YearsCode\", \"YearsCodePro\", \"Age1stCode\", \"CareerSat\", \"JobSat\", \"CompTotal\", \n",
    "     'ConvertedComp', \"MgrIdiot\", \"MgrMoney\", \"MgrWant\"]]\n",
    "df_main.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Respondent         int64\n",
       "Employment        object\n",
       "EdLevel           object\n",
       "EduOther          object\n",
       "OrgSize           object\n",
       "DevType           object\n",
       "YearsCode         object\n",
       "YearsCodePro      object\n",
       "Age1stCode        object\n",
       "CareerSat         object\n",
       "JobSat            object\n",
       "CompTotal        float64\n",
       "ConvertedComp    float64\n",
       "MgrIdiot          object\n",
       "MgrMoney          object\n",
       "MgrWant           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Transform Data especially attributes like: YearsCode and YearsCodePro that have 'Less than 1 year' as a value**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the YearsCodePro to Number\n",
    "def to_num(df, col):\n",
    "    '''\n",
    "    input: \n",
    "        df - dataframe \n",
    "        col - column name of attribute that needs to be converted\n",
    "    output:\n",
    "        df - dataframe with df[col] transformed to integers. non representable values are replaced with NaN\n",
    "    '''\n",
    "    df.dropna(subset=[col], inplace=True) \n",
    "    df = df.replace('Less than 1 year', '0')\n",
    "    #df = df.replace('More than 50 years', '51')\n",
    "    df[col] = pd.to_numeric(df[col], errors='coerce')\n",
    "    return df\n",
    "\n",
    "df_main = to_num(df_main, 'YearsCodePro')\n",
    "df_main = to_num(df_main, 'YearsCode')\n",
    "df_main = to_num(df_main, 'Age1stCode')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Missing values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Respondent       0.000000\n",
      "Employment       0.003230\n",
      "EdLevel          0.007116\n",
      "EduOther         0.017023\n",
      "OrgSize          0.060157\n",
      "DevType          0.029613\n",
      "YearsCode        0.002792\n",
      "YearsCodePro     0.000876\n",
      "Age1stCode       0.004872\n",
      "CareerSat        0.028245\n",
      "JobSat           0.048333\n",
      "CompTotal        0.183316\n",
      "ConvertedComp    0.184903\n",
      "MgrIdiot         0.179649\n",
      "MgrMoney         0.179265\n",
      "MgrWant          0.178718\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Find proportion of the each atribute with missing values\n",
    "print(df_main.isnull().sum()/len(df_main))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows and columns with NaN values are not removed at this stage. They will be removed accordingly as we answer each of the questions. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Respondent</th>\n",
       "      <th>YearsCode</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>Age1stCode</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18269.000000</td>\n",
       "      <td>18218.000000</td>\n",
       "      <td>18253.000000</td>\n",
       "      <td>18180.000000</td>\n",
       "      <td>1.492000e+04</td>\n",
       "      <td>1.489100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44566.486562</td>\n",
       "      <td>14.933692</td>\n",
       "      <td>9.875308</td>\n",
       "      <td>15.259351</td>\n",
       "      <td>1.340483e+12</td>\n",
       "      <td>2.491618e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25558.740406</td>\n",
       "      <td>10.288632</td>\n",
       "      <td>8.927571</td>\n",
       "      <td>5.420642</td>\n",
       "      <td>1.157753e+14</td>\n",
       "      <td>4.515211e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22529.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44539.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.020000e+05</td>\n",
       "      <td>1.100000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66695.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.400000e+05</td>\n",
       "      <td>1.600000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88880.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>1.000000e+16</td>\n",
       "      <td>2.000000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Respondent     YearsCode  YearsCodePro    Age1stCode     CompTotal  \\\n",
       "count  18269.000000  18218.000000  18253.000000  18180.000000  1.492000e+04   \n",
       "mean   44566.486562     14.933692      9.875308     15.259351  1.340483e+12   \n",
       "std    25558.740406     10.288632      8.927571      5.420642  1.157753e+14   \n",
       "min        4.000000      0.000000      0.000000      5.000000  0.000000e+00   \n",
       "25%    22529.000000      7.000000      3.000000     12.000000  7.500000e+04   \n",
       "50%    44539.000000     12.000000      7.000000     15.000000  1.020000e+05   \n",
       "75%    66695.000000     20.000000     14.000000     18.000000  1.400000e+05   \n",
       "max    88880.000000     50.000000     50.000000     66.000000  1.000000e+16   \n",
       "\n",
       "       ConvertedComp  \n",
       "count   1.489100e+04  \n",
       "mean    2.491618e+05  \n",
       "std     4.515211e+05  \n",
       "min     0.000000e+00  \n",
       "25%     8.000000e+04  \n",
       "50%     1.100000e+05  \n",
       "75%     1.600000e+05  \n",
       "max     2.000000e+06  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4) Answer questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Are people with high school degrees paid at same rate as people with college education?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>EdLevel</th>\n",
       "      <th>Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bachelor’s degree (BA, BS, B.Eng., etc.)</td>\n",
       "      <td>10442</td>\n",
       "      <td>0.575666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Master’s degree (MA, MS, M.Eng., MBA, etc.)</td>\n",
       "      <td>3418</td>\n",
       "      <td>0.188434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>2195</td>\n",
       "      <td>0.121010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Associate degree</td>\n",
       "      <td>807</td>\n",
       "      <td>0.044490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Other doctoral degree (Ph.D, Ed.D., etc.)</td>\n",
       "      <td>634</td>\n",
       "      <td>0.034952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>421</td>\n",
       "      <td>0.023210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Professional degree (JD, MD, etc.)</td>\n",
       "      <td>100</td>\n",
       "      <td>0.005513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Primary/elementary school</td>\n",
       "      <td>61</td>\n",
       "      <td>0.003363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>I never completed any formal education</td>\n",
       "      <td>61</td>\n",
       "      <td>0.003363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             EdLevel  Count  Percentage\n",
       "0           Bachelor’s degree (BA, BS, B.Eng., etc.)  10442    0.575666\n",
       "1        Master’s degree (MA, MS, M.Eng., MBA, etc.)   3418    0.188434\n",
       "2  Some college/university study without earning ...   2195    0.121010\n",
       "3                                   Associate degree    807    0.044490\n",
       "4          Other doctoral degree (Ph.D, Ed.D., etc.)    634    0.034952\n",
       "5  Secondary school (e.g. American high school, G...    421    0.023210\n",
       "6                 Professional degree (JD, MD, etc.)    100    0.005513\n",
       "7                          Primary/elementary school     61    0.003363\n",
       "8             I never completed any formal education     61    0.003363"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observe the distribution of the dataset based on educational level\n",
    "df = df_main\n",
    "counts = df.groupby('EdLevel').size().sort_values(ascending=False).reset_index(name='Count')\n",
    "counts[\"Percentage\"] = counts['Count'] / np.sum(counts['Count'])\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**More than 60% of those who ocmpleted the survey in the United States have a bachelor's, Master's, Doctorate or Professional Degree. Only about 5% have a High School Diploma with an additional 12% having some college education.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bachelor’s degree (BA, BS, B.Eng., etc.)',\n",
