Skip to content

Instantly share code, notes, and snippets.

@acstrahl

acstrahl/salary_prediction.ipynb

Last active October 23, 2025 15:17
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save acstrahl/7467611752a933f0d51cf17521490de3 to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save acstrahl/7467611752a933f0d51cf17521490de3 to your computer and use it in GitHub Desktop.
Download ZIP
Stack Overflow Developer Survey Salary Prediction with Linear Regression -- Project Lab Solution Code
Display the source blob
Display the rendered blob
Raw
salary_prediction.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ccc86051",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error"
]
},
{
"cell_type": "markdown",
"id": "b667d41f",
"metadata": {},
"source": [
"## Initial data cleaning (dropping columns, filtering out nulls, etc.)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "81846123",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 48019 entries, 0 to 48018\n",
"Data columns (total 33 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 MainBranch 48019 non-null object \n",
" 1 Age 48019 non-null object \n",
" 2 Employment 48007 non-null object \n",
" 3 RemoteWork 47940 non-null object \n",
" 4 EdLevel 48019 non-null object \n",
" 5 LearnCode 47935 non-null object \n",
" 6 LearnCodeOnline 38414 non-null object \n",
" 7 YearsCode 47950 non-null object \n",
" 8 YearsCodePro 47825 non-null object \n",
" 9 DevType 47904 non-null object \n",
" 10 OrgSize 47982 non-null object \n",
" 11 Country 48019 non-null object \n",
" 12 LanguageHaveWorkedWith 47883 non-null object \n",
" 13 DatabaseHaveWorkedWith 41764 non-null object \n",
" 14 PlatformHaveWorkedWith 37375 non-null object \n",
" 15 WebframeHaveWorkedWith 37865 non-null object \n",
" 16 MiscTechHaveWorkedWith 31769 non-null object \n",
" 17 ToolsTechHaveWorkedWith 44055 non-null object \n",
" 18 NEWCollabToolsHaveWorkedWith 47435 non-null object \n",
" 19 OpSysPersonal use 47534 non-null object \n",
" 20 OpSysProfessional use 45030 non-null object \n",
" 21 OfficeStackAsyncHaveWorkedWith 41999 non-null object \n",
" 22 OfficeStackSyncHaveWorkedWith 47015 non-null object \n",
" 23 AISearchHaveWorkedWith 29485 non-null object \n",
" 24 AIDevHaveWorkedWith 13940 non-null object \n",
" 25 AISelect 48019 non-null object \n",
" 26 AIToolCurrently Using 19550 non-null object \n",
" 27 TBranch 46156 non-null object \n",
" 28 ICorPM 32639 non-null object \n",
" 29 WorkExp 32638 non-null float64\n",
" 30 ProfessionalTech 31726 non-null object \n",
" 31 Industry 27747 non-null object \n",
" 32 ConvertedCompYearly 48019 non-null float64\n",
"dtypes: float64(2), object(31)\n",
"memory usage: 12.1+ MB\n"
]
}
],
"source": [
"df = pd.read_csv('survey_results_slim.csv')\n",
"\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "397b7106",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing data percentage:\n",
" AIDevHaveWorkedWith 70.969824\n",
"AIToolCurrently Using 59.286949\n",
"dtype: float64\n",
"\n",
"Total columns with more than 50% missing data: 2\n"
]
}
],
"source": [
"# Calculate missing percentage\n",
"missing_pct = (df.isnull().sum() / len(df) * 100).sort_values(ascending=False)\n",
"print(\"Missing data percentage:\\n\", missing_pct[missing_pct > 50])\n",
"print(\"\\nTotal columns with more than 50% missing data:\", (missing_pct > 50).sum())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a2a33477",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dropped 12 columns\n",
"Remaining columns: 21\n",
"\n",
"Remaining columns:\n",
"Index(['MainBranch', 'Age', 'Employment', 'RemoteWork', 'EdLevel', 'LearnCode',\n",
" 'YearsCode', 'YearsCodePro', 'DevType', 'OrgSize', 'Country',\n",
" 'LanguageHaveWorkedWith', 'DatabaseHaveWorkedWith',\n",
" 'PlatformHaveWorkedWith', 'WebframeHaveWorkedWith',\n",
" 'ToolsTechHaveWorkedWith', 'OpSysProfessional use', 'AISelect',\n",
" 'WorkExp', 'Industry', 'ConvertedCompYearly'],\n",
" dtype='object')\n"
]
}
],
"source": [
"# Get columns with >50% missing\n",
"cols_to_drop = missing_pct[missing_pct > 50].index.tolist()\n",
"\n",
"# Add all columns to drop in one consolidated list\n",
"cols_to_drop.extend([\n",
" # Less critical columns\n",
" 'LearnCodeOnline', 'OpSysPersonal use',\n",
" \n",
" # Meta columns\n",
" 'ICorPM', 'ProfessionalTech', 'TBranch',\n",
" \n",
" # High missingness tech columns\n",
" 'AISearchHaveWorkedWith', # 38.6% missing\n",
" 'MiscTechHaveWorkedWith', # 33.8% missing\n",
" \n",
" # Office tools - less relevant\n",
" 'OfficeStackAsyncHaveWorkedWith',\n",
" 'OfficeStackSyncHaveWorkedWith',\n",
" 'NEWCollabToolsHaveWorkedWith'\n",
"])\n",
"\n",
"# Remove duplicates and drop\n",
"cols_to_drop = list(set(cols_to_drop))\n",
"df_cleaned = df.drop(columns=cols_to_drop)\n",
"\n",
"print(f\"Dropped {len(cols_to_drop)} columns\")\n",
"print(f\"Remaining columns: {df_cleaned.shape[1]}\")\n",
"print(f\"\\nRemaining columns:\\n{df_cleaned.columns}\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6030ad84",
"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>MainBranch</th>\n",
" <th>Age</th>\n",
" <th>Employment</th>\n",
" <th>RemoteWork</th>\n",
" <th>EdLevel</th>\n",
" <th>LearnCode</th>\n",
" <th>YearsCode</th>\n",
" <th>YearsCodePro</th>\n",
" <th>DevType</th>\n",
" <th>OrgSize</th>\n",
" <th>...</th>\n",
" <th>LanguageHaveWorkedWith</th>\n",
" <th>DatabaseHaveWorkedWith</th>\n",
" <th>PlatformHaveWorkedWith</th>\n",
" <th>WebframeHaveWorkedWith</th>\n",
" <th>ToolsTechHaveWorkedWith</th>\n",
" <th>OpSysProfessional use</th>\n",
" <th>AISelect</th>\n",
" <th>WorkExp</th>\n",
" <th>Industry</th>\n",
" <th>ConvertedCompYearly</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>I am a developer by profession</td>\n",
" <td>25-34 years old</td>\n",
" <td>Employed, full-time</td>\n",
" <td>Remote</td>\n",
" <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
" <td>Books / Physical media;Colleague;Friend or fam...</td>\n",
" <td>18</td>\n",
" <td>9</td>\n",
" <td>Senior Executive (C-Suite, VP, etc.)</td>\n",
" <td>2 to 9 employees</td>\n",
" <td>...</td>\n",
" <td>HTML/CSS;JavaScript;Python</td>\n",
" <td>Supabase</td>\n",
" <td>Amazon Web Services (AWS);Netlify;Vercel</td>\n",
" <td>Next.js;React;Remix;Vue.js</td>\n",
" <td>Docker;Kubernetes;npm;Pip;Vite;Webpack;Yarn</td>\n",
" <td>MacOS;Windows;Windows Subsystem for Linux (WSL)</td>\n",
" <td>Yes</td>\n",
" <td>10.0</td>\n",
" <td>Information Services, IT, Software Development...</td>\n",
" <td>285000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>I am a developer by profession</td>\n",
" <td>45-54 years old</td>\n",
" <td>Employed, full-time</td>\n",
" <td>Hybrid (some remote, some in-person)</td>\n",
" <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
" <td>Books / Physical media;Colleague;On the job tr...</td>\n",
" <td>27</td>\n",
" <td>23</td>\n",
" <td>Developer, back-end</td>\n",
" <td>5,000 to 9,999 employees</td>\n",
" <td>...</td>\n",
" <td>Bash/Shell (all shells);Go</td>\n",
" <td>NaN</td>\n",
" <td>Amazon Web Services (AWS);Google Cloud;OpenSta...</td>\n",
" <td>NaN</td>\n",
" <td>Cargo;Docker;Kubernetes;Make;Nix</td>\n",
" <td>MacOS;Other Linux-based</td>\n",
" <td>No, and I don't plan to</td>\n",
" <td>23.0</td>\n",
" <td>Information Services, IT, Software Development...</td>\n",
" <td>250000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>I am a developer by profession</td>\n",
" <td>25-34 years old</td>\n",
" <td>Employed, full-time</td>\n",
" <td>Hybrid (some remote, some in-person)</td>\n",
" <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
" <td>Colleague;Friend or family member;Other online...</td>\n",
" <td>12</td>\n",
" <td>7</td>\n",
" <td>Developer, front-end</td>\n",
" <td>100 to 499 employees</td>\n",
" <td>...</td>\n",
" <td>Bash/Shell (all shells);HTML/CSS;JavaScript;PH...</td>\n",
" <td>PostgreSQL;Redis</td>\n",
" <td>Cloudflare;Heroku</td>\n",
" <td>Node.js;React;Ruby on Rails;Vue.js;WordPress</td>\n",
" <td>Homebrew;npm;Vite;Webpack;Yarn</td>\n",
" <td>iOS;iPadOS;MacOS</td>\n",
" <td>No, and I don't plan to</td>\n",
" <td>7.0</td>\n",
" <td>NaN</td>\n",
" <td>156000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>I am a developer by profession</td>\n",
" <td>25-34 years old</td>\n",
" <td>Employed, full-time;Independent contractor, fr...</td>\n",
" <td>Remote</td>\n",
" <td>Bachelor’s degree (B.A., B.S., B.Eng., etc.)</td>\n",
" <td>Books / Physical media;Online Courses or Certi...</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>Developer, full-stack</td>\n",
" <td>20 to 99 employees</td>\n",
" <td>...</td>\n",
" <td>HTML/CSS;JavaScript;TypeScript</td>\n",
" <td>BigQuery;Elasticsearch;MongoDB;PostgreSQL</td>\n",
" <td>Amazon Web Services (AWS);Firebase;Heroku;Netl...</td>\n",
" <td>Express;Gatsby;NestJS;Next.js;Node.js;React</td>\n",
" <td>Docker;npm;Webpack;Yarn</td>\n",
" <td>Other (Please Specify):</td>\n",
" <td>Yes</td>\n",
" <td>6.0</td>\n",
" <td>Other</td>\n",
" <td>23456.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>I am a developer by profession</td>\n",
" <td>35-44 years old</td>\n",
" <td>Employed, full-time</td>\n",
" <td>Remote</td>\n",
" <td>Some college/university study without earning ...</td>\n",
" <td>Books / Physical media;Colleague;Online Course...</td>\n",
" <td>21</td>\n",
" <td>21</td>\n",
" <td>Developer, back-end</td>\n",
" <td>100 to 499 employees</td>\n",
" <td>...</td>\n",
" <td>Bash/Shell (all shells);HTML/CSS;JavaScript;Ru...</td>\n",
" <td>BigQuery;Cloud Firestore;PostgreSQL;Redis</td>\n",
" <td>Amazon Web Services (AWS);Cloudflare;Google Cloud</td>\n",
" <td>Angular;Express;NestJS;Node.js</td>\n",
" <td>Docker;Homebrew;Kubernetes;npm;pnpm;Terraform</td>\n",
" <td>MacOS</td>\n",
" <td>Yes</td>\n",
" <td>22.0</td>\n",
" <td>Other</td>\n",
" <td>96828.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" MainBranch Age \\\n",
"0 I am a developer by profession 25-34 years old \n",
"1 I am a developer by profession 45-54 years old \n",
"2 I am a developer by profession 25-34 years old \n",
"3 I am a developer by profession 25-34 years old \n",
"4 I am a developer by profession 35-44 years old \n",
"\n",
" Employment \\\n",
"0 Employed, full-time \n",
"1 Employed, full-time \n",
"2 Employed, full-time \n",
"3 Employed, full-time;Independent contractor, fr... \n",
"4 Employed, full-time \n",
"\n",
" RemoteWork \\\n",
"0 Remote \n",
