diff --git a/ Stack_Overflow_2019 .ipynb b/ Stack_Overflow_2019 .ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "72783dcf",
+   "metadata": {},
+   "source": [
+    "# EDA ON STACK OVERFLOW 2019 DATA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "454852c7",
+   "metadata": {},
+   "source": [
+    "Importing required libraries and modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "4638bf45",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29dbed43",
+   "metadata": {},
+   "source": [
+    "### Data Collection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b8e1a0b",
+   "metadata": {},
+   "source": [
+    "**Loading 2019 stack overflow data**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "b2d1b8d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df= pd.read_csv(\"/Users/yodhasudarsi/Stackoverflow-Analysis/Data/survey_results_sample_2019.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "c0159bd6",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .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": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Printing first 5 rows\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "ebca6a9f",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(99, 85)"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Dimension\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "a3ab43b3",
+   "metadata": {
+    "scrolled": true
+   },
+   "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": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Features\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "e1ba7aa2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Respondent       int64\n",
+       "MainBranch      object\n",
+       "Hobbyist        object\n",
+       "OpenSourcer     object\n",
+       "OpenSource      object\n",
+       "                 ...  \n",
+       "Sexuality       object\n",
+       "Ethnicity       object\n",
+       "Dependents      object\n",
+       "SurveyLength    object\n",
+       "SurveyEase      object\n",
+       "Length: 85, dtype: object"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Datatypes\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e2b4444",
+   "metadata": {},
+   "source": [
+    "### Data Cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "09c9616b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Respondent       0\n",
+       "MainBranch       0\n",
+       "Hobbyist         0\n",
+       "OpenSourcer      0\n",
+       "OpenSource       2\n",
+       "                ..\n",
+       "Sexuality       16\n",
+       "Ethnicity       23\n",
+       "Dependents       7\n",
+       "SurveyLength     3\n",
+       "SurveyEase       2\n",
+       "Length: 85, dtype: int64"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_values=df.isnull().sum()\n",
+    "missing_values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "850e90b1",
+   "metadata": {},
+   "source": [
+    "Therefore missing/null value exists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "42c8f666",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of cells:  8415\n",
+      "Total number of missing values:  1151\n",
+      "Missing Percentage:  13.677956030897207 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_cells=np.product(df.shape)\n",
+    "total_missing=missing_values.sum()\n",
+    "percent=(total_missing/total_cells)*100\n",
+    "print('Total number of cells: ', total_cells)\n",
+    "print('Total number of missing values: ', total_missing)\n",
+    "print('Missing Percentage: ', percent, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edecc46b",
+   "metadata": {},
+   "source": [
+    "Nearly 14% missing values exist in the dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "236892db",
+   "metadata": {},
+   "source": [
+    "## Problem Statement: Investigate participation rates based on ethnicity and differences in income between men and women."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6be28dd1",
+   "metadata": {},
+   "source": [
+    "Selecting only required features for the EDA : ethnicity, income and gender"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0900d65f",
+   "metadata": {},
+   "source": [
+    "### Data Preparation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f897bf5",
+   "metadata": {},
+   "source": [
+    "**Ethnicity**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "0777e633",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing percentage of the 'Ethnicity' column: 23.232323232323232 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Ethnicity'].isnull().sum()\n",
+    "missing_percentage_ethnicity = (df['Ethnicity'].isnull().sum() / len(df['Ethnicity'])) * 100\n",
+    "\n",
+    "print(\"Missing percentage of the 'Ethnicity' column:\", missing_percentage_ethnicity, \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "01fe2613",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ethnicity\n",
+       "Biracial                                                                                                                                                                                                      1\n",
+       "Black or of African descent                                                                                                                                                                                   3\n",
+       "Black or of African descent;East Asian;Hispanic or Latino/Latina;Middle Eastern;Native American, Pacific Islander, or Indigenous Australian;South Asian;White or of European descent;Biracial;Multiracial     1\n",
+       "East Asian                                                                                                                                                                                                    5\n",
+       "East Asian;South Asian                                                                                                                                                                                        1\n",