       "       'Master’s degree (MA, MS, M.Eng., MBA, etc.)',\n",
       "       'Some college/university study without earning a degree',\n",
       "       'Associate degree', 'Other doctoral degree (Ph.D, Ed.D., etc.)',\n",
       "       'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)',\n",
       "       'Professional degree (JD, MD, etc.)',\n",
       "       'I never completed any formal education', nan,\n",
       "       'Primary/elementary school'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List educational levels present in the dataset\n",
    "df.EdLevel.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group the data into highschool / post high school\n",
    "\n",
    "In other to see how people with only Hgh School degree compare in the survey engineering market, they are grouped with those with some college and compared to those who have a bachelor's, Master's, Doctorate or Professional Degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Respondent</th>\n",
       "      <th>Employment</th>\n",
       "      <th>EdLevel</th>\n",
       "      <th>EduOther</th>\n",
       "      <th>OrgSize</th>\n",
       "      <th>DevType</th>\n",
       "      <th>YearsCode</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>Age1stCode</th>\n",
       "      <th>CareerSat</th>\n",
       "      <th>JobSat</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "      <th>MgrIdiot</th>\n",
       "      <th>MgrMoney</th>\n",
       "      <th>MgrWant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Taken an online course in programming or softw...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Data or business analyst;Designer;Developer, b...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "      <td>Very dissatisfied</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>103000.0</td>\n",
       "      <td>Somewhat confident</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Taught yourself a new language, framework, or ...</td>\n",
       "      <td>10,000 or more employees</td>\n",
       "      <td>Designer;Developer, back-end;Developer, deskto...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Very satisfied</td>\n",
       "      <td>Very satisfied</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>114000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>Taken an online course in programming or softw...</td>\n",
       "      <td>500 to 999 employees</td>\n",
       "      <td>Developer, full-stack;DevOps specialist;Engine...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "      <td>47300.0</td>\n",
       "      <td>47300.0</td>\n",
       "      <td>Somewhat confident</td>\n",
       "      <td>Not sure</td>\n",
       "      <td>Not sure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>Taught yourself a new language, framework, or ...</td>\n",
       "      <td>100 to 499 employees</td>\n",
       "      <td>Developer, back-end;DevOps specialist</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Slightly satisfied</td>\n",
       "      <td>Neither satisfied nor dissatisfied</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>107000.0</td>\n",
       "      <td>Somewhat confident</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Taken an online course in programming or softw...</td>\n",
       "      <td>20 to 99 employees</td>\n",
       "      <td>Database administrator;Designer;Developer, bac...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Very satisfied</td>\n",
       "      <td>Very satisfied</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>66000.0</td>\n",
       "      <td>Very confident</td>\n",
       "      <td>No</td>\n",
       "      <td>Not sure</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Respondent          Employment  \\\n",
       "21          22  Employed full-time   \n",
       "25          26  Employed full-time   \n",
       "52          53  Employed full-time   \n",
       "59          60  Employed full-time   \n",
       "60          61  Employed full-time   \n",
       "\n",
       "                                              EdLevel  \\\n",
       "21  Some college/university study without earning ...   \n",
       "25  Some college/university study without earning ...   \n",
       "52  Secondary school (e.g. American high school, G...   \n",
       "59  Secondary school (e.g. American high school, G...   \n",
       "60  Some college/university study without earning ...   \n",
       "\n",
       "                                             EduOther  \\\n",
       "21  Taken an online course in programming or softw...   \n",
       "25  Taught yourself a new language, framework, or ...   \n",
       "52  Taken an online course in programming or softw...   \n",
       "59  Taught yourself a new language, framework, or ...   \n",
       "60  Taken an online course in programming or softw...   \n",
       "\n",
       "                     OrgSize  \\\n",
       "21  10,000 or more employees   \n",
       "25  10,000 or more employees   \n",
       "52      500 to 999 employees   \n",
       "59      100 to 499 employees   \n",
       "60        20 to 99 employees   \n",
       "\n",
       "                                              DevType  YearsCode  \\\n",
       "21  Data or business analyst;Designer;Developer, b...       35.0   \n",
       "25  Designer;Developer, back-end;Developer, deskto...       12.0   \n",
       "52  Developer, full-stack;DevOps specialist;Engine...        5.0   \n",
       "59              Developer, back-end;DevOps specialist       10.0   \n",
       "60  Database administrator;Designer;Developer, bac...       15.0   \n",
       "\n",
       "    YearsCodePro  Age1stCode           CareerSat  \\\n",
       "21          18.0        12.0  Slightly satisfied   \n",
       "25           8.0         8.0      Very satisfied   \n",
       "52           3.0        10.0  Slightly satisfied   \n",
       "59           9.0        13.0  Slightly satisfied   \n",
       "60           8.0        10.0      Very satisfied   \n",
       "\n",
       "                                JobSat  CompTotal  ConvertedComp  \\\n",
       "21                   Very dissatisfied   103000.0       103000.0   \n",
       "25                      Very satisfied   114000.0       114000.0   \n",
       "52                  Slightly satisfied    47300.0        47300.0   \n",
       "59  Neither satisfied nor dissatisfied   107000.0       107000.0   \n",
       "60                      Very satisfied    66000.0        66000.0   \n",
       "\n",
       "              MgrIdiot  MgrMoney   MgrWant  \n",
       "21  Somewhat confident        No        No  \n",
       "25                 NaN       NaN       NaN  \n",
       "52  Somewhat confident  Not sure  Not sure  \n",
       "59  Somewhat confident       Yes        No  \n",
       "60      Very confident        No  Not sure  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HighSchool_SomeCollege = df[df.EdLevel.isin(['Some college/university study without earning a degree', 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)'])]\n",
    "AboveHighSchool = df[df[\"EdLevel\"].isin(['Bachelor’s degree (BA, BS, B.Eng., etc.)',\n",
    "       'Master’s degree (MA, MS, M.Eng., MBA, etc.)', 'Other doctoral degree (Ph.D, Ed.D., etc.)',\n",
    "        'Professional degree (JD, MD, etc.)'])]\n",
    "HighSchool_SomeCollege.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2616, 16)\n",
      "(14594, 16)\n"
     ]
    }
   ],
   "source": [
    "print(HighSchool_SomeCollege.shape)\n",