"1 Hybrid (some remote, some in-person) \n",
"2 Hybrid (some remote, some in-person) \n",
"3 Remote \n",
"4 Remote \n",
"\n",
" EdLevel \\\n",
"0 Bachelor’s degree (B.A., B.S., B.Eng., etc.) \n",
"1 Bachelor’s degree (B.A., B.S., B.Eng., etc.) \n",
"2 Bachelor’s degree (B.A., B.S., B.Eng., etc.) \n",
"3 Bachelor’s degree (B.A., B.S., B.Eng., etc.) \n",
"4 Some college/university study without earning ... \n",
"\n",
" LearnCode YearsCode YearsCodePro \\\n",
"0 Books / Physical media;Colleague;Friend or fam... 18 9 \n",
"1 Books / Physical media;Colleague;On the job tr... 27 23 \n",
"2 Colleague;Friend or family member;Other online... 12 7 \n",
"3 Books / Physical media;Online Courses or Certi... 6 4 \n",
"4 Books / Physical media;Colleague;Online Course... 21 21 \n",
"\n",
" DevType OrgSize ... \\\n",
"0 Senior Executive (C-Suite, VP, etc.) 2 to 9 employees ... \n",
"1 Developer, back-end 5,000 to 9,999 employees ... \n",
"2 Developer, front-end 100 to 499 employees ... \n",
"3 Developer, full-stack 20 to 99 employees ... \n",
"4 Developer, back-end 100 to 499 employees ... \n",
"\n",
" LanguageHaveWorkedWith \\\n",
"0 HTML/CSS;JavaScript;Python \n",
"1 Bash/Shell (all shells);Go \n",
"2 Bash/Shell (all shells);HTML/CSS;JavaScript;PH... \n",
"3 HTML/CSS;JavaScript;TypeScript \n",
"4 Bash/Shell (all shells);HTML/CSS;JavaScript;Ru... \n",
"\n",
" DatabaseHaveWorkedWith \\\n",
"0 Supabase \n",
"1 NaN \n",
"2 PostgreSQL;Redis \n",
"3 BigQuery;Elasticsearch;MongoDB;PostgreSQL \n",
"4 BigQuery;Cloud Firestore;PostgreSQL;Redis \n",
"\n",
" PlatformHaveWorkedWith \\\n",
"0 Amazon Web Services (AWS);Netlify;Vercel \n",
"1 Amazon Web Services (AWS);Google Cloud;OpenSta... \n",
"2 Cloudflare;Heroku \n",
"3 Amazon Web Services (AWS);Firebase;Heroku;Netl... \n",
"4 Amazon Web Services (AWS);Cloudflare;Google Cloud \n",
"\n",
" WebframeHaveWorkedWith \\\n",
"0 Next.js;React;Remix;Vue.js \n",
"1 NaN \n",
"2 Node.js;React;Ruby on Rails;Vue.js;WordPress \n",
"3 Express;Gatsby;NestJS;Next.js;Node.js;React \n",
"4 Angular;Express;NestJS;Node.js \n",
"\n",
" ToolsTechHaveWorkedWith \\\n",
"0 Docker;Kubernetes;npm;Pip;Vite;Webpack;Yarn \n",
"1 Cargo;Docker;Kubernetes;Make;Nix \n",
"2 Homebrew;npm;Vite;Webpack;Yarn \n",
"3 Docker;npm;Webpack;Yarn \n",
"4 Docker;Homebrew;Kubernetes;npm;pnpm;Terraform \n",
"\n",
" OpSysProfessional use AISelect \\\n",
"0 MacOS;Windows;Windows Subsystem for Linux (WSL) Yes \n",
"1 MacOS;Other Linux-based No, and I don't plan to \n",
"2 iOS;iPadOS;MacOS No, and I don't plan to \n",
"3 Other (Please Specify): Yes \n",
"4 MacOS Yes \n",
"\n",
" WorkExp Industry \\\n",
"0 10.0 Information Services, IT, Software Development... \n",
"1 23.0 Information Services, IT, Software Development... \n",
"2 7.0 NaN \n",
"3 6.0 Other \n",
"4 22.0 Other \n",
"\n",
" ConvertedCompYearly \n",
"0 285000.0 \n",
"1 250000.0 \n",
"2 156000.0 \n",
"3 23456.0 \n",
"4 96828.0 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_cleaned.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "69d66f7d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing data in remaining columns:\n",
"Industry 42.216623\n",
"WorkExp 32.031071\n",
"PlatformHaveWorkedWith 22.166226\n",
"WebframeHaveWorkedWith 21.145796\n",
"DatabaseHaveWorkedWith 13.026094\n",
"ToolsTechHaveWorkedWith 8.255066\n",
"OpSysProfessional use 6.224619\n",
"YearsCodePro 0.404007\n",
"LanguageHaveWorkedWith 0.283221\n",
"DevType 0.239489\n",
"LearnCode 0.174931\n",
"RemoteWork 0.164518\n",
"YearsCode 0.143693\n",
"OrgSize 0.077053\n",
"Employment 0.024990\n",
"dtype: float64\n",
"\n",
"Data types:\n",
"object 19\n",
"float64 2\n",
"Name: count, dtype: int64\n",
"\n",
"Unique values in key columns:\n",
"Country: 171 unique values\n",
"DevType: 33 unique values\n",
"EdLevel: 8 unique values\n",
"Employment: 14 unique values\n"
]
}
],
"source": [
"# Check remaining missing data in your filtered dataset\n",
"print(\"Missing data in remaining columns:\")\n",
"missing_after = (df_cleaned.isnull().sum() / len(df_cleaned) * 100).sort_values(ascending=False)\n",
"print(missing_after[missing_after > 0])\n",
"\n",
"# Check data types\n",
"print(\"\\nData types:\")\n",
"print(df_cleaned.dtypes.value_counts())\n",
"\n",
"# Check unique values for some key categorical columns\n",
"print(\"\\nUnique values in key columns:\")\n",
"for col in ['Country', 'DevType', 'EdLevel', 'Employment']:\n",
" if col in df_cleaned.columns:\n",
" print(f\"{col}: {df_cleaned[col].nunique()} unique values\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "daf94549",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final dataset shape: (16460, 21)\n",
"Rows retained: 16460 (34.3% of filtered data)\n"
]
}
],
"source": [
"# After dropping your high-missing columns, just do:\n",
"df_model = df_cleaned.dropna().copy()\n",
"\n",
"print(f\"Final dataset shape: {df_model.shape}\")\n",
"print(f\"Rows retained: {len(df_model)} ({len(df_model)/48019*100:.1f}% of filtered data)\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "21a138a7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Salary statistics:\n",
"count 1.646000e+04\n",
"mean 9.412425e+04\n",
"std 1.309772e+05\n",
"min 1.000000e+00\n",
"25% 4.362100e+04\n",
"50% 7.496300e+04\n",
"75% 1.231530e+05\n",
"max 1.031937e+07\n",
"Name: ConvertedCompYearly, dtype: float64\n",
"\n",
"Salaries > $500k: 51\n",
"Salaries < $10k: 1100\n"
]
}
],
"source": [
"# Check your target variable distribution\n",
"print(\"\\nSalary statistics:\")\n",
"print(df_model['ConvertedCompYearly'].describe())\n",
"\n",
"# Check for outliers\n",
"print(f\"\\nSalaries > $500k: {(df_model['ConvertedCompYearly'] > 500000).sum()}\")\n",
"print(f\"Salaries < $10k: {(df_model['ConvertedCompYearly'] < 10000).sum()}\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1e46d168",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 10 salaries:\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>Country</th>\n",
" <th>DevType</th>\n",
" <th>YearsCodePro</th>\n",
" <th>ConvertedCompYearly</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>45679</th>\n",
" <td>New Zealand</td>\n",
" <td>Engineering manager</td>\n",
" <td>40</td>\n",
" <td>10319366.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20505</th>\n",
" <td>Canada</td>\n",
" <td>Developer, full-stack</td>\n",
" <td>2</td>\n",
" <td>7435143.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44175</th>\n",
" <td>Brazil</td>\n",
" <td>Developer, full-stack</td>\n",
" <td>22</td>\n",
" <td>4451577.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44794</th>\n",
" <td>Brazil</td>\n",
" <td>Developer, mobile</td>\n",
" <td>4</td>\n",
" <td>2028761.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29024</th>\n",
" <td>United States of America</td>\n",
" <td>Developer, back-end</td>\n",
" <td>18</td>\n",
" <td>1800000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4394</th>\n",
" <td>Japan</td>\n",
" <td>Other (please specify):</td>\n",
" <td>23</td>\n",
" <td>1792064.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30077</th>\n",
" <td>United States of America</td>\n",
" <td>Other (please specify):</td>\n",
" <td>4</td>\n",
" <td>1250000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38087</th>\n",
" <td>Germany</td>\n",
" <td>Developer, back-end</td>\n",
" <td>22</td>\n",
" <td>1069822.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24801</th>\n",
" <td>Canada</td>\n",
" <td>Developer, full-stack</td>\n",
" <td>5</td>\n",
" <td>929393.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41471</th>\n",
" <td>Sweden</td>\n",
" <td>Data scientist or machine learning specialist</td>\n",
" <td>4</td>\n",
" <td>921414.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country \\\n",
"45679 New Zealand \n",
"20505 Canada \n",
"44175 Brazil \n",
"44794 Brazil \n",
"29024 United States of America \n",
"4394 Japan \n",
"30077 United States of America \n",
"38087 Germany \n",
"24801 Canada \n",
"41471 Sweden \n",
"\n",
" DevType YearsCodePro \\\n",
"45679 Engineering manager 40 \n",
"20505 Developer, full-stack 2 \n",
"44175 Developer, full-stack 22 \n",
"44794 Developer, mobile 4 \n",
"29024 Developer, back-end 18 \n",
"4394 Other (please specify): 23 \n",
"30077 Other (please specify): 4 \n",
"38087 Developer, back-end 22 \n",
"24801 Developer, full-stack 5 \n",
"41471 Data scientist or machine learning specialist 4 \n",
"\n",
" ConvertedCompYearly \n",
"45679 10319366.0 \n",
"20505 7435143.0 \n",
"44175 4451577.0 \n",
"44794 2028761.0 \n",
"29024 1800000.0 \n",
"4394 1792064.0 \n",
"30077 1250000.0 \n",
"38087 1069822.0 \n",
"24801 929393.0 \n",
"41471 921414.0 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Bottom 10 salaries:\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>Country</th>\n",
" <th>DevType</th>\n",
" <th>YearsCodePro</th>\n",
" <th>ConvertedCompYearly</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1192</th>\n",
" <td>India</td>\n",
" <td>Developer, full-stack</td>\n",
" <td>11</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4861</th>\n",
" <td>Viet Nam</td>\n",
" <td>Developer, full-stack</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10078</th>\n",
" <td>China</td>\n",
" <td>Developer, front-end</td>\n",
" <td>2</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21795</th>\n",
" <td>Uganda</td>\n",
" <td>Developer, desktop or enterprise applications</td>\n",
" <td>5</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6073</th>\n",
" <td>Syrian Arab Republic</td>\n",
" <td>Developer, front-end</td>\n",
" <td>2</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8307</th>\n",
" <td>Iran, Islamic Republic of...</td>\n",
" <td>Developer, back-end</td>\n",
" <td>25</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11842</th>\n",
" <td>Hungary</td>\n",
" <td>Developer, mobile</td>\n",
" <td>30</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24100</th>\n",
" <td>Indonesia</td>\n",
" <td>Developer, full-stack</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27998</th>\n",
" <td>United Republic of Tanzania</td>\n",
" <td>Developer, front-end</td>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35753</th>\n",
" <td>Uzbekistan</td>\n",
" <td>Developer, front-end</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country \\\n",
"1192 India \n",
"4861 Viet Nam \n",
"10078 China \n",
"21795 Uganda \n",
"6073 Syrian Arab Republic \n",