+       "Hispanic or Latino/Latina                                                                                                                                                                                     5\n",
+       "Hispanic or Latino/Latina;Multiracial                                                                                                                                                                         1\n",
+       "Middle Eastern                                                                                                                                                                                                3\n",
+       "Multiracial                                                                                                                                                                                                   1\n",
+       "South Asian                                                                                                                                                                                                   8\n",
+       "White or of European descent                                                                                                                                                                                 46\n",
+       "White or of European descent;Multiracial                                                                                                                                                                      1\n",
+       "Name: Ethnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Ethnicity')['Ethnicity'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "b5078ff9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[df['Ethnicity'].str.match('Biracial') == True, 'Ethnicity'] = 'Biracial'\n",
+    "df.loc[df['Ethnicity'].str.match('Black or of African descent') == True, 'Ethnicity'] = 'Black or African descent'\n",
+    "df.loc[df['Ethnicity'].str.match('East Asian') == True, 'Ethnicity'] = 'East Asian'\n",
+    "df.loc[df['Ethnicity'].str.match('Hispanic or Latino') == True, 'Ethnicity'] = 'Hispanic or Latino'\n",
+    "df.loc[df['Ethnicity'].str.match('Indigenous') == True, 'Ethnicity'] = 'Indigenous'\n",
+    "df.loc[df['Ethnicity'].str.match('Middle Eastern') == True, 'Ethnicity'] = 'Middle Eastern'\n",
+    "df.loc[df['Ethnicity'].str.match('South') == True, 'Ethnicity'] = 'South Asian'\n",
+    "df.loc[df['Ethnicity'].str.match('White or of European descent') == True, 'Ethnicity'] = 'White or European descent'\n",
+    "df.loc[df['Ethnicity'].str.match('Multiracial') == True, 'Ethnicity'] = 'Multiracial'\n",
+    "df.loc[df['Ethnicity'].str.match('Native American') == True, 'Ethnicity'] = 'Native American'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "4461d043",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ethnicity\n",
+       "Biracial                      1\n",
+       "Black or African descent      4\n",
+       "East Asian                    6\n",
+       "Hispanic or Latino            6\n",
+       "Middle Eastern                3\n",
+       "Multiracial                   1\n",
+       "South Asian                   8\n",
+       "White or European descent    47\n",
+       "Name: Ethnicity, dtype: int64"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Ethnicity')['Ethnicity'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "3e5a5052",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "e3449b11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Ethnicity']=df.groupby(['Country'])['Ethnicity'].bfill().ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "a2f03fe6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Ethnicity'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7c40163",
+   "metadata": {},
+   "source": [
+    "**Gender**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "d87e9f6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing percentage of the 'Gender' column: 4.040404040404041 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['Gender'].isnull().sum()\n",
+    "missing_percentage_gender = (df['Gender'].isnull().sum() / len(df['Gender'])) * 100\n",
+    "\n",
+    "print(\"Missing percentage of the 'Gender' column:\", missing_percentage_gender, \"%\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "1d479d28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Man                                                  87\n",
+       "Non-binary, genderqueer, or gender non-conforming     1\n",
+       "Woman                                                 7\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Gender')['Gender'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "3500f0e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Gender'] = df['Gender'].fillna('Non-binary, genderqueer, or gender non-conforming')\n",
+    "df['Gender'].replace('Female;Male;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Male;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
+    "df['Gender'].replace('Male;Transgender;Non-binary, genderqueer, or gender non-conforming', 'Male', inplace =True)\n",
+    "df['Gender'].replace('Transgender;Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) ##not sure\n",
+    "df['Gender'].replace('Female;Male', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Male;Transgender', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Female;Transgender', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Male;Transgender', 'Female', inplace =True)\n",
+    "df['Gender'].replace('Non-binary, genderqueer, or gender non-conforming', 'Non-conforming', inplace =True) #\n",
+    "df['Gender'].replace('Transgender', 'Male', inplace =True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "6098f137",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()['Gender']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "894d9ec9",
+   "metadata": {},
+   "source": [
+    "**Income**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a91598e",
+   "metadata": {},
+   "source": [