    "print(AboveHighSchool.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**15,330 of the respondents in the from the United States have post high school education while 3,892 have only high school/some college education**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABOgAAAIcCAYAAABM7JHcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzs3XuU1XW9//Hnm+HikCCK2aCConIRUsxAE9TES94yzFTUMseDVMfymEoJx0C8/QxNsyMnszKwUrLykpaVN9BU1DDJGykqWgopxwuIBgh8fn98v0Mz4x7Ys2fGDXuej7X22u7v9/P9fN57O2vVevm5REoJSZIkSZIkSeXRodwFSJIkSZIkSe2ZAZ0kSZIkSZJURgZ0kiRJkiRJUhkZ0EmSJEmSJEllZEAnSZIkSZIklZEBnSRJkiRJklRGBnSSJEmSJElSGRnQSZIkSZIkSWVkQCdJkiRJkiSVkQGdJEmSPnARURsRKSJqW9DHIRHxUEQsyfua3noVllTP9LyO7ctZhyRJ2vgY0EmSpIoXER+PiGsiYn5EvBMR/4qI5yPiZxFxULnrK5eI6BkR346IpyLi3fz1UkTcHRHnRsRHWtD39m0ZmkVEX+AWoA/wY+C8/HObaY1QUZIkqZCO5S5AkiSprUREB+A7wBnAKuAe4FbgPWAH4HDgCxExKaV0QdkKLYOI2BZ4EOgNzAWmAcuA7YEhwGTgAeDVNirhZuAhYFGJzx8AdAHOTCn9otWqapkJwLeBV8pdiCRJ2rgY0EmSpEp2IVk4Nxc4OqX0fP2bEVENfA3oWYbayu08snCuYDgZEbsAb7XV4CmlJcCSFnSxdf7+z1Yop1WklBZReuAoSZLaMZe4SpKkihQROwHfBF4HDmkczgGklP6VUroUOLfec/0j4pKI+EtEvB4RyyPi2Xwp6KYFxpmVL3vsEhHnR8RzEfFeREyu16ZzRJyZ9/lORLwdEX+KiM80UXvR7evte7ZDRJyRL1ddUcTS0r3y9ysL3UwpPZFS+kejsT4bETPy7/huvvfbnyLic43a1QIL8o8n5fXVvfara1NouWhE7B4Rv46Iv+ff49WImB0R4/P720dEIgsYAWbW63v75tTZaNxdI+LnEfFyPu6iiPhDRByR359ONssQYFr971Svjyb3oIuIk/L98pblr4ci4qQC7fbL+5ic/xZ/zP/9L4mImwv1LUmSNn7OoJMkSZWqFqgCrk4prXOZZkppRb2PRwFjgJnALLL/oPkJ4GzgkxGxb0rpvQLd3ES2NPSPwBvACwAR0QX4A7Af8BhwDdCJbHntbyLitJTS1LpOmtu+nivzOn8H/Jb1L019I3/fCZiznrZ1LgZWAveTzRT7MPAZ4NcR8V8ppbqwby7wPeB04K803BvuxaY6j4jdyJbdrgZ+A7wE9AAGA2PJlo++RRbO7Qd8Eri2Xp91M/6KrbNu3M8CM8j+Xd8GPANsBexJ9rdwW/4degCj8trmruuHatT/d4Gvky19vQZIwOeA6RExJKV0ZoHHhgLfIPsbvBr4GHAksEtEfDSltLzY8SVJ0obPgE6SJFWqEfn7Pc187mfA5SmllfUvRsQksmDoWOC6As9tDeyaUnqj0fVJZGHSZOD8lFLK++uW13ZZRNyUUlpYYvs6uwIfSyn9vcjv+Suy3+i2iPhfsiBobkpp2TqeOSyl9EL9C/mswgeBCyLimpTSuymluRFxBVlANzelNLnImk4k21duVErp1kbj9ARIKb0FTM5nKH4SmJ5SmlVKnfn1rYCfku1RuE9K6bFGz22bj3tLRNQFdLeklKYX84UiYh+ycG4esFe+tJeIOJdsD74z8n+f9zd69HDguJTSDfX6+mn+Gx0JbCj77kmSpFbgEldJklSpavL3l5vzUErplcbhXK5u1tqBTTx6buNwLrJDKv4TeI56YVs+ztvA+UBnsll7zW7fyKXNCOcgm3F3ObAFcAHwJ2BpvkT22xHRq/EDjUOv/NoyYDqwGTCsGeOvy78KjPN6sQ83s86TgE2ByxqHc/lzzfr7KaA2f59cF87l/S7h38t0a3m/++qHc7mf5O+t9TtLkqQNhDPoJEmS6omIAE4mC00+Shbo1P+PmlsXeAzgkQLXBgCbAwuBc7OuG/hw/j6wxPbrG79JKaU1wFkRcTFwGNny2KHAx4FBwJcj4pCU0sN1z+SzzcYDhwLbAdWNum3qtynWr8lmm90SEb8E7gTub2bw2Nw698jf7yip4vX7WP4+q8C9umu7Fbj3lwLX6sLCHi0rSZIkbWgM6CRJUqX6J1mQtQ3ZnmLF+h+yk13/AdxKtodZ3R5155ItwSyk0J5vW+Tvg/NXUz5UYvv1jb9eKaX/I1vi+VOAiKghmy34OeCHZPvqERFbAH8G+gAPAHeR7fm2mixgGkXTv02xtcyOiP2BCcDx5DPLIuJR4BsppZnr66OEOuvCrldaUvs6dAfWAIsL3Hs1v7dZgXuFTrhdlb9XtU5pkiRpQ2FAJ0mSKtUDZHu5HUCR+9DlM6++CjxOtl/Yu/Xu1VDvtNfG6i9HrWdp/n5jSunoIkpobvsGJTSzfeFOUvpnRJwIfBrYNSJ65stLx5CFXt9KKV1U/5n8hNVRrTT+vcC9EVFNdkjDEcCpwO8iYpdCp/E20tw66w6W2IZ1HGDRAkvJZmB+GHit0b2t8ntLGz8kSZLaF/egkyRJlWo62aypL0XEh9fVMD85FWAHIIC76odzuX1KqGEeWfgyNCI6tUH7trICaHxS7Y75+628X6HfZnX+XtJsr5TSv1JKs1JKZwH/j2yZalP7/9XX3DrrlgZ/qoi+S/lOdfva7Vfg3ifz96JPhJUkSZXJgE6SJFWklNJzwCXAlsDvI6Jv4zYRsUlEnEl2YirAS/n78PzAhrp22wLfLqGGVcBVZPugfadQ6BYRH81n7jW7fUtExFkRUWgvO4D/Ijs44W/1Dmeo+232btTPCWR72DX2Jtmsvm2bUdM+EdG9wK2P5O/vOzyigObWeS2wjGw/vvftBRcR29T7WHcISNHfKe8fsj0F1363/J/PbdRGkiS1Uy5xlSRJlexbwCbAGcAzEXEP8CTZ7LC+ZDOyeubtSCktiogbyfZfmxMRd5OFQ58mWya7Qwk1nAvsThZ6HR4R95LtR7YNsAvZHm978e/lj81tX6oTyULAJ4CH8/565H1/jCwM+8967X8GnA1cGREjyYKwXcl+w5todLJsSmlZRPwZ2DcipgHzyfZbu34dhz6cBRwUETOBF4DlZL/FAWQn295cxPdqbp2vRcQXgV8Aj0TErWR7Fm5JtsT2ReDIvPns/Hf5eh6wLc77aDK8TSndFxFXAqcBT+Z/X5HX0Rv4n5TSfUV8L0mSVMEM6CRJUsXKTyo9MyKuJwub9s1fHcgOf7gDmJZSurPeY7VkocznyEKVvwOXA1OAlSXUsCIiDiXbG+2LwNFkhxS8CjwN/AB4otT2LXAy2f5u+wMHkwWRq8kCrauA76aU5ter6+WI+CTZrMQDyf5/5F/Ilob2plHwlTsR+C5ZwLUZWTD1ENlvWshVZIcj7En27ynythcCV6SU3l7flyqlzpTSzRGxJ9nhFJ8EPgP8H9nS0x/Va/dGRBxNNuPyP/n36bDrnF2ZUvqviHgsf+ZL+eWngHNTStPW950kSVLli8L7GUuSJEmSJEn6ILgHnSRJkiRJklRGBnSSJEmSJElSGRnQSZIkSZIkSWVkQCdJkiRJkiSVkQGdJEmSJEmSVEYGdJIkSZIkSVIZdSx3ARuziAhga+DtctciSZIkSZKkDUI3YGFKKRX7gAFdy2wNvFzuIiRJkiRJkrRB2RZ4pdjGBnQt8zbAP/7xD7p3717uWiRJkiRJklRGS5cupXfv3tDM1ZYGdK2ge/fuBnSSJEmSJEkqiYdESJIkSZIkSWVkQCdJkiRJkiSVkQGdJEmSJEmSVEbuQSdJkiRJkjZYKSVWrVrF6tWry12KRFVVFR07diQiWrVfAzpJkiRJkrRBWrlyJYsWLeLdd98tdynSWl27dqVXr1507ty51fo0oJMkSZIkSRucNWvWsGDBAqqqqth6663p3Llzq89akpojpcTKlStZvHgxCxYsoF+/fnTo0Dq7xxnQSZIkSZKkDc7KlStZs2YNvXv3pmvXruUuRwKgurqaTp068dJLL7Fy5Uo22WSTVunXQyIkSZIkSdIGq7VmKEmtpS3+Jv0rlyRJkiRJksrIgE6SJEmSJEkqIwM6SZIkSZKkMpg+fTo9evRo1jO1tbUceeSRbVRRYS+++CIRwdy5c9t0nFJ+j0rhIRGSJEmSJGmjsv34332g47347cOb1b62tpa33nqLW265pcH1WbNmMXLkSN5880169OjB6NGjOeyww1qz1IJWr17NJZdcwrXXXstLL71EdXU1/fv358tf/jInn3xym4+v9TOgkyRJkiRJKoPq6mqqq6vbfJzJkyfzwx/+kKlTpzJ06FCWLl3KnDlzePPNN9t8bBXHJa6SJEmSJEllUGhJ54UXXshWW21Ft27dOOWUUxg/fjy77bbb+579zne+Q69evejZsydf/epXee+995oc57bbbuPUU0/lmGOOoW/fvgwZMoQxY8Zw5plnrm2zZs0apkyZwk477USXLl3o06cPF110UYN+XnjhBUaOHEnXrl0ZMmQIs2fPbnD/xhtvZPDgwXTp0oXtt9+eyy67rMH9N998ky9+8YtsvvnmdO3alUMPPZT58+cX/XtVMgM6SZIkSZKkDcB1113HRRddxJQpU3j00Ufp06cPV1111fvazZw5k+eff56ZM2dy7bXXMn36dKZPn95kvzU1Ndxzzz0sXry4yTYTJkxgypQpTJw4kaeffprrr7+ej3zkIw3anHPOOYwbN465c+fSv39/jj/+eFatWgXAo48+yrHHHstxxx3HE088weTJk5k4cWKDumpra5kzZw633nors2fPJqXEYYcdts5wsb1wiaskSZIkSVIr++1vf8umm27a4Nrq1avX+cyVV17JmDFj1u4LN2nSJO644w6WLVvWoN3mm2/O1KlTqaqqYuDAgRx++OHcfffdjB07tmC/l19+OUcffTQ1NTUMHjyY4cOHM2rUKA499FAA3n77bb73ve8xdepUTjrpJAB23HFH9t577wb9jBs3jsMPz/bjO++88xg8eDDPPfccAwcO5PLLL+eAAw5g4sSJAPTv35+nn36aSy+9lNraWubPn8+tt97KAw88wPDhw4EskOzduze33HILxxxzzHp/00rmDDpJkiRJkqRWNnLkSObOndvg9eMf/3idzzzzzDPsscceDa41/gwwePBgqqqq1n7u1asXr732WpP9Dho0iCeffJKHHnqIk08+mVdffZUjjjiCU045BYB58+axYsUKDjjggHXWt+uuuzYYE1g77rx58xgxYkSD9iNGjGD+/PmsXr2aefPm0bFjR/bcc8+193v27MmAAQOYN2/eOsdtD5xBJ0mSJEmS1Mo+9KEPsdNOOzW49vLLL6/3uYho8Dml9L42nTp1et8za9asWWe/HTp0YNiwYQwbNowzzjiDn//855x44omcc845RR9UUX/cujrrxk0prbP2Qt+jqefaIwM6FeWDPsK6XJp7dLYkSZIkSa1lwIABPPLII5x44olrr82ZM6dNxho0aBAA77zzDv369aO6upq777577ay6Uvq7//77G1x78MEH6d+/P1VVVQwaNIhVq1bx8MMPr13i+vrrr/Pss8+y8847t+zLVAADOkmSJEmSpA3AaaedxtixYxk6dCjDhw/nhhtu4PHHH2eHHXZoUb9HH300I0aMYPjw4dTU1LBgwQImTJhA//79GThwIB07duTss8/mm9/8Jp07d2bEiBEsXryYp556ijFjxhQ1xllnncWwYcO44IILGD16NLNnz2bq1Kl8//vfB6Bfv36MGjWKsWPHcvXVV9OtWzfGjx/PNttsw6hRo1r0/SqBAZ0kSZIkSdqoVOrqp89//vO88MILjBs3juXLl3PsscdSW1vLI4880qJ+Dz74YGbMmMHFF1/MkiVLqKmpYf/992fy5Ml07JhFQxMnTqRjx45MmjSJhQsX0qtXL77yla8UPcbuu+/OL3/5SyZNmsQFF1xAr169OP/886mtrV3bZtq0aZx++ul8+tOfZuXKley7777cfvvt71uy2x5FU2uAtX4R0R1YsmTJErp3717uctqUS1wlSZIkSR+k5cuXs2DBAvr27csmm2xS7nLK5qCDDqKmpoaf/exn5S5FuXX9bS5dupTNNtsMYLOU0tJi+3QGnSRJkiRJ0gbg3Xff5Qc/+AEHH3wwVVVVzJgxg7vuuos777yz3KWpjRnQSfVN3qzcFXwwJi8pdwWSJEmSpEYigttvv50LL7yQFStWMGDAAG688UYOPPDAcpemNmZAJ0mSJEmStAGorq7mrrvuKncZKoMO5S5AkiRJkiRJas8M6CRJkiRJkqQyMqCTJEmSJEmSysiATpIkSZIkSSojAzpJkiRJkiSpjAzoJEmSJEmSpDLqWO4CJEmSJEmSmmXyZh/weEs+2PE+YNOnT+frX/86b731VtHP1NbW8tZbb3HLLbe0YWUNvfjii/Tt25fHHnuM3Xbbrc3GKeX3aCln0EmSJEmSJLWi2tpaIoKIoFOnTuywww6MGzeOd955p9X6P/LII0tuN2vWLCJibQA1evRonn322VapbV1Wr17NxRdfzMCBA6murmaLLbbgE5/4BNOmTWvzsTd0zqCTJEmSJElqZYcccgjTpk3jvffe409/+hOnnHIK77zzDldddVW5S3uf6upqqqur23ycyZMn88Mf/pCpU6cydOhQli5dypw5c3jzzTfbfOwNnTPoJEmSJEmSWlmXLl2oqamhd+/enHDCCXz+859vsBz03nvvZY899qBLly706tWL8ePHs2rVqrX3f/3rX7PLLrtQXV1Nz549OfDAA3nnnXeYPHky1157Lb/5zW/WztKbNWtWi2qdPn06PXr0aHDtwgsvZKuttqJbt26ccsopjB8/vuCy0u985zv06tWLnj178tWvfpX33nuvyXFuu+02Tj31VI455hj69u3LkCFDGDNmDGeeeebaNmvWrGHKlCnstNNOdOnShT59+nDRRRc16OeFF15g5MiRdO3alSFDhjB79uwG92+88UYGDx5Mly5d2H777bnssssa3H/zzTf54he/yOabb07Xrl059NBDmT9/ftG/V1vYaAO6iNg0Iq6IiIURsTwi5kbEcUU8VxsRqYlXzQdRuyRJkiRJal+qq6vXhlevvPIKhx12GMOGDeOvf/0rV111Fddccw0XXnghAIsWLeL444/nP/7jP5g3bx6zZs3iqKOOIqXEuHHjOPbYYznkkENYtGgRixYtYvjw4a1a63XXXcdFF13ElClTePTRR+nTp0/BmX8zZ87k+eefZ+bMmVx77bVMnz6d6dOnN9lvTU0N99xzD4sXL26yzYQJE5gyZQoTJ07k6aef5vrrr+cjH/lIgzbnnHMO48aNY+7cufTv35/jjz9+bbj56KOPcuyxx3LcccfxxBNPMHnyZCZOnNigrtraWubMmcOtt97K7NmzSSlx2GGHrTNcbGsb8xLXm4BhwHjgWeAEYEZEdEgpXV/E8ycDf2t07fXWLVGSJEmSJLV3jzzyCNdffz0HHHAAAN///vfp3bs3U6dOJSIYOHAgCxcu5Oyzz2bSpEksWrSIVatWcdRRR7HddtsBsMsuu6ztr7q6mhUrVlBTs/55Rr/97W/ZdNNNG1xbvXr1Op+58sorGTNmDCeffDIAkyZN4o477mDZsmUN2m2++eZMnTqVqqoqBg4cyOGHH87dd9/N2LFjC/Z7+eWXc/TRR1NTU8PgwYMZPnw4o0aN4tBDDwXg7bff5nvf+x5Tp07lpJNOAmDHHXdk7733btDPuHHjOPzwwwE477zzGDx4MM899xwDBw7k8ssv54ADDmDixIkA9O/fn6effppLL72U2tpa5s+fz6233soDDzywNti87rrr6N27N7fccgvHHHPMen/TtrBRzqCLiMOAg4BTU0pXp5RmppTGAncCl0ZEVRHdPJlSeqjRq3xRqSRJkiRJqhh1wdgmm2zCXnvtxb777suVV14JwLx589hrr72IiLXtR4wYwbJly3j55ZcZMmQIBxxwALvssgvHHHMMP/rRj0rep23kyJHMnTu3wevHP/7xOp955pln2GOPPRpca/wZYPDgwVRV/TuC6dWrF6+99lqT/Q4aNIgnn3yShx56iJNPPplXX32VI444glNOOQXIfpcVK1asDTKbsuuuuzYYE1g77rx58xgxYkSD9iNGjGD+/PmsXr2aefPm0bFjR/bcc8+193v27MmAAQOYN2/eOsdtSxtlQAd8FlgG/KrR9WnA1sCe73tCkiRJkiTpA1IXjD3zzDMsX76cm266ia222gqAlFKDcK7uGkBEUFVVxZ133snvf/97Bg0axJVXXsmAAQNYsGBBs+v40Ic+xE477dTgtc0226z3uabqq69Tp07ve2bNmjXr7LdDhw4MGzaMM844g5tvvpnp06dzzTXXsGDBgqIPqqg/bl2ddeOu67dt6ns09dwHaWMN6D4KzEsprWp0/fF699fntxGxOiLeiIibImK9z0REl4joXvcCujWzbkmSJEmS1A7UBWPbbbfd+4KsQYMG8eCDDzYIix588EG6deu2NjyLCEaMGMF5553HY489RufOnbn55psB6Ny583qXqbbEgAEDeOSRRxpcmzNnTpuMNWjQIADeeecd+vXrR3V1NXfffXeL+rv//vsbXHvwwQfp378/VVVVDBo0iFWrVvHwww+vvf/666/z7LPPsvPOO5c8bkttrHvQ9QReKHD9jXr3m/JP4CLgIWApsAvZPnYPRcSIlNJf1/HsBODc5pcrSZIkSZKUOfXUU7niiis47bTT+NrXvsYzzzzDueeey5lnnkmHDh14+OGHufvuu/nUpz7FVlttxcMPP8zixYvXBkjbb789f/zjH3nmmWfo2bMnm2222ftCwJY47bTTGDt2LEOHDmX48OHccMMNPP744+ywww4t6vfoo49mxIgRDB8+nJqaGhYsWMCECRPo378/AwcOpGPHjpx99tl885vfpHPnzowYMYLFixfz1FNPMWbMmKLGOOussxg2bBgXXHABo0ePZvbs2UydOpXvf//7APTr149Ro0YxduxYrr76arp168b48ePZZpttGDVqVIu+X0tsrAEdQOE5ieu5l1L6A/CHepfui4jfAU8A5wPr+rdxMXB5vc/dgJfXX6okSZIkSWo1k5eUu4IW2Wabbbj99tv5xje+wZAhQ9hiiy0YM2YM3/rWtwDo3r079913H1dccQVLly5lu+2247LLLlt7mMLYsWOZNWsWQ4cOZdmyZcycOZP99tuv1er7/Oc/zwsvvMC4ceNYvnw5xx57LLW1te+bVddcBx98MDNmzODiiy9myZIl1NTUsP/++zN58mQ6dswiqokTJ9KxY0cmTZrEwoUL6dWrF1/5yleKHmP33Xfnl7/8JZMmTeKCCy6gV69enH/++dTW1q5tM23aNE4//XQ+/elPs3LlSvbdd19uv/32Vg05myuaWnu7IYuI2UBVSmmPRtcHA08CX04p/bCZff4e2D2l9JH1Nv73M92BJUuWLKF79+7NGW6js/3435W7hA/Ei5ucUO4SPhgb+f+YSZIkSap8y5cvZ8GCBfTt25dNNtmk3OW0ewcddBA1NTX87Gc/K3cpZbeuv82lS5ey2WabAWyWUlpabJ8b6wy6J4DjI6Jjo33o6s4cfrKEPgNY906GkiRJkiRJFe7dd9/lBz/4AQcffDBVVVXMmDGDu+66izvvvLPcpVWsjfWQiJuBTYHPNbp+ErAQePh9T6xDRPQFRpDtSydJkiRJktRuRQS33347++yzDx//+Me57bbbuPHGGznwwAPLXVrF2ihn0KWUfh8RdwJX5ctMnwOOBw4BvpBSWg0QEdeQhXY7ppReyq/dBdxHduJr3SER3yTbt27iB/1dJEmSJEmSNiTV1dXcdddd5S6jXdkoA7rcUWSnsZ4PbAH8DTg+pfSLem2q8lfUu/YEMBoYB1QDrwH3ABeklJ79AOqWJEmSJEmS1tpoA7qU0jLg9PzVVJtaoLbRtTPatDBJkiRJktRqNsbDLVXZ2uJvcmPdg06SJEmSJFWwTp06AdmBBdKGpO5vsu5vtDVstDPoJEmSJElS5aqqqqJHjx689tprAHTt2pWIWM9TUttJKfHuu+/y2muv0aNHD6qqqlqtbwM6SZIkSZK0QaqpqQFYG9JJG4IePXqs/dtsLQZ0kiRJkiRpgxQR9OrVi6222or33nuv3OVIdOrUqVVnztUxoJMkSZIkSRu0qqqqNglFpA2Fh0RIkiRJkiRJZWRAJ0mSJEmSJJWRAZ0kSZIkSZJURgZ0kiRJkiRJUhkZ0EmSJEmSJEllZEAnSZIkSZIklZEBnSRJkiRJklRGBnSSJEmSJElSGRnQSZIkSZIkSWVkQCdJkiRJkiSVkQGdJEmSJEmSVEYGdJIkSZIkSVIZGdBJkiRJkiRJZWRAJ0mSJEmSJJWRAZ0kSZIkSZJURgZ0kiRJkiRJUhkZ0EmSJEmSJEllZEAnSZIkSZIklZEBnSRJkiRJklRGBnSSJEmSJElSGRnQSZIkSZIkSWVkQCdJkiRJkiSVkQGdJEmSJEmSVEYGdJIkSZIkSVIZGdBJkiRJkiRJZWRAJ0mSJEmSJJWRAZ0kSZIkSZJURgZ0kiRJkiRJUhkZ0EmSJEmSJEllZEAnSZIkSZIklZEBnSRJkiRJklRGBnSSJEmSJElSGRnQSZIkSZIkSWVkQCdJkiRJkiSVkQGdJEmSJEmSVEYGdJIkSZIkSVIZGdBJkiRJkiRJZWRAJ0mSJEmSJJWRAZ0kSZIkSZJURgZ0kiRJkiRJUhkZ0EmSJEmSJEllZEAnSZIkSZIklZEBnSRJkiRJklRGBnSSJEmSJElSGRnQSZIkSZIkSWVkQCdJkiRJkiSVkQGdJEmSJEmSVEYGdJIkSZIkSVIZGdBJkiRJkiRJZWRAJ0mSJEmSJJWRAZ0kSZIkSZJURgZ0kiRJkiRJUhkZ0EmSJEm0lwSSAAAgAElEQVSSJEllZEAnSZIkSZIklZEBnSRJkiRJklRGBnSSJEmSJElSGRnQSZIkSZIkSWVkQCdJkiRJkiSVkQGdJEmSJEmSVEYGdJIkSZIkSVIZGdBJkiRJkiRJZWRAJ0mSJEmSJJWRAZ0kSZIkSZJURgZ0kiRJkiRJUhlttAFdRGwaEVdExMKIWB4RcyPiuBL6uTAiUkQ82RZ1SpIkSZIkSevSsdwFtMBNwDBgPPAscAIwIyI6pJSuL6aDiNgNGAe82mZVSpIkSZIkSeuwUQZ0EXEYcBBwQkppRn55ZkRsB1waETeklFavp4+OwDTgamAIsGVb1ixJkiRJkiQVsrEucf0ssAz4VaPr04CtgT2L6GM8sAVwTuuWJkmSJEmSJBVvo5xBB3wUmJdSWtXo+uP17j/Y1MMRMQj4FnBUSmlZRBQ1aER0AbrUu9St6IolSZIkSZKkAjbWGXQ9gTcKXH+j3v2CIqID8BPgppTS7c0cdwKwpN7r5WY+L0mSJEmSJDWwsQZ0AKnEe2cC/YCvlzDmxcBm9V7bltCHJEmSJEmStNbGusT1dQrPktsify80u46I6AOcT7b/3MqI6JHf6gh0yD+vSCn9q9DzKaUVwIp6/ZVWvSRJkiRJkpTbWGfQPQHsnJ/EWt8u+fuTTTy3A1ANfA94s95rBLBz/s8Xt3q1kiRJkiRJUhM21hl0NwNjgc8BN9S7fhKwEHi4iefmAiMLXL+CbMnqybivnCRJkiRJkj5AJQd0+Umo3wT2BXoBnZtomlJKrRoEppR+HxF3AldFRHfgOeB44BDgCyml1XmN15CFdjumlF5KKb0FzCrwXd4COqaU3ndPkiRJkiRJakslBWcRsRdwF9lyUcj2hFvWWkUV6SjgIrI95bYA/gYcn1L6Rb02VfnLzeIkSZIkSZK0QSp1ZtvFZOHcFcCFKaWChzK0pZTSMuD0/NVUm1qgtoi+9mutuiRJkiRJkqTmKDWgGwrMTSmd2ZrFSJIkSZIkSe1Nqae4riTb902SJEmSJElSC5Qa0N0P7NKahUiSJEmSJEntUakB3X8DvSPirNYsRpIkSZIkSWpvSt2DbndgGnBJRBwB3Am8DKRCjVNKPy1xHEmSJEmSJKmilRrQTScL4wLYF9iniXaRtzOgkyRJkiRJkgooNaA7nyZmy0mSJEmSJEkqXkkBXUppcivXIUmSJEmSJLVLpR4SIUmSJEmSJKkVlLrEda2I6AUMB7YmW/a6CHgwpbSopX1LkiRJkiRJla7kgC4iPgxcCXyO98/EWxMRNwKnpZQWt6A+SZIkSZIkqaKVFNBFxGbAfcAA4F/AHcCL+e3tgE8BxwJDIuITKaUlLS9VkiRJkiRJqjylzqAbTxbO/Qr4WuNZchGxJTCVLKQ7G/jvlhQpSZIkSZIkVapSD4n4LPAP4AuFlrCmlP4PODFv87nSy5MkSZIkSZIqW6kB3XbAAyml95pqkN97AOhT4hiSJEmSJElSxSs1oPsXsGUR7bbM20qSJEmSJEkqoNSA7lHgkxHx8aYa5Pf2A+aUOIYkSZIkSZJU8UoN6L4LdALujohzI6JfRHTOX/0iYjJwF1CVt5UkSZIkSZJUQEkBXUrpduAcYFNgEvA34N389TdgItAN+FZK6fetU6okSZIkSZJUeUqdQUdK6WLgE8DPgReB9/LXi8DPgL3yNpIkSZIkSZKa0LElD6eU5gAntVItkiRJkiRJUrtT8gw6SZIkSZIkSS1nQCdJkiRJkiSVUVFLXCPiBSABB6aUFuSfi5VSSjuWVJ0kSZIkSZJU4Yrdg277/L1To8+SJEmSJEmSWqCogC6l1GFdnyVJkiRJkiSVxqBNkiRJkiRJKqOSArqImBQRnymi3RERMamUMSRJkiRJkqT2oNQZdJOBI4to9xng3BLHkCRJkiRJkipeWy9xrQLWtPEYkiRJkiRJ0karrQO6wcCbbTyGJEmSJEmStNEq6hRXgIj4SaNLexe4Vr/fAcBQ4JYSa5MkSZIkSZIqXtEBHVBb758TsFP+WpfHgW80syZJkiRJkiSp3WhOQDcyfw/gHuAPwJQm2q4EFqaUXmpBbZIkSZIkSVLFKzqgSyndW/fPEXEtcH/9a5IkSZIkSZKarzkz6NZKKZ3c2oVIkiRJkiRJ7VFJp7hGxF4R8ZOI2GsdbYbnbfYsvTxJkiRJkiSpspUU0AFfA0YD89bRZh5wHHBqiWNIkiRJkiRJFa/UgO4TwGMppbeaapBSehP4CzCixDEkSZIkSZKkildqQLc18Pci2v0d6FXiGJIkSZIkSVLFKzWgewfYsoh2WwIrSxxDkiRJkiRJqnilBnR/BfaOiG2bapDf2wd4vMQxJEmSJEmSpIpXakD3E2AT4LaI+Fjjm/m1W4HOeVtJkiRJkiRJBXQs5aGU0nURcSTwOeDPEfEX4HkgATsBu5OFfzenlK5trWIlSZIkSZKkSlNSQJcbDfw3cCYwNH/VeQv4LvD/WtC/JEmSJEmSVPFKDuhSSmuACyNiClk41zu/9Q9gTkrpvVaoT5IkSZIkSapoLZlBB0AexM3OX5IkSZIkSZKaodRDIiRJkiRJkiS1ghbNoIuIPsARQD+gGxAFmqWU0piWjCNJkiRJkiRVqpIDuoiYBEyk4Sy8uoAu1fucAAM6SZIkSZIkqYCSlrhGxGhgMtmBEF8C7sxvHQz8J3AvWTh3ObB/i6uUJEmSJEmSKlSpM+hOBVYCI1NKL0XE3gAppbqg7uqIOAO4BLil5WVKkiRJkiRJlanUQyJ2BR5MKb2Uf04AEbF2D7qU0neBZ4BvtahCSZIkSZIkqYKVGtB1Af5Z7/Py/L1Ho3Z/BYaVOIYkSZIkSZJU8UoN6BYBNfU+v5K/D27UblugqsQxJEmSJEmSpIpXakD3BDCw3udZZIdCnB8RmwJExLHAPsBTLSlQkiRJkiRJqmSlBnS3ATURcSBASukBYCawH/BGRLwOzCDbm+6CVqhTkiRJkiRJqkilBnQ/B3YG/lLv2meBHwJvAJsCTwMnppT+0KIKJUmSJEmSpArWsZhGEdEHWJZSegMgpbSC7ITWtVJKS4Gv5C9JkiRJkiRJRSh2Bt0C4NK6DxHxk4j4j7YpSZIkSZIkSWo/ig3oolHbWmDvVq9GkiRJkiRJameKDeiWAL3bshBJkiRJkiSpPSpqDzrgz8D+ETGNbLkrwG4RMamIZ1NKyZNcJUmSJEmSpAKKDejGA7cBJ9W7tlv+Wp8EGNBJkiRJkiRJBRQV0KWU/hIRA4BhZEtdpwP3A9e0XWmSJEmSJElS5St2Bh0ppWXATICImA48l1K6to3qkiRJkiRJktqFogO6RvoCy1qzEEmSJEmSJKk9KimgSym91NS9iBgIfBT4e0rpkVILkyRJkiRJktqDkgK6iBgNfBmYkFJ6uN71bwPfqPf5JmB0SmlNSwuVJInJm5W7gg/G5CXlrkCSJEnSB6hDic99AfgYMLfuQkTsCXwTeBv4BfAicBRwfMtKLCwiNo2IKyJiYUQsj4i5EXFcEc8dGBF35s+tiIjXIuKeiDisLeqUJEmSJEmS1qXUgO6jwOMppRX1rn0RSMCxKaXPA0PJwrovtazEJt0EnAScBxwK/BmYEREnrOe5nsBTwBnAp8hmAr4H/C4ivtBGtUqSJEmSJEkFlXpIxFbA7EbXRgKvpZTuAEgpvRkRfwI+3oL6Cspnux0EnJBSmpFfnhkR2wGXRsQNKaXVhZ5NKd0A3NCov98CC8jCxJ+3dr2SJEmSJElSU0qdQfcu0LXuQ0RsAQwA7m3U7i1g8xLHWJfPkp0i+6tG16cBWwN7NqezlNJ7ZLWuapXqJEmSJEmSpCKVOoPuBWCviOiUh1tH5dfvaNSuBnit1OLW4aPAvJRS40Dt8Xr3H1xXBxHRgSyg3IpsmWt/4Oz1PNMF6FLvUrdm1CxJkiRJkiS9T6kz6H4CfBi4LyIuBy4lm9H2m7oGEdGRbB+6Z1taZAE9gTcKXH+j3v31uZ1s77lXgK+TnTb7u/U8MwFYUu/1clHVSpIkSZIkSU0oNaC7Bvgl2VLSr5PNxPtySun1em2OADYD7mlRhU1LJd6rcxqwBzAK+CNwQ0Ss78TZi8m+U91r2yLGkSRJkiRJkppU0hLXfGnpcRFxNtkS0b+llN5u1GwB2V5xD7WsxIJep/AsuS3y90Kz6xpIKc2v9/HWiPg98L/5ARNrmnhmBbD25NqIKL5iSZIkSZIkqYBSZ9ABkFJ6KaX05wLhHCmluSml36SUXm3JGE14Atg5X0Zb3y75+5Ml9PkI2YEWH25JYZIkSZIkSVJztCigK6ObgU2BzzW6fhKwEHi4OZ1FNhXuk2Qnub6+nuaSJEmSJElSqylqiWtETCLb1+1/U0pv5J+LlVJKF5RUXdMd/j4i7gSuiojuwHPA8cAhwBdSSqvzuq8hC+12TCm9lF/7DfBXYC5ZGLc1UEsW0H21wMmwkiRJkiRJUpspdg+6yWQB3Q1k+7vVfS5mE7YEtGpAlzsKuAg4n2zvub8Bx6eUflGvTVX+ql/nA8DRwNeA7mSz5uYAny7iFFdJkiRJkiSpVRUb0J2cvy9q9LlsUkrLgNPzV1Ntaslmx9W/dglwSVvWJkkftO3Ht4//vvDiJuWuQJIkSZJaX1EBXUrp2nV9liRJkiRJklSakg6JiIg+EbFFEe02j4g+pYwhSZIkSZIktQelnuK6ALi0iHaXAC+UOIYkSZIkSZJU8UoN6ILiDoioaytJkiRJkiSpgFIDumJtCfyrjceQJEmSJEmSNlrFnuJKROzb6FJNgWv1+x0AHAI8WWJtkiRJkiRJUsUrOqADZgGp3ueD81dTIm9/WfPLkiRJkiRJktqH5gR0P+XfAd1JwPPAA020XQksBG5LKf2l9PIkSZIkSZKkylZ0QJdSqq3754g4Cbg/pfQfbVGUJEmSJEmS1F40ZwbdWimltj5cQpIkSZIkSWoXDNokSZIkSZKkMippBl2diOgDHAH0A7qRHQzRWEopjWnJOJIkSZIkSVKlKjmgi4hJwEQazsKrC+hSvc8JMKCTJEmSJEmSCihpiWtEjAYmA/8AvgTcmd86GPhP4F6ycO5yYP8WVylJkiRJkiRVqFJn0J0KrARGppReioi9AVJKdUHd1RFxBnAJcEvLy5QkSZIkSZIqU6mHROwKPJhSein/nAAiYu0edCml7wLPAN9qUYWSJEmSJElSBSs1oOsC/LPe5+X5e49G7f4KDCtxDEmSJEmSJKnilRrQLQJq6n1+JX8f3KjdtkBViWNIkiRJkiRJFa/UgO4JYGC9z7PIDoU4PyI2BYiIY4F9gKdaUqAkSZIkSZJUyUoN6G4DaiLiQICU0gPATGA/4I2IeB2YQbY33QWtUKckSZIkSZJUkUoN6H4O7Az8pd61zwI/BN4ANgWeBk5MKf2hRRVKkiRJkiRJFaxjKQ+llFaQndBa/9pS4Cv5S5IkSZIkSVIRSp1BJ0mSJEmSJKkVlDSDrpCI2As4HNgSWAjcnFJ6orX6lyRJkiRJkipRUTPoImKfiLg9Ik5r4v53gfuBCcCXgHOBxyJiQqtVKkmSJEmSJFWgYpe4jgIOpuGhEABExHHA6UAAc4DLgN/kny+MiBGtU6okSZIkSZJUeYpd4joC+GdK6YEC984EEnALcHRKKQFExMnANcCXgULPSZIkSZIkSe1esTPoegOPNr4YEVsCH88/Tq4L5wBSStOABcDwlhYpSZIkSZIkVapiA7otgaUFrg8nW8q6qIkDIZ4EepVYmyRJkiRJklTxig3oVlA4aNsrf3+kiefeIVv+KkmSJEmSJKmAYgO6vwF75Uta6xtFFsDd18Rz2wKLSqxNkiRJkiRJqnjFBnS/BjYBfhsR+0XERyPiSmAgsAr4VeMHIqKabH+6F1qrWEmSJEmSJKnSFHuK6/8AJwB7AHc3vpdSeqXAM8cA1QXaS5IkSZIkScoVNYMupbQCGAn8L9mS1VXAi8A5wNlNPPYVYAlwe4urlCRJkiRJkipUsTPoSCm9BZyWv4ppP7zUoiRJkiRJkqT2otg96CRJkiRJkiS1AQM6SZIkSZIkqYwM6CRJkiRJkqQyMqCTJEmSJEmSysiATpIkSZIkSSojAzpJkiRJkiSpjAzoJEmSJEmSpDIqKaCLiDci4t7WLkaSJEmSJElqb0qdQdcReLk1C5EkSZIkSZLao1IDuqeAbVqzEEmSJEmSJKk9KjWguxLYOyL2bs1iJEmSJEmSpPamY4nP3Q/8GPhjRPwYuA34O7C8UOOU0t9LHEeSJEmSJEmqaKUGdC8CCQjga/mrKakF40iSJEmSJEkVrdTg7D6y4E2SJEmSJElSC5QU0KWU9mvlOiRJkiRJkqR2qdRDIiRJkiRJkiS1glbZGy4i+gFbAq+nlJ5tjT4lSZIkSZKk9qDkGXQRUR0RUyLideBvZCe7jq93/+SI+EtE7NYKdUqSJEmSJEkVqaSALiI+BNwLjANWAL8jO9G1vvuA3YDRLSlQkiRJkiRJqmSlzqA7GxgK/Ajom1L6TOMGKaXnyWbWHVh6eZIkSZIkSVJlKzWgGw28CHw1pbRiHe1eArYtcQxJkiRJkiSp4pUa0PUBHk0prV5Pu6XA5iWOIUmSJEmSJFW8UgO6d8hObV2fvsDrJY4hSZIkSZIkVbxSA7pHgT0iondTDSJiMPAxYHaJY0iSJEmSJEkVr9SAbipQDdwUETs1vhkR2wE/zfufWnp5kiRJkiRJUmUrKaBLKd0GfBf4OPBMRDwJJOBTETEHmE82e+6SlNKsVqpVkiRJkiRJqjilzqAjpXQWcBzwBDAICGBrYHfgeeDElNKE1ihSkiRJkiRJqlQdW/JwSumXwC8j4sPAdkAV8HJK6ZXWKE6SJEmSJEmqdC0K6OqklBYDi1ujL0mSJEmSJKk9aXFA9//bu/Mwyar6/uPvDyCIghhQcEEh4i4YEwGXaETRX1BxAVwAUVDI4m7cQKMRUSOuUTQuEYQYFdxFxQVUBnfUIJuKBnVYRJDNYUZgWPz+/ji3nZqa6u7q6u6p6Z7363nq6alzz7331K177tz7rbMkeQCwC3A74KdV9YUufRNgk6q6Zrb7kCRJkiRJkharkcegS3KfJD8A/hf4APBG4Mk9WZ4DXJ1kj9kVUZIkSZIkSVq8RgrQJdkO+BawK3Ai8EraJBG9TgBuBPaZTQElSZIkSZKkxWzUFnSvA7YEDqyqvavqHf0Zqupq4GfAQ2ZRPkmSJEmSJGlRGzVA9/fAT6rqf6bJdwFwpxH3IUmSJEmSJC16owbotgJ+NUS+Am454j6mlGSzJO9KckmS65OcmWTfIdbbO8nxSc5Pcl2SpUk+luQe81FOSZIkSZIkaSqjzuJ6BfCXQ+S7D/DbEfcxnc/SZo89DPglsD9wfJINqurjU6x3KHAp8Cbg18BdgFcDZyR5cFX9dJ7KK0mSJEmSJK1h1ADdacC+Sf62qr47KEOSPYF70WZ4nVNJHgc8Bti/qo7vkk/tJq94W5JPVNXNk6z+hKr6fd/2vgksBf4FOGSuyytJkiRJkiRNZtQurm+mzdD6xSQHJ7n9xIKu6+kBwLHAtcAaE0jMgb2AFcCn+tKPpY1596DJVuwPznVplwAX01rTSZIkSZIkSWvNSAG6qjoXeAawMfBftC6jBTwLWAb8N3Ar4JlVNcxYdTO1I/DzqrqpL/3snuVDS3I3YDtgyu6tSTZJcpuJF7D5TPYjSZIkSZIk9Ru1BR1V9RlaIOw9wM+B62it6n5NC9r9VVV9bi4KOcBWwFUD0q/qWT6UJBsBx9Ba5P3HNNlfRQtATrwuHnY/kiRJkiRJ0iCjjkEHQFUtBV4yN0WZ+e5HXPZnSUILzj0c2KeqLppmlTcD7+x5vzkG6SRJkiRJkjQLIwXokpwB/KqqnjrH5RnWlQxuJbdl93dQ67rVdMG5o4EDgAOr6sTp1qmqlcDKnm0MVVhJkiRJkiRpMqN2cb0XrTvruJwD3Kfrntprp+7vuVOt3BOcezZwSFV9dO6LKEmSJEmSJE1v1ADd/zGDcd7mweeAzYB9+tIPBC4BTp9sxS449yFacO6fqurY+SqkJEmSJEmSNJ1Rx6A7BnhbkntX1XlzWaBhVNVXkpwCvL+bTfV8YD9gD+CAqroZIMkxtKDdDlV1Qbf6UcDBwIeBc5I8uGfTK6vqJ2vrc0iSJEmSJEkjBeiq6j1J7gecluRI4IvAhVV1w5yWbmp7A28CjqCNPXcesF9VndCTZ8Pu1TtY3BO6v8/pXr0uALafj8JKkiRJkiRJg4w6ScTNE/8E3t69Jps0oapqVrPFTrLRFcCLu9dkeQ4CDupL236uyyJJkiRJkiSNatTA2UVAzWVBJEmSJEmSpPXRqF1ct5/jckiSJEmSJEnrpVFncZUkSZIkSZI0BwzQSZIkSZIkSWM068kbutlc7wFszuqzpf5ZVX1ktvuRJEmSJEmSFqORA3RJHg28D9hhqmy0ySQM0EmSJEmSJEkDjBSgS7IzcBIt+PZxYKfudSQtYPdo4C+AY4EL56SkkiRJkiRJ0iI0agu6V3Xr7lFVpyQ5Ftipqv4VIMltgQ8CewI7z0lJJUmSJEmSpEVo1EkiHgr8pKpOGbSwqv4APAv4E/DGEfchSZIkSZIkLXqjBui2BM7veX8DQJJbTyRU1Urg28BjRi6dJEmSJEmStMiNGqC7HLhN33uAu/Xl2xTYYsR9SJIkSZIkSYveqAG681l99tYf0mZs/aeJhCR3Bx4F/Hrk0kmSJEmSJEmL3KgBui8D90iyY/f+q8AFwHOTnJ7kM8CPgFsCx8y+mJIkSZIkSdLiNOosrh8BlgEFUFU3JHki8Elgl+71J+Bo4N1zUE5JkiRJkiRpURopQFdVlwIf7Es7B7hPknsDfwGcX1WXD1pfkiRJkiRJUjNqC7pJVdV5c71NSZIkSZIkabEaOkCX5FHAtsCPq+pn0+S9L7AzcFFVnTq7IkqSJEmSJEmL11ABuiR3AU4CLgIeOMQqFwGfA7ZNco+qumT0IkqSJEmSJEmL17CzuB4CbAy8sqqWT5e5y/MKYFPg4NGLJ0mSJEmSJC1uwwboHgNcXlWfH3bDVfUF4DLgsaMUTJIkSZIkSVofDBuguzfwoxG2/2PgXiOsJ0mSJEmSJK0Xhg3Q3RpYNsL2lwGbjbCeJEmSJEmStF4YNkB3NbDNCNvfpltXkiRJkiRJ0gDDBuh+Bjw4yabDbjjJrYCHdOtKkiRJkiRJGmDYAN0Xad1cXzODbb+GNovrF2daKEmSJEmSJGl9MWyA7oPApcBhSV6TZNL1kmyQ5LXAYbRZXD84+2JKkiRJkiRJi9NGw2SqqmuT7A18A3g98A9JPgWcAVzeZbs98DfAU4FtgeuBfarq2jkvtSRJkiRJkrRIDBWgA6iqHyR5CPBRYEfgXwZkS/f3p8ABVXXW7IsoSZIkSZIkLV5DB+gAqups4P5J/h54PPDXwFa0wNwVwJnASVX11bkuqCRJkiRJkrQYzShAN6GqvgZ8bY7LIkmSJEmSJK13hp0kQpIkSZIkSdI8MEAnSZIkSZIkjZEBOkmSJEmSJGmMDNBJkiRJkiRJY2SATpIkSZIkSRojA3SSJEmSJEnSGBmgkyRJkiRJksbIAJ0kSZIkSZI0RgboJEmSJEmSpDEyQCdJkiRJkiSNkQE6SZIkSZIkaYwM0EmSJEmSJEljZIBOkiRJkiRJGiMDdJIkSZIkSdIYGaCTJEmSJEmSxsgAnSRJkiRJkjRGBugkSZIkSZKkMTJAJ0mSJEmSJI2RATpJkiRJkiRpjAzQSZIkSZIkSWNkgE6SJEmSJEkaIwN0kiRJkiRJ0hgZoJMkSZIkSZLGyACdJEmSJEmSNEYG6CRJkiRJkqQxMkAnSZIkSZIkjZEBOkmSJEmSJGmMDNBJkiRJkiRJY2SATpIkSZIkSRojA3SSJEmSJEnSGBmgkyRJkiRJksbIAJ0kSZIkSZI0RgboJEmSJEmSpDEyQCdJkiRJkiSNkQE6SZIkSZIkaYwM0EmSJEmSJEljZIBOkiRJkiRJGiMDdJIkSZIkSdIYLdgAXZLNkrwrySVJrk9yZpJ9h1hv226905L8IUklOWgtFFmSJEmSJElaw4IN0AGfBQ4EXg88FvgRcHyS/adZ7+7AM4AbgC/PawklSZIkSZKkaWw07gKMIsnjgMcA+1fV8V3yqUm2A96W5BNVdfMkq3+rqm7fbWdnYL/5L7EkSZIkSZI02EJtQbcXsAL4VF/6scCdgAdNtmJV/WkeyyVJkiRJkiTNyEIN0O0I/LyqbupLP7tnuSRJkiRJkrTOW5BdXIGtgF8PSL+qZ/mcS7IJsElP0ubzsR9JkiRJkiStPxZqCzqAGnHZbLwKWNbzunie9iNJkiRJkqT1xEIN0F3J4FZyW3Z/rxqwbC68Gdii57XtPO1HkiRJkiRJ64mFGqA7B7hPkv4uujt1f8+dj51W1cqqumbiBSyfj/1IkiRJkiRp/bFQA3SfAzYD9ulLPxC4BDh9rZdIkiRJkiRJGsGCnCSiqr6S5BTg/UluA5wP7AfsARxQVTcDJDmGFrTboaoumFg/yVO6f96t+7tzkhXdtj+9lj6GJEmSJEmStDADdJ29gTcBR9DGnjsP2K+qTujJs2H3St+6n+p7//zuxYC8kiRJkiRJ0rxZsAG6qloBvLh7TZbnIOCgAekG4SRJkiRJkrROWKhj0EmSJEmSJEmLggE6SZIkSZIkaYwM0EmSJEmSJEljZIBOkiRJkiRJGiMDdJIkSZIkSdIYGaCTJEmSJEmSxsgAnSRJkiRJkjRGBugkSZIkSZKkMTJAJ0mSJEmSJI3RRuMugCRJ0mK2/WEnjbsIa8XSIx8/7iJIkiQtWAboJEmSNHuHbzHuEqwdhy8bdwkkSdIiZBdXSZIkSZIkaYwM0EmSJEmSJEljZIBOkiRJkiRJGiMDdJIkSZIkSdIYGVhpKvcAACAASURBVKCTJEmSJEmSxsgAnSRJkiRJkjRGBugkSZIkSZKkMTJAJ0mSJEmSJI2RATpJkiRJkiRpjAzQSZIkSZIkSWNkgE6SJEmSJEkaIwN0kiRJkiRJ0hgZoJMkSZIkSZLGaKNxF0CSJEmSxmX7w04adxHWiqVHPn7cRZAkTcEWdJIkSZIkSdIY2YJOkiRJkha7w7cYdwnWjsOXjbsEkjQSW9BJkiRJkiRJY2SATpIkSZIkSRojA3SSJEmSJEnSGBmgkyRJkiRJksbIAJ0kSZIkSZI0RgboJEmSJEmSpDEyQCdJkiRJkiSNkQE6SZIkSZIkaYwM0EmSJEmSJEljZIBOkiRJkiRJGiMDdJIkSZIkSdIYGaCTJEmSJEmSxsgAnSRJkiRJkjRGBugkSZIkSZKkMTJAJ0mSJEmSJI2RATpJkiRJkiRpjDYadwEkSZIkSZL6bX/YSeMuwlqx9MjHj7sIWgfYgk6SJEmSJEkaIwN0kiRJkiRJ0hgZoJMkSZIkSZLGyACdJEmSJEmSNEZOEiFJkiRJkjQuh28x7hKsHYcvG3cJ1mm2oJMkSZIkSZLGyACdJEmSJEmSNEYG6CRJkiRJkqQxMkAnSZIkSZIkjZEBOkmSJEmSJGmMDNBJkiRJkiRJY2SATpIkSZIkSRojA3SSJEmSJEnSGBmgkyRJkiRJksbIAJ0kSZIkSZI0RgboJEmSJEmSpDEyQCdJkiRJkiSNkQE6SZIkSZIkaYwM0EmSJEmSJEljZIBOkiRJkiRJGiMDdJIkSZIkSdIYGaCTJEmSJEmSxsgAnSRJkiRJkjRGBugkSZIkSZKkMTJAJ0mSJEmSJI3Rgg3QJdksybuSXJLk+iRnJtl3yHW3TnJckiuSXJvk+0l2n+8yS5IkSZIkSf0WbIAO+CxwIPB64LHAj4Djk+w/1UpJNgG+AewOvBh4EnAZ8NUkj5jXEkuSJEmSJEl9Nhp3AUaR5HHAY4D9q+r4LvnUJNsBb0vyiaq6eZLVDwZ2BB5aVd/vtncqcBbwVuBB81t6SZIkSZIkaZWF2oJuL2AF8Km+9GOBOzF1kG0v4BcTwTmAqroJ+Ciwa5I7z3FZJUmSJEmSpEkt1ADdjsDPu8Bar7N7lk+17tkD0ifS7jfLskmSJEmSJElDW5BdXIGtgF8PSL+qZ/lU6141IH3adbvx6zbpSdoc4Jprrplid4vDn1ZeO+4irBXXpMZdhLVjPThn1zfW0UXGOrqoWD8XGevnomMdXWSso4uK9XORWU/q56gxooUaoAOY6gye7uwedd1XAa/rT7zLXe4yze60UGwx7gKsLUeuN59Ui8x6c+ZaR7UArTdnrfVTC9R6c+ZaR7UArTdn7fpXPzcHho7WLdQA3ZUMbum2Zfd3UAu5uVj3zcA7B6w31TpaODYHLga2BZaPuSyS1mQdldZd1k9p3WYdldZd1s/FaXPgkpmssFADdOcA+yXZqG8cup26v+dOs+5OA9KnXbeqVgIr+5LXjzaa64EkE/9cXlV+r9I6xjoqrbusn9K6zToqrbusn4vWjL/LhTpJxOeAzYB9+tIPpEUoT59m3Xsn+fNMr0k2Ag4ATq+qGUU4JUmSJEmSpNlYkC3oquorSU4B3p/kNsD5wH7AHsABVXUzQJJjaEG7Harqgm71DwPPBz6V5DDg98DzgHsBj167n0SSJEmSJEnruwUZoOvsDbwJOII2Dtx5wH5VdUJPng2715/bjFbVyiS7A28F3gPcCjgTeGxVnbaWyq5100rg9azZjVnSusE6Kq27rJ/Sus06Kq27rJ8CIFXryXS+kiRJkiRJ0jpooY5BJ0mSJEmSJC0KBugkSZIkSZKkMTJAJ0mSJEmSJI2RAToNLcnnklyX5LZT5PlYkhuTbLM2y7Y2JHlBkmcNSL97kkpywAy3lyRvTnJRkpuSXNGlfyfJ1+eq3N02L05y9FxuU2tPkgd19e/CJCuTXJbk+0ne0ZdvSZIlfWmV5PAR97s0yZeGyHffJIcn2X7AsuOSLB1l//MtyfOSHDQgffvuuK2xbIhtvrH7nm5K8ocubY3vZba67+a4udymGuvb9PrPvyS7dZ99t/ne9xRlsj6vvs2Dus99fZLtBixfkuTcEbe9xnmW5NVJnjxFOXYeZV/rmiQP7erfGvfCo54bSXZP8uMkf+yO1ZN7jtv2c1Dsif0cnmRBDz7ef42dj+M0QpkmO/dHui4m2TjJB5L8LsnNSc7s0ufjOjHy/1lzsO9F81zZ/5yV5NHdsX3YGMvkc+sCtZBncdXadwzwZGB/4H39C5NsAewFfKmqLlvLZVsbXgBcDHykL/0i4CHA+TPc3t7AYbSZiE8Gru/S/xFY0DdQmjtJHg98AVgCvBL4HXBHYGdgX+Bl02ziIbTzdj7dF3gdrYxL53lfc+l5wBXAcX3pv6Mdt1/NZGNJngT8K22G8a+waiau582qlFprrG8jO4P22X82xjJYnwfbBHgj8Mw53OYbgHf3pb0a+DTw+Tncz7roobT6dxzwh75lMz43kgT4JPBL4InAH4Ff0J7RHkI7fzW5kxj/cZrs3B/1uvhc4J+AFwL/C6zo0vcCrhm9mOucxfxc+UPad//TMZbB59YFygCdZuIrwCXAcxhwIQX2AzalXXBnLcmmVXXdXGxrPlXVSuAHI6y6Y/f33VV1Vc/2xvmAo3XPK4HfAH9fVTf1pJ+Q5JXTrVxVo5yb67U5qNNHVdXve7ZnnV44rG8jqKprGK3OzDvrM18F9k/y9qo6ay42WFUzCnauDUluVVXXjrMMI54bdwK2BD5XVd/oW3b57Eu1bkqyIbBRVz9HVlWXs44ep1lcF3cErquq9/Zt7ydzUrB1x6J9rlzk/yf63DrP7OKqoVXVzcB/Aw9MstOALM+m/YL1lYmEJJsk+bckv+i6Cv0+yTFJbte7YteU9fNJnprkzCTXA/+a5LRBXTCSbJDkN0lOnKrMXRPj05Jc1TWjviDJp5PcsifPEUl+2OW5Jsn/dk3m01s+4F7A7l2z4EpyfrdsjabCSbZJcnTXDHhlksu7JsCP7Nne4V32K7v1X9MtW6Op8AyO48ZJ3p7k0iTXJvl2Fkm3kvXYVsAVfcECAKrqT9OtPKj7QpKHpXXZuz7Jb5O8IckhmaSbSJI9kpzR1aHzkjynZ9lBwKe6t6f21I+DJinPN7ptpC89Sc5PctI0n+dRad2IruzKc2GSzyS5VU+e1yU5vadOn5Hk4L46vRS4H/CInjIv7Zat0SUuye2T/Fdfnf5ukkf3bO+NXfbLeo97BneF3DjJa7pjMbG9Y5Pcvi/fLZK8tadOfyfJrlMdI82K9W31fEOdfxnQlSvJ3ZKckOSSrOoq/I0kD+jJY32e//r8VuBK4C3TZezOi+el3Yddl+TqtHumu/XlW62La1q3yVsDB/Yc/yV9m988yfuTXNF9359NcqcBZXh6V1/+mGRFkq8l+esB+1+RZKckJydZDvQHt3rzT/l9d3kek+TEtPvR67v68cH03Gd158Dbure/6fmsu3XLB50bz01yVlfe5d058u8925tocfuWvvN2YNfNtPvab3R14druc+w+4DM/vvseV6bdL798suMzYN0lSc5NskvafeS1SX6d5LAkG/TlvWuSj6bdl65M8vMkL+vN11MHX9nVk9/QWqQ+cooy3CbJh7pzZUWSrya554B8axynJH+d5Es9ZbokyUlJtu3J89S068qyns/34Z7lt0zyju4YLku7/nw/rWVt7/4nPfczwnWx294hwKbpu75nQBfX7ji9vfuOb0j7P+ZdSW49yvFcmxboc+VQz1kZ0MU17Xnxk2ldl1d22/h672ePz60+t2ILOs3ch2nNW58D/MtEYpL7ArsCR3YX3Ilfx74EPJh2Y/gDYHta09hdkuxaVdf3bHtXWnT+jbRuOytozcM/k2S3qlrSk3fPblv/NFlBk+zQ7f9U4CBas/A7A3sAt2BV09ztgPfTmvymK+/7ab9q/nuX5wnA54DfAy/q0nrL3u9j3Wd5Da3bwl8AD6Q9/E1s70VduR7TfdaLJvkcMzmOH6b94vQ22s3q/WlN7m8zRVm1bvs+cEiSo2jn1RlVdeOoG0tyf+AU2nl5IHAt8M/AZGNR/BXwDuBI4DLajeMxSc6vqm/Rupe8mlZXnk+rszB5d7J3AycCuwO9/6E/FtiBVfVrUNm37/b3bdo16A+sqtMbd58FWv34IHBh9/7BwHu6vEd0aXvRuqQsY1W3pKl+yf8f4G9oXd5+Cdy2ez9Rp/eiff6Du/IsY5KujmkPLicCD6c9PH+Pdh16PbAkyc49v/J+CHgW8Hba97Yj8Flg8ynKqtFZ31Y3m/Pvy8CGtFaJFwK3o3UPvC1Yn1l79Xk57b7q3UkeVVXfnCLvB2n3JUcBh9Jadv0b8L0kfzVFN7OHAN+k3W+9oUvr74p3NO373h+4C+0+5aPAoyYyJHl1V9Zju78bA68Avt3d6/S21NiY1h39g7T6MtUzzXTfN7T68P2unMto591Lge8k2am7DhzdHZMX0rp7TXSrHNiCJMm+tJZB7wFeDvwJuDutm/rEMTmLdg68B/g4U5y33QP1R2jn24HAjbT74K8l+fuJFnhpAbsTu8+zL6vq4UzG8boD7Rr4Dtq5vBfwZlqLp490+7k97XzfGHgt7d59T9r5vQNrdvl9Ee34v5x2fvzfJJ8ztHvXh9Lq+I+Av6UnUDOZLjB1Cq0l9PNp19E70IKBm3d5HgJ8onsdTruf346ec5HWNXzL7rP8tvuMjwY+m+TZVTXRbXCYc7/XlNfFbnuv7co7UZ6B1/e0HzJOA7al/Z9wNu2HiiOAnZI8uqpqNsdzLVgwz5U95R31OesrtGvAK1j13f8t7RnR51afW1epKl++ZvSijXtzOXCLnrS30/qf36Mn7YAu7Yl96z+oS/+HnrSLgRuAHfrybki7qH66L/1k2hgdmaKcT+/2c78ZfLYNaDd5rwcu61t2HvD1AevcvdvPAT1p1wFvm2Zfb+zWu21f+nd69zPscaRdWAt4a1++Z3XpR4/73PE18xftP8dvd99hdfXku7Qbms368i4BlvSlFXB4z/tP0v5jvV1P2ga0cTIK2L4nfWl3Lt+1J+2WtNYYH+hJe0q37m4Dyn8csLRvX78CPt+X78u08TCmqtP7dPv5qxkcv4k6/Vra+FTpWXZu//Hq0rfv9nNQT9py4D+m2dfh3Xq360tf7XuhPTAVsHdfvp279Od27+/dvX9nX779u/Tjxn1+LraX9W21PEOff8BuvWXqjmMBL55i+9bnSY7nHJ3LB3Xb3ZkWXPgV7cE8PZ/j3J78D+7yv7RvO9vSgqVvmew869JWDPoMPeX4z770V3Tpd+je34UWcDqqL99mtEDYJ/r2X8CzhzwW037fffnTnWd3pe/+ixZcWq3uTnFuvAe4epp9TZyfL5/kuG3fvb8V7VrwhQF14kzg9J60H9CCSrfsSdu8W7+G+PxLun3v2pf+U+CrPe/fPEm+99ECEffs+4zn0/PsMMX+9+jyv6gv/dWseY3tP04P7N4/aYrtv6zLs8UMzokNu3PiaNoPN8Oc+7sxw+tiz/m9YkD6Ula/7h4G3Azs3Jdv4tr62Jkez3G8WDjPlUM/Z9GCuQU8rHu/Tff++VNs3+fWSY7n+vayi6tGcQwt6v9EgCQb0Srjt6uq99ewPWk3A19OstHEizbg6RW0/7h6nVl945pU+9XkP4EnJblzt7970i58/1ldTZ7ET2g3e0cneVaSvxyUKau6Cyyj/Ud3I+0X462TbDVonSH8EDg4yb+mzQh4ixG3A8Mfx4muAh/rW/8E2o2SFqCqurKqHg7sQrsZOxG4J+3G+Jz+5uJDeATwzaq6omcff6IFEgY5s6ou7Ml7Pe3Xte1muN/efb0X2DPJXeHPvxruAbxvmjp9Ju2G67+SHJi+blcT0rrNfb2vTh9BuzneepRy0+r0QWndcx48B3X6D8AX++r0mcClTF+nPwms0QVTs2d9W81szr+raAGhVyR5aVqXs/57TutzM+/1uapuoLWM2Bl42iTZ9qQ9FH2073NcSmvltdssi/GFvvdnd38nzu2/pz1ofqRv/9fTWgkN2v9nhtz3tN93kq3TZs68iPZ93Ahc0C2+z5D7GbTf2yY5PsmTRrh+9HoorUXXf/cdnw1o4wzukuTWXQuyXYDPVk9roqpaDnxxBvu7tKp+2Jd2Nqtfix4F/GxAvuNoQc5H9aV/oYZrkTxZXfn4EOueD1xN6zL8z11LrH4/6v5+MsnTJp4v+qV1j/xukhWsOicOZvTzYZjr4kzsSfth4sy+c+JrdIHBLt9sjufasFCeK2fznHU5LTB4WJKXJHnAgO/e59ZmvX9uNUCnUUx0I3l29/5xtF8G+gfx3IZ2A33jgNftulevyWZgOpp2Ez/R7PgFtF9zj5uqkFX1S1oz3CtpTX9/nTamyAsm8nTN3L9Ku8AdQrsB2oXWXQLa4KSjeAqtS8U/0n7JvDJtzJRRHiaGPY4TF+VLe1fubsyvHmG/WodU1Y+r6i1V9VRaM/b/oP0qPe3A9X22onX56DdZ16UrB6StZPS6Aa1J+3W0rn7QuqFc16VPqrvRejStyf5/Ar9K8qskL57Ikzae08nd23+gdR/YhTYTI7Mo99NpY6UcQus2dFWSjyS5wwjb2obWneUG1qzTd2D6On0Tg78XzRHrGzCL8697yNmd9qD4Slq3osuTHJVk8y6P9Zm1Wp9PoH0Pb5rk4WsbWlDlMtb8HA9mzXu2mer/jBNdOSe+w4nulz8asP+nD9j/tdUGYh/GlN9396B8Mq3b6ltp5+6utM/dW8YZqar/oXXd244WTPx92rhnjxlhcxPH59OseXwOpX13W9K6pm1A33nWGZQ2mWGuRVsx+N79kp7lvYadaXUr4Kaq6i/DtOWvqmW0H0bOpHX3+2naeG+vnzjvqw0X8GS6gDBwcdqYe/tNbCfJ3rTg+W9pwaKH0K49H6a1bJ6xYa6LM7QNrUtg//mwnHY+9F57Rjqea8mCeK5kFs9Z3Y9lj6R1v34VLRj3+7TxAjfr8vjcis+t4Bh0GkFVXZfkeOAfktyRdvOxnFUDV0+4gnajt+ckm+q/sRr4q0VVXZ3kf4B/TPIu2rgb/zPMjVlVnQac1kXudwZeDLwnyaVV9Wlav/eVwJ7dBQGAJE+ZbtvT7Pfybl8vTrId8CRaC4zbMfnxmMywx3HiP9470PPwl2RjuvENtDhU1Y1JXk8br2PH6fL3uZLB49CM8mA6kqpaluS/aWN9vZ12U/bxqvrDEOt+mzYe0Ya0Ov1C4F1JLquqE2jdzW6k1ek/tx5I8uRZlvkK4CXAS7qWSE+k3RBtTWuNNBNX0L6HydZb3v3trdO/nVjYXc9G/ZVUM7Qe17dZnX9VdQGttclEC4Wn0bqNbkwXLLQ+r736XFWV5FDaA+I/DshyBe0+7OEMHgdtVrNtDmGilelTWNVybSpTtXRZPeP03/eOtPEfD6qq/55YL8ndh93HFPs+Fji2a9n2d7SuaF9Kcs+ujgxr4vi8kMlnYLyMNlZVMfgaM9fXnSuBOw5In5j844q+9GG/syuBjZJs1RdUGqr8VXUOsG+S0AJYB9FaGF1HF8ioqhOBE5NsQgvEvgr4eJKlVfV9WlDuN8DTe1tVdflHNsx1cQau6D7Tc6ZYDrM8nvNtAT1Xzuo5q6qW0n1XSe5F++HgdbR4zAu6PD63+txqCzqN7BhaP/5X0H7pOKHWnN7+S3R97rvWCP2vX85gf0d12/o0beDI906dfXVVdVNV/YDuAkgbHBjaxftGeprSpg26OmgA75FaMVTVBVV1FG0Q2b+ZLv8Awx7HU7u/z+hbf1+s6wtWd7MyyEQXi0smWT6Z04BHZfWZ6TYAnjpC8Sb0t4QYxlG0//g/TWt9MtM6fXNVnU5rDQSr1+mbaL8uApBkU+CZAzYzap2+sKreS3vQHbVObwVsOEmd/kWXb0n3t79OPw1/YJsX1rfVLOn+zvr8q6pfVtUbgXMYUGesz2unPlfV12mf899oY7v1+hKt1c2dJ/kc50yz+dm29Pwa7bveYZL9/3gW2/6zSb7viQf5/iDkoAHjR6l/VNUfq+ortNafG9MG85+J79K6Ut93suNTVTdU1R9p3dX2zuozP25OG+h9Ln0DuG+S/nozMYbUqWuuMpTJ7mf3n8lGqjmrqv6FduwGXXtWdkGRQ7ukiRmDC7ihLzh3B1rgot+o154pr4tD+BJtMo4rJzkflnb55uR4zrOF8Fw5Z89ZVfWLqjqCNsHMoPPS59b1mDf4GklV/TjJ2bRfJMOazZCh9Snfnza71LuAH9Nuvu5MG5fi01U11HgYVfWzJKfQmv4uqaqfTrdOkufTfgn+Cm22nE3pfrVi1Wx2J9FmpflokqNpDzCvZNXscb3OAfZJ8jTar2rXVdWgqbq3ot1oHk8boHMFbWDMx9BmjJqpoY5jVZ2b5ATgZUlupl1Yd6LNQrZ88Ka1AHwtbXrzL9LOpw2AB9AGOV5Bm6VxJt5Eu0n/RpI3sarr26275aOM+zBRD/4xyXLamEG/qTW7U/xZVf0yyVdps0l+p6rOmm4nSf6Zds6fRKvTt2TVL8e9dfqltF/C/4v24PxyBrf+mPiV/enAr4HrBz2EJtmCdiPxcdp3sJzWpWAP2ux7M3UC7Ybky0neTXuYupE2GPsjgROr6nNV9fMkH6W1+rix+4w7smoGPM0969uqdUY+/9Jmr30vrQXE/9G6Ez2K1prlyC6P9Xk89flQ2lhAW9MG/Qegqr7bHeNjk+wMfAv4I62F1MOAc6rq/VNs9xxgtyRPoHUtW94TnJxWVS1N8m+0Lrh3o3Xjupr2oLcr8Meqet0MPicw9Pd9Hm1ssCO7VldX0ertoK6oE+fUi7uWqTcCv6g2xlv/vj9Eq/PfpR2TO9Baai1j1ThoQ6mqFUleSBuDbktaYOH3wO1prf9uX1XP7bK/lnb8TknyDlrg41Da97nlTPY7jf+gBeNO6r67C4DH02Zvff8MAya9Tqadf2/tWh7+mNa9fVBgfjVJ9uz2/3nadSC0rsu3pQVlSXIErX5+gzaZwG1prYdupP2oAi3IsHeS99GO9V1ox/V3wD36djvUuT/MdXGG3kWbEOJbSf6DNkbgBrTJTf4f8I7uh4+Rj+fashCeK2fznNUFsd9JO5f+j3auPZoWqH9Dl8fnVp9bm1oHZqrwtTBftAtEAT+dIs8taL+GnEW7SVkO/JzWt36HnnwX0zfL3IBtHdztb58hy/dQ2hTTF9AeYK6gVf7H9eU7hDZzz/W0G7RX0rp/FLBtT76/pP0nt7xbdn6XvtpsOLQL6gdo/1Euo90Q/Zz2i3XvjFpDzYYzw+O4Ce0/gN+z6qZw1+74rrez4SzkF611xcdoA8Uvp93QXUAbN+U+fXmXMM2skl3aw2hdZK6n3Uy+tTvvV5vVjDag7ZcGlGnQfl5Muxm+qdvOQV36cfTN9tezzoFd3qcPeSweTHugWtpTp5cAT+jL92zaTcZEnT6M9uBfrD5r5na0m5JrumVLu/Tt+z7DJl1dO6ur09d22z8cuFXP9g7v1pty1scubSNa0OfMvjr9AeDuPfk2ps1mdlmX7/vdcViKs7ha3+axvs3k/GPN2Qq3Bo7tzukV3bE8i/bwtWGXx/o8j/WZnllcByz7WLfs3AHLnk07X1d0x+Z82vhtD+zJs8Z5RgsSfYd2z1MTx2iycvSfMz3pT6Ldqy3rvvOltIDG7n37X2OWy0mOw7Df931o93jX0AJ0n6QFZQbV6X+ndVO+mdXP+9XODVrw6pu0MZZWdut8AtipJ8/23TamnMW1J/3vaMGjK2nXp4u790/py/eE7jOvpF3DDu0+cw1xzJZMcm4M+t7v2p1PV3TlOY8WdN5gus84TRm2oAVpru7OqZOBe/V/H/3Hqcvzcdp5ey2t5dzpwIE96zyeNpP1xd3xuYwW+HhYXxkOpQU2rqe1dDpk0DFk8nN/N2Z4XZzq/GbAdYL2Y88buuO+svu8Z9OeBbaZ6fEc54t1/LmyVl1Ppn3OYs1ZXO/Qfa8TAbBraP9fvIiuruBzq8+t3WtiqnVpnZfkRFrT87tVG1RZ0hxJcjLtBveea3Gfn6E9nG5fw83sJi0K1jdJksbH50qtq+ziqnVaNxjr39BmT3oC8CIvotLsJHknbQapi2jdXZ5Ba8p+8FTrzdG+J+r0rsBewEsNFmgxs75JkjR+PldqITBAp3XdXYDv0ZoCv697SZqdDYEjaE3ui9Z145lV9dG1sO87sqpOfxB4z1rYpzRO1jdJksbP50qt8+ziKkmSJEmSJI3Rej2FrSRJkiRJkjRuBugkSZIkSZKkMTJAJ0mSJEmSJI2RATpJkiRJkiRpjAzQSZIkSZIkSWNkgE6SJGmBSbI0yUuGzHurJJ9Jck2SSnLbeSrT9t32HzAf21+XJDkuyefHXQ5JkrR4GKCTJEmaB0m2TvLBJBcmWZnk0iRfS/KQGWzjoCR/GLBoF+C/htzMgcDDgYcCdwSWDbv/Kco1KEB1Ubf9c2e7/Wn2fXiSM+dzH5IkSWvbRuMugCRJ0iL1GeAWtADZr4FtgN2BLWe74aq6fAbZdwB+XlXzGjirqpuBS+dzH5IkSYuVLegkSZLmWNeN9GHAoVV1alVdUFU/rKo3V9VJPflemuScJH9MclGS9yXZrFu2G3AssEXXdbSSHN4tW62La9eqbKKl3iVJjurSlwAvA/6uW39Jl35Akh8nWd617Pt4kq37PsP9kpzUdY1dnuTbSXboynAg8KSecu02qItrkkck+WFXrt8lOTLJRj3LlyQ5Kslbk1zVleXwWR77jbvt/bY7rqd3x5IkWyS5Lskefevs3eWdOPZ3TvKJJFcnuTLJiUm2n025JEmSpmKATpIkae6t6F5PTrLJFPn+BLwI2JEW9HoU8NZu2feAlwDXx2+87wAABRRJREFU0LqO3hF4e/8GkjwF+Bfgn4B7AE8GzukW7w18CPh+t/7eXfrGwGuBv+ry/yVwXM827wx8C7i+K9MDgQ/Tel+8Hfgk8NWecn1vQLnuDHwZ+FG3n+cCBwOv6ct6IPBH4EHAK4F/S/KYgUdrOMcCfwvsC9wf+BTw1ST3qKplwEnAM/rW2R84sapWJLkVcCrt+/s7WqB1RbeNjWdRLkmSpEnZxVWSJGmOVdVNSQ6iBcf+OckZwGnACVV1dk++d/Ws9pskrwXeDzyvqm5Isqxlq6m6jt6V1rX061V1I3Ah8MNu+1cluRa4oXcbVfXhnvV/neRFwA+TbFZVK4Dn08aq27fbJsAvJ1ZIch2wSe82k/SX63m0celeUFUFnJfkTsBbkhxRVX/q8p1dVa/v/v1/SV5A6wp8yhSfeaAkOwD7AdtW1SVd8tu7FnPPBl4NfAz4SJJbVdW1SW4DPB7Yp8u/Ly1wekhXbpI8G/gDsBtw8kzLJUmSNB1b0EmSJM2DqvoMcCfgicDXaMGdM7rAHQBJHpnklK475nLgI8BWSW49g119CtiUFmj7UJK9eruRDpLkr7tumxd0+13SLbpr9/cBwLd7gnOjuA/w/YkgV+e7wGbAtj1pZ7O63wFbM5q/AQL8MsmKiRfwCNpYfNBa0N1E+16gBeaWsyrw9kDg7sDynvWvAm7Zsw1JkqQ5ZYBOkiRpnlTV9VV1SlUdUVUPpXUjfT1Aku1oXUDPpQWJHkhruQZtcolh93ERcK9u3euA9wHfSjJwG13w72Rat80DaDPC7tUtnujCed2w+59CgBqQRl96fxCwGP0edQPgZtqxfEDP6z7AiwGq6gbg07RurXR/P1FVN/Vs43/71n8AcE/g4yOWS5IkaUp2cZUkSVp7fkYb8w1gZ9q92MsmunsmeVpf/huADafbaFVdB3wB+EKS/wTOA3YCzhiQ/d7A7YDDuuAeSXbuy3M2cGCSW0zSim6Ycv0M2CdJelrRPZTWWu23032mEf2kK9fWVfXtKfJ9DDg5yf2AR9LG45twBvB04PdVdc08lVOSJGk1tqCTJEmaY0m2SvLNbrbU+yf5yyRPpU2CcGKX7Ve0AN0Lk9wtyTOBf+7b1FJgsyS7J7ldN4FB/74OSnJwkh2T3A14Jq0F3AWTFO9CWoBtYr9PZPUAFcB7gdsAJyTZOck9kjwzyb16ynX/JPfqyjWotd77gLsA70ly7yRPorUefGfP+HOj2jTJA/ped6+qX7JqjLm9u+O+S5JDkzyuZ/3TgMu6vEur6gc9yz4GXAGcmOTh3TYekeTdSXq75kqSJM0ZA3SSJElzbwVwOm121W/RurG+gTZpxAsAqupM4KXAod3yZwCv6t1IVX0P+ADwCeByWoCv3x+Af6CN73Y2bYKFJ1TVlYMKVlWXAwcBT6W1cjsMeHlfnitps7duRgtm/W+3j4nWdB8CfgH8uCvX3w7Yz2+BxwG7Amd1n+MY4I2DyjVD96S1lut9Hd0tezZtLL93dGX8Am2G2It6ylbA8bTZZT/WV+5rabO3Xgh8Fvg5bQbbTWkz6kqSJM25rD5uryRJkiRJkqS1yRZ0kiRJkiRJ0hgZoJMkSZIkSZLGyACdJEmSJEmSNEYG6CRJkiRJkqQxMkAnSZIkSZIkjZEBOkmSJEmSJGmMDNBJkiRJkiRJY2SATpIkSZIkSRojA3SSJEmSJEnSGBmgkyRJkiRJksbIAJ0kSZIkSZI0Rv8fUZUg2lLsDM4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How do they compare in career satisfaction?\n",