"8307 Iran, Islamic Republic of... \n",
"11842 Hungary \n",
"24100 Indonesia \n",
"27998 United Republic of Tanzania \n",
"35753 Uzbekistan \n",
"\n",
" DevType YearsCodePro \\\n",
"1192 Developer, full-stack 11 \n",
"4861 Developer, full-stack 3 \n",
"10078 Developer, front-end 2 \n",
"21795 Developer, desktop or enterprise applications 5 \n",
"6073 Developer, front-end 2 \n",
"8307 Developer, back-end 25 \n",
"11842 Developer, mobile 30 \n",
"24100 Developer, full-stack 3 \n",
"27998 Developer, front-end 1 \n",
"35753 Developer, front-end 3 \n",
"\n",
" ConvertedCompYearly \n",
"1192 1.0 \n",
"4861 1.0 \n",
"10078 1.0 \n",
"21795 1.0 \n",
"6073 2.0 \n",
"8307 2.0 \n",
"11842 2.0 \n",
"24100 2.0 \n",
"27998 2.0 \n",
"35753 2.0 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"After salary filtering:\n",
"Rows: 15309\n",
"Salary range: $10,000 - $500,000\n",
"\n",
"New salary statistics:\n",
"count 15309.000000\n",
"mean 97043.815925\n",
"std 67251.276938\n",
"min 10000.000000\n",
"25% 50332.000000\n",
"50% 80317.000000\n",
"75% 128507.000000\n",
"max 500000.000000\n",
"Name: ConvertedCompYearly, dtype: float64\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x400 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Check the extreme values\n",
"print(\"Top 10 salaries:\")\n",
"display(df_model.nlargest(10, 'ConvertedCompYearly')[['Country', 'DevType', 'YearsCodePro', 'ConvertedCompYearly']])\n",
"\n",
"print(\"\\nBottom 10 salaries:\")\n",
"display(df_model.nsmallest(10, 'ConvertedCompYearly')[['Country', 'DevType', 'YearsCodePro', 'ConvertedCompYearly']])\n",
"\n",
"# Apply reasonable salary filters\n",
"# Reasoning: \n",
"# - Min $10k: Removes part-time/student/hobby responses or data errors\n",
"# - Max $500k: Removes extreme outliers while keeping high earners\n",
"df_model = df_model[\n",
" (df_model['ConvertedCompYearly'] >= 10000) & \n",
" (df_model['ConvertedCompYearly'] <= 500000)\n",
"]\n",
"\n",
"print(f\"\\nAfter salary filtering:\")\n",
"print(f\"Rows: {len(df_model)}\")\n",
"print(f\"Salary range: ${df_model['ConvertedCompYearly'].min():,.0f} - ${df_model['ConvertedCompYearly'].max():,.0f}\")\n",
"print(f\"\\nNew salary statistics:\")\n",
"print(df_model['ConvertedCompYearly'].describe())\n",
"\n",
"# Visualize the distribution\n",
"\n",
"plt.figure(figsize=(12, 4))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.hist(df_model['ConvertedCompYearly'], bins=50, edgecolor='black')\n",
"plt.xlabel('Salary (USD)')\n",
"plt.ylabel('Count')\n",
"plt.title('Salary Distribution After Cleaning')\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"plt.hist(np.log10(df_model['ConvertedCompYearly']), bins=50, edgecolor='black')\n",
"plt.xlabel('Log10(Salary)')\n",
"plt.ylabel('Count')\n",
"plt.title('Log-Transformed Salary Distribution')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "fca9b0db",
"metadata": {},
"source": [
"## Feature Transformation"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "562f5365",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Creating count features for multi-select columns...\n",
"\n",
"Count feature statistics:\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>Num_Languages</th>\n",
" <th>Num_Databases</th>\n",
" <th>Num_Platforms</th>\n",
" <th>Num_Webframes</th>\n",
" <th>Num_Tools</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>15309.000000</td>\n",
" <td>15309.000000</td>\n",
" <td>15309.000000</td>\n",
" <td>15309.000000</td>\n",
" <td>15309.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5.888366</td>\n",
" <td>3.409890</td>\n",
" <td>2.412698</td>\n",
" <td>3.691097</td>\n",
" <td>5.575805</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.918188</td>\n",
" <td>2.176719</td>\n",
" <td>1.643505</td>\n",
" <td>2.409808</td>\n",
" <td>3.192943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>7.000000</td>\n",
" <td>4.000000</td>\n",
" <td>3.000000</td>\n",
" <td>5.000000</td>\n",
" <td>7.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>41.000000</td>\n",
" <td>30.000000</td>\n",
" <td>17.000000</td>\n",
" <td>32.000000</td>\n",
" <td>37.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Num_Languages Num_Databases Num_Platforms Num_Webframes \\\n",
"count 15309.000000 15309.000000 15309.000000 15309.000000 \n",
"mean 5.888366 3.409890 2.412698 3.691097 \n",
"std 2.918188 2.176719 1.643505 2.409808 \n",
"min 1.000000 1.000000 1.000000 1.000000 \n",
"25% 4.000000 2.000000 1.000000 2.000000 \n",
"50% 5.000000 3.000000 2.000000 3.000000 \n",
"75% 7.000000 4.000000 3.000000 5.000000 \n",
"max 41.000000 30.000000 17.000000 32.000000 \n",
"\n",
" Num_Tools \n",
"count 15309.000000 \n",
"mean 5.575805 \n",
"std 3.192943 \n",
"min 1.000000 \n",
"25% 3.000000 \n",
"50% 5.000000 \n",
"75% 7.000000 \n",
"max 37.000000 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Total Skills distribution:\n",
"count 15309.000000\n",
"mean 20.977856\n",
"std 9.170052\n",
"min 5.000000\n",
"25% 15.000000\n",
"50% 19.000000\n",
"75% 25.000000\n",
"max 140.000000\n",
"Name: Total_Skills, dtype: float64\n",
"\n",
"Correlation with salary:\n",
"ConvertedCompYearly 1.000000\n",
"Num_Tools 0.114356\n",
"Num_Languages 0.074025\n",
"Total_Skills 0.044754\n",
"Num_Platforms 0.006805\n",
"Num_Databases -0.003100\n",
"Num_Webframes -0.072698\n",
"Name: ConvertedCompYearly, dtype: float64\n"
]
}
],
"source": [
"# Count features for all tech stack columns\n",
"print(\"Creating count features for multi-select columns...\")\n",
"\n",
"# Languages\n",
"df_model['Num_Languages'] = df_model['LanguageHaveWorkedWith'].str.count(';') + 1\n",
"\n",
"# Databases\n",
"df_model['Num_Databases'] = df_model['DatabaseHaveWorkedWith'].str.count(';') + 1\n",
"\n",
"# Platforms\n",
"df_model['Num_Platforms'] = df_model['PlatformHaveWorkedWith'].str.count(';') + 1\n",
"\n",
"# Web Frameworks\n",
"df_model['Num_Webframes'] = df_model['WebframeHaveWorkedWith'].str.count(';') + 1\n",
"\n",
"# Tools/Tech\n",
"df_model['Num_Tools'] = df_model['ToolsTechHaveWorkedWith'].str.count(';') + 1\n",
"\n",
"# Check the new features\n",
"print(\"\\nCount feature statistics:\")\n",
"count_cols = ['Num_Languages', 'Num_Databases', 'Num_Platforms', 'Num_Webframes', 'Num_Tools']\n",
"display(df_model[count_cols].describe())\n",
"\n",
"# Create a \"total skills\" feature\n",
"df_model['Total_Skills'] = (\n",
" df_model['Num_Languages'] + \n",
" df_model['Num_Databases'] + \n",
" df_model['Num_Platforms'] + \n",
" df_model['Num_Webframes'] + \n",
" df_model['Num_Tools']\n",
")\n",
"\n",
"print(\"\\nTotal Skills distribution:\")\n",
"print(df_model['Total_Skills'].describe())\n",
"\n",
"# Quick correlation check with salary\n",
"print(\"\\nCorrelation with salary:\")\n",
"correlation_data = df_model[count_cols + ['Total_Skills', 'ConvertedCompYearly']].corr()['ConvertedCompYearly'].sort_values(ascending=False)\n",
"print(correlation_data)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e6e8601e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 20 languages:\n",
"LanguageHaveWorkedWith\n",
"JavaScript 11851\n",
"SQL 9565\n",
"HTML/CSS 9423\n",
"TypeScript 8868\n",
"Python 7193\n",
"Bash/Shell (all shells) 6042\n",
"C# 5108\n",
"Java 4645\n",
"PHP 3059\n",
"Go 2840\n",
"PowerShell 2747\n",
"C++ 2028\n",
"Rust 1988\n",
"C 1691\n",
"Kotlin 1635\n",
"Ruby 1538\n",
"Dart 909\n",
"Lua 857\n",
"Groovy 758\n",
"Swift 704\n",
"Name: count, dtype: int64\n",
"\n",
"Top 15 databases:\n",
"DatabaseHaveWorkedWith\n",
"PostgreSQL 8827\n",
"MySQL 6112\n",
"Redis 4865\n",
"Microsoft SQL Server 4599\n",
"SQLite 4586\n",
"MongoDB 4273\n",
"Elasticsearch 3181\n",
"MariaDB 2800\n",
"Dynamodb 2339\n",
"Oracle 1348\n",
"Cloud Firestore 1066\n",
"BigQuery 981\n",
"Firebase Realtime Database 951\n",
"Cosmos DB 934\n",
"H2 732\n",
"Name: count, dtype: int64\n",
"\n",
"Top 15 platforms:\n",
"PlatformHaveWorkedWith\n",
"Amazon Web Services (AWS) 9556\n",
"Microsoft Azure 5162\n",
"Google Cloud 4082\n",
"Cloudflare 2673\n",
"Digital Ocean 2482\n",
"Firebase 2157\n",
"Heroku 1843\n",
"Vercel 1627\n",
"Netlify 1352\n",
"VMware 956\n",
"Hetzner 741\n",
"Linode, now Akamai 637\n",
"Managed Hosting 624\n",
"OVH 616\n",
"OpenShift 528\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"# Explore what languages/skills are most common\n",
"print(\"Top 20 languages:\")\n",
"all_langs = df_model['LanguageHaveWorkedWith'].str.split(';').explode()\n",
"print(all_langs.value_counts().head(20))\n",
"\n",
"print(\"\\nTop 15 databases:\")\n",
"all_dbs = df_model['DatabaseHaveWorkedWith'].str.split(';').explode()\n",
"print(all_dbs.value_counts().head(15))\n",
"\n",
"print(\"\\nTop 15 platforms:\")\n",
"all_platforms = df_model['PlatformHaveWorkedWith'].str.split(';').explode()\n",
"print(all_platforms.value_counts().head(15))\n",
"\n",
"# After seeing these, we'll create binary features for:\n",
"# - High-paying languages (Rust, Go, Scala, etc.)\n",
"# - Popular languages (Python, JavaScript, SQL, etc.)\n",
"# - Specialized databases (PostgreSQL, MongoDB, etc.)\n",
"# - Cloud platforms (AWS, Azure, Google Cloud)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "892f833c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== ALL Skill Correlations (sorted) ===\n",
"Has_Terraform 0.177524\n",
"Has_AWS 0.142293\n",
"Has_Kubernetes 0.128906\n",
"Has_Go 0.123166\n",
"Has_Rust 0.082603\n",
"Has_Python 0.079271\n",
"Has_Docker 0.064240\n",
"Has_Redis 0.061930\n",
"Has_React 0.061303\n",
"Has_Elasticsearch 0.053530\n",
"Has_PostgreSQL 0.049394\n",
"Has_Scala 0.046563\n",
"Has_TypeScript 0.037359\n",
"Has_GCP 0.035238\n",
"Has_Swift 0.023807\n",
"Has_Kotlin 0.014656\n",
"Has_SQL 0.005919\n",
"Has_Azure -0.014817\n",
"Has_JavaScript -0.015086\n",
"Has_NextJS -0.017373\n",
"Has_Java -0.017985\n",
"Has_CSharp -0.020396\n",
"Has_NodeJS -0.024481\n",
"Has_SpringBoot -0.035489\n",
"Has_MongoDB -0.095041\n",
"Name: ConvertedCompYearly, dtype: float64\n",
"\n",
"Final shape with binary skill features: (15309, 41)\n",
"\n",
"Feature types:\n",
"int64 25\n",
"object 14\n",
"float64 2\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"# Create binary features for specific high-value and popular skills\n",
"\n",
"# === LANGUAGES ===\n",
"# High-paying/specialized languages\n",
"df_model['Has_Rust'] = df_model['LanguageHaveWorkedWith'].str.contains('Rust', na=False).astype(int)\n",
"df_model['Has_Go'] = df_model['LanguageHaveWorkedWith'].str.contains('Go', na=False).astype(int)\n",