+    "**Took Compensation as income in this dataset**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "1a9ad0ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing percentage of the 'CompTotal' column: 2.0202020202020203 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "df['CompTotal'].isnull().sum()\n",
+    "missing_percentage_CompTotal = (df['CompTotal'].isnull().sum() / len(df['CompTotal'])) * 100\n",
+    "\n",
+    "print(\"Missing percentage of the 'CompTotal' column:\", missing_percentage_CompTotal, \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "0622ff09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['CompTotal'].fillna(method='ffill', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "3aecaf59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dropna(subset=['CompTotal'], inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "1f67609f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['CompTotal'].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "id": "05e8d482",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Man               85\n",
+       "Non-conforming     5\n",
+       "Woman              7\n",
+       "Name: CompTotal, dtype: int64"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Gender')['CompTotal'].count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a0a6645",
+   "metadata": {},
+   "source": [
+    "# Participation rate based on ethnicity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "810f51d1",
+   "metadata": {},
+   "source": [
+    "### Data Visualization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "381e3047",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df.groupby('Ethnicity')['Ethnicity'].count()\n",
+    "plt.figure(figsize=(8,8))\n",
+    "plt.pie(list(lst.values), labels=list(lst.keys()), autopct='%1.1f%%')  # Plot the pie chart\n",
+    "plt.title('Participation rate based on ethnicity')  # Add a title\n",
+    "#plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "7abe7d11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "25064844",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['White or European descent', 'South Asian', 'East Asian', 'Hispanic or Latino', 'Black or African descent', 'Middle Eastern', 'Multiracial', 'Biracial']\n",
+      "[58, 15, 8, 7, 4, 3, 1, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "participation_rate = df['Ethnicity'].value_counts().keys().tolist()\n",
+    "print(participation_rate)\n",
+    "count = df['Ethnicity'].value_counts().tolist()\n",
+    "print(count)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "b7367e11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PP//E119/jZiYGNy8eRPr16+HUqlE7dq1YWlpiYkTJ2L8+PFYt24d4uLicPr0aXz77bdYt24dAGD48OGIi4vDhAkTcPnyZYSHh7/yGkAdOnRA/fr10bdvX5w6dQrHjx9H//794efnp9XhTpXJkyfjyJEjGDlyJGJiYnD16lX8/vvvGD16tNqy/+eff3Dnzp0inUGmLT8/P7Rp0wY9e/ZEZGQk4uPjsWvXLuzevTvf+Qv7jH300UdYt24dYmNjMWDAgBKLmfQXE5qy4gQAKwAF/6gCVIepvV8qV+2Pquk6qPLH1tYWa9euxebNm+Hp6Yn58+dj0aJFRXptz5490alTJ7Rr1w62trZqpyGrGBsbIyIiAnZ2dujcuTO8vb0xf/58tTEHL3vrrbfg7u6ONm3aoHfv3ujatStCQ0MBAAYGBti0aRNOnjwJLy8vjB8/Hl9++aXa642MjPD111/j+++/h5OTEwIDA/N9n/nz56Nnz57o168fGjVqhGvXrmHPnj2oXLlykdqviWnTpqFRo0bo2LEj2rZtKyV5uU2cOBGGhobw9PSUDv05OTnh8OHDyM7ORseOHeHl5YWxY8eiUqVKMDAoeHdlZmaGyZMnIzg4GC1atICpqSk2bdokTV+6dCkqV64MX19fdO3aFR07dkSjRo2k6dbW1ti2bRvat2+PunXrYsWKFdi4cSPq1asHAJg1axa++OILzJs3D3Xr1kXHjh3xxx9/SOOZqlevjq1bt+KPP/5AgwYNsGLFCsydO7fQZaQ63b9y5cpo06YNOnTogBo1auCXX37RdHGrqV+/Pg4cOICrV6+idevWaNiwIaZNm6Z2yG7mzJm4ceMGatasqdF1Y7SxdetWNGnSBH369IGnpydCQkIKTE4L+4x16NABjo6O6NixI5ycnEo0ZtJPMqHNwV/SvVbISWpMAKQBqAHgYwCj8f9p52MAngBcAawC4AzgCIChALyQ03vDFFWvDBw4EE+ePCnyrROIKqq0tDQ4OTnhxx9/RI8ePUo7HCqDOIamrKgHYDhyxss8BBAGYByA5wA+/W+eyshJZIKRk8AAgDGAJf+9lskMEZUzSqUSiYmJWLx4MSpVqoRu3bqVdkhURjGhKSu+f+l5J+QcfvoBOQmNQM7ZTpsAfAagGYC7AEKRk/y8C8D+9YRKRPS6JCQkwM3NDdWqVcPatWs1GixOFQsPOZVlnQAcBJAKYDmAkQAOA8h9eYvLAOoAmIqcU7eJiIgqIB6kKKsEck7lbvDf880A3KCezAA542kMANx+bZERERGVOUxoyqovAcQj53ozQM4F9R4jZ0xNbr8AUALQ/ixPIiIivcdDTqXtOwDHAHQE4ATgDoCNAHYgZ+zMvP/mWw9gAHIuoDcGOWdD/Q3gKwCNAOwDoHidgRMREZUdFSahUSqVuHv3LiwtLfO93HhpkW+UQ75aDoM4A8iSZRCVBLIbZyNzaCay/LPU5jX60wjGXxvD8JIhkA4oayqR2TMTGSMzchIcIiKickYIgWfPnsHJyanQ61FVmITm9u3bajeBIyIiIv1x69YtVKtW8BVky8T5b3fu3MHkyZOxa9cuPH/+HB4eHli9ejUaN24MICc7mzFjBlauXInHjx+jWbNm+Pbbb6WreBaF6m61t27dgpWVVYm0Q9cyMzMRERGBgICAPDeEK0/YzvKF7Sw/KkIbAbazrEtOToazs7PaXefzU+oJzePHj9GyZUu0a9cOu3btgp2dHeLi4mBtbS3Ns3DhQixZsgRr166Fh4cHZs+eDX9/f1y+fPmVDVRRHWaysrLSq4TGzMwMVlZWerXxaYrtLF/YzvKjIrQRYDv1xauGi5R6QrNgwQI4OztjzZo1Upmrq6v0vxACYWFh+Pzzz6XLXa9btw729vYIDw/HsGHDXnfIREREVMaUekLz+++/o2PHjujVqxcOHDiAqlWrYsSIERgyZAgAID4+HomJiQgICJBeo1Ao4Ofnh6ioqAITmvT0dLU7tSYnJwPIyVAzMzNLsEW6o4pTX+LVFttZvrCd5UdFaCPAdpZ1RY231AcFm5jknJ4zYcIE9OrVC8ePH8e4cePw/fffo3///oiKikLLli1x584dtTusDh06FDdv3sSePXvyrTc0NBQzZszIUx4eHg4zM7OSaQwRERHpVFpaGoKDg/H06dNCh4yUeg+NUqmEj48P5s6dCwBo2LAhLly4gOXLl6N///7SfC8fOxNCFHo8bcqUKZgwYYL0XDWoKCAgQK/G0ERGRsLf318vj3cWFdtZvrCd5UdFaCPAdpZ1qiMsr1LqCY2joyM8PT3VyurWrYutW7cCABwcHAAAiYmJcHR0lOZJSkqCvX3Bd2NUKBRQKPJeaU4ul+vVigT0M2ZtsJ3lC9tZflSENgJsZ1lV1FhL/dYHLVu2xOXLl9XKrly5AhcXFwCAm5sbHBwcEBkZKU3PyMjAgQMH4Ovr+1pjJSIiorKp1Htoxo8fD19fX8ydOxe9e/fG8ePHsXLlSqxcuRJAzqGmcePGYe7cuXB3d4e7uzvmzp0LMzMzBAcHl3L0REREVBaUekLTpEkTbN++HVOmTMHMmTPh5uaGsLAw9O3bV5onJCQEz58/x4gRI6QL60VERBT5GjRERERUvpV6QgMAXbp0QZcuXQqcLpPJEBoaitDQ0NcXFBEREemNUh9DQ0RERFRcTGiIiIhI7zGhISIiIr3HhIaIiIj0HhMaIiIi0ntl4iwnfef66Y4SqVdhKLCwKeAVugfp2YXfNl1bN+a/UyL1EhERvU7soSEiIiK9x4SGiIiI9B4TGiIiItJ7TGiIiIhI7zGhISIiIr3HhIaIiIj0HhMaIiIi0ntMaIiIiEjvMaEhIiIivceEhoiIiPQeExoiIiLSe0xoiIiISO8xoSEiIiK9x4SGiIiI9B4TGiIiItJ7TGiIiIhI7zGhISIiIr3HhIaIiIj0HhMaIiIi0ntMaIiIiEjvMaEhIiIivceEhoiIiPQeExoiIiLSe0xoiIiISO8xoSEiIiK9x4SGiIiI9B4TGiIiItJ7TGiIiIhI7zGhISIiIr3HhIaIiIj0HhMaIiIi0ntMaIiIiEjvMaEhIiIivceEhoiIiPQeExoiIiLSe0xoiIiISO8xoSEiIiK9x4SGiIiI9B4TGiIiItJ7TGiIiIhI7zGhISIiIr3HhIaIiIj0HhMaIiIi0ntMaIiIiEjvlXpCExoaCplMpvZwcHCQpgshEBoaCicnJ5iamqJt27a4cOFCKUZMREREZU2pJzQAUK9ePdy7d096nDt3Tpq2cOFCLFmyBMuWLUN0dDQcHBzg7++PZ8+elWLEREREVJaUiYTGyMgIDg4O0sPW1hZATu9MWFgYPv/8c/To0QNeXl5Yt24d0tLSEB4eXspRExERUVlhVNoBAMDVq1fh5OQEhUKBZs2aYe7cuahRowbi4+ORmJiIgIAAaV6FQgE/Pz9ERUVh2LBhBdaZnp6O9PR06XlycjIAIDMzE5mZmTqNX2EodFqfVK+BUPtbEnS9LIoTQ1mIpSSxneVLRWhnRWgjwHaWdUWNVyaEKLlvyyLYtWsX0tLS4OHhgfv372P27Nm4dOkSLly4gMuXL6Nly5a4c+cOnJycpNcMHToUN2/exJ49ewqsNzQ0FDNmzMhTHh4eDjMzsxJpCxEREelWWloagoOD8fTpU1hZWRU4X6knNC9LTU1FzZo1ERISgubNm6Nly5a4e/cuHB0dpXmGDBmCW7duYffu3QXWk18PjbOzMx48eFDoAtGGV2jBiVVxKAwEZvkoMe2EAdKVshJ5j/OhHUukXk1kZmYiMjIS/v7+kMvlpR1OiWE7y5eK0M6K0EaA7SzrkpOTUaVKlVcmNGXikFNu5ubm8Pb2xtWrVxEUFAQASExMVEtokpKSYG9vX2g9CoUCCoUiT7lcLtf5ikzPLplkQ6pfKSux9yhLG3VJrJuyiO0sXypCOytCGwG2s6wqaqxlYlBwbunp6YiNjYWjoyPc3Nzg4OCAyMhIaXpGRgYOHDgAX1/fUoySiIiIypJS76GZOHEiunbtiurVqyMpKQmzZ89GcnIyBgwYAJlMhnHjxmHu3Llwd3eHu7s75s6dCzMzMwQHB5d26ERERFRGlHpCc/v2bfTp0wcPHjyAra0tmjdvjqNHj8LFxQUAEBISgufPn2PEiBF4/PgxmjVrhoiICFhaWpZy5ERERFRWlHpCs2nTpkKny2QyhIaGIjQ09PUERERERHqnzI2hISIiItIUExoiIiLSe0xoiIiISO8xoSEiIiK9x4SGiIiI9B4TGiIiItJ7TGiIiIhI7zGhISIiIr3HhIaIiIj0HhMaIiIi0ntMaIiIiEjvMaEhIiIivceEhoiIiPQeExoiIiLSe0xoiIiISO8xoSEiIiK9x4SGiIiI9B4TGiIiItJ7TGiIiIhI72md0LRt2xbr16/H8+fPdRkPERERkca0TmgaN26MkJAQODg4YMiQITh69Kgu4yIiIiIqMq0TmsWLF+POnTtYv349/v33X7Rp0waenp5YtGgR7t+/r8sYiYiIiApVrDE0hoaGCAwMxG+//YY7d+4gODgY06ZNg7OzM4KCgvD333/rKk4iIiKiAulkUPDx48fxxRdfYNGiRbCzs8OUKVNgZ2eHrl27YuLEibp4CyIiIqICGWn7wqSkJGzYsAFr1qzB1atX0bVrV2zatAkdO3aETCYDAPTu3RtBQUFYtGiRzgImIiIiepnWCU21atVQs2ZNDB48GAMHDoStrW2eeZo2bYomTZoUK0AiIiKiV9E6odm7dy9at25d6DxWVlbYt2+ftm9BREREVCRaj6Fp3bo1srKy8Ndff+H777/Hs2fPAAB3795FSkqKzgIkIiIiehWte2hu3ryJTp06ISEhAenp6fD394elpSUWLlyIFy9eYMWKFbqMk4iIiKhAWvfQjB07Fj4+Pnj8+DFMTU2l8u7du2Pv3r06CY6IiIioKLTuoTl06BAOHz4MY2NjtXIXFxfcuXOn2IERERERFZXWPTRKpRLZ2dl5ym/fvg1LS8tiBUVERESkCa0TGn9/f4SFhUnPZTIZUlJSMH36dHTu3FkXsREREREVidaHnJYuXYp27drB09MTL168QHBwMK5evYoqVapg48aNuoyRiIiIqFBaJzROTk6IiYnBxo0bcerUKSiVSnz44Yfo27ev2iBhIiIiopKmdUKTlpYGMzMzDB48GIMHD9ZlTEREREQa0XoMjZ2dHT744APs2bMHSqVSlzERERERaUTrhGb9+vVIT09H9+7d4eTkhLFjxyI6OlqXsREREREVidYJTY8ePbB582bcv38f8+bNQ2xsLHx9feHh4YGZM2fqMkYiIiKiQmmd0KhYWlpi0KBBiIiIwJkzZ2Bubo4ZM2boIjYiIiKiIil2QvPixQv8+uuvCAoKQqNGjfDw4UNMnDhRF7ERERERFYnWZzlFRETg559/xm+//QZDQ0O8++672LNnD/z8/HQZHxEREdEraZ3QBAUFoUuXLli3bh3eeecdyOVyXcZFREREVGRaJTRZWVmYP38+evXqBUdHR13HRERERKQRrcbQGBkZ4dNPP0VGRoau4yEiIiLSmNaDgps1a4bTp0/rMhYiIiIirWg9hmbEiBH45JNPcPv2bTRu3Bjm5uZq0+vXr1/s4IiIiIiKQuuE5r333gMAjBkzRiqTyWQQQkAmkyE7O7v40REREREVgdYJTXx8vC7jICIiItKa1gmNi4uLLuMgIiIi0prWCc369esLnd6/f3+t6p03bx4+++wzjB07FmFhYQAAIQRmzJiBlStX4vHjx2jWrBm+/fZb1KtXT6v3ICIiovJF64Rm7Nixas8zMzORlpYGY2NjmJmZaZXQREdHY+XKlXkGFC9cuBBLlizB2rVr4eHhgdmzZ8Pf3x+XL1+GpaWltk0gIiKickLr07YfP36s9khJScHly5fRqlUrbNy4UeP6UlJS0LdvX/zwww+oXLmyVC6EQFhYGD7//HP06NEDXl5eWLduHdLS0hAeHq5t+ERERFSOaN1Dkx93d3fMnz8fH3zwAS5duqTRa0eOHIl33nkHHTp0wOzZs6Xy+Ph4JCYmIiAgQCpTKBTw8/NDVFQUhg0blm996enpSE9Pl54nJycDyOlJyszM1Ci2V1EYCp3WJ9VrINT+lgRdL4vixFAWYilJbGf5UhHaWRHaCLCdZV1R49VpQgMAhoaGuHv3rkav2bRpE06dOoXo6Og80xITEwEA9vb2auX29va4efNmgXXOmzcPM2bMyFMeEREBMzMzjeJ7lYVNdVpdHrN8lCVW986dO0usbk1FRkaWdgivBdtZvlSEdlaENgJsZ1mVlpZWpPm0Tmh+//13tedCCNy7dw/Lli1Dy5Yti1zPrVu3MHbsWERERMDExKTA+WQyWZ73e7kstylTpmDChAnS8+TkZDg7OyMgIABWVlZFjq8ovEL36LQ+FYWBwCwfJaadMEC6suC2Fsf50I4lUq8mMjMzERkZCX9//3J9k1O2s3ypCO2sCG0E2M6yTnWE5VWKdbft3GQyGWxtbdG+fXssXry4yPWcPHkSSUlJaNy4sVSWnZ2Nf/75B8uWLcPly5cB5PTU5L4RZlJSUp5em9wUCgUUCkWecrlcrvMVmZ5dMsmGVL9SVmLvUZY26pJYN2UR21m+VIR2VoQ2AmxnWVXUWLVOaJRK3RwGeeutt3Du3Dm1skGDBqFOnTqYPHkyatSoAQcHB0RGRqJhw4YAgIyMDBw4cAALFizQSQxERESk33Q2hiYrKwsvXryAhYWFRq+ztLSEl5eXWpm5uTlsbGyk8nHjxmHu3Llwd3eHu7s75s6dCzMzMwQHB+sqfCIiItJjGp+2vXPnTmzYsEGtbM6cObCwsIC1tTUCAgLw+PFjnQUIACEhIRg3bhxGjBgBHx8f3LlzBxEREbwGDREREQHQIqFZtGiR2gCdqKgofPHFF5g2bRp+/fVX3Lp1C7NmzSpWUPv375euEgzkjM8JDQ3FvXv38OLFCxw4cCBPrw4RERFVXBonNOfPn4evr6/0fMuWLfD395cufLd48WL88ccfOg2SiIiIqDAaJzTPnj2DjY2N9PzQoUNo37699LxevXoaX4eGiIiIqDg0TmicnJwQGxsLIOd2BWfOnFG77szDhw91fuE6IiIiosJonNC8++67GDduHDZs2IAhQ4bAwcEBzZs3l6afOHECtWvX1mmQRERERIXR+LTt6dOn4+7duxgzZgwcHBzw008/wdDQUJq+ceNGdO3aVadBEhERERVG44TGzMwsz2nbue3bt69YARERERFpSuNDTkRERERlDRMaIiIi0ntMaIiIiEjvMaEhIiIivceEhoiIiPSeRmc5ff3110Wed8yYMRoHQ0RERKQNjRKapUuXqj3/999/kZaWBmtrawDAkydPYGZmBjs7OyY0RERE9NpodMgpPj5eesyZMwdvvvkmYmNj8ejRIzx69AixsbFo1KhRse+2TURERKQJrcfQTJs2Dd98843abQ5q166NpUuXYurUqToJjoiIiKgotE5o7t27h8zMzDzl2dnZuH//frGCIiIiItKE1gnNW2+9hSFDhuDEiRMQQgDIuTHlsGHD0KFDB50FSERERPQqWic0P/74I6pWrYqmTZvCxMQECoUCzZo1g6OjI1atWqXLGImIiIgKpfHNKVVsbW2xc+dOXLlyBZcuXYIQAnXr1oWHh4cu4yMiIiJ6Ja0TGhUPDw8mMURERFSqNEpoJkyYgFmzZsHc3BwTJkwodN4lS5YUKzAiIiKiotIooTl9+rR0ZtPp06cLnE8mkxUvKiIiIiINaJTQ7Nu3L9//iYiIiEpTsW5OKYTAgwcP8PDhQ13FQ0RERKQxrRKaxMRE9O/fH5UrV4a9vT3s7OxQuXJlDB48mBfVIyIiotdO47OckpOT4evri5SUFAwaNAh16tSBEAIXL17Exo0bcejQIZw6dQoWFhYlES8RERFRHhonNF999RUMDQ1x4cIF2Nraqk2bOnUqWrZsia+//hqfffaZzoIkIiIiKozGh5x27NiBzz77LE8yAwB2dnaYMmUK/vjjD50ER0RERFQUGic0V65cga+vb4HTfX19cfny5WIFRURERKQJjROa5ORkWFtbFzjd2toaycnJxYmJiIiISCMaJzRCCBgYFPwymUwm3X2biIiI6HXQeFCwEAIeHh4FXg2YyQwRERG9bhonNGvWrCmJOIiIiIi0pnFCM2DAgJKIg4iIiEhrxbr1AREREVFZwISGiIiI9B4TGiIiItJ7TGiIiIhI72mV0GRmZqJGjRq4ePGiruMhIiIi0phWCY1cLkd6enqB16IhIiIiep20PuQ0evRoLFiwAFlZWbqMh4iIiEhjGl+HRuXYsWPYu3cvIiIi4O3tDXNzc7Xp27ZtK3ZwREREREWhdUJjbW2Nnj176jIWIiIiIq1ondDwFghERERUVhTrtO2srCz89ddf+P777/Hs2TMAwN27d5GSkqKT4IiIiIiKQusemps3b6JTp05ISEhAeno6/P39YWlpiYULF+LFixdYsWKFLuMkIiIiKpDWPTRjx46Fj48PHj9+DFNTU6m8e/fu2Lt3r06CIyIiIioKrXtoDh06hMOHD8PY2Fit3MXFBXfu3Cl2YERERERFpXUPjVKpRHZ2dp7y27dvw9LSslhBEREREWlC64TG398fYWFh0nOZTIaUlBRMnz4dnTt3LnI9y5cvR/369WFlZQUrKyu0aNECu3btkqYLIRAaGgonJyeYmpqibdu2uHDhgrZhExERUTmkdUKzdOlSHDhwAJ6ennjx4gWCg4Ph6uqKO3fuYMGCBUWup1q1apg/fz5OnDiBEydOoH379ggMDJSSloULF2LJkiVYtmwZoqOj4eDgAH9/f+msKiIiIiKtx9A4OTkhJiYGGzduxKlTp6BUKvHhhx+ib9++aoOEX6Vr165qz+fMmYPly5fj6NGj8PT0RFhYGD7//HP06NEDALBu3TrY29sjPDwcw4YN0zZ8IiIiKke0TmgAwNTUFIMHD8bgwYN1Ekx2djY2b96M1NRUtGjRAvHx8UhMTERAQIA0j0KhgJ+fH6KiogpNaNLT05Geni49T05OBpBzp/DMzEydxCvFZCh0Wp9Ur4FQ+1sSdL0sihNDWYilJLGd5UtFaGdFaCPAdpZ1RY1XJoTQ+tvy8uXL+OabbxAbGwuZTIY6depg1KhRqFOnjkb1nDt3Di1atMCLFy9gYWGB8PBwdO7cGVFRUWjZsiXu3LkDJycnaf6hQ4fi5s2b2LNnT4F1hoaGYsaMGXnKw8PDYWZmplF8REREVDrS0tIQHByMp0+fwsrKqsD5tO6h2bJlC/r06QMfHx+0aNECAHD06FF4e3sjPDwcvXr1KnJdtWvXRkxMDJ48eYKtW7diwIABOHDggDRdJpOpzS+EyFP2silTpmDChAnS8+TkZDg7OyMgIKDQBaINr9CCE6viUBgIzPJRYtoJA6QrC2+vts6HdiyRejWRmZmJyMhI+Pv7Qy6Xl3Y4JYbtLF8qQjsrQhsBtrOsUx1heRWtE5qQkBBMmTIFM2fOVCufPn06Jk+erFFCY2xsjFq1agEAfHx8EB0dja+++gqTJ08GACQmJsLR0VGaPykpCfb29oXWqVAooFAo8pTL5XKdr8j07JJJNqT6lbISe4+ytFGXxLopi9jO8qUitLMitBFgO8uqosaq9VlOiYmJ6N+/f57yDz74AImJidpWCyCnByY9PR1ubm5wcHBAZGSkNC0jIwMHDhyAr69vsd6DiIiIyg+te2jatm2LgwcPSj0rKocOHULr1q2LXM9nn32Gt99+G87Oznj27Bk2bdqE/fv3Y/fu3ZDJZBg3bhzmzp0Ld3d3uLu7Y+7cuTAzM0NwcLC2oRMREVE5o3VC061bN0yePBknT55E8+bNAeSModm8eTNmzJiB33//XW3egty/fx/9+vXDvXv3UKlSJdSvXx+7d++Gv78/gJxDW8+fP8eIESPw+PFjNGvWDBEREbwaMREREUm0TmhGjBgBAPjuu+/w3Xff5TsNyBnQm98tElRWr15d6PvIZDKEhoYiNDRU21CJiIionNM6oVEqlbqMg4iIiEhrWg8KJiIiIioripXQHDhwAF27dkWtWrXg7u6Obt264eDBg7qKjYiIiKhItE5ofvrpJ3To0AFmZmYYM2YMRo0aBVNTU7z11lsIDw/XZYxEREREhdJ6DM2cOXOwcOFCjB8/XiobO3YslixZglmzZvG0aiIiInpttO6huX79ep47ZQM5p2jHx8cXKygiIiIiTWid0Dg7O2Pv3r15yvfu3QtnZ+diBUVERESkCa0POX3yyScYM2YMYmJi4OvrC5lMhkOHDmHt2rX46quvdBkjERERUaG0Tmg+/vhjODg4YPHixfj1118BAHXr1sUvv/yCwMBAnQVIRERE9CpaJzQA0L17d3Tv3l1XsRARERFphRfWIyIiIr2nUQ9N5cqVIZPJijTvo0ePtAqIiIiISFMaJTRhYWElFAYRERGR9jRKaAYMGFBScRARERFprVhjaOLi4jB16lT06dMHSUlJAIDdu3fjwoULOgmOiIiIqCi0TmgOHDgAb29vHDt2DNu2bUNKSgoA4OzZs5g+fbrOAiQiIiJ6Fa0Tmk8//RSzZ89GZGQkjI2NpfJ27drhyJEjOgmOiIiIqCi0TmjOnTuX7zVobG1t8fDhw2IFRURERKQJrRMaa2tr3Lt3L0/56dOnUbVq1WIFRURERKQJrROa4OBgTJ48GYmJiZDJZFAqlTh8+DAmTpyI/v376zJGIiIiokJpndDMmTMH1atXR9WqVZGSkgJPT0+0adMGvr6+mDp1qi5jJCIiIiqU1vdyksvl+PnnnzFz5kycPn0aSqUSDRs2hLu7uy7jIyIiInolrROaAwcOwM/PDzVr1kTNmjV1GRMRERGRRrQ+5OTv74/q1avj008/xfnz53UZExEREZFGtE5o7t69i5CQEBw8eBD169dH/fr1sXDhQty+fVuX8RERERG9ktYJTZUqVTBq1CgcPnwYcXFxeO+997B+/Xq4urqiffv2uoyRiIiIqFDFupeTipubGz799FPMnz8f3t7eOHDggC6qJSIiIiqSYic0hw8fxogRI+Do6Ijg4GDUq1cPf/75py5iIyIiIioSrc9y+uyzz7Bx40bcvXsXHTp0QFhYGIKCgmBmZqbL+IiIiIheSeuEZv/+/Zg4cSLee+89VKlSRZcxEREREWlE64QmKipKl3EQERERaa1YY2g2bNiAli1bwsnJCTdv3gQAhIWF4X//+59OgiMiIiIqCq0TmuXLl2PChAno3Lkznjx5guzsbAA5d+EOCwvTVXxEREREr6R1QvPNN9/ghx9+wOeffw5DQ0Op3MfHB+fOndNJcERERERFoXVCEx8fj4YNG+YpVygUSE1NLVZQRERERJrQOqFxc3NDTExMnvJdu3bB09OzODERERERaUTrs5wmTZqEkSNH4sWLFxBC4Pjx49i4cSPmzZuHVatW6TJGIiIiokJpndAMGjQIWVlZCAkJQVpaGoKDg1G1alV89dVXeP/993UZIxEREVGhtE5oAGDIkCEYMmQIHjx4AKVSCTs7O13FRURERFRkxUpoHjx4gBs3bkAmk8HV1VVHIRERERFpRqtBwRcuXECbNm1gb2+PZs2aoWnTprCzs0P79u1x+fJlXcdIREREVCiNe2gSExPh5+cHW1tbLFmyBHXq1IEQAhcvXsQPP/yA1q1b4/z58zz8RERERK+NxgnN0qVL4eLigsOHD8PExEQq79SpEz7++GO0atUKS5cuxbx583QaKBEREVFBND7kFBkZicmTJ6slMyqmpqaYNGkS9uzZo5PgiIiIiIpC44Tm+vXraNSoUYHTfXx8cP369WIFRURERKQJjROaZ8+ewcrKqsDplpaWSElJKVZQRERERJrQ6rTtZ8+e5XvICQCSk5MhhChWUERERESa0DihEULAw8Oj0OkymaxYQRERERFpQuOEZt++fSURBxEREZHWNE5o/Pz8SiIOIiIiIq1pdaVgXZo3bx6aNGkCS0tL2NnZISgoKM/VhoUQCA0NhZOTE0xNTdG2bVtcuHChlCImIiKisqbUE5oDBw5g5MiROHr0KCIjI5GVlYWAgACkpqZK8yxcuBBLlizBsmXLEB0dDQcHB/j7++PZs2elGDkRERGVFcW6OaUu7N69W+35mjVrYGdnh5MnT6JNmzYQQiAsLAyff/45evToAQBYt24d7O3tER4ejmHDhpVG2ERERFSGaJTQnD17Fl5eXjAwKLmOnadPnwIA3njjDQBAfHw8EhMTERAQIM2jUCjg5+eHqKioAhOa9PR0pKenS8+Tk5MBAJmZmcjMzNRpzArDkjlNXWEg1P6WBF0vi+LEUBZiKUlsZ/lSEdpZEdoIsJ1lXVHjlQkNLhpjaGiIe/fuwc7ODjVq1EB0dDRsbGy0DvJlQggEBgbi8ePHOHjwIAAgKioKLVu2xJ07d+Dk5CTNO3ToUNy8ebPA2yyEhoZixowZecrDw8NhZmams5iJiIio5KSlpSE4OBhPnz4t9MK+GvXQWFtbIz4+HnZ2drhx4waUSmWxA81t1KhROHv2LA4dOpRn2svXtnnV9W6mTJmCCRMmSM+Tk5Ph7OyMgICAQheINrxCS+beVQoDgVk+Skw7YYB0Zclc2+d8aMcSqbdA9wGDJQaQxchyHo9lSP8mHbudd8Pf3x9yuRwAINsmg9H7+W+eWVFZED76d/HGzMxMREZGqrWzPGI7y4+K0EaA7SzrVEdYXkWjhKZnz57w8/ODo6MjZDIZfHx8YGhomO+8mt7PafTo0fj999/xzz//oFq1alK5g4MDACAxMRGOjo5SeVJSEuzt7QusT6FQQKFQ5CmXy+U6X5Hp2SV7IcF0pazE3uO1b9RHAewH0BCAHEAEYOhjCNx/ad3EADADsDdvFUY+RmVg9Jf2SmIbLIvYzvKjIrQRYDvLqqLGqtHXwsqVK9GjRw9cu3YNY8aMwZAhQ2BpaalVgCpCCIwePRrbt2/H/v374ebmpjbdzc0NDg4OiIyMRMOGDQEAGRkZOHDgABYsWFCs96ZS8O5/DwDoB0AOCC8B3H9pvhMA3gTQ/DXGRkREekvj37mdOnUCAJw8eRJjx44tdkIzcuRIhIeH43//+x8sLS2RmJgIAKhUqRJMTU0hk8kwbtw4zJ07F+7u7nB3d8fcuXNhZmaG4ODgYr03lbJTADwB5O1IA04C+OD1hkNERPpL6477NWvW4MmTJzhx4gRkMhlq1qwJa2trjetZvnw5AKBt27Z56h84cCAAICQkBM+fP8eIESPw+PFjNGvWDBEREcVOpqgUpQG4DGBAPtOuAXgM4CcAywGYA2gMYB6Apq8rQCIi0idaJTQ3btzAyJEjsWfPHunO2jKZDJ06dcKyZcvg6upa5LqKcpKVTCZDaGgoQkNDtQmXyqIzALIBNMpnWiKAKQBaALD+b96ZAN4CcBpArdcTIhER6Q+NE5pbt26hefPmkMvlmDVrFurWrQshBGJjY7F8+XK0aNEC0dHRagN7ifI4+d/f/BKaVv89VFoDcAHQDcBGANNKNjQiItI/Gic006dPR+3atbFnzx6YmJhI5d27d8f48ePRqVMnTJ8+HatXr9ZpoFTOnAJgCKBBEedv8t/fxJIJh4iI9JvGCc3u3bvx66+/qiUzKqamppg1axbef/99nQRH5dgpAHWQc2p2US4Ceem/v0VNgIiIqELR+B4GDx8+LHSMTI0aNfDw4cPixETlXTqAi8j/cFN+UgCEAKgKgLkyERHlQ+MeGicnJ1y4cKHAMTLnz59XuwAekeQ3AFkAbiGnV0YJYAsgy5IBxv/NE4icRKchcs5uOg9gMYBnAHYB0O1FnomIqJzQOKEJDAzEpEmT0KhRI9ja2qpNS0pKwuTJkxEUFKSr+Ki8SALQ/aWyn3MehlUMgVUABHLOagoH8CVykp7qyBkMPBmA82uLloiI9IxWg4J37tyJmjVr4oMPPkCdOnUAABcvXkR4eDgcHBzwxRdf6DxQ0nN2yElY8pGVmQXsBCADsO41xkREROWGxglN5cqVcezYMXz22WfYtGkTnjx5AiDnxpXBwcGYM2cO3njjDV3HSURERFQgrS6sV7lyZSxfvhzfffcd/v33XwCAra1toXe/JiIiIiopxbpnsUwmg52dna5iISIiItKKxqdtExEREZU1TGiIiIhI7zGhISIiIr3HhIaIiIj0XrESmlGjRuHRo0e6ioWIiIhIKxonNLdv35b+Dw8PR0pKCgDA29sbt27d0l1kREREREWk8WnbderUgY2NDVq2bIkXL17g1q1bqF69Om7cuIHMzKLcNpmIiIhItzTuoXn69Ck2b96Mxo0bQ6lUonPnzvDw8EB6ejr27NmDxMTEkoiTiIiIqEAaJzSZmZlo2rQpPvnkE5iamuL06dNYs2YNDA0N8eOPP6JmzZqoXbt2ScRKRERElC+NDzlZWVmhYcOGaNmyJTIyMpCWloaWLVvCyMgIv/zyC6pVq4bjx4+XRKxERERE+dK4h+bu3buYOnUqFAoFsrKy4OPjg9atWyMjIwOnTp2CTCZDq1atSiJWIiIionxpnNBUqVIFXbt2xbx582BmZobo6GiMHj0aMpkMEydOhJWVFfz8/EoiViIiIqJ8FfvCepUqVULv3r0hl8vx999/Iz4+HiNGjNBFbERERERFUqy7bZ89exZVq1YFALi4uEAul8PBwQHvvfeeToKjssX10x0lUq/CUGBhU8ArdA/Ss2U6r//G/Hd0XicREZUtxUponJ2dpf/Pnz9f7GCIiIiItMF7OREREZHeY0JDREREeo8JDREREek9JjRERESk95jQEBERkd5jQkNERER6jwkNERER6T0mNERERKT3mNAQERGR3mNCQ0RERHqPCQ0RERHpPSY0RCXlBICeAKoDMAeM3jSC259ugCjluIiIyiEmNEQl4VcAzQA8BRAG4H+AaCJQf1V9GCzkx46ISNeKdbdtIirAZAB1AeyG9CnL9stG6t5UWGyxAKaWYmxEROUQfyoSlYQk5CQ0uX8yZAHyVDlEVR5zIiLSNSY0RCWhH4A/AWwF8AzAFcCwnyFk2TJkz84u3diIiMohJjREJWEGgEYA3gVgBaA2ACWwP2w/4FWagRERlU9MaIh0bRdyEhhXAH8AiATQF5D9IcMbF98ozch0KwuAOQCZ+kNuLEdgUCDkxnJge6lGSEQVCAcFE+lSIoA+AN4HsCJXeQdAxAl4rfEC5pZOaDqnBLA3b3HWjSwY9jWEaCxgEMjfTET0ejChIdKlCOScqt03n2mugMlxE2Qps15vTCXFGEDzvMUGCw0gIJC9LBsGBkxoiOj14N6GSJdUucrdl8r/BWR/y/CkxpPy/anbAxhsN0B853igYWkHQ0QVSXnetRK9foEAbAGMAbAcwD4APwDwBZAKnBtyrhSDK2HpAEYBwlHgUvCl0o6GiCoYHnIi0iUbAEcBhCJnrMx95CQ4bwFZk7Pw+NrjUgyuhM0DcA3I/ikbWWbl5LAaEekNJjREulYDwPp8yjMBXHvNsbwucQAWAPAHRG8B7CztgIioouEhJyIqvtHIuenmt6UdCBFVVGUiofnnn3/QtWtXODk5QSaT4bffflObLoRAaGgonJycYGpqirZt2+LChQulEywRqduKnGvvfArAvZRjIaIKq0wkNKmpqWjQoAGWLVuW7/SFCxdiyZIlWLZsGaKjo+Hg4AB/f388e/bsNUdKRGpSAYwHUBM5CQ0RUSkpE2No3n77bbz99tv5ThNCICwsDJ9//jl69OgBAFi3bh3s7e0RHh6OYcOGvc5QiSi3UAC3kHNXcZPSDYWIKrYykdAUJj4+HomJiQgICJDKFAoF/Pz8EBUVVWBCk56ejvT0dOl5cnIyACAzMxOZmZk6jVFhWDJ3T1YYCLW/JUGTZaGv7dT1+taWKo6yEk+xXQCMvjKC6CmQ3T47Z9AzymE7C1AR2lkR2giwnWVdUeOVCSFK7ttSCzKZDNu3b0dQUBAAICoqCi1btsSdO3fg5OQkzTd06FDcvHkTe/bsybee0NBQzJgxI095eHg4zMzMSiR2IiIi0q20tDQEBwfj6dOnsLKyKnC+Mt9DoyKTydSeCyHylOU2ZcoUTJgwQXqenJwMZ2dnBAQEFLpAtOEVmn9SVVwKA4FZPkpMO2GAdGXBbS2O86EdizyvvrZTkzaWpMzMTERGRsLf3x9yuby0wykxbGf5URHaCLCdZZ3qCMurlPmExsHBAQCQmJgIR0dHqTwpKQn29vYFvk6hUEChUOQpl8vlOl+R6dklk2xI9StlJfYemiwLfW1nWfvglsQ2WBaxneVHRWgjwHaWVUWNtUyc5VQYNzc3ODg4IDIyUirLyMjAgQMH4OvrW4qRERERUVlRJnpoUlJScO3a/19CNT4+HjExMXjjjTdQvXp1jBs3DnPnzoW7uzvc3d0xd+5cmJmZITg4uBSjJiIiorKiTCQ0J06cQLt27aTnqrEvAwYMwNq1axESEoLnz59jxIgRePz4MZo1a4aIiAhYWlqWVshERERUhpSJhKZt27Yo7GQrmUyG0NBQhIaGvr6giIjyEwmgMyDPksNsBc+YJCorykRCQ0SkFy4C6AXACBDmAmn2aaUdERH9p8wPCiYiKhOSALyDnISmGiDeFEDJnvhHRBpgQkNE9CovAAQC8AAwH0AcIBqWqWuSElV4PORERFQYAWAAgDQAEQCic8rEm0xoiMoS9tAQERXmcwCHAewAYAngRE4xe2iIyhb20BC9xPXTHSVSr8JQYGHTnFtIlMQVkW/Mf0fndVZ4awEsA/APgGr/lZ0AYA6gNoD40gmLiPJiDw0RUX6OARgO4HsArgCe/Pc4AcATQDIgyyxHo4KPIGeckCtyErZKgGEbQ9gfL/gWM0RlCRMaIqL8bAaQDiAYQOVcj3gA0YDcTg67GLtSDFDHziJn0PNCADsB/ADI0mRoNr8ZZEfKUeJG5RYPORER5WcQgC4vlR0FMAXANCCrTRYePHrw+uMqKcPyFmVXy4ZRSyPIDsiANq8/JCJNMKEhIspPvXzKjv739z1AeAhk78x+nRG9fgk5f0Q9DoCmso+HnIiIiuokAFMAdUo7kBKiBJAF4F8AmwHD8YZIaJcA0ZUJDZV97KEhqqB4NpcWNuf6X1lqUZScvgA2/fe/MaBcqsTpqqfhCMfSjIqoSNhDQ0REOaYj5+yubQCaAgbjDVDlXJVSDoqoaNhDQ0REOXIfSnsLwBuAS4QLMLm0AiIqOvbQEBFRXlmALFsGYcDxM6QfmNAQEVFe83L+3Gtxr3TjICoiHnIiIqrI3kHOBfV8AdgCuA3gJwB7gOxh2bjX/B4aomFpRkhUJExoiIgqsvoAdgH4ETl3FLcB0BjANkDZRZlz1WAiPcBDTkREFdk8ADEAngLIBJCInDuLdy/FmErSfQATIQ16hgyQrSqHt3aoKO3MhQkNERFVHAcB/I2cm3A2+a+sPB5RqyjtzIUJDRERVRzvAjgFYDUAOwByQHiVwzO5Kko7c2FCQ0REFdMpAJ4AFKUdSAmrIO1kQkNERBVPGoDLyBkAXZ5VlHaCCQ0REVVEZwBkA2hU2oGUsIrSTjChISKiiujkf3/L+xd9RWknmNAQEVFFdAqAIYAGpR1ICaso7QQTGiIiqohOIedmnGalHUgJqyjtBBMaIiKqaNIBXET5PwxTUdr5H976gIiIKobfAGQBuIWcqyIrAWwBZFkywLg0A9Ox31Ax2vkSJjREVK65frqjROpVGAosbAp4he5BerbuLyl/Y/47Oq+zQktC3ts5/JzzMKxiCKwqhZhKQkVpZz54yImIiMo/OwAi/0fW3azSjEy3Kko788GEhoiIiPQeExoiIiLSe0xoiIiISO8xoSEiIiK9x7OciIj0nL6eyQXwbC7SHfbQEBERkd5jQkNERER6jwkNERER6T0mNERERKT3mNAQERGR3mNCQ0RERHqPCQ0RERHpPSY0REREpPeY0BAREZHeY0JDREREeo8JDREREek93suJiIj0gr7es0rT+1VVlHbqGntoiIiISO/pVULz3Xffwc3NDSYmJmjcuDEOHjxY2iERERFRGaA3Cc0vv/yCcePG4fPPP8fp06fRunVrvP3220hISCjt0IiIiKiU6U1Cs2TJEnz44Yf46KOPULduXYSFhcHZ2RnLly8v7dCIiIiolOnFoOCMjAycPHkSn376qVp5QEAAoqKi8n1Neno60tPTpedPnz4FADx69AiZmZk6jc8oK1Wn9Un1KgXS0pQwyjRAtlL3A7gA4OHDh0WPR0/bqUkbAbazuNhO3agIn02gYrST22zxPHv2DAAghCh8RqEH7ty5IwCIw4cPq5XPmTNHeHh45Pua6dOnCwB88MEHH3zwwUc5eNy6davQXEEvemhUZDL1jFIIkadMZcqUKZgwYYL0XKlU4tGjR7CxsSnwNWVNcnIynJ2dcevWLVhZWZV2OCWG7Sxf2M7yoyK0EWA7yzohBJ49ewYnJ6dC59OLhKZKlSowNDREYmKiWnlSUhLs7e3zfY1CoYBCoVArs7a2LqkQS5SVlZVebXzaYjvLF7az/KgIbQTYzrKsUqVKr5xHLwYFGxsbo3HjxoiMjFQrj4yMhK+vbylFRURERGWFXvTQAMCECRPQr18/+Pj4oEWLFli5ciUSEhIwfPjw0g6NiIiISpneJDTvvfceHj58iJkzZ+LevXvw8vLCzp074eLiUtqhlRiFQoHp06fnOXRW3rCd5QvbWX5UhDYCbGd5IRPiVedBEREREZVtejGGhoiIiKgwTGiIiIhI7zGhISIiIr3HhIaIiIj03mtJaGQyGX777bcCp+/fvx8ymQxPnjx5HeHQa9C2bVuMGzcOQOmt39DQULz55puv9T21tXbt2iJd+DH3ci0pN27cgEwmw86dOyGTyRATE1PsOl+1DygtquWZlpaGnj17wsrKqtBtVbVsdLFMCoqlMK6urggLCyt0Wu5lXZLxlkVF2c4GDhyIoKCgEo9F089qUfeTr2udarPffh37p8IUOaFZsWIFLC0tkZWVJZWlpKRALpejdevWavMePHgQMpkMV65cKVLdvr6+uHfvnnQlwKLu3F8n1crN7/HyFYzLoqSkJAwbNgzVq1eHQqGAg4MDOnbsiCNHjuj0fUpigx46dCgMDQ2xadMmjV43ceJE7N27FwMHDsx3vXXq1EmatzjbXGEf/IJ2ns7OzpDJZLh48SKAnMsSFOXzsm3bNsyaNUurOFXx5F4GNjY26NSpE86ePZtnXnt7e+kSCaWtuMmpah0ZGhqqXbtKtTzXrVuHgwcPIioqSm1f9DJnZ+ciLxPVss7vWlkjRoyATCbDwIED88RS1pTUvi80NBRvvPEGZDIZLC0t8yRq+S0jTRT0xf/VV19h7dq1WtWpCW3Xp6rdBX1GNdkGi+Pl72V9UOSEpl27dkhJScGJEyeksoMHD8LBwQHR0dFIS0uTyvfv3w8nJyd4eHgUqW5jY2M4ODiUiXssvepO3JcvX8a9e/fUHnZ2dlq/X0ZGhtav1UTPnj1x5swZrFu3DleuXMHvv/+Otm3b4tGjR6/l/bWVlpaGX375BZMmTcLq1as1eq2FhQVsbGwAAJ06dcqz3jZu3FgSIb/SoUOHpPUeHh4OADA1NS3SdvTGG2/A0tKyWO+fe1ns3bsXRkZG6NKlS575DA0N4eDgACOj13e5quJ8Hl712QWAMWPGYNOmTdL+SrU84+LiULduXXh5eRW4L8rIyNB4mTg7O2PTpk14/vy5VPbixQts3LgR1atXV5tXF+u2JOl636fi7OyM1NRUtXVf0DLShUqVKhX640VX++TirM/CPqOv2gaFEGodD9oqS9/LRabJXa+dnJzEvHnzpOchISFi5MiRwtPTU0RGRkrl7du3F3379pWeAxA//PCDCAoKEqampqJWrVrif//7nzR93759AoB4/Pix9H/ux/Tp04UQQqSnp4tJkyYJJycnYWZmJpo2bSr27dtXaMw3b94U3bp1E+bm5sLS0lL06tVLJCYmStOnT58uGjRoIFavXi3c3NyETCYTSqUyTz25YyyIn5+fGDt2rFpZYGCgGDBggPTcxcVFzJo1SwwYMEBYWVmJ/v37CyGE2LJli/D09BTGxsbCxcVFLFq0SK0eFxcXMXPmTNGnTx9hbm4uHB0dxddff602z5MnT8SQIUOEra2tsLS0FO3atRMxMTHi8ePHAoD4+eefRbdu3YSdnZ0wNzcXPj4+auvt5s2bwtTUVBgbGwu5XC6MjIyEk5OT+P7774UQQgwYMEAEBgaqvefYsWNFq1atRL9+/YSRkVGedRcfHy8tu969ewu5XC4ACAsLC7Fu3Tqpnhs3boguXboIa2trYWZmJjw9PcWOHTvE2rVrRfPmzcWRI0eEgYGBMDc3FxYWFqJVq1bi2rVrYt++faJJkybC2NhYGBgYCJlMJmrUqCG+/fZbad327NlTABALFiwQlStXFgCEgYGBePPNN8XJkyfz3easra2FiYmJtK0WFF98fHye1+Ze3/kts4EDB4o+ffoIAMLFxUUolUqxZs0aUalSJSGEEN9++61wdnYWMplMyGQyYWRkJBo1aiSio6OFn5+f8Pf3F5UqVRLbt28XDg4OQiaTCUNDQ1GlShXRp08fcf/+fXHt2jXRrVs3qb0eHh7C3d1dGBoaisqVK4tLly4JFxcXMWfOHPH2228LANK6VrVpx44dAoA4ceKEGDx4sHB1dRUKhUKYm5sLhUKhth6EEGL16tXSNuzg4CBGjhwp9u/fL5o0aSIt08mTJ4vMzEwxY8YMYWdnJxo3bixGjhwpxo8fL2xsbESbNm2k5fTdd9+JGjVqCLlcLmxsbISzs7PacgQgli9fLrp16yaMjY2Fo6OjsLCwEPb29tJyEELku45atWolfWarVq2qNs3NzU0MGjRIyGQyYWVlJVq0aCF9VlV1+fj4CBMTE/HGG2+Inj17ioCAAGFpaam2TAYMGCDatGkjLCwshIWFhbCyshJt2rQRs2fPFt7e3tK+QbV/tLGxEYaGhtI2d//+fdGlSxdhYmIiXF1dxU8//SRcXFzE0qVLpWnGxsZCoVAIuVwu5HK5GD58uAAgtm/fLp48eSLef/99AUCYmZlJ+4MLFy6It99+W5iamkqfc3Nzc2kbUzl06JBo06aNMDY2FgBEu3btxKNHj4QQQiiVSrFgwQLh5uYmfU68vb2FjY2NsLKyEvXr1xcAxF9//SUaN24sTE1NRbVq1YSjo6MwNjYW1tbWhd5V+eeffxbe3t6idevWws7OTpiYmAhDQ0PRunVrkZKSIsUol8tFu3btpP0pADF69GhRqVKlPHW2aNEi38+kn59fvtvg4sWLhZeXlzAzMxPVqlUTH3/8sXj27JnaNqhaRqampsLa2loEBARIy+jl74MNGzaIxo0b57uNCvH/3zF9+vTJs8/4559/BACRlJQkbYOnT59We93u3btF48aNhVwuF3///be0Dyhofy+EEC9evBCTJk0S1apVE8bGxqJWrVpi1apVavWqvvMePHgg3n//fVG1alVhamoqvLy8RHh4uFp9+X0Hvk4ajaFp27Yt9u3bJz3ft28f2rZtCz8/P6k8IyMDR44cQbt27dReO2PGDPTu3Rtnz55F586d0bdv33x7B3x9fREWFgYrKyspQ504cSIAYNCgQTh8+DA2bdqEs2fPolevXujUqROuXr2ab7xCCAQFBeHRo0c4cOAAIiMjERcXh/fee09tvmvXruHXX3/F1q1bX8ux5i+//BJeXl44efIkpk2bhpMnT6J37954//33ce