    "a = HighSchool_SomeCollege.CareerSat.value_counts(normalize=True)\n",
    "b = AboveHighSchool.CareerSat.value_counts(normalize=True)\n",
    "\n",
    "a = a.reset_index()\n",
    "a.columns = ['Satisfaction Level', 'High School']\n",
    "\n",
    "b = b.reset_index()\n",
    "b.columns = ['Satisfaction Level', 'Post High School']\n",
    "\n",
    "c = pd.merge(a, b, on='Satisfaction Level')\n",
    "\n",
    "# How do they compare in career satisfaction?\n",
    "fig, axes = plt.subplots(ncols=1, figsize=(15, 6), dpi=100)\n",
    "c.plot(kind=\"bar\", x='Satisfaction Level', ax=axes, rot=0)\n",
    "\n",
    "axes.set_ylabel('Career Satisfaction', size=15, labelpad=10)\n",
    "axes.tick_params(axis='x', labelsize=12)\n",
    "axes.tick_params(axis='y', labelsize=12)\n",
    "axes.set_title('Career Satisfaction', fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interestingly, in terms of career satisfaction, both population show similar distribution: about 53% say they are very satisfied, about 31% say they are slightly satisfied. The remaining 15-20% are somewhat dissatisfied or neutral.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What about Job Satisfaction?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How do they compare in Job Satisfaction?\n",
    "a = HighSchool_SomeCollege.JobSat.value_counts(normalize=True)\n",
    "b = AboveHighSchool.JobSat.value_counts(normalize=True)\n",
    "\n",
    "a = a.reset_index()\n",
    "a.columns = ['Satisfaction Level', 'High School']\n",
    "\n",
    "b = b.reset_index()\n",
    "b.columns = ['Satisfaction Level', 'Post High School']\n",
    "\n",
    "c = pd.merge(a, b, on='Satisfaction Level')\n",
    "\n",
    "# How do they compare in career satisfaction?\n",
    "fig, axes = plt.subplots(ncols=1, figsize=(15, 6), dpi=100)\n",
    "\n",
    "c.plot(kind=\"bar\", x='Satisfaction Level', ax=axes, rot=0)\n",
    "\n",
    "axes.set_ylabel('Job Satisfaction', size=15, labelpad=10)\n",
    "axes.tick_params(axis='x', labelsize=12)\n",
    "axes.tick_params(axis='y', labelsize=12)\n",
    "axes.set_title('Job Satisfaction', fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Interestingly, in terms of job satisfaction, both population show similar distribution: about 41% say they are very satisfied, about 34% say they are slightly satisfied. The rest are either dissatisfied or neutral**\n",
    "2. **Also, people are more satisfied with their career in Software Engineering than they are with their actual jobs.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Are people with high school degrees paid at same rate as people with college education?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Respondent', 'Employment', 'EdLevel', 'EduOther', 'OrgSize', 'DevType',\n",
       "       'YearsCode', 'YearsCodePro', 'Age1stCode', 'CareerSat', 'JobSat',\n",
       "       'CompTotal', 'ConvertedComp', 'MgrIdiot', 'MgrMoney', 'MgrWant'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be an error in the values above..a max of $1x10^16 is definitely an error. Since we are dealing with the United States, ConvertedComp should equal CompTotal. ConvertedComp is currency adjusted value of CompTotal i.e. converting to US Dollar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Respondent</th>\n",
       "      <th>YearsCode</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>Age1stCode</th>\n",
       "      <th>CompTotal</th>\n",
       "      <th>ConvertedComp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12878.000000</td>\n",
       "      <td>12871.000000</td>\n",
       "      <td>12876.000000</td>\n",
       "      <td>12826.000000</td>\n",
       "      <td>1.287800e+04</td>\n",
       "      <td>1.287800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44519.633173</td>\n",
       "      <td>14.691166</td>\n",
       "      <td>9.635290</td>\n",
       "      <td>15.158506</td>\n",
       "      <td>1.207046e+05</td>\n",
       "      <td>1.207046e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>25593.372774</td>\n",
       "      <td>9.945693</td>\n",
       "      <td>8.540177</td>\n",
       "      <td>5.221942</td>\n",
       "      <td>7.711404e+04</td>\n",
       "      <td>7.711404e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22435.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000e+04</td>\n",
       "      <td>8.000000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>44617.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.070000e+05</td>\n",
       "      <td>1.070000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>66529.250000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.440000e+05</td>\n",
       "      <td>1.440000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88878.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>2.000000e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Respondent     YearsCode  YearsCodePro    Age1stCode     CompTotal  \\\n",
       "count  12878.000000  12871.000000  12876.000000  12826.000000  1.287800e+04   \n",
       "mean   44519.633173     14.691166      9.635290     15.158506  1.207046e+05   \n",
       "std    25593.372774      9.945693      8.540177      5.221942  7.711404e+04   \n",
       "min        4.000000      0.000000      0.000000      5.000000  0.000000e+00   \n",
       "25%    22435.500000      7.000000      3.000000     12.000000  8.000000e+04   \n",
       "50%    44617.500000     12.000000      7.000000     15.000000  1.070000e+05   \n",
       "75%    66529.250000     20.000000     14.000000     18.000000  1.440000e+05   \n",
       "max    88878.000000     50.000000     50.000000     55.000000  2.000000e+06   \n",
       "\n",
       "       ConvertedComp  \n",
       "count   1.287800e+04  \n",
       "mean    1.207046e+05  \n",
       "std     7.711404e+04  \n",
       "min     0.000000e+00  \n",
       "25%     8.000000e+04  \n",
       "50%     1.070000e+05  \n",
       "75%     1.440000e+05  \n",
       "max     2.000000e+06  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In order to account for this error, select only rows where \n",
    "# Total Compensation [CompTotal] == Converted Compensation [ConveretedComp]\n",
    "df = df_main[df_main[\"ConvertedComp\"]==df_main[\"CompTotal\"]]\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdLevel         69\n",
       "Employment      13\n",
       "YearsCodePro     2\n",
       "CompTotal        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select only the needed columns\n",
    "df = df[['EdLevel', 'Employment', 'YearsCodePro', 'CompTotal']]\n",
    "\n",
    "# View missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rows with N/A values in the 'CompTotal' column are dropped to maintain the purity of our analysis; Filling NaN fields with values like the mean and median can bias the result of our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with NaN values in CompTotal\n",
    "df = df.dropna(subset=['CompTotal'], axis=0)"
   ]
  },
  {
   "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>EdLevel</th>\n",
       "      <th>Employment</th>\n",
       "      <th>YearsCodePro</th>\n",
       "      <th>CompTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>18.0</td>\n",
       "      <td>103000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>8.0</td>\n",
       "      <td>114000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>3.0</td>\n",
       "      <td>47300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Secondary school (e.g. American high school, G...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>9.0</td>\n",
       "      <td>107000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Some college/university study without earning ...</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>8.0</td>\n",
       "      <td>66000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              EdLevel          Employment  \\\n",
       "21  Some college/university study without earning ...  Employed full-time   \n",
       "25  Some college/university study without earning ...  Employed full-time   \n",
       "52  Secondary school (e.g. American high school, G...  Employed full-time   \n",
       "59  Secondary school (e.g. American high school, G...  Employed full-time   \n",
       "60  Some college/university study without earning ...  Employed full-time   \n",
       "\n",
       "    YearsCodePro  CompTotal  \n",
       "21          18.0   103000.0  \n",