"df_model['Has_Scala'] = df_model['LanguageHaveWorkedWith'].str.contains('Scala', na=False).astype(int)\n",
"df_model['Has_Kotlin'] = df_model['LanguageHaveWorkedWith'].str.contains('Kotlin', na=False).astype(int)\n",
"df_model['Has_Swift'] = df_model['LanguageHaveWorkedWith'].str.contains('Swift', na=False).astype(int)\n",
"\n",
"# Popular/versatile languages\n",
"df_model['Has_Python'] = df_model['LanguageHaveWorkedWith'].str.contains('Python', na=False).astype(int)\n",
"df_model['Has_JavaScript'] = df_model['LanguageHaveWorkedWith'].str.contains('JavaScript', na=False).astype(int)\n",
"df_model['Has_TypeScript'] = df_model['LanguageHaveWorkedWith'].str.contains('TypeScript', na=False).astype(int)\n",
"df_model['Has_Java'] = df_model['LanguageHaveWorkedWith'].str.contains('Java', na=False).astype(int)\n",
"df_model['Has_CSharp'] = df_model['LanguageHaveWorkedWith'].str.contains('C#', na=False).astype(int)\n",
"df_model['Has_SQL'] = df_model['LanguageHaveWorkedWith'].str.contains('SQL', na=False).astype(int)\n",
"\n",
"# === DATABASES ===\n",
"df_model['Has_PostgreSQL'] = df_model['DatabaseHaveWorkedWith'].str.contains('PostgreSQL', na=False).astype(int)\n",
"df_model['Has_MongoDB'] = df_model['DatabaseHaveWorkedWith'].str.contains('MongoDB', na=False).astype(int)\n",
"df_model['Has_Redis'] = df_model['DatabaseHaveWorkedWith'].str.contains('Redis', na=False).astype(int)\n",
"df_model['Has_Elasticsearch'] = df_model['DatabaseHaveWorkedWith'].str.contains('Elasticsearch', na=False).astype(int)\n",
"\n",
"# === CLOUD PLATFORMS ===\n",
"df_model['Has_AWS'] = df_model['PlatformHaveWorkedWith'].str.contains('Amazon Web Services|AWS', na=False).astype(int)\n",
"df_model['Has_Azure'] = df_model['PlatformHaveWorkedWith'].str.contains('Azure', na=False).astype(int)\n",
"df_model['Has_GCP'] = df_model['PlatformHaveWorkedWith'].str.contains('Google Cloud', na=False).astype(int)\n",
"\n",
"# === WEB FRAMEWORKS (modern ones tend to pay better) ===\n",
"df_model['Has_React'] = df_model['WebframeHaveWorkedWith'].str.contains('React', na=False).astype(int)\n",
"df_model['Has_NextJS'] = df_model['WebframeHaveWorkedWith'].str.contains('Next.js', na=False).astype(int)\n",
"df_model['Has_NodeJS'] = df_model['WebframeHaveWorkedWith'].str.contains('Node.js', na=False).astype(int)\n",
"df_model['Has_SpringBoot'] = df_model['WebframeHaveWorkedWith'].str.contains('Spring Boot', na=False).astype(int)\n",
"\n",
"# === DEVOPS/INFRASTRUCTURE TOOLS (these should correlate well) ===\n",
"df_model['Has_Docker'] = df_model['ToolsTechHaveWorkedWith'].str.contains('Docker', na=False).astype(int)\n",
"df_model['Has_Kubernetes'] = df_model['ToolsTechHaveWorkedWith'].str.contains('Kubernetes', na=False).astype(int)\n",
"df_model['Has_Terraform'] = df_model['ToolsTechHaveWorkedWith'].str.contains('Terraform', na=False).astype(int)\n",
"\n",
"# Recalculate correlations for ALL skill features\n",
"skill_features = [col for col in df_model.columns if col.startswith('Has_')]\n",
"skill_corr = df_model[skill_features + ['ConvertedCompYearly']].corr()['ConvertedCompYearly'].sort_values(ascending=False)\n",
"\n",
"print(\"=== ALL Skill Correlations (sorted) ===\")\n",
"print(skill_corr[skill_corr.index != 'ConvertedCompYearly'])\n",
"\n",
"# Now drop the original string columns AND the count features\n",
"cols_to_drop_final = [\n",
" 'LanguageHaveWorkedWith',\n",
" 'DatabaseHaveWorkedWith', \n",
" 'PlatformHaveWorkedWith',\n",
" 'WebframeHaveWorkedWith',\n",
" 'ToolsTechHaveWorkedWith',\n",
" # Drop count features since binary features are better\n",
" 'Num_Languages',\n",
" 'Num_Databases',\n",
" 'Num_Platforms',\n",
" 'Num_Webframes',\n",
" 'Num_Tools',\n",
" 'Total_Skills'\n",
"]\n",
"\n",
"df_model = df_model.drop(columns=cols_to_drop_final)\n",
"\n",
"print(f\"\\nFinal shape with binary skill features: {df_model.shape}\")\n",
"print(f\"\\nFeature types:\")\n",
"print(df_model.dtypes.value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f5200a6c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 15309 entries, 0 to 48017\n",
"Data columns (total 41 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 MainBranch 15309 non-null object \n",
" 1 Age 15309 non-null object \n",
" 2 Employment 15309 non-null object \n",
" 3 RemoteWork 15309 non-null object \n",
" 4 EdLevel 15309 non-null object \n",
" 5 LearnCode 15309 non-null object \n",
" 6 YearsCode 15309 non-null object \n",
" 7 YearsCodePro 15309 non-null object \n",
" 8 DevType 15309 non-null object \n",
" 9 OrgSize 15309 non-null object \n",
" 10 Country 15309 non-null object \n",
" 11 OpSysProfessional use 15309 non-null object \n",
" 12 AISelect 15309 non-null object \n",
" 13 WorkExp 15309 non-null float64\n",
" 14 Industry 15309 non-null object \n",
" 15 ConvertedCompYearly 15309 non-null float64\n",
" 16 Has_Rust 15309 non-null int64 \n",
" 17 Has_Go 15309 non-null int64 \n",
" 18 Has_Scala 15309 non-null int64 \n",
" 19 Has_Kotlin 15309 non-null int64 \n",
" 20 Has_Swift 15309 non-null int64 \n",
" 21 Has_Python 15309 non-null int64 \n",
" 22 Has_JavaScript 15309 non-null int64 \n",
" 23 Has_TypeScript 15309 non-null int64 \n",
" 24 Has_Java 15309 non-null int64 \n",
" 25 Has_CSharp 15309 non-null int64 \n",
" 26 Has_SQL 15309 non-null int64 \n",
" 27 Has_PostgreSQL 15309 non-null int64 \n",
" 28 Has_MongoDB 15309 non-null int64 \n",
" 29 Has_Redis 15309 non-null int64 \n",
" 30 Has_Elasticsearch 15309 non-null int64 \n",
" 31 Has_AWS 15309 non-null int64 \n",
" 32 Has_Azure 15309 non-null int64 \n",
" 33 Has_GCP 15309 non-null int64 \n",
" 34 Has_React 15309 non-null int64 \n",
" 35 Has_NextJS 15309 non-null int64 \n",
" 36 Has_NodeJS 15309 non-null int64 \n",
" 37 Has_SpringBoot 15309 non-null int64 \n",
" 38 Has_Docker 15309 non-null int64 \n",
" 39 Has_Kubernetes 15309 non-null int64 \n",
" 40 Has_Terraform 15309 non-null int64 \n",
"dtypes: float64(2), int64(25), object(14)\n",
"memory usage: 4.9+ MB\n"
]
}
],
"source": [
"df_model.info()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "9de647c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Remaining categorical columns:\n",
"\n",
"MainBranch: 2 unique values\n",
"MainBranch\n",
"I am a developer by profession 14757\n",
"I am not primarily a developer, but I write code sometimes as part of my work/studies 552\n",
"Name: count, dtype: int64\n",
"\n",
"Age: 8 unique values\n",
"Age\n",
"25-34 years old 7575\n",
"35-44 years old 4552\n",
"18-24 years old 1450\n",
"45-54 years old 1326\n",
"55-64 years old 357\n",
"65 years or older 35\n",
"Under 18 years old 7\n",
"Prefer not to say 7\n",
"Name: count, dtype: int64\n",
"\n",
"Employment: 10 unique values\n",
"Employment\n",
"Employed, full-time 12724\n",
"Employed, full-time;Independent contractor, freelancer, or self-employed 1412\n",
"Independent contractor, freelancer, or self-employed 787\n",
"Employed, part-time 205\n",
"Independent contractor, freelancer, or self-employed;Employed, part-time 77\n",
"Employed, full-time;Employed, part-time 49\n",
"Employed, full-time;Independent contractor, freelancer, or self-employed;Employed, part-time 48\n",
"Employed, full-time;Retired 3\n",
"Employed, full-time;Independent contractor, freelancer, or self-employed;Retired 2\n",
"Independent contractor, freelancer, or self-employed;Retired 2\n",
"Name: count, dtype: int64\n",
"\n",
"RemoteWork: 3 unique values\n",
"RemoteWork\n",
"Remote 7361\n",
"Hybrid (some remote, some in-person) 6440\n",
"In-person 1508\n",
"Name: count, dtype: int64\n",
"\n",
"EdLevel: 8 unique values\n",
"EdLevel\n",
"Bachelor’s degree (B.A., B.S., B.Eng., etc.) 7564\n",
"Master’s degree (M.A., M.S., M.Eng., MBA, etc.) 3852\n",
"Some college/university study without earning a degree 1933\n",
"Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.) 731\n",
"Associate degree (A.A., A.S., etc.) 548\n",
"Professional degree (JD, MD, Ph.D, Ed.D, etc.) 473\n",
"Something else 143\n",
"Primary/elementary school 65\n",
"Name: count, dtype: int64\n",
"\n",
"LearnCode: 554 unique values\n",
"Top 10:\n",
"LearnCode\n",
"Books / Physical media;Online Courses or Certification;On the job training;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc) 604\n",
"Other online resources (e.g., videos, blogs, forum) 568\n",
"Books / Physical media;Online Courses or Certification;On the job training;Other online resources (e.g., videos, blogs, forum) 555\n",
"Books / Physical media;Online Courses or Certification;Other online resources (e.g., videos, blogs, forum) 520\n",
"Online Courses or Certification;On the job training;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc) 463\n",
"Books / Physical media;Other online resources (e.g., videos, blogs, forum) 454\n",
"Online Courses or Certification;On the job training;Other online resources (e.g., videos, blogs, forum) 416\n",
"Books / Physical media;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc) 402\n",
"Online Courses or Certification;Other online resources (e.g., videos, blogs, forum) 397\n",
"Books / Physical media;On the job training;Other online resources (e.g., videos, blogs, forum);School (i.e., University, College, etc) 396\n",
"Name: count, dtype: int64\n",
"\n",
"YearsCode: 52 unique values\n",
"Top 10:\n",
"YearsCode\n",
"10 1320\n",
"15 1021\n",
"20 912\n",
"8 906\n",
"12 855\n",
"7 738\n",
"13 668\n",
"9 644\n",
"6 629\n",
"5 609\n",
"Name: count, dtype: int64\n",
"\n",
"YearsCodePro: 51 unique values\n",
"Top 10:\n",
"YearsCodePro\n",
"5 1197\n",
"10 1148\n",
"4 988\n",
"6 984\n",
"8 977\n",
"3 969\n",
"7 925\n",
"2 880\n",
"15 676\n",
"12 672\n",
"Name: count, dtype: int64\n",
"\n",
"DevType: 33 unique values\n",
"Top 10:\n",
"DevType\n",
"Developer, full-stack 6700\n",
"Developer, back-end 3556\n",
"Developer, front-end 817\n",
"Engineering manager 560\n",
"Developer, desktop or enterprise applications 422\n",
"Other (please specify): 399\n",
"Senior Executive (C-Suite, VP, etc.) 358\n",
"DevOps specialist 357\n",
"Cloud infrastructure engineer 256\n",
"Developer, mobile 240\n",
"Name: count, dtype: int64\n",
"\n",
"OrgSize: 10 unique values\n",
"OrgSize\n",
"20 to 99 employees 3702\n",
"100 to 499 employees 3167\n",
"10,000 or more employees 1856\n",
"1,000 to 4,999 employees 1753\n",
"2 to 9 employees 1515\n",
"10 to 19 employees 1433\n",
"500 to 999 employees 1109\n",
"5,000 to 9,999 employees 573\n",
"I don’t know 140\n",
"Just me - I am a freelancer, sole proprietor, etc. 61\n",
"Name: count, dtype: int64\n",
"\n",
"Country: 140 unique values\n",
"Top 10:\n",
"Country\n",
"United States of America 4042\n",
"United Kingdom of Great Britain and Northern Ireland 1205\n",
"Germany 1164\n",
"Canada 684\n",
"India 589\n",
"France 513\n",
"Brazil 455\n",
"Netherlands 414\n",
"Poland 414\n",
"Australia 404\n",
"Name: count, dtype: int64\n",
"\n",
"OpSysProfessional use: 947 unique values\n",
"Top 10:\n",
"OpSysProfessional use\n",
"MacOS 3557\n",
"Windows 2284\n",
"Windows;Windows Subsystem for Linux (WSL) 941\n",
"Ubuntu 919\n",
"MacOS;Ubuntu 636\n",
"Ubuntu;Windows;Windows Subsystem for Linux (WSL) 482\n",
"MacOS;Windows 367\n",
"Ubuntu;Windows 362\n",
"Arch 189\n",
"MacOS;Other Linux-based 177\n",
"Name: count, dtype: int64\n",
"\n",
"AISelect: 3 unique values\n",
"AISelect\n",
"Yes 7271\n",
"No, and I don't plan to 4073\n",
"No, but I plan to soon 3965\n",
"Name: count, dtype: int64\n",
"\n",
"Industry: 12 unique values\n",
"Industry\n",
"Information Services, IT, Software Development, or other Technology 7400\n",
"Financial Services 1993\n",
"Other 1549\n",
"Healthcare 1008\n",
"Manufacturing, Transportation, or Supply Chain 978\n",
"Retail and Consumer Services 968\n",
"Higher Education 401\n",
"Advertising Services 351\n",
"Insurance 350\n",
"Legal Services 116\n",
"Oil & Gas 115\n",
"Wholesale 80\n",
"Name: count, dtype: int64\n"
]
}
],
"source": [
"# Check remaining categorical columns\n",
"categorical_cols = df_model.select_dtypes(include='object').columns.tolist()\n",
"\n",
"print(\"Remaining categorical columns:\")\n",
"for col in categorical_cols:\n",
" n_unique = df_model[col].nunique()\n",
" print(f\"\\n{col}: {n_unique} unique values\")\n",
" if n_unique <= 20:\n",
" print(df_model[col].value_counts())\n",
" else:\n",
" print(f\"Top 10:\\n{df_model[col].value_counts().head(10)}\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "5f7c3e13",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Simplified Employment:\n",
"Employment_Simple\n",
"Full-time 12776\n",
"Full-time + Freelance 1462\n",
"Freelance 866\n",
"Part-time 205\n",
"Name: count, dtype: int64\n",
"\n",
"Numeric columns check:\n",
" YearsCode YearsCodePro WorkExp\n",
"count 15289.000000 15073.000000 15309.000000\n",
"mean 15.429394 10.708286 11.621073\n",
"std 8.859019 7.684437 8.334593\n",
"min 1.000000 1.000000 0.000000\n",
"25% 9.000000 5.000000 5.000000\n",
"50% 13.000000 9.000000 10.000000\n",
"75% 20.000000 15.000000 16.000000\n",
"max 50.000000 50.000000 50.000000\n",
"\n",
"Column types:\n",
"bool 44\n",
"int64 27\n",
"float64 5\n",
"Name: count, dtype: int64\n",
"\n",
"All numeric? True\n",
"\n",
"Remaining nulls:\n",
"YearsCode 20\n",
"YearsCodePro 236\n",
"OrgSize_Ordinal 140\n",
"dtype: int64\n"
]
}
],
"source": [
"# === 1. DROP: Too complex to be useful ===\n",
"# LearnCode has 554 combinations - not useful for prediction\n",
"# OpSysProfessional use has 947 combinations - too fragmented\n",
"df_model = df_model.drop(columns=['LearnCode', 'OpSysProfessional use'])\n",
"\n",
"# === 2. ORDINAL ENCODING: Natural order ===\n",
"# Age - clear ordinal relationship\n",
"age_order = {\n",
" 'Under 18 years old': 0,\n",
" '18-24 years old': 1,\n",
" '25-34 years old': 2,\n",
" '35-44 years old': 3,\n",
" '45-54 years old': 4,\n",
" '55-64 years old': 5,\n",
" '65 years or older': 6,\n",
" 'Prefer not to say': 2 # Assign to middle age as neutral\n",
"}\n",
"df_model['Age_Ordinal'] = df_model['Age'].map(age_order)\n",
"df_model = df_model.drop(columns=['Age'])\n",
"\n",
"# EdLevel - clear ordinal relationship\n",
"edlevel_order = {\n",
" 'Primary/elementary school': 0,\n",
" 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)': 1,\n",
" 'Some college/university study without earning a degree': 2,\n",
" 'Associate degree (A.A., A.S., etc.)': 3,\n",
" \"Bachelor’s degree (B.A., B.S., B.Eng., etc.)\": 4,\n",
" \"Master’s degree (M.A., M.S., M.Eng., MBA, etc.)\": 5,\n",
" 'Professional degree (JD, MD, Ph.D, Ed.D, etc.)': 6,\n",
" 'Something else': 2 # Assign to some college as neutral\n",
"}\n",
"df_model['EdLevel_Ordinal'] = df_model['EdLevel'].map(edlevel_order)\n",
"df_model = df_model.drop(columns=['EdLevel'])\n",
"\n",
"# === 3. SIMPLIFY: Employment (consolidate rare categories) ===\n",
"def simplify_employment(emp):\n",
" if pd.isna(emp):\n",
" return 'Unknown'\n",
" elif 'Employed, full-time' in emp and 'Independent contractor' in emp:\n",
" return 'Full-time + Freelance'\n",
" elif 'Employed, full-time' in emp:\n",
" return 'Full-time'\n",
" elif 'Independent contractor' in emp or 'freelancer' in emp or 'self-employed' in emp:\n",
" return 'Freelance'\n",
" elif 'part-time' in emp:\n",
" return 'Part-time'\n",
" else:\n",
" return 'Other'\n",
"\n",
"df_model['Employment_Simple'] = df_model['Employment'].apply(simplify_employment)\n",
"df_model = df_model.drop(columns=['Employment'])\n",
"print(\"\\nSimplified Employment:\")\n",
"print(df_model['Employment_Simple'].value_counts())\n",
"\n",
"# === 4. ONE-HOT ENCODE: Low cardinality ===\n",
"# MainBranch (2 unique), RemoteWork (3), AISelect (3), Industry (12)\n",
"one_hot_cols = ['MainBranch', 'RemoteWork', 'AISelect', 'Employment_Simple']\n",
"\n",
"for col in one_hot_cols:\n",
" dummies = pd.get_dummies(df_model[col], prefix=col, drop_first=True)\n",
" df_model = pd.concat([df_model, dummies], axis=1)\n",
" df_model = df_model.drop(columns=[col])\n",
"\n",
"# Industry - one-hot encode\n",
"industry_dummies = pd.get_dummies(df_model['Industry'], prefix='Industry', drop_first=True)\n",
"df_model = pd.concat([df_model, industry_dummies], axis=1)\n",
"df_model = df_model.drop(columns=['Industry'])\n",
"\n",
"# === 5. NUMERIC: Already numeric, keep as-is ===\n",
"# YearsCode, YearsCodePro, WorkExp - just convert to numeric\n",
"df_model['YearsCode'] = pd.to_numeric(df_model['YearsCode'], errors='coerce')\n",
"df_model['YearsCodePro'] = pd.to_numeric(df_model['YearsCodePro'], errors='coerce')\n",
"\n",
"print(\"\\nNumeric columns check:\")\n",
"print(df_model[['YearsCode', 'YearsCodePro', 'WorkExp']].describe())\n",
"\n",
"# === 6. HANDLE HIGH CARDINALITY ===\n",
"# DevType (33 unique) - keep top 10, group rest as \"Other\"\n",
"top_10_devtypes = df_model['DevType'].value_counts().head(10).index\n",
"df_model['DevType_Grouped'] = df_model['DevType'].apply(\n",
" lambda x: x if x in top_10_devtypes else 'Other'\n",
")\n",
"devtype_dummies = pd.get_dummies(df_model['DevType_Grouped'], prefix='DevType', drop_first=True)\n",
"df_model = pd.concat([df_model, devtype_dummies], axis=1)\n",
"df_model = df_model.drop(columns=['DevType', 'DevType_Grouped'])\n",
"\n",
"# OrgSize - ordinal encoding (size matters)\n",
"orgsize_order = {\n",
" 'Just me - I am a freelancer, sole proprietor, etc.': 0,\n",
" '2 to 9 employees': 1,\n",
" '10 to 19 employees': 2,\n",
" '20 to 99 employees': 3,\n",
" '100 to 499 employees': 4,\n",
" '500 to 999 employees': 5,\n",
" '1,000 to 4,999 employees': 6,\n",
" '5,000 to 9,999 employees': 7,\n",
" '10,000 or more employees': 8,\n",
" \"I don't know\": 4 # Assign to middle as neutral\n",
"}\n",
"df_model['OrgSize_Ordinal'] = df_model['OrgSize'].map(orgsize_order)\n",
"df_model = df_model.drop(columns=['OrgSize'])\n",
"\n",
"# Country (140 unique) - keep top 15, group rest as \"Other\"\n",
"top_15_countries = df_model['Country'].value_counts().head(15).index\n",
"df_model['Country_Grouped'] = df_model['Country'].apply(\n",
" lambda x: x if x in top_15_countries else 'Other'\n",
")\n",
"country_dummies = pd.get_dummies(df_model['Country_Grouped'], prefix='Country', drop_first=True)\n",
"df_model = pd.concat([df_model, country_dummies], axis=1)\n",
"df_model = df_model.drop(columns=['Country', 'Country_Grouped'])\n",
"\n",
"print(f\"\\nColumn types:\")\n",
"print(df_model.dtypes.value_counts())\n",
"print(f\"\\nAll numeric? {df_model.select_dtypes(include='object').shape[1] == 0}\")\n",
"\n",
"# Check for any remaining nulls\n",
"print(f\"\\nRemaining nulls:\")\n",
"null_counts = df_model.isnull().sum()\n",
"print(null_counts[null_counts > 0])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "1152a461",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Shape after dropping nulls: (14924, 76)\n",
"Rows lost: 385\n",
"Percentage retained: 97.5%\n",
"\n",
"Remaining nulls: 0\n",
"All numeric/boolean? True\n",
"\n",
"=== FINAL CLEAN DATASET ===\n",
"Rows: 14924\n",
"Columns: 76\n",
"Features: 75\n",
"Target: ConvertedCompYearly\n"
]
}
],
"source": [
"# Drop rows with any remaining nulls\n",
"df_model = df_model.dropna()\n",
"\n",
"print(f\"\\nShape after dropping nulls: {df_model.shape}\")\n",
"print(f\"Rows lost: {15309 - len(df_model)}\")\n",
"print(f\"Percentage retained: {len(df_model)/15309*100:.1f}%\")\n",
"\n",
"# Verify completely clean\n",
"print(f\"\\nRemaining nulls: {df_model.isnull().sum().sum()}\")\n",
"print(f\"All numeric/boolean? {df_model.select_dtypes(include='object').shape[1] == 0}\")\n",
"\n",
"print(f\"\\n=== FINAL CLEAN DATASET ===\")\n",
"print(f\"Rows: {len(df_model)}\")\n",
"print(f\"Columns: {df_model.shape[1]}\")\n",
"print(f\"Features: {df_model.shape[1] - 1}\")\n",
"print(f\"Target: ConvertedCompYearly\")"
]
},
{
"cell_type": "markdown",
"id": "a2153728",
"metadata": {},
"source": [
"## ML Modeling"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "035bd66f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Features shape: (14924, 75)\n",
"Target shape: (14924,)\n",
"\n",
"=== TOP 20 POSITIVE CORRELATIONS WITH SALARY ===\n",
"Country_United States of America 0.600111\n",
"YearsCodePro 0.310453\n",
"WorkExp 0.305963\n",
"YearsCode 0.302707\n",
"Age_Ordinal 0.258526\n",
"OrgSize_Ordinal 0.211776\n",
"Has_Terraform 0.175568\n",
"RemoteWork_Remote 0.142808\n",
"Has_AWS 0.140094\n",
"Has_Kubernetes 0.126078\n",
"Has_Go 0.121580\n",
"DevType_Engineering manager 0.106681\n",
"DevType_Senior Executive (C-Suite, VP, etc.) 0.097640\n",
"Has_Rust 0.085012\n",
"Has_Python 0.083086\n",
"Has_React 0.063394\n",
"Has_Docker 0.061214\n",
"Industry_Financial Services 0.060895\n",
"Has_Redis 0.058601\n",
"Country_Switzerland 0.057682\n",
"dtype: float64\n",
"\n",
"=== TOP 20 NEGATIVE CORRELATIONS WITH SALARY ===\n",
"AISelect_Yes -0.032045\n",
"Industry_Manufacturing, Transportation, or Supply Chain -0.032050\n",
"Has_SpringBoot -0.035668\n",