7cOYSGhmLatGl5ukW//PJL1K9fH6dOncKUKVMwfvx46f5WQgi88847SExMxM6dO3Hy5Ek0atQIb731FjIyMmBhYYGdO3fC398ff/31F06fPo2OHTuia9euSEhIkJaVUqmEiYkJxo4di9q1a0OhUODjjz/GpUuXCmzP9evXsW/fPmzevBkNGjRA9erVYWZmho8++gjOzs7SfDt37sTixYuxa9cu2NraYuDAgdK6GzlyJNLT0/HPP//g3LlzWLBgASwsLLB69Wp06dIF77zzDhwcHNCnTx+cPHkSgwcPxosXLxAUFITKlSujUqVKWLp0KRYuXIiQkBBMmzYtz7pctmwZBg8ejJ07d6JDhw64du0aOnfuDG9vbwQFBcHMzAwAYGNjAxMTE/To0UPaVocOHZpvfM7Ozti6dSuA//8F+9VXXxW4rJ49e4bNmzfD398fQE4P1P79+6XpJ06cwJgxY6T1sWXLFkyePBmffvop5HK5NF9aWhrmzJmDQYMGISwsDF5eXnB0dER8fDwGDhyIlJQUdO7cGYsWLQKQc3j45s2baN++PQwMDDB48GAAwKJFi/Ds2TO4uLhg5MiR+PjjjxEXF6cWs1KpRLVq1bBs2TKYmJigbt26kMlkmDVrFgYPHoysrCwsX74cI0eOxNChQ3Hu3Dn8/vvvsLW1RefOndGkSRMAwPDhw7Fq1Sq0bNkSq1evxqFDh2BhYYF169bByMgIhw8fxvfffw8A2L59O8aOHYtPPvkE58+fR+PGjXHr1i21/Q8ATJ8+HYGBgZg5cybmzp2LM2fO4LfffpOWA5DTA/Duu+9K62jOnDlQKpVSHV26dMGQIUPQokULVKtWDY8fP4aPjw+cnJyQnp6Oo0eP4tdff8W0adOknhYrKytER0fju+++w/bt23Hp0iX8/fff0rap+oWclZWFbt26wcvLC0ePHoW7uztmzpyJ4OBgtXbMmDEDdnZ2+OCDD6Rtrm/fvrhx4wb+/vtvbNmyBd999x2SkpIA5BzOio+PR7Vq1eDl5QVPT0/IZDL88ccfAP5/f/Dvv/8CyOkJbNSoEdq1a4c2bdrgzTffRNWqVeHv74+mTZvC29tbbRuLiYnBW2+9hXr16uHbb78FkNNrkJ2dDQCYOnUq1qxZg+XLl+PChQvo3r07YmNjsWTJEhw9ehTVqlUDAHz66adYvHgx5syZg3v37sHa2hpXr17F//73P/j7+8Pa2hrt27eHlZUV3NzcpH3+jz/+iE6dOiEqKgouLi44e/YsbG1tcf36dYwaNUpt2R0+fFjan+b+fKi2u+XLl8Pd3b3QOzXntw0aGBjg66+/xvnz57Fu3Tr8/fffCAkJkV6TexkdOXIEhw4dQteuXaVl9LKMjAzMmjUr3220MCkpKfj5559Rq1Ytqcc5PyEhIZg3bx5iY2NRv359aR+Q3/5epX///ti0aRO+/vprxMbGYsWKFbCwsMi3/hcvXqBx48b4888/cf78eQwdOhT9+vXDsWPHXtmG10aT7GflypXC3NxcZGZmiuTkZGFkZCTu378vNm3aJHx9fYUQQhw4cEAAEHFxcdLrAIipU6dKz1NSUoRMJhO7du0SQuTNBHP/WlW5du2akMlk4s6dO2rlb731lpgyZUq+8UZERAhDQ0ORkJAglV24cEEAEMePHxdC5PTQyOVykZSUVGjbVTGam5urPTw8PKR5itpDExQUpDZPcHCw8Pf3VyubNGmS8PT0VHtdp06d1OZ57733xNtvvy2EEGLv3r3CyspKvHjxQm2emjVriu+//15s2bJFVK5cWZiYmAhfX18xZcoUcebMGeHp6Sm++eYbaVlVrVpVfPDBB2rLqnLlymL58uX59jaMGDFCyGQysWnTJmkZDBs2TJiamkrL4qeffhIApHmEENKv/0mTJgkhhPD29hahoaFqdV+5ckXI5XIxduxY4ebmJjZv3iycnZ1Fdna2EEKIhw8fCgDC1tY2zy+FWbNmiWrVqqn10Kh6F8zNzYWpqam0Pv/44w9pm1Ntq7/++quwsbGRtlVXV9c88akU1ns3YMAAYWhoKL2vQqEQBgYGwsTERAAQw4cPF3379pXef+vWrcLKykpYWlqKtWvX5qlP1UMDQBw9elQqj42NFQDEmjVrBADpl6Qqtr/++kt4enqKpk2bCgMDAwFAyGQyAUA4OjqKkydPCqVSKezs7MTs2bPVemhUvwSnTJki3NzcREZGhhgxYoTo2bOn9P5OTk7i888/V4v1s88+E7Vr1xZKpVIAEJs3bxZNmjQRMplM3Lx5U2rPm2++maedvr6+YsiQIdLz6dOni0qVKonOnTtLZQDEuHHj8l0nx48fl5ZDdna2sLOzk9bRv//+K+Ryubh69ar0mR07dqzw8/MTLi4u0vav+qza2dmJ5cuXCyGEmDt3rgAgoqKipGVib28vZDKZWs+vat0HBgaKf//9VygUChEfHy/i4uIEAPHTTz+p9dBMnTpViiUlJUXqVchvHU+ZMkUAEF999ZUwNDQUt27dkqYNHTpUABAzZswQVlZW4tKlS2rrsHLlytJ+RbWN3bp1SwAQly9flt6rT58+omXLlmrbUO79HgBRvXp1tfZ++OGHok+fPkIIIf766y8BQMyePVsIkdPboeoJe/78ubROK1euLAIDA0W1atWEkZGRiI+PFzdu3BAmJiaiV69ewsXFRdp/uri4iNGjRwsDAwOpDrlcLmrXrq22TYwePVoAEPv375fa/u233wp7e3u19aJS0Db4MtU+Ib9llJ9X9Vbk3kaFUO+hyb3PyP0ZFUIU2EPz22+/vbINqv29EEJcvnxZAMjTa6NSlKMSnTt3Fp988kmR21zSNOqhadeuHVJTUxEdHY2DBw/Cw8MDdnZ28PPzQ3R0NFJTU7F//35Ur14dNWrUUHtt/fr1pf/Nzc1haWkp/dooilOnTkEIAQ8PD1hYWEiPAwcO5PlFqRIbGwtnZ2e1XgJPT09YW1sjNjZWKnNxcYGtrW2R4jh48CBiYmKkx549e4rcBhUfH588cbZs2VKtrGXLlrh69apatt+iRQu1eVq0aCG14+TJk0hJSYGNjY3a8omPj0dcXBx69uyJq1evomvXrrhy5QoWLlyIBg0aIDY2FgkJCdKyMjIyktaVallZWFgUuK6ePHkCIYRabCYmJqhdu7b0XNULM3jwYCmunj17AoA0KHbMmDGYPXs2WrZsienTp+Ps2bNYvXo1OnbsiCtXrqB169bo1q0bUlNT8ddffwHIOUb9/vvv499//8UHH3wAhUIBMzMzWFhYYPbs2Xl6AJcvX46uXbuiSpUqMDDI2fTT0tKk9qempgLI6bXo378/Hj58CACwtLREu3bt8sRXVO3atZO2lzp16iAkJASrVq0CAPTu3Rvbtm2TxnT4+/vDxcUFWVlZGDRoELy8vDBz5sw827iRkRF8fHxw+vRpBAYGomPHjgCAYcOGAQAuXbqEkJAQ6Rdgt27dcOnSJaSmpkq/XO3t7TFq1CgEBATg7bffRkJCAhwcHPDgwYM8bVixYgW+/fZb3L17F5UrV8YPP/wg/dJLSkrC3bt38dZbb6m9JjY2Fi1atJCOwY8fPx6JiYkQL91t5eXPg+q1L38mzM3N1T63uV+rWg4uLi6wtLRE27ZtAQAJCQmIiIjAixcvpNdUqVIFAQEB+PHHH/O8L6C+r/Lx8YGDg4O0/V+7dg1AzpgnIOdXert27SCEwOXLl/PUlZ6ejqlTp8LIyAh16tRBnTp1AABPnz4t8D3Nzc1hamoKQ0NDtWVTp04dWFtb4/79+zAyMkJ2djaqV6+OatWqSdNcXV0BAHFxcUhJSUHDhg0B5OwrLCws8PjxY1y6dAkWFhZ48eIFBg4cKL3m0KFD0nupeh9yU+371q9fDwD4999/pc+zubk51qxZg23btqFSpUrSeA/VOI9evXpJ+7JBgwZh+/btar1khoaG8PT0xLp167BmzRq88847uHDhAhISEvDzzz/DwsICCQkJWLFiBZRKJeLj46XX5tfzYmZmpnafP0dHx0K/b/LbBvft2wd/f39UrVoVlpaW0j5BtZ/IbxkVprBt9GW59xnHjh2TPqM3b94schtSU1MREhKith+/dOmS9H4xMTEwNDSEn59fkeLPzs7GnDlzUL9+fel7JiIiIt/4S4tGCU2tWrVQrVo17Nu3D/v27ZMWhIODA9zc3HD48GHs27cP7du3z/Pa3N3lQM7pdbk36FdRKpUwNDTEyZMn1RKK2NjYArv4hRD5Dmh6udzc3LzIcbi5uaFWrVrSQ7UzAHK6KF/eWec3UPHl98svzpfrKYjqdUqlEo6OjmrLJiYmBpcvX8akSZMAQDq8tXLlSpw+fRq9evWCkZERMjIy1GLIva5UcSiVynzbV5TBZ6r1vH//fimuX375BUDOIQMA+Oijj3D9+nX069cP586dQ+PGjbF8+XLs2LEDu3fvxoYNG2BmZoZHjx6pDQ5WnRnx7rvvol69ejAwMMCaNWtw/vx5fPTRR2pxrF69GteuXcN3330nHaqzsrJCYmIili5dKiU5ixYtkrrZMzMzIZPJ0KZNG7X4fHx88M0337yy7UDO+q5VqxYyMjJw5swZLFy4EP379wcAdOjQAc+fP8fRo0cB5CRPp06dwu+//47Bgwfj3r17mD9/Pjw9PbF9+3a1etPS0hAQEAALCwv89NNPsLKywpgxYwAACxcuxNatW/Hhhx8CyPky8vb2hlKplL6MDQ0NUbNmTaxevRqpqan44YcfIJPJ8qzjiIgIjB8/Hm5ubujQoQNiYmIwaNAgafCkqr6Xvbxd+/v7S18qRfn85ffZfbnM3NwcqampasshOjpaWlYZGRn48ccfkZycDCAnmTEyMsLOnTuxbt26fD9nubd/c3NztX3Vy/Pnbnt+8Z46dQonT57ExIkTUaVKFdjb28PKyirPwNP89o8F1VkY1fxCCDg6OmLnzp0AgF9++QUxMTFo06YN3n77bcTExODixYuIiIhASEgIWrZsieHDh0vLLb91qtr3Va1aFQCwa9cu6fPcpEkT1KtXD99//z2ioqKkhF213JydnaW6FQoFRowYgTVr1qjV37RpU6xduxbr1q3D4MGDoVQq4erqiq5duyImJgZVq1bFpEmTcPXqVdSsWVN6nbGxcZ5Y81uehe1TX94Gb968ic6dO8PLywtbt27FyZMn1fYJBS2jgrxqG80vHtV3TNOmTdU+o0Vtw6RJk7B161bMmTNHSka9vb1f+bktyOLFi7F06VKEhITg77//RkxMDDp27PjaTmwpCo2vQ9OuXTvs378f+/fvlzJMAPDz88OePXtw9OjRPONnNGVsbJznOGTDhg2RnZ2NpKQktYSiVq1acHBwyLceT09PJCQk4NatW1LZxYsX8fTpU9StW7dYMebH1tYW9+7dk55nZ2fj/Pnzr3ydp6en2q8jAIiKioKHhwcMDQ2lMtWXXu7nql98jRo1QmJiIoyMjPIsnypVqgDI+VIbOHAgunfvDm9vbzRs2FBKSFTLKneColpWJiYm+bYPyPngy2QyKTZjY2OkpaWpnYLs7u4OIOcXnSom1dkLuXvGnJ2dMXz4cGzbtg2BgYFISUnB6dOnMXz4cDg5OSE6OhqbN2/Gb7/9JvWe2Nvbo2rVqvDy8sKpU6fg7e2NgwcPolatWqhcubJarKdPn8aYMWPQuXNnaf0/ffoUt27dglKplHaCVatWxd27d/Osp9zxffLJJ9LORbVDLejYucrq1avRpk0bnDlzRtrhHzx4ECEhIfjnn3+k+YyMjNChQwesWrUKCQkJyMzMRJMmTdS+ALKysrBt2zY8ePAA8+fPh52dHZKTk6Xj36dPn8bAgQOlSyrY2dnhxo0b+cYlk8lgYGCgdiZObqdOnYKvry+6d++OixcvwsXFRa3HyNLSEq6urti7d6/a6zw9PREVFSV9kXTr1k1K5A4ePFjosqpbt26ez0Rqamq+n9tLly5Jy6F169aoU6eOlDg9efIE//vf/zBjxgwA6kl1SkqKxmf5qbZl1bKqX78+9u3bB5lMlu9ZnQ8fPsSYMWMwbdo0CCGQmZkpJVeFMTAwQHZ2ttpZpZcvX8aTJ09gb2+PrKwsGBoaIiEhAXfv3pWmqdZxjRo1pP0BAFSvXh21atVCq1atcO3aNbi6uqJWrVrw9/fH3LlzcejQIfTs2VPaxurXr59nfap4enpCoVAgISFB+jyfPHkSkyZNwoABA1CvXr08CQUAaT8SGhqK/fv34/bt29L+xtjYGB4eHsjIyEBGRgY6duyIRo0a4dmzZ7CyskKtWrXg5OSEzMxM1KpVC8bGxkhOTi70B1VRP5f5OXHiBLKysrB48WI0b94cHh4eefYJhS2jlxW2jRbFqz6j+Xl5f+/g4KC2D1D9wDlw4ECR6wsMDMQHH3yABg0aoEaNGgWOXy0tWiU0hw4dQkxMjFpXlZ+fH3744Qe8ePGi2AmNq6srUlJSsHfvXjx48ABpaWnw8PBA37590b9/f2zbtg3x8fGIjo7GggULpF8hL+vQoQPq16+Pvn374tSpUzh+/Dj69+8PPz+/fLsYiyIpKQmJiYlqD1XG3r59e+zYsQM7duzApUuXMGLEiCJdlOiTTz7B3r17MWvWLFy5cgXr1q3DsmXLpMHQKocPH8bChQtx5coVfPvtt9i8eTPGjh0rtbVFixYICgrCnj17cOPGDURFRWHq1Kn466+/0L59eygUCvz888/YsWMHvvzyS8yYMUPa4amW1YMHD3Dr1i21ZaUaLNu+fXucOHEC69evx9WrVzF9+nTExsbCwcEBkyZNwt69e2FlZYXNmzcDyNnpK5VK6ZCfKhmIj4/HhQsXAEAa5Dlu3Djs2bMH8fHxOHXqFPbu3YuqVauiQYMGmDVrFl68eIHZs2fDxcUFlStXxtixY7F3715MmTIF/fr1w5w5c6TBy1ZWVlizZk2ea+w4Ojpi1apV+OeffxAREQEg59dilSpVkJ2dLXUl79q1C8uXL1d7bXh4uFp8f//9t/Tl6uLiAplMhj///BP//vsvUlJS8qzjzMxMbNiwAX369IGXlxfc3NwA5Hw5fPTRR7h58yays7Px559/YtGiRXj//fexadMmLF26FNnZ2UhISFD7MpfL5fj6669hZGSEqVOnok+fPqhdu7Z0GrizszO2bdsmHSIZMmSI9GtZ9YsqOzsb9+/fx+jRo5GSkoKuXbvmu31Wr14dJ06cgKenJx4/fgxvb28cPXoU6enp2LBhAy5fvozQ0FAsXrwYX3/9Na5evYpTp07BxMQEt27dwujRowEAx44dw/bt29GrVy98+OGH2LJlS77vB+T8uly7di1WrFiBq1ev4siRI3j69CmCgoKkhAQA7t27h+rVq8PY2BjffPMNrl+/jt9//126/seOHTtgY2ODQYMGQSaTIS4uDvb29qhRowa6dOmSJ0F/FdU1haZNm4bz58+jQYMG+Pfff+Hs7Ixbt27h6tWr0jIBcn41b9iwAVeuXMGGDRtQo0aNIv0yNjQ0hLe3N4YMGYJjx47h5MmT+Oijj6TT+zt16oRVq1ahevXq6N69O95//30YGxtjx44dAIAGDRqgRYsWGDp0KADg7t27iIqKQkpKCpKSktC7d2/06tUL4eHh2LBhA9555x0cP35c2samTJmC6OhojBgxQkpew8LCcOHCBaSmpmL48OEYO3YsVq9ejbi4OFStWhVz587FvHnzcOzYMcyZM0etPWvXrpV6JBISErBhwwbpx8/Tp09RtWpV/PPPP9i7dy8OHToEQ0NDTJ48GY8ePcLRo0cRExODN998E6tWrULPnj1x/vx5DBgwoNBlaGdnB1NTU+zevVvji3rWrFkTWVlZ0ja1YcMGrFixQm2e3Mvo7NmzuHTpEpYvX57vIdvCttH8pKenS98vsbGxr/yM5qdWrVrYtm0bYmJicObMGQQHB6sdFXF1dcWAAQMwePBgaZDy/v378euvvxZYX2RkJKKiohAbG4thw4aVvWuwaTroRjUgqU6dOmrlqoFlNWvWzPMa/HcaYW6VKlUSa9asEULkP/ho+PDhwsbGRiDXadsZGRniiy++EK6urkIulwsHBwfRvXt3cfbs2QLjLepp26+S36m9qseRI0ek+D7++GPxxhtvCDs7OzFv3rx8BwUvXbo0T/2q07blcrmoXr26+PLLL9Wmu7i4iBkzZojevXsLMzMzYW9vL8LCwtTmSU5OFqNHjxZOTk5CLpcLZ2dn0bdvX3H16lXx6aefCi8vL+nUaiMjI9GpUyfRqlUraRBX7tO2cy+rBg0aSOvgiy++EPb29qJSpUpi/PjxYtSoUaJVq1bigw8+EGZmZsLGxkZUr15dGnia+7TtkJAQad2p1q1qYPioUaNEzZo1hUKhEDY2NkImk4nVq1dLbTtz5owICAgQZmZmQi6XCzMzM3Hs2DERFBQkHB0dhZGRkZDL5cLAwEBYW1uLNm3aiN69e6sNCs7vYW9vL5YuXSqWLFkizMzMpPKgoCBpm6xUqZJ46623pPhsbW1Fv379xIMHD6T4Zs6cKZ1Cnd9p21u2bBEGBgbStvfyNq86bfLgwYOidevWQi6XS6dtV65cWYwaNUo8f/5c7bTtrVu3SoNdZTKZaNSokfj999+lAb3t2rUTCoVCABALFy4Ufn5+om7dumrtVygUokmTJmLLli1CCCEaNGggxo4dqzYo+NixY2LgwIGiUqVKwtLSUhrAaWBgIFq3bi2dALBixQpRu3ZtIZfLhaOjoxg9enSBp23/8ssvwsTERHh6ehY4iPDl07bzW3/16tUTQggRHh4unVbeokULaTm4u7uLESNG5LuOtm7dKmQymfjoo4/UBgWrPp+q/3Nv//mdtt2jRw/x1ltvCTMzM2FpaSktkwEDBoi2bdsKHx8foVAohLu7u9i8ebNUb+5Bwdu3b1cbUFmpUiURFhYm3nnnHaFQKET16tXF+vXrpdfeu3dPvPPOO0IulwuFQiGMjIyEkZGRGDZsmFRfcnKyVL+RkZG0Pzhw4IAIDAyUtjH8N+B35MiR0mBbIYTYv3+/8PX1lS61kN+jevXqQi6XC2tra2FlZSWMjY2Fu7u7CA0NFQDE3LlzhRBCbN++XXh7ewsg5xTy5s2bi507dwoXFxdpn+Ts7Cxtrypt2rSRTsc3MzMTlSpVEgqFQjg7O4u1a9cKuVwuOnbsKM0P/P9p20II8cMPPwhnZ2dpf5T7M6lS0EDWJUuWCEdHR2Fqaio6duwo1q9fn+d7SrWMFAqFsLa2Fh07dpSmv1xvQdvoy4N7VZd0UD0sLS3VPqMFDQp+efBufHy8aNeunTA1NRXOzs5i2bJleWJ6/vy5GD9+vHQ6fa1atcSPP/6Yb70PHz4UgYGBwsLCQtjZ2YmpU6eK/v37F2lZvi4yIYo4WINKlaurK8aNG1eql5WmsmHt2rUYN24cbxVCRJQLb05JREREeo8JDREREek9HnIiIiIivcceGiIiItJ7TGiIiIhI7zGhISIiIr3HhIaI9FbuezQVpZyIyi+j0g6AiEgbBw8ehIGBARISEuDj44Pr168jMTERjRo1QkZGBho1aqTxvZCISH/xLCciIiLSezzkRERERHqPCQ0RERHpPSY0REREpPeY0BAREZHeY0JDREREeo8JDREREek9JjRERESk95jQEBERkd5jQkNERER67/8ATYNGoHb+cQQAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()    \n",
+    " \n",
+    "ax.bar(participation_rate,count)\n",
+    "plt.title('Participation rate based on ethnicity',size=10)\n",
+    "plt.xlabel('Total Count',size = 1)\n",
+    "plt.ylabel('# Of Developer Did Survey',size = 10) \n",
+    "for i, v in enumerate(count):\n",
+    "    ax.text(i-.15, \n",
+    "              v+3,\n",
+    "              count[i],\n",
+    "              style = 'italic',\n",
+    "              fontsize=12,\n",
+    "              color = 'magenta')\n",
+    "ax.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53594653",
+   "metadata": {},
+   "source": [
+    "**The visualization shows that people of White or European descent participate more than those from other ethnic backgrounds.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4ec803c",
+   "metadata": {},
+   "source": [
+    "# Differences in income between men and women"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "id": "9ccb70ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Income vs Gender')"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x ='Gender', y='', data=df)\n",
+    "plt.title('Income vs Gender', fontsize = 14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "id": "580b3777",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lst=df.groupby('Gender')['Ethnicity'].count()\n",
+    "plt.figure(figsize=(12,4))\n",
+    "plt.bar(list(lst.keys()), lst.values, color='skyblue')  # Plotting the bars\n",
+    "\n",
+    "# Adding labels and title\n",
+    "plt.xlabel('Age Group')  # Label for x-axis\n",
+    "plt.ylabel('Counts')      # Label for y-axis\n",
+    "plt.title('Age')  # Title of the plot\n",
+    "#plt.xticks(rotation=45)  # Rotate labels by 90 degrees\n",
+    "\n",
+    "# Display the plot\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48449f8e",
+   "metadata": {},
+   "source": [
+    "**Based on the data analysis above, it's evident that men generally earn more than women.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "228f754d",
+   "metadata": {},
+   "source": [
+    "# Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "837987db",
+   "metadata": {},
+   "source": [
+    "Overall, we performed EDA on the Stack overflow 2019 developer survey and derived insights from it. We found the participation rates based on ethnicity and differences in income between men and women."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9712adf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}