       "25           8.0   114000.0  \n",
       "52           3.0    47300.0  \n",
       "59           9.0   107000.0  \n",
       "60           8.0    66000.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For those who are currently employed, does their income compare wih education level\n",
    "HighSchool = df[df.EdLevel.isin(['Some college/university study without earning a degree', 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)'])]\n",
    "PostHighSchool = df[df[\"EdLevel\"].isin(['Bachelor’s degree (BA, BS, B.Eng., etc.)',\n",
    "       'Master’s degree (MA, MS, M.Eng., MBA, etc.)', 'Other doctoral degree (Ph.D, Ed.D., etc.)',\n",
    "        'Professional degree (JD, MD, etc.)'])]\n",
    "\n",
    "HighSchool.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot results \n",
    "\n",
    "g1 = HighSchool.groupby(\"YearsCodePro\").mean().reset_index()\n",
    "g2 = PostHighSchool.groupby(\"YearsCodePro\").mean().reset_index()\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(20, 6))\n",
    "g1.plot(drawstyle=\"steps-post\", x=\"YearsCodePro\", y=\"CompTotal\", linewidth=2, color='b', ax =ax, label='HighSchool');\n",
    "g2.plot(drawstyle=\"steps-post\", x=\"YearsCodePro\", y=\"CompTotal\", ax=ax, color='r', label='PostHighSchool');\n",
    "#g2.plot(kind=\"scatter\", x=\"YearsCode\", y=\"CompTotal\", ax=ax, color='r', label='PostHighSchool');\n",
    "plt.legend(fontsize=15)\n",
    "\n",
    "ax.set_ylabel('Average Salary', size=15, labelpad=20)\n",
    "ax.set_xlabel('Years of Coding', size=15, labelpad=20)\n",
    "ax.tick_params(axis='x', labelsize=12)\n",
    "ax.tick_params(axis='y', labelsize=12)\n",
    "ax.set_title('Salary Performance with Years of Coding', fontsize=14);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From the plot above, people with advanced degrees are clearly earning more than those with only High School degree with the same years of Coding Experience. However, as the years of experience increase, the difference becomes smaller.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. How do they feel about becoming managers (growth opportunity)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_main[['EdLevel', 'MgrWant' ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "HighSchool = df[df.EdLevel.isin(['Some college/university study without earning a degree', 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)'])]\n",
    "PostHighchool = df[df[\"EdLevel\"].isin(['Bachelor’s degree (BA, BS, B.Eng., etc.)',\n",
    "       'Master’s degree (MA, MS, M.Eng., MBA, etc.)', 'Other doctoral degree (Ph.D, Ed.D., etc.)',\n",
    "        'Professional degree (JD, MD, etc.)'])]\n",
    "fig, axes = plt.subplots(1, 2, figsize=(20, 6))\n",
    "HighSchool['MgrWant'].value_counts(normalize=True).plot(kind=\"bar\", ax=axes[0], rot=45, label=\"High School\");\n",
    "HighSchool['MgrWant'].value_counts(normalize=True).plot(kind=\"bar\", ax=axes[1], rot=45, label=\"Post High School\");\n",
    "\n",
    "axes[0].set_ylabel('Percentage', size=15, labelpad=20)\n",
    "axes[0].tick_params(axis='x', labelsize=12)\n",
    "axes[0].tick_params(axis='y', labelsize=12)\n",
    "axes[1].tick_params(axis='x', labelsize=12)\n",
    "axes[1].tick_params(axis='y', labelsize=12)\n",
    "\n",
    "axes[0].set_title('High School/Some College', fontsize=14)\n",
    "axes[1].set_title('Post High School Degree', fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We observe a similar distribution among those with high school degrees and those with advanced degrees. Might be worth doing, looking at what what factors are common among those who desire to become Managers.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Modelling\n",
    "\n",
    "We will try to predict a person's salary based on their level of education, Developer type, and years of professional coding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Employed full-time',\n",
       "       'Independent contractor, freelancer, or self-employed',\n",
       "       'Not employed, but looking for work', 'Employed part-time',\n",
       "       'Not employed, and not looking for work', 'Retired', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get all employment statuses\n",
    "df_main.Employment.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16206, 16)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Limit modelling to only those that are full-time employed\n",
    "df = df_main[df_main['Employment']=='Employed full-time']\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python37\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ed Status\n",
       "High School         13283\n",
       "Post High School     2057\n",
       "Name: Respondent, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def EduStatus(val):\n",
    "    '''\n",
    "    function classifies an educational level into post high school, high school or nan if pre-high school \n",
    "    input:\n",
    "        val - string value of the educational level\n",
    "    '''\n",
    "    if val in (['Some college/university study without earning a degree', 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)']):\n",
    "        return \"Post High School\"\n",
    "    else:\n",
    "        if val in (['Bachelor’s degree (BA, BS, B.Eng., etc.)',\n",
    "       'Master’s degree (MA, MS, M.Eng., MBA, etc.)', 'Other doctoral degree (Ph.D, Ed.D., etc.)',\n",
    "        'Professional degree (JD, MD, etc.)']):\n",
    "            return \"High School\"\n",
    "        else:\n",
    "            return np.nan\n",
    "        \n",
    "df['Ed Status'] = df.EdLevel.apply(EduStatus)\n",
    "df.groupby('Ed Status').count()['Respondent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop non relevant fields\n",
    "df = df[['Ed Status', 'YearsCodePro', 'CompTotal',]]\n",
    "df.dropna(inplace=True)      \n",
    "df = pd.get_dummies(df, columns=['Ed Status']) # Perform one hot encoding for the categorical attribute 'ED Status'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0002527805924861237\n",
      "2.527167045696368e+28\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>Features</th>\n",
       "      <th>Coefs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YearsCodePro</td>\n",
       "      <td>0.863681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ed Status_Post High School</td>\n",
       "      <td>0.070579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ed Status_High School</td>\n",
       "      <td>0.065740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Features     Coefs\n",
       "0                YearsCodePro  0.863681\n",
       "2  Ed Status_Post High School  0.070579\n",
       "1       Ed Status_High School  0.065740"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create x and y values\n",
    "y = df['CompTotal']\n",
    "X = df.drop(['CompTotal'], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Using random forest classifier\n",
    "Classifier = RandomForestRegressor(n_estimators=500)\n",
    "Classifier.fit(X_train, y_train)\n",
    "y_test_preds = Classifier.predict(X_test)\n",
    "print(r2_score(y_test, y_test_preds))\n",
    "print(mean_squared_error(y_test, y_test_preds))\n",
    "\n",
    "coefs_df = pd.DataFrame()\n",
    "\n",
    "coefs_df['Features'] = X_train.columns\n",
    "coefs_df['Coefs'] = Classifier.feature_importances_\n",
    "coefs_df.sort_values('Coefs', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Evaluation\n",
    "\n",
    "1. From the result in the modelling step above, using only `Education Level` and `Years of Professional Coding Experience` to predict compensation, `Years of Professional Coding Experience` plays the most crucial role in determining compensation. Having a post high school degree has a comparatively similar effect as not having one when determining compensation as seen in the low values above. This follows along with our analysis to Q2 about how compensation varies between the two population. <br>\n",
    "2. However, as seen from the population distribution of those who were `Employed full-time`,13,283 of them ha post high school education while only 2057 had highest education as high school or some college, it is possible that it is more challenging to get a job when you only have high school diploma and some college; but if you do get one, you can expect to receive a competitive salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}