"Country_Netherlands -0.044026\n",
"Employment_Simple_Part-time -0.050640\n",
"DevType_Developer, full-stack -0.060592\n",
"Country_Portugal -0.061218\n",
"DevType_Developer, front-end -0.061295\n",
"Industry_Information Services, IT, Software Development, or other Technology -0.062504\n",
"Country_Germany -0.065142\n",
"Country_Sweden -0.071242\n",
"Country_Poland -0.074110\n",
"Country_Spain -0.078691\n",
"Country_France -0.088070\n",
"Has_MongoDB -0.093737\n",
"Country_Italy -0.100666\n",
"RemoteWork_In-person -0.117706\n",
"Country_Brazil -0.132113\n",
"Country_India -0.181109\n",
"Country_Other -0.277522\n",
"dtype: float64\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAASmCAYAAAAzjMgKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjcsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvTLEjVAAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3QWYVOX////30tLdjYSAEhYSgkkpooSANAboB4uPErYgpSIIIn4MQEFEQQzCQCkxQRAELKRFOkV6/tfr/v3PfGeX2dlZ2LP5fFzX6O6cmdOzzOu87/s+MYFAIGAAAAAAACDJZUr6WQIAAAAAACF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAgQ9q4caPFxMTYc889l9KrkupMmjTJ7Rvto6Te35p3WqH1ffLJJ6N+7X/+8x9LLbTeWiekPEI3AABIl/RlM5rHwoULfV2Pf//913r16mU1a9a0fPnyWe7cua1WrVo2ZswYO3HixBmv379/v915551WpEgRy5Url1111VX2448/RrWsJk2axLudv/zyiw9bZzZ+/Pg0FaJS0vr16+2uu+6yihUrWo4cOSxv3rzWoEEDdy7oPEkv3n77bRs9erSlR19//bULs/qcJrVdu3bZfffdZ9WqVbPzzjvPihYtapdddpn179/fDh8+nOTLQ/LJkozLAgAASDZvvfVWrN/ffPNN+/zzz894/oILLvB1PRSm1qxZYy1atLDy5ctbpkyZ3Bf3Bx54wL777jsXUDynT5+2li1b2k8//WQPPfSQFS5c2IVahenly5db5cqVE1xe6dKlbdiwYWc8X7JkSfOD1k/r2b17d1/mn17MmTPH2rVrZ9mzZ7euXbu6izDHjx+3r776yh1rnSP/+9//LD3QOf3zzz/b/fffH+v5cuXKuc9D1qxZLa3Q+mbJ8n+RSZ/dp556yp3v+fPnT7Ll7N271y655BI7ePCg9ezZ0wXvPXv22KpVq+zll1+2Pn36uAt2SJsI3QAAIF3q3LlzrN+//fZbF7rjPu+3ggULumWH6t27t6t6jxs3zkaNGmXFixd3z8+YMcN9qX/vvfesbdu27rn27dtblSpV7IknnogV0OOj+Sb3Nia1QCBgR48eddW+9GDDhg3WoUMHFzq//PJLK1GiRHDaPffcY3/88YcL5X7uNz2fLVs2d9EnpajFhSr8aUlyre/rr79umzdvtqVLl1r9+vVjTVMQ17FLSentM5ncaF4OAAAyrH/++cf69etnZcqUcRXIqlWruv69+oIZrq/m1KlT3Wv0Rfziiy+2xYsXn/WyVfWW0GaqCt3FihWzW265JficmpkreH/44Yd27NgxO1eahwL8+eef77ZZ2/7www+fMe+JEyfa1Vdf7Zq46nXVq1d3Fbe426AK7aJFi4LN2FWVj9SfNFxfYc3nhhtusE8//dRV+/TF/pVXXgnuH1VMvWOk9R4xYoRrFRDqnXfeccckT548rtn2hRde6JptR+uFF15woVjLbty4savUhu4LrfOKFSvOeN/QoUMtc+bMtm3btnjnPXLkSNc8WMEqNHB7tE1qVuw5efKkDR482CpVquS2Wftn0KBBZxyj+PabukxofbVPHn30UStVqpTlzJnThTdRC4tmzZq5CzR6XtursJcQnYNqiaFWE1ovrZ/W89SpU8HX6PjrAsKmTZuC54R3rsfXp1sXIho1auS6U6h6fNNNN9m6detivcY7n3SBwqsya/179OhhR44cibjeL774ojtGoZ+1559/3s3vwQcfDD6n7dD5o+bc4fp06/9qlSAVKlQIbl/cfu8ffPCBa8mgfVSjRg375JNPoup6oHWsV6/eGdN0PoeG/yVLlrhWE2XLlg1+htVyJpouCtF8riOdWzpX1D0mHP1tbNq0aYLrkBFR6QYAABmSgnWrVq1swYIFrs917dq13RdMfalWgFIIC6VgOX36dLv33nvdl1U1q1Zw+f77790X7ISoKbFCj74YL1u2zIV7hTwFLo9CXd26dc+oRqpfp5oe//bbby5MRqLgsHv37ljP6Qu7mqYqqGqb1aRZ/cbVtH716tVuWzVvhQWPvogrMOj1al778ccf29133+3moeqsqN9u37593bwfeeQR95wuGpyNX3/91Tp27Oj6PN9xxx3uC7zClL7k63joeYUMtQQYOHCgbd++PdhvWC0Y9N5rrrnGBXJRaFOQDA2z8VHXg0OHDrntUjVPYV3BRPtG26NWB5qmiy516tSJ9V49p6CpYBsf7Tv1445bwYzP7bffbpMnT3bL1UUhhWR1GdA2zZo1K8H95lEgVoX0v//9rwvs+lkBt3nz5u4ChS6+6FzzgpjCnM61+Cgs61grqOr/mtfjjz/uzutnn33WvUbnwYEDB2zr1q3Bz1CkZtHz589366P9o1Crz8fYsWNdX3eNZeAFdo8uQCnwan9o+muvveYCpHfcw1Gg13mr815BUrSt2nb9P/Tzp4sjV155Zdj56GKYPifTpk1z26ZuFd6FMY+W8f7777vPigK8An+bNm1cFbtQoULxrqP+Fuizq+4v3bp1s0jUEkafDTU51zz1N0j7TPtc0yKJ5nMd6dzSsdTPuigV+nfvhx9+cPtGF3kQRgAAACADuOeee1S+Dv7+wQcfuN+HDBkS63Vt27YNxMTEBP7444/gc3qdHsuWLQs+t2nTpkCOHDkCN998c1TLnzZtWnA+elxyySWBVatWxXpNrly5Aj179jzjvXPmzHHv+eSTTyIuo3HjxrGW4T26devmpr/11luBTJkyBZYsWRLrfRMmTHCvW7p0afC5I0eOnDH/pk2bBipWrBjruRo1arjlxvXEE0/E2t+eiRMnuuc3bNgQfK5cuXJht2/w4MFun/z222+xnh8wYEAgc+bMgc2bN7vf77vvvkDevHkDJ0+eDCSG1kHLPe+88wJbt24NPv/dd9+55x944IHgcx07dgyULFkycOrUqeBzP/74o3udtik+Bw4ccK+56aabolqnlStXutfffvvtsZ7/73//657/8ssvE9xvCxYscM/rWIUex9OnTwcqV67sjqN+9ug1FSpUCFx33XURj1O4c+Kuu+4K5MyZM3D06NHgcy1btnTrFt/+Dt1ftWvXDhQtWjSwZ8+e4HM//fSTO0+7du16xvkU9/Ohz1+hQoUCkeiY6fx4+OGHg/tB72nXrp07jw4dOuSeHzVqlFvuvn37gu/VMrVsz7PPPnvGfgl9bbZs2WL97dC26PmxY8dGXMe///47UKRIEffaatWqBXr37h14++23A/v37z/jteGOw7Bhw9zfLf1divQZjPZzHd+5pfXR373+/fvHev7ee+91n9XDhw9H3M6MiublAAAgQ5o7d65rzqnKdShVFvX9ed68ebGev+KKK1x10KOqq5rBqjoe2rw2PhqFXBVZVaLUp1uDSal5eyhV+VRFj8trWhpN81FVBrWc0Ieaj4uWreq2BmlSNdx7qMopqvp7QvtuqnKp16nq/Oeff7rfk5qql3Gbpmp9VaUsUKBArPW99tpr3T73mverqbH2pbb1bLRu3TpWpVrV3ssvv9ydIx4NfvbXX3/F2keqcms/qZIZH69Jt6qe0fCWGdrs2TsvJW7f73D7zaOKaehxXLlypf3+++/WqVMnN0iXtz+179RKQPszbrP9UKHzUssAvVfHR1XXsxkdX60VtE5qLq6xDzwXXXSRXXfddbH2v0efnVBavrbF28/hqKKtVgbe+aIWA3rPgAED3Gf9m2++cc+r6q3q7bkMkKZzU83uQ7dFzcP1uYlELSo0gKK2b9++fTZhwgR3nFTFV4uF0C4vocdBx07HQdun14TrAhEqMZ/rcOeWmvTr756q/d466bOoVkD6HKmLAM5E83IAAJAhqc+p+qbGDUPeaOaaHircyOEa4EyBQ7f68QZDi/Sl2mt6rWbD6gusYKEQ5L1XX4jD9dtWk2dvekL0pVdf/MPRshQ4QpvDhtq5c2fwZzXNVvNjBZK4fWb15VxfvpOSvuCHW1+N3pzQ+qp57LvvvuuaKSs8X3/99a4Zspr/RyO+Y6t5enSs1B9bQVsBVeFUwUMBJFKgVuDyQmo0dN4pJIZ2OxCdIwqDcc/LcPstvmnanxKp+bKOrS5yhKP++2o+rGblcUPu2VyI8bYltEl86OdQF7QUKkODnC52hfLWVUHV29fhKJx7zdcVrnUs1ZVD/ZP1u46vmobrvDkXcdfPW0etX0K0Tmr+ra4rOlbafjWbVxN+TVO3A1FTdT330UcfnTHfhI5DYj7X8Z1bugClkK39pqb46iKwY8cO69KlS4LbmFERugEAAFKAgrf6v2pwKvWZFH2xVvUvLu+5c73tl4Ki+oRrxPRwNCCTN6iTgqUq4nqtnld/YFUe1Zc1UjXUE24QNYmvVUC4CwpajsKQV6kPF4xF1UBVTBVS1EJBD/VTVjhQ3+ikoFYRqjy++uqrLhQpvKjyndBI8QqCOm6hA7NFI779F1ekCzFxp3nHTf2vNYZBOPH1v9YgZKqIanuefvppV81VCwz1q9bAY9GcE0l1HMKJO/hhXA0bNrQTJ064sKmwqBAu+r9+V6VeF8+855N7/eIee53bemjgOl0U0sUehW59fvSZ0C3GtN/1GdVFCY17oBYDkY5DYj/X8Z1bqn7rAuKUKVNc6Nb/dVEovot9IHQDAIAMSgMXqUKjCmRopdJrJqvp4aqEoTRwkEZ/jq8SG4nXVDy0MqUgpACgL7+hg6lpIC0txwuZZ0tBSU1Y9cU7UqjT4EqquKuSFlq5C21a7YlvPl4FUmEttLlu3EptQuurga2i+TKv8HDjjTe6h/afqt8abfmxxx47o2ocV3zHNu4gXgrxGvVa+0fBXsc9mtGaNXiXBsJT4FM3hUh03mn9tU6h95BXJVH7Mu55mRhes2cF58QGJI2IribZGiQsdKAx3Q7tbC8YeNuiAbvi0udQA5UlVXNldRnQOaLPlx7eKOTaFl1I+eKLL4K/RxLttiUVDTCnz5J34U2D++nc1MUknY+eaLpWJOZzHc0FKA2sp0q8BmDU4GrxXXAAtwwDAAAZVIsWLVzVSPfKDqWKj75Yq6lyKAUmVfU8W7ZscVVqNWWO9GVTfSbDVbk06rLodjyh1W+FKwWb0Perb7PCZLj+3omhprOqiClkhLsI4PUx97YndL11cUDV47gUikJvxRQ34IXeVk3zT0zlWeur/a4Kdlxapm6tJQqDoXTBQn1pJZrbrCk0hN7yS6NB60JH3HNA89RDx27mzJnu3tsaATohqtRrP6lSqeMbrgLp3d5M56V4I7N7vNYJqnyeLY1JoOOikfN1MSMuVXrjE+6c0Ij8qvrHpW2Nprm5WnboQpPOidBzSK0CPvvss+C+SAqqyl966aWuS4CaZ4dWunXua5Rx7Ztwt3QL5V0ECHfOnwudb3HHePDORZ3fXhP8cMdBP0dze7zEfK4ToqbkatquVjo6lxJq8ZHRUekGAAAZkkKsBjdTE2/dZ1d9O/VFX0Fa94UOHQxJNMCSqpqhtwyTp556KuJy1PRSgyJpkCFVrVRZV4hUZUrr4A1i5oVu3adX9x5eu3atq/RpObo4kNByov2irH7KGqxJ1S3dlknzVlVRz3v35NWFBK9y7H2pVlBXM+64zd8V5NQPdciQIa6irNdomzQPVdN0OzZVFfWF/4033nDVYYWeaOh9qsqpUqyms1qWgomqfbqnuY6b9pHCrJrbarmlS5d21XTdQkmBLrRaHB+tt5of6xZMCukKvLoVU7hm7aou6hZcEm3Q0Ln09ttv26233urWR/PQ+aTQqlug6aKKtk90HqrPtSrjXpNuBS8FU51DOmfPli5G6IKBLibotlE6z9QHXhccdD6oAq5qaDgaqEsVV62bPgO6MKXbW4W7oKTjpD6/GgxOQVdN1nUuhaOm7loftQDQueLdMkx9i737YycVBezhw4e7eXu33tP5qkCrart3DCLxBlPU3w1ddNGAiNq2c63Ia1+qCfnNN9/slqHPn8Zf0GdGFwx0n3ZR03CdTzoHddx0zHQBKJo+44n5XCdEt87TOewNzqj+8YggpYdPBwAASIlbholuFaTbQulWUFmzZnW3U9ItgUJvpyR6n94/ZcoU95rs2bMH6tSp427NlJAffvjB3ZqobNmy7n26rU7dunXd7YlOnDhxxuv37t0b6NWrl7ulkW7FpNtxaR7R0Gt1C69Ijh8/HhgxYoR7ndanQIECgYsvvjjw1FNPudtbeT766KPARRdd5G4PVL58efeeN95444zbJelWR7pFVJ48edy00NuHLV++PHD55Ze72yhp+7XN8d0yTPMIR8do4MCBgfPPP9/Np3DhwoH69esHnnvuObctMmPGjMD111/vbj3lLUu3stq+fXvEfeHdwkrH/Pnnnw+UKVPG7ZNGjRq5Wz2Fo3nqNlNVqlQJJJZufXbHHXe4/an11D5r0KCBu51U6C23dF7oeOg2XjovtV7aB6GvibTfvFuGvffee2HXY8WKFYFbbrnFnWPaXs2nffv2gS+++CL4mnDHSbeUq1evnrvFmj4zugXXp59+6l4X+lnQbaM6deoUyJ8/v5vm3T4s3C3DZP78+W4/aL66tdeNN94YWLt2bazXeLe/2rVrV6znw61nfLxb7zVv3jzW87o9m55//fXXz3hP3FuGebeyK1WqlLu9WOiyvb8TcWn7vdv2xUe3D3zooYfc34aCBQsGsmTJEihRooT726Fb04XSvrn22msDuXPndp8HnVPerclC9224W4ZF+7mO9Jn0jBw50r1v6NChEV+HQCBG/4kUygEAADI6VfXuueeeM5qiI+NRc381Qdbo0eovDmRUatL+wAMPuBYn4UZtx/+hTzcAAAAQJQ0epSb53B4JGZnqtq+//rrr/kDgThh9ugEAAIAE6N7U6mf/zDPPuL7VcUc2BzICjamgcRY0BoDGVtAYGEgYoRsAAABIgO5NrUHPNPicBvoCMiKNcK/bhek2gBrcrVWrVim9SmkCfboBAAAAAPAJfboBAAAAAPAJoRsAAAAAAJ/QpxsAgAzi9OnT9tdff1mePHncLbAAAMDZU0/tQ4cOWcmSJS1Tpvjr2YRuAAAyCAXuMmXKpPRqAACQrmzZssVKly4d73RCNwAAGYQq3N6Xg7x581p6r+prlN0iRYpErD7A32PQpk0bmzlzJscgBfFZSHkcg/R7HA4ePOguZnv/vsaH0A0AQAbhNSlX4M4Iofvo0aNuO/mSm3LHIEuWLByDFMZnIeVxDNL/cUioyxZHHQAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8EkWv2YMAACA1KH8gDnJvsxMFrDKyb5UAEh9qHQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAGlU+fLl7YMPPkjp1QAAABEQugEACfrqq6+sefPmVqBAAcufP7/VqlXLRo4cacePH/d1uU2aNLHRo0f7Nv+YmBjLmTOn5c6d24oVK2adOnWyXbt2ndW8Jk2aZLVr107ydQQAAGkboRsAENHs2bNd4G7atKn9/vvvtn//fps+fbqtXbvWtm/fnqLrFggE7NSpU+c0j6+//toOHz5sq1evtm3bttn999+f6HmcOHHinNYBAACkX4RuAEDEUHvvvfda//79XRgtXLiwe75atWqusluuXDlbtmyZNWjQwFXAq1evbtOmTQu+v3v37rFCrAK7qssbN24MTr/jjjusQ4cOlidPHqtataotXLjQTevXr58tWbLELVuVaAV/r0n1sGHDrF69eq5KPW7cOKtYsaJbV8+3335rBQsWtKNHj0a9rUWLFrV27drZqlWr3O+RtuvJJ5+0G264wfr06eOW06xZM+vdu7cL7lpXPTZv3pzg9h87dsy9T/OoUKGCvf7667Gmf/bZZ3bJJZdYvnz5rESJEnb33Xfbv//+m+jjCAAAUg6hGwAQL1W2N2zYYB07dgw7XSFSgVOhWc2yX375ZReily5dGvUyVDVX8NS8unTp4oKqPP/889aoUSMbMWKEq0TPmzcv+B4F/smTJ7vnFXyPHDliixYtCk6fOHGiW+ccOXJEvR5///23vfvuu1a3bt2otuuTTz6xyy+/3Hbu3Gkff/yxTZgwwS688EK3TnqULVs2wWUOGTLEhfs1a9bYypUrbdasWbGmn3feefbqq6/a3r173bIXLFhgo0aNinqbFOoPHjwY6wEAAJIXoRsAEC+vf3OpUqXCTp8zZ44VKVLE+vbta1mzZrXGjRu7ftEKxNFq0aKF67udOXNm69Gjh23atMn27NkT8T0K2qqK6z3ZsmWzbt26uSAuqm4ryGte0VCwV1/1yy67zCpVqmQvvPBCVNtVs2ZNd4EgS5YsruJ+Nt5++20bMGCAq2Krmv3EE0+csW516tRx26lq/l133RVsCRANtQjQfL1HmTJlzmo9AQDA2SN0AwDi5TUnV1/ncLZu3eqae4dSONTz0SpevHjw51y5crn/Hzp0KOJ74laRe/bsaTNnznQVZlWLNV3NsqOhJuz79u1zzcFVIVdT72i2K5pKdkL++uuvWEE47jx/+OEHu/baa90gb3nz5rVBgwbZ7t27o57/wIED7cCBA8HHli1bznmdAQBA4hC6AQDxqlKliguf77zzTtjppUuXDvY/9uh3PS/q26ym357EDryWKVOmqJ5X1Vsjqs+YMcNVvKOtcscnoe0Ktw7h1jWh7S9ZsmSsIKzgH0pN5K+66ir7888/XdPwoUOHxuq7npDs2bO7sB76AAAAyYvQDQCIlwb1Gjt2rA0fPtz932v2/dtvv1mvXr2sYcOGrk/z+PHj7eTJk65qPHXqVOvatat7nfpHf/rppy5sqnr91FNPJWr5qvCuX78+qtdqfdQPfPHixda5c2c7F2ryHmm74ltXbWfoQGcJbb9CtW69pv7kqkQPHjw41nQFbQ3kphYA69atc33LAQBA2kLoBgBEpFG6NYiZ+jmrz7NCYNu2bd0I5uqLrGlTpkyxQoUK2Z133umCocK4KPyqP7Req3tYt2zZMlHL1sjf8+fPd8vUekTSvn171x9co5yrP/a5UB/vSNsVztVXX+1GVFf/d62vqtYJbf+jjz7qKvQaHV3TFfa9CrW88sor9txzz7mKuQab08BuAAAgbYkJJKadGgAAqZguCowZMybBgJ5affPNN25QOQ0Gp1YGSU2Vcw2opqp6em9qfvr0addaQbeCi6+bQkZSfsCcZF9mJgtY5RVj3QUsjkHK4bOQ8jgG6fc4RPvvKkcdAJAuqN/5qVOngvfzTgv0j79uA6b11qBqGvisTZs2vgRuAACQMrKk0HIBAEgyF1xwgbuXtW7ppdtredQkW03E4ypXrpy7N3ZKU9h+4IEH7I8//nC3HbvuuuvsxRdfTOnVAgAASYjQDQBI8zTIWDgTJkxwj9RKfeJXrlyZ0qsBAAB8RPNyAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCcMpAYAAJDObRzeMkXuidu8+dhkXy4ApDZUugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ9wyzAAAIB0rvyAOcm+zEwWsMrJvlQASH2odAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwDOWffu3e3++++31Oro0aN28803W/78+e2yyy6z9OKDDz6w8uXLp/RqAACACAjdANKlJk2aWPbs2S1PnjyWL18+q1mzpvXr18927dp1zvNesmSJ5c6dO/iIiYmxnDlzBn8fOnSoJTdvHfLmzWsFCxa0K664wkaPHm0nTpxI9nVJjWbMmGG//vqr7dixw77//ntflqHwqxAMAAAQitANIN0aMWKEHTp0yPbv32/vvvuubdu2zS6++GIXvM5Fo0aN7PDhw8GHfP3118HfBw0aZClB63Dw4EG3fcOHD7fJkyfbjTfeaIFAwNIrbdupU6cSfN2GDRusSpUq7kJMOFycAAAAfiF0A0j3VAWuXr26TZkyxVWCn3/++eC0H3/80a666ipXHT7//PPt1Vdfdc/v3LnTsmXLZps2bQq+9tixY1agQAH75ptv4g1uxYoVs4ULF8Z6/oILLrDp06cH12XMmDFWtWpV19T51ltvtQMHDgRfu379eheUixQpYuXKlbMhQ4bY6dOnE7W9WbNmtcaNG9v7779vixYtsnnz5gWnvfPOO3bRRRe5ZV966aUuqMusWbOsUqVKsebz3XffudepabbMnz/fNc3WczVq1LCPPvoo3nVYtmyZNWjQwL1W+37atGnBaU8++aTdcMMN1qtXL3c8Kleu7JYfGqRffPFFq1atmnu/Wi2sW7cuVkV52LBhVq9ePVfdX7t2bcT9oRYOgwcPttmzZ7uWCE888YQ7Rpr3yy+/bGXLlrX69eu71+oc0fHStIYNG7rzw6P1GDhwoDVt2tS1oKhbt66tXr3aTWvXrp1t3rzZOnbs6JbRu3fveNcnvmOQ0DJk69atdv3117v9pgtICW07AABIeYRuABlGlixZrHXr1i6Iyt9//23XXXed9enTxzU7V9NgBbIvvvjCihYt6sKNQpjn448/dmFYTbfjC7tdunSxSZMmBZ9TQFflWcv1vPXWW7ZgwQLbuHGj7du3L9gX+siRI3bNNde4h6ryasaugDZx4sSz2t4KFSq4YOZt79y5c+2///2vW7+9e/e6cKeAv2fPHmvZsqVrEbB06dJY66kwmSNHDlu1apX7WRV0vfeVV15x26om23FpPs2aNbMOHTq4/apge8cdd8Sa9yeffOICvOY1atQoF1Z1wUH0+tdff93t7927d9stt9zi1vP48ePB92sbVMlXywJdwIhEF1nU+kBBX69/6qmn3PNqBfHTTz/ZL7/84vbR4sWL3bmgbdN6t23b1m1H6EUR7ZORI0e643bJJZdY37593fPvvfeeC++6uKBlTJgwIey6RDoGCS1DOnXqZCVKlHDn7tSpU4MXieKjC0Vq/RD6AAAAyYvQDSBDKVWqlAs7Xri58sorrX379pY5c2bX77tHjx729ttvu+ldu3Z1r/HoZwXNSFS9nTlzZrDZucKVglJos+aHH37YSpYs6SqdqsBqeapmz5kzx1XSFcJVZVeIu++++4Lrc67b+9JLL9lDDz3kqqeZMmVyYVbVZAVBLU9Vd297VbVXdV77QBRENVja1Vdf7d6rKrBCrJrtx6Xt0MUJhUWv6q59oJDsUVPvu+66y10IUehUawOvGq71fPrpp10FXNPvvfde+/fff13l3aNwrLCt46Z1Pxva57qIoGq5Htr2zp07u3NC663joOOh7fFoeq1atdx6devWzZYvX56oZUY6BgktY8uWLe5CzLPPPuvWV++LVFEXtQjQmAbeo0yZMoneTwAA4NwQugFkKKogqym5qNKssKPw6z3UrHn79u1ueqtWrVxFUQNvqeKq6mxCoVtNkxXeNXCXmmUruPbs2TPWa9RsPPRnVXBVWdX6/Pzzz7HWR02jtQ5Jtb2q+IbOf+XKle41ooCtEK3qqPaLmjcrXHvvVfU29L0ffvih/fXXX2csU02g446oXbFiRfd8uH3g/e6th5al4Bm6LFV9Q9+vCxLnStuneUdab7UWCF1u8eLFgz/nypUreHElHA2o5w2u17x586iOQaRlaF+r1YFaYcS3H+NSJV2Veu+h4A4AAJJXlmReHgCkmJMnT7qg2KJFC/e7qn66jZSacIejgKMm1aqAqqp6+eWXR3V7JlW7VeFWdVuhSFXNUOonrnmJ+gGrUqvKsNZHzcG//fbbJNleBTxVSR955JHg9qr6HF91VH2kCxcu7Po+q+qs4Ks+6N57VXVXZTghpUuXdsuOuy563hPaV97bD16/ai1LI6+raXd8VCU+V3HnEc16J2Z+CtdxB9VL6BhEotYRupCj8Qa84K39FonOwfgGjwMAAMmDSjeADEH9dtVUV9W+Bx980D2nqvWXX37pmoOrObUeqjr+8MMPwfep+uv1q/aaWidEzbQVdhVQ41a5Rc2DVbVU3+fHH3/c9X1WYFNzbfX/Hj9+vAtXGpVbfabjDsyWEG2HmiG3adPGNe32wus999zjlq1102Bl6kOuwdFCK7naJ2PHjnVNqkO3V03BtQ/UF13rpWq4+quHDnDm0UUNBUNthy50aF3U/zh0fr/99pvrj6zpWpaOg/abt57aL15/cfVD1sUS9cGOjy5ynOv9qnWRQeupvudaL+0H9bX2LtIkRIPoef3S4xPNMYiPArsGpxswYIBrbq/9o2b/AAAgdSN0A0i3+vfvH7xPt/rOqtmuRtVWOPL6O3/66acuuGhwKj2vUBQ62JSaV2seGiVaVe9o6PV6rYL+bbfdFjbcqQ+zquB6rUYzFzVDVgDTQG4KkIUKFXJ9oaNtXq5KseanKqj6DWs5GozMq1ar77QuBGhQM/VVVtNpLTt0dHSFbg0oVqdOHTeau0e/q/r96KOPuqq89t1jjz3mwndcmrdGTNcgdNqGO++80w2O5jVVF10IUEVfTd9VQddr1Ydb/vOf/7j+4zpmGqVbTfYT6teuiq8C6bnQBQoFbbVU0HrrYou2I7QJeiSqao8bN869/u677w77mmiOQSTaD2oirmOscyPcRR0AAJC6xATS8w1cASCFaCAwjfitvt2hFIBXrFhhtWvXtoxKtwxTiwKNFp9UNOK7Aq8COuKnC0q6CKUWH7qgkZ7pQobXFD8puiOkdeUH/N+AgMklkwWs8oqx7uIVxyDl8FlIeRyD9Hscov13lT7dAJDENCiamk6H3joM/lLrAAAAgNSISy0AkISeeeYZ1zRc971W9TWpaOAtbyTs0EeNGjWSbBkAAABIejQvBwAgg6B5ecZF8/KMi89CyuMYpA4p2bycow4AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hFuGAQAApHMbh7dMkUGLmjcfm+zLBYDUhko3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE24ZBgAAkM6VHzAn2ZeZyQJWOdmXCgCpD5VuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAADSQ+iuXbu2TZo0ydKTXbt22dVXX2158+a1du3aWWq0efNmy507tx04cMDSgxo1atjs2bNTejVSjW+//daqV69uefLksRdffDGlVyfVK1++vH3wwQcpvRrpWkxMjK1cudLSEj/PiyeffNJat27ty7wBAEA6C91NmjSx0aNHW3r+Qrdw4ULLnz9/1K9/5ZVXLHPmzLZ//3577733LDWIu3/Kli1rhw8ftnz58vmyvHnz5tlll13m5l+gQAG79NJLbe7cueaXNWvW2A033GCpYR/37t3bXdDQI0eOHO5c8H7XQxc8ErJ9+3br1KmTFS9e3AXnihUr2gMPPBD1ujz66KPWsWNHO3TokN17772pOlTq86V9p32jC1XFihWz5s2b24cffpjSq5bqbdy40e07/a1JTZL7fJs6dWrw85UrV67g+eQ9NB0AACA1SZfNy0+cOJFsy9qwYYOrvGbKdHa78uTJk5aWrV+/3lX4Bw0aZHv37nUB8rnnnnPhMbUfu6QwYcIEd0FDD/184YUXBn/XQxc8EtKlSxcX2H/55RfXGuHzzz93rUIScw5quclN524gEEj0+3RxRvvm4MGD9scff7jt79Wrlw0dOtSX9UTa/vzFXafbbrst+PnSBTjZunVr8DlNBwAASBeh26sIv/baa1amTBkrVKiQPfzww7FeM27cuOC0Rx55JMHmdpqf5is//vij1atXz1XDChcubDfeeKN7XhVVqV+/vqtq6Iu6VwGaOHGinX/++Va6dGlXKezevXus+Q8fPtxV1RJb3R84cKA1bdrUBcm6deva6tWr3TSFzTfffNPGjx/v1uX11193z0+ZMsUuuOACtz0NGzZ02xI6P+2n66+/3lVpVCVWpWjYsGGuQqzntI4KsHfffbebR+XKle3rr78OzkPzr1mzplsfhbrHHnssGH4i7R+vQqYvsdomvbdIkSJ26623umbyHr1WAVLL0P5v1apVvE3TV6xY4aqVOpaq8io8Nm7c2Bo1ahQrmOv4aVnlypWzIUOG2OnTp900dTdQwHziiSdcpbdDhw5Wq1Ytt19DaZ9oH4WrrCmkXn755W5flShRIvg6mT9/vtsnmqaLIx999FGs91100UVuP2ob+vTpYynVPLxHjx5uHXXxplKlStatW7fgdFWw77zzTrdteqi6/s8//7hp2mcK3ap063jffPPNrrru/a7Xjhgxwu1Xz8UXX+w+W542bdrY888/n+C55Z0b+lzrNTpXFXIiHd+EaDmq8mueTz/9tDvvvXP08ccfd/tCfz90Dv71119u2n333Wc9e/aMNR9toz6jovVVM/tq1aq5farP3Lp16+Jdh4Q+rw899JD7v9b1iiuuiDWvxH52I21XQp8977Otv2+Jqeh+9tlnVqdOHXexQ3+/9Jnw6G+kLni0b9/eLU/Ljkv7U+eH1rlgwYLWrFkz+/PPP4N/A+Oeb6HndXx/QxL7NyFaCR17Xej5z3/+45ap9dJx27JlS3D6b7/95j4bOtb6OxY6LaG/i8uWLbMGDRq45aq7x7Rp0+JdT11s0vmq/an9GrcF2dixY4P/dqoli9c1S+eP/lZ5/056dP5Onz496v0EAACSWSARGjduHHjhhRfczwsWLAhkypQp8MADDwT+/fffwNq1awM5c+Z0z8sXX3wRyJs3b+Drr78OHDt2LDBo0KBA5syZAxMnTnTTn3jiicBNN90Ua/758uULvv+KK64IDBkyJHDq1KnA0aNHA4sWLQq+Tqu9YsWK4O8bNmxwz7Vu3Tqwb9++wD///BNYvXp1IHfu3IFDhw4FX1e1atXAu+++G3EbtXytR+g2lypVKrBy5crAiRMnAnfccYd7ztOtW7fAfffdF/xd66nl6v/Hjx93+6tIkSKB/fv3B+en37/77rvA6dOnA0eOHAmUK1cuUKtWrcDmzZvd62rUqBGoXLlyYObMmYGTJ08GHn/88cCFF14YXMbcuXMDv/76q3u/9kPRokUDU6ZMSXD/aN/IU089FahZs2Zg06ZNbv/ceuutgeuuuy7W+6+66qrAjh073Hvq1Knjjlc4f/75ZyBHjhyB3r17B+bNmxfYs2dPrOk6Fto+7QedB1qmtu+1115z03U+6Lx4+umn3XS9/tlnnw1ce+21wXls3749kDVrVrd/RPObNWuW+/nHH38MnHfeeYEZM2a4/a39980337hpP/30UyB//vzuXNR5tGTJEndO/vLLL256iRIlAm+++ab7+fDhw4GlS5cGohV3H3vbouOYWNdff32gbt26gcmTJ7vjGlePHj3c8di9e3dg165d7hzSeegJ3R/hfte5VqxYMffz3r173c8FCxYMHDx40J1D+ln7MdpzS5/Nbdu2uc9lQsc3oc+XR+/V3xMtXx566KHA1VdfHfjrr7/ctH79+gUaNWrkpv3www/uOOqz49EyvfV86aWXAhdddFHgt99+c5/ZMWPGBCpVquTmE3f/RPN5LVCgQKy/Y/psar7evBLz2Y20XQl99uJ+jqPx+++/u8+n1kfr/N5777nPiz633t8v/f7JJ5+4z4iOZ1w6L0uWLBlYtWqV+1v/4IMPBqpXrx5rH4Sebwltx9n8TYhP3H2S0LG/+eabA02bNnXnr7ZX570+U9526Fhp32g7mzdv7vZPNNuk3wsVKhR48cUX3Xm0cOHCQK5cuQJfffXVGf/eab30b5HOBS1Hf6f0t2jq1Klu+vz5893fLX1utd6PPfZYIEuWLMF/O3XOhK6Xzk2do/o8hqPnDxw4EHxs2bLFbYt+Tu90jPXvh/6PQKBc/9nJ/qjQ/2P3bxzHIGXxWUh5HIP0exz072k0/66eU/NyfQ9RhULVTV1pV3V1+fLlbpqqMGrmp8pQtmzZXGVblaBoZc2a1TZt2uSqQNmzZ7crr7wywfeoMqIqQ86cOV01QtWGGTNmuGnffPONq+aqOpFYnTt3dtXXLFmyuAqkt43hvPXWW+71Wl9tw/333+/6Oc+ZMyf4GlX2VLVS5eS8885zz6nKqsqGqlEtWrRwFY5bbrnFVY9Vif7555/t+PHj7rWqplWpUsW9XxUQVZniVj4i0TqqeqJKpqpTo0aNclXf0IqbqvFFixZ1+1OV0Pi2uUKFCrZ06VJX8bz99ttd5eq6664LVsK03dp+7QedB1qmKpVvv/12cB7aZrWE0HQdO503ixYtsm3btrnpqhipcq79E9f//vc/VwnTOmp/a15eFVf97VXJ00B3qiCriqm+4O+++66brter4qTzQuemzt+UoLEAVPVTtUvVeFXhvP2j6p8+S6qm6pxQqw+1XlBLgGiryaps//vvv7Z27Vp3nujc1LYuWbIk2Pffa84ezbmlc6NkyZLucxnN8Y2G3qttU5VYf1fUekTnpSr7mqa/MzrPVHm85JJLXLXX6weu1haqtqrKLy+99JKrmqvKrM+s+rlr+7/77ruz+rzq/Ar9O7Zjxw5XxfVE+9lNaLsS+9mLhqqfqvZqfbQv2rZt6z4HoVVYtbpR1VWfEX3+wu0j7UN1YdDfep1/Wt/vv/8+4rLj246z+ZsQrUjHXsdt1qxZ7m+Gzl9tr1oA6LzzqIWC/qZpO/V3KO6+j7RN+tvXt29fdx6pSq6/85MnTz5jHbUu6obj/dup1jaqvnuDjGo/aNn6N0Lbr9Ymof92qmXCzJkz3d9c0fu0LH0ew9HfDu1P7xHu7ygAAPDXOYVuNbEL/UKkLwZqCisKcAoPHn0R0RfNaL3xxht29OhRFxjUVFDNTxMSt/+smqB6X2T0f32Rie+LSSRq4ujxmtTGR30L1eQ0lL7E6fn41lPUZNCjfRr3d31hP3LkiPv9008/daFJXxb1JUpNHnfv3h319sRdRy9Aha5j3G32jms4arKqL+Z6v5pnal0VZERN2xU69CXVe/Tr18/+/vvv4PtLlSoVq0+8zhMFZa/5rAJm165dwy5bF2b0BTscLVv7JnTZCmrexQV9Ade6Va1a1X359sJ4ctPnSGFOzZr37dvngoK2V81idUFAgS30eGmgtWPHjkV9zBX+dNFiwYIF9uWXX9pVV11l11xzTfB3hTKF7GjPrdDzN5rjGw1to5aj5rb6v5rPKwh789T5qADihVP1A/e6IOj/CkDe3yKtk86/0HXSfg09vxPzeQ33d8y7IJSYz24025XYz15Cwm2fzp+E/h5Fmof+VuhvRrj9GSq+7TibvwnRinTs9bdC6x5pexPa9/FNj2Y/e/Sc9p+Oe7jX6u9TaDCO+2+nLnDrorIuKOvfSF1YidvdIpS6EqkZvPcIPdcAAEAaH0hNXyr0Jcejvmi6uu9RhdULkaIvo+pv51E/N32Z1hcx9Rv/73//G6wqeAEhrrhf0lSlUz87Vfj0xUT9Zv2mCpy++IXS73o+vvVMbDhR1equu+5yX/z1JUr9KOP2u03MOmofK8SFruPZ0nFT1crr964vj7pwov7k3kPH2RsAKb79oVClIK8v5wryClXhKBCpWh2Olq11CV22Lpi8/PLLwYsFqhgpDKmapGqRqmEpSZ8LBRAFXp23qp7py3no8dLPCg+hFbpQ4fangrYXsnVBQ4/QEB7tuRV3/tEc32i8//77bjvVSkGVYoVVVQRD56uKpdcaQRfQvvjiC7eeqtrqfAldJ7UeCH2v/tbo78HZfF7D/R1TKEysaLYrkrP5u5EUf4/izkPniYKhN4/ErtfZ/k2Idt7xHXv9rdDfOT9CZzT7OfS12n+hA8SFvlb/doauowYsDP2306t260KyLhxqu/S3LD76W6ELe6EPAACQTkK3vuSoUqkvmPqSpiZ/3uBPoi8JavKtEZt1tV6jX4eGRQVuBSA95w0wpYqdqJKkgXgSoi8XCmsKU6peqZrpN1VZtN1qMqovSxoQZ8+ePa7ZaVLQl0btL32B15cp7d+4TXkT2j9aR6+JqELogw8+aNdee637spdYaqKsJrNe9VgB/tVXXw2GCDXn1nHUa7Tep06dsl9//TXB5vBqKqywo4st+llhNJw77rjDhS59+dT+VlD0mv4qPGpwPYVLLVf7TuecKsg6JxXqVQXTueXdJk5NUkXVX1Wfk4K+HMetgoXSQF1q5q110kMXmfRZUTDRuun8VVNbNb3WuaTPikJmfMEk3PFXsNaAWto/quyrqbAqa2rGrwAe7bkV19keX4/OP10QU7NcXfhQs2Ntl8K+Lj544UPbHTpQlMKVmkkrfCisexcO5J577nGDlWk9RIFOLRzCVYyj+bxquaF/x3QhJHQgumhFs12RaLmaR9xjq3PLa9ETl5q361ho+7V9urixePHiRA1Opn2klka6CKRzRF1TdNHBG9gt2r/HSXXORBLp2Gs9b7rpJncMFGLVPUNdE3QMzpXOl507d7pt0n7W30WdV+Fa6Gi/aV20ntqfurCo884bPFH/dupzpwvGCuZqhh76b6d3XHURWoODRqpyAwCAdB66FeIGDx7sQq+axukLjprEefRFX6FI4UwjjisEhN5mSiPsqh+1wpa+KD377LPBfqear5rg6gu6vnREoi/lP/30U7JUuUV9+fQFSstVeHnnnXfcCOWJufd3JNpH6reo0ax1UeGZZ55xX8BCJbR/vNHY1U9VX9j1xU4jOJ8NLUNNkhUQ1dxSF1P0nNeXUcdPx1JVSS1L+0QhMqHmx6oI6tzRvONrWh5ardZ+UNNkNb1UkBRdZFEgV0hQYFFQULDTF13RF1ude9qnCn36Xesn6iOskYiTQkLz0vooBGnZar6qCwUKCl5QHzNmjPtZYxSoz7fWWf2C46NQrpCkc059VEWfHV1Q0MUE0cUsnavads032nMrrrM5vgr+3n261TJC26sLNVrv0H6oOj/1d0LrpfNLFw1C6bzQ+aFQGHrBTv1j1ZdfVXstQ+dEfBcPovm8KtT079/fnV8a+0Aj53sXZxIrmu2Kj8Z/0LgV6nev9dM2ed0M4rsIoHNFQVvv0/rrooEuUKk5c7S0n/X5UFjW+am/px9//HFwH4Q73yI5278J0Ujo2Ovvki7YaFwAra8CuFoanCv9zdN5o7+j2h59htSiRheG4lJz8dmzZ7vQrP2pcUZ04VP7wPu3U8dLd4TQdIV4jbMQ2jVK545GjtdFa26RBgBA6hej0dQsHVPgUZ9fVWK9QAVEogq7QrCq4klB/acVShQAkLboIoXCjwb9So10gUn97iPdngppm1pY6N+uTz75JNbFO11AWbVqVXCw0Gip+q/uK7r4ld6bmutiv1ogaPC7c+nWlV6UH/B/A0Qml0wWsMorxrqLUhyDlMNnIeVxDNLvcYj239WzK9ekEWq2qPv36h60BG5ES30kkypwiyp6gB9UqdcD6YtaJ6hFg74cqKWO/v3SPcU9GmBRrUPi61YAAABSl3R7qWXDhg3uaoMqQWomG0pNdNXEMe5DTQ0BAEhJGm9C3bI0zobuavDRRx8FRzvXv2dqlt+yZUvXigYAAKR+6bbSrYHT4g4+40nsyMoAMqakGNwLSCz1u4+PBlXUAwAApB3pttINAAAAAEBKI3QDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPkm3o5cDAADg/9k4vGWyL1P3mm/efGyyLxcAUhsq3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE+4ZRgAAAB8sXLLfjt/0Fw7bTEZ4jZpABAOlW4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgCkC02aNLHRo0ef8XxMTIytXLnSl2WWL1/ezjvvPMudO7cVLlzYWrZsaX/88Yf5beHChZY/f37flwMAAM4doRsAgHMwbdo0O3z4sG3cuNEF4Z49e6b0KgEAgFSE0A0AyBBWrFhhDRs2tIIFC1qRIkWsY8eOtmfPnuD0qVOnWuXKlS1PnjxWqlQpGzx4cKLmr2p3p06dbPny5cHnTpw4YY8//rhVqlTJChUqZK1atbK//vorOP3hhx+2cuXKuWVWr17d3nvvvVjz1Lyuvvrq4Dr37dvXrXPz5s3twIEDbpl6LFmyJOw6HTt2zA4ePBjrAQAAkhehGwCQIWTKlMmGDx9uO3bssJ9//tm2bdtmAwYMcNP++ecf6969u73++ut26NAhW7NmjTVr1ixR81cIfuutt6xKlSrB5x555BFbunSpffXVV7Z9+3Y3rUOHDsHptWrVsh9++MH279/vwnmXLl1sw4YNbprWT4G7bdu2Lqhv2rTJ2rdv78L7vHnzLF++fK7CrkejRo3CrtOwYcPc67xHmTJlznLvAQCAs0XoBgCkGwMHDnRNvEMfoQFXle6sWbNasWLF7MEHH3R9oz16ft26da4arPddeumlUS3ztttuc4FW71E1fcqUKe75QCBg48ePt1GjRlmJEiUsW7ZsNmTIEBfCt2zZEnxv0aJFLXPmzC6MV6tWzb7++ms3TfO5+OKL7e6777YcOXJYzpw54w3XkfaHLgZ4D2+5AAAg+RC6AQDphiq7qhqHPjwa4Oymm26ykiVLWt68ea1z5862e/duNy1Xrlz28ccf24cffuiqwQrnCxYsiGqZapauQPvLL7/YyZMnbf369e55zVsV9CuvvDJ4AaB48eIufHvh94UXXrAaNWoEQ7sq8N46qbKt5u7nInv27G5bQx8AACB5EboBABlC7969XV/ttWvXumq2KsmqRnuuueYamzt3rgu97dq1s9atW9vp06ejnn/VqlXtueeesz59+ti///7rmoGrOv3dd9/FugigafXr13dNzp988kl78803bd++fW5azZo1g+ukvt7xjYSupvIAACBt4F9tAECGoKCtActU7VWl+dlnnw1OUz/vWbNmuf7cWbJkca/R/xNLQV1h+6WXXnLBWEG/X79+wcq2BkGbPn16cH3UrFwDpCncv/HGG67S7VHT8++//94mTJjgBkQ7cuRIcMA0NY/Xuu7cuTMJ9gwAAPAToRsAkCGob/Xs2bNdoFYz8zZt2gSnKfSOGTPGNS1XU2+F5hkzZiS6oqx7gqsf9ciRI13TcjV3v+KKK9yAaAr86qP92WefuddqoDYNknbhhRe6Ju8avK1BgwbBeZUuXdq++OILe/vtt13I1j3BtU5eVb1Xr15uxHM1S1fVHAAApE4xgdC2dQAAIN1SdV0XFdQHPb3379aFFLUE0EB1NMdPuWNQouYVlqvVY3baYpJ9+RuHt0z2ZaZGfBZSHscg/R6HaP9d5agDAAAAAOATQjcAAPEYOnSo5c6dO+wDAAAgGoRuAADiMWjQIDt8+HDYBwAAQDQI3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPsvg1YwAAAGRstcvkt3lDW3BvYgAZGn8BAQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmjlwMAAKRT5QfMSbFlZ7KAVU6xpQNA6kGlGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAEFfffWVNW/e3AoUKGD58+e3WrVq2ciRI+348eO+LrdJkyY2evRoX5cxadIku/zyyy137txWqFAhq1Onjg0bNsz++ecfX5cLAAAyNkI3AMCZPXu2C9xNmza133//3fbv32/Tp0+3tWvX2vbt21N03QKBgJ06deqs39+/f397/PHH7ZFHHnHbsmfPHps6dar9/fff9scff5zVPE+cOHHW6wMAADIOQjcAwIXae++914XT+++/3woXLuyer1atmqsQlytXzpYtW2YNGjRwFfDq1avbtGnTgu/v3r27e59HgT0mJsY2btwYnH7HHXdYhw4dLE+ePFa1alVbuHChm9avXz9bsmSJW7aq0Ar+Ur58eVeJrlevnuXMmdPGjRtnFStWdOvq+fbbb61gwYJ29OjReLdt/fr19vzzz9s777xjrVq1cssXbcOYMWNcNd8zf/58u+yyy9w21qhRwz766KNY29irVy9r37695c2b1yZMmOAq9A8//LBdc801litXLreu27ZtsyeffNKKFClipUuXtlmzZgXn8dlnn9kll1xi+fLlsxIlStjdd99t//77b3C6tlktCzQfrWfjxo1ty5YtbtoDDzzg1iHU8OHDg/sLAACkToRuAICrbG/YsME6duwYdrpCdLNmzVxo3rVrl7388ssuRC9dujTqZahq3rt3bzevLl26BAOkAnGjRo1sxIgRdvjwYZs3b17wPQr8kydPds/36dPHjhw5YosWLQpOnzhxolvnHDlyxLtcBemSJUta/fr1I67fqlWrrF27di7I7t2711555RW3nr/++mvwNbrQoOCtbdD/vedefPFF9x4vKOtCgCrqTz31lNtPXlX8vPPOs1dffdW9VvtuwYIFNmrUqFjrMWXKFDdP7WcF+ccee8w9r+XNnDnT7YvQ/dOzZ894t+nYsWN28ODBWA8AAJC8CN0AABfwpFSpUmGnz5kzx1Vu+/bta1mzZnXBslOnTi4QR6tFixauMpw5c2br0aOHbdq0yTXzjkRBW1VxvSdbtmzWrVs3FzRF1W0Fec0rkt27d7vQHeq6665z1Wyvgi4K2boQcPXVV1umTJmsYcOGdsMNN9i7774bfN/111/vmt9rut4rnTt3dlXx7Nmz28033+z6iKvVQJYsWdwFAW2jtlV0cUF9ybU9qtrfddddwYq/R9XvChUquAsJt912my1fvtw9X7NmTVednzFjhvv9m2++ccdN1fv4qKWAqureo0yZMhH3FQAASHqEbgBAsDm5mkaHs3XrVtf0OZRCo56PVvHixYM/q4Irhw4diviesmXLxvpdVV2v2qtm25qu5toJbdtff/0V67nPP//cVavVlPzkyZPuOTWFV5NxhXHv8eGHH8Z6b9z1kWLFigV/VhCP+7t41ekffvjBrr32WvcaNVEfNGiQuygQaT+F7iNtv3fRQf9XKFfYj8/AgQPtwIEDwYfXVB0AACQfQjcAwKpUqeJCtfo9h6O+yV7/bI9+1/Oivthq+u1J7MBrqhxH87yq3uqDrWqvQmdCVW5Rf2tdTFD/70hUBb7vvvtcGPceCstqSp/QekZLle+rrrrK/vzzT9fUe+jQobH6qEfzfvWt1+B20VT5FcgV7kMfAAAgeRG6AQBu0LOxY8e6/sz6v9fs+7fffnN9idXUeufOnTZ+/HhXGdbAZxr9u2vXru51devWtU8//dSFbVVm1Zc5MVT51YBn0dD6qB/44sWLXdPuhJx//vluEDL1R//4449dkFbQ1bZp9HKPmnqrj7j6WWukdPWHVhPudevWWVJR0FYFXRVszTc00EdDoblNmzauab+aoKupOgAASN0I3QAAR/2XNYiZ+m9XqlTJhcO2bdu6Ecw10ramaZAv3eP6zjvvdIFRYVwUftXPW6+tXbu2tWzZMlHL1sjnGvBMy9R6RKLRw9VHWqN2q595NJ577jk3IJkuBhQtWtS979Zbb3V9uL1qsQKsBjB79NFH3XT1b9d7FL6TivqNa13UMkCDyulCQGLposNPP/0UVZUfAACkvJhAYtq1AQCQCuiigG73lVBAT482b95slStXdn3NdQEksZV2Daim/t3pvan56dOnXesMXWQ5124BaVn5AXNSbNmZLGCVV4x1F+wy8jFIaXwWUh7HIP0eh2j/XeWoAwDSFPU7V/PvjHh/am23bq2man9iAzcAAEgZWVJouQAAJNoFF1zg7nGtW5XptlseNdVW0/e4ypUrZ2vWrLH0QPdR123D1Jd77ty5Kb06AAAgSoRuAECaEd+gZrrVlx7pmcK27gEOAADSFpqXAwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD5hIDUAAIB0auPwlil6T9zmzcem2PIBILWg0g0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiEW4YBAACkI+UHzLHUIJMFrHJKrwQApAJUugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwCAFHL//fdb9+7dU3o1AACAjwjdAICoffXVV9a8eXMrUKCA5c+f32rVqmUjR46048eP+7rcJk2a2OjRo32b/7x58+yyyy6zfPnyuW279NJLbe7cub4tDwAAZByEbgBAVGbPnu0Cd9OmTe3333+3/fv32/Tp023t2rW2ffv2FF23QCBgp06dOqv3rl+/3tq1a2eDBg2yvXv3um157rnnLE+ePEm+ngAAIOMhdAMAogq19957r/Xv3981iS5cuLB7vlq1ajZp0iQrV66cLVu2zBo0aOAq4NWrV7dp06YF368m1HqfR4E9JibGNm7cGJx+xx13WIcOHVzYrVq1qi1cuNBN69evny1ZssQtO3fu3C74S/ny5W3YsGFWr149y5kzp40bN84qVqzo1tXz7bffWsGCBe3o0aPxbtuKFSusWLFi1rp1a8ucObPlyJHDGjdubI0aNXLT+/TpYwMGDAjuhyJFirj19Fx88cU2c+ZM9/Phw4ftP//5j5UtW9aKFi1qXbt2tQMHDgRfu3jxYrvwwgvddtxyyy126NChMy4A3HjjjW4Z2qdDhgyx06dPu2naz7Vr17bBgwe7eWud/az+AwCApEHoBgAkSJXtDRs2WMeOHcNOV4hu1qyZC6O7du2yl19+2YXopUuXRr0MVc179+7t5tWlS5dgX+fnn3/eBeARI0a4UKum4B4F0cmTJ7vnFY6PHDliixYtCk6fOHGiW2cF6fgoNP/111/u/Z988omrdoe66qqrbMGCBe7nVatWWd68eYPL2Ldvn3tOr5GePXu69+s57a8TJ064EO69tlWrVu53bWOPHj1sypQpweVo3a+55hr32LZtm7vQ8M4777ht8KxZs8ZdYNB07a+HHnrIBfX4HDt2zA4ePBjrAQAAkhehGwCQIAVpKVWqVNjpc+bMcdXZvn37WtasWV2luFOnTi4QR6tFixau77aqzQqkmzZtsj179kR8j4KyquJ6T7Zs2axbt24uiIuq2wqmmlckFSpUcBcHFNxvv/12tx3XXXed/fnnn2661unHH390gfXLL7+0Nm3auEq/mtWrGl+zZk1XTdc+UsX7pZdectX+XLly2dNPP+3WQU3f1Ty/ZMmSdtddd1mWLFlcRfvqq6+OtQ/Vn1wtArQtqpbfd9999vbbbwdfo+Wq8q99rPVStX/lypXxbptaAqifuvcoU6ZM1McDAAAkDUI3ACBBXnNyVVjD2bp1qwuAodTUW89Hq3jx4sGfFVglbvPruBRMQ6nSrOCrAD1r1iw3/ZJLLklw2XXr1rW33nrLre9vv/3mmpF37tzZTVNTbgV7VZ4VulXVVjVa1W/97gVnNZVXU3CFeIVuPTQgW6ZMmezvv/921XQ1GQ8V+rve//PPPwffq4cCtt7rUZPyUNpPkfbRwIEDXfN277Fly5YE9wUAAEhahG4AQIKqVKniQrWaO4dTunTpYP9sj37X86I+zGo+7UnswGsKrtE8r3CsEdVnzJjhKt4JVbnDqVSpkqswr169Ovicgvbnn39u33zzjWvqrqDthW6vabmqyFofhWs1H/ceqrirhYCq3Kreh9q8eXPwZ71fTd1D36vqupqUn63s2bO75vChDwAAkLwI3QCABGnQs7Fjx9rw4cPd/71m36oK9+rVyxo2bGg7d+608ePH28mTJ11VeOrUqW4gMa+S/Omnn7qwrcrsU089lajlq8Ibqe9yKK2P+oFr0DKvWh2J1lXrrbAsqiy/+uqrVr9+/eBrFKzVt1oXH3QBQc3nv/jiC7f9V155ZbBSr8HY1Gd79+7dwXmp4i4tW7Z0LQU0b+0jNSdXaPfccMMNtmPHDrcuCupqkv7rr78GB5QDAABpE6EbABAVhUINYqawqGqwmj+3bdvWjWBeokQJN00DgxUqVMjuvPNON5iawrgo/Cqo6rUagVsBNDHUz3n+/PlumVqPSNq3b+8qyhrlXP2zE6J+1LogoCqzmmvrAoGeC+2Prv7TuljgNSVX/2gFcL0ntHqs6rrXrFzPqyq+fPlyN039vj/88EMbM2aMe81rr71mt912W/C9CvPaRoV5tSrQflS/+NDm5QAAIO2JCYTeWwUAgHRAFwUUbhMK6BmNmqvrgoH6d6f3pubqX6/WF+qTH1/3hPSq/IA5lhpksoBVXjHWXZDLaMcgNcnIn4XUgmOQfo9DtP+uctQBAOmK+p2rabZ3P28AAICUlCVFlw4AQBK64IIL3H2y1TRctxHz6P7foffEDh09/FwGKgMAAEgIoRsAkG6sW7cu7PMTJkxwDwAAgORG83IAAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8wujlAAAA6cjG4S0tNTh9+rQ1bz42pVcDAFIclW4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAn3DIMAAAgnSg/YI6lFpksYJVTeiUAIBWg0g0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAA+OTJJ5+01q1b+zb/2rVr26RJk3ybPwAAOHeEbgBAkvnqq6+sefPmVqBAAcufP7/VqlXLRo4cacePH/d1uU2aNLHRo0f7Nv+YmBjLmTOn5c6d24oVK2adOnWyXbt2+bY8AACQfhC6AQBJYvbs2S5wN23a1H7//Xfbv3+/TZ8+3dauXWvbt29P0XULBAJ26tSpc5rH119/bYcPH7bVq1fbtm3b7P7770+y9QMAAOkXoRsAkCSh9t5777X+/fu7MFq4cGH3fLVq1Vzz53LlytmyZcusQYMGrgJevXp1mzZtWvD93bt3jxViFdhVXd64cWNw+h133GEdOnSwPHnyWNWqVW3hwoVuWr9+/WzJkiVu2apEK/hL+fLlbdiwYVavXj1XpR43bpxVrFjRravn22+/tYIFC9rRo0ej3taiRYtau3btbNWqVe73SNsV18MPP+z2hbZBr33vvfeC07Q9msdrr71mZcqUsUKFCrnXh9I2eNMeeeSRqNcZAACkHEI3AOCcqbK9YcMG69ixY9jpCtHNmjVzoVnNsl9++WUXopcuXRr1MlQ17927t5tXly5dXBCX559/3ho1amQjRoxwleh58+YF36PAP3nyZPd8nz597MiRI7Zo0aLg9IkTJ7p1zpEjR9Tr8ffff9u7775rdevWTfR2qbn9Dz/84N73+OOPu+3QfvMcOnTItQzQ/lRT/Zdeeil4ceHLL790QVvL9loO/PzzzxHX9dixY3bw4MFYDwAAkLwI3QCAc+b1by5VqlTY6XPmzLEiRYpY3759LWvWrNa4cWPXL1qBOFotWrRwfbczZ85sPXr0sE2bNtmePXsivkdBW1VxvSdbtmzWrVu34MBjqm4ryGte0VCwV1/1yy67zCpVqmQvvPBCorfrtttuc5VyrY+CuloCqNm6R1X4IUOGuIsAF1xwgdWvX9+WL1/upk2dOtW9/4orrnDbokHacuXKFXGdVenPly9f8KEqOQAASF6EbgDAOfOak6uvczhbt251zb1Dqam3no9W8eLFgz97YVOV4UjKli0b6/eePXvazJkzXeV71qxZbvoll1wS1fLVhH3fvn22efNmVyFXs/TEbpeCeo0aNVwAVlNyVap3794dnJ43b17XFD50O71t/Ouvv1zTdI9CfokSJSKu88CBA+3AgQPBx5YtW6LaVgAAkHQI3QCAc1alShUXPt95552w00uXLh3sn+3R73pe1BdbTb89iR14LVOmTFE9r6q3mnjPmDHDVbyjrXLHJ6HtCqXm4qpOv/nmmy68q4l5zZo1Y/Uxj6RkyZKuuu85ceJEgvspe/bsLsiHPgAAQPIidAMAzpkGPRs7dqwNHz7c/d9r9v3bb79Zr169rGHDhrZz504bP368nTx50lWN1Vy6a9eu7nXqH/3pp5+6EKnK7lNPPZWo5es2XuvXr4/qtVof9QNfvHixde7c2c6FmrxH2q5Q6k+tZuVqjn769Gl74403EuyTHUp9zzXv7777zt2C7emnn7Z//vnnnNYfAAD4j9ANAEgSN9xwgxvETP2c1edZzafbtm3r+i2rGbSmTZkyxY28feedd7pBxxTGReFX/aH12tq1a1vLli0TtWyNfD5//ny3TK1HJO3bt3cVY41yrgB8LtTHO9J2hdKAa9ofF154oatar1mzxo16Hq1rr73WBg8ebG3atHH7U8FdlXIAAJC6xQSibdcGAEA6oYsCY8aMSTCgpzeqtqs/ufp3p/em5roooVYIGrguvu4H6VH5AXMstchkAau8Yqy7MJWRjkFqk1E/C6kJxyD9Hodo/13lqAMAMhT1Oz916lTwft4AAAB+yuLr3AEASEV0G669e/e6W3qpf7VH9/9WE/G4NFq4moEDAACcLUI3ACDDWLduXdjnJ0yY4B4AAABJjeblAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITRywEAANKJjcNbWmpx+vRpa958bEqvBgCkOCrdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITRywEAANK48gPmWGqTyQJWOaVXAgBSASrdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAKRSQ4cOtY4dO6b0agAAgHNA6AYAJIuvvvrKmjdvbgUKFLD8+fNbrVq1bOTIkXb8+HFfl9ukSRMbPXq0b/OfN2+eXXbZZZYvXz63bZdeeqnNnTs3SeY9aNAgmzZtWpLMCwAApAxCNwDAd7Nnz3aBu2nTpvb777/b/v37bfr06bZ27Vrbvn17iq5bIBCwU6dOndV7169fb+3atXPheO/evW5bnnvuOcuTJ0+SrycAAEibCN0AAN9D7b333mv9+/e3+++/3woXLuyer1atmk2aNMnKlStny5YtswYNGrgKePXq1WNVd7t37+7e51Fgj4mJsY0bNwan33HHHdahQwcXdqtWrWoLFy500/r162dLlixxy86dO7cL/lK+fHkbNmyY1atXz3LmzGnjxo2zihUrunX1fPvtt1awYEE7evRovNu2YsUKK1asmLVu3doyZ85sOXLksMaNG1ujRo3cdK2j1vXVV191yyxUqJDdfffdwer+4cOH7aabbrKiRYu6SvmVV15pP/30U3D+Tz75pJu3R/OaMGGC1axZ0/LmzWutWrWyAwcOJMFRAgAAfiF0AwB8pcr2hg0b4u2brBDdrFkzF5p37dplL7/8sgvRS5cujXoZqpr37t3bzatLly4uiMvzzz/vAvCIESNcwFVTcI8C/+TJk93zffr0sSNHjtiiRYuC0ydOnOjWWUE6PhdffLH99ddf7v2ffPKJq3aHM2vWLFu5cqWtXr3avv76axf45fTp09apUye3f3bs2GF16tSx9u3bxwr/cb377rv25Zdf2ubNm23r1q32wgsvxPvaY8eO2cGDB2M9AABA8iJ0AwB8pSAtpUqVCjt9zpw5VqRIEevbt69lzZrVVYoVRBWIo9WiRQvXd1vV5h49etimTZtsz549Ed+joKyquN6TLVs269atmwviouq2grzmFUmFChXcxQEF99tvv91tx3XXXWd//vlnrNepYq0qfsmSJW3gwIH21ltvuedVrb711lstV65cLtw/9dRT9ttvv7kgH5+HH37YVcY1vzZt2tjy5cvjfa3CvSro3qNMmTIRtwcAACQ9QjcAwFdec/Jt27aFna5qrZpeh1JTbz0freLFiwd/VoCVQ4cORXxP2bJlY/3es2dPmzlzpgvQqkxr+iWXXJLgsuvWretCtNZXgVlV6s6dO8d6jZrQh/7s7Yt///3XNTfX9iuAe/th9+7dUW9rpO1UwFfzc++xZcuWBLcHAAAkLUI3AMBXVapUcWHynXfeCTu9dOnSwf7ZHv2u50V9sdX025PYgdcyZcoU1fOqemtE9RkzZriKd0JV7nAqVapk9913n2tGHkqVd4+ahXtVfzV/V6VaI7ur6be3HyI1L0+M7NmzuzAf+gAAAMmL0A0A8JUG/xo7dqwNHz7c/d9r9q2qcK9evaxhw4a2c+dOGz9+vJ08edINfDZ16lTr2rVrsJL86aefurCtqq6aYCeGBjrTKOPR0PooCC9evPiManU4Wlett9cc/O+//3aDptWvXz/W655++mnX31yvU5Pv2267zT2voK1m5brVmCrsGgUdAACkL4RuAIDvbrjhBjeImfpvqxqs/sht27Z1I5iXKFHCTZsyZYob3fvOO+90g6kpjIvCr/p567W1a9e2li1bJmrZGvl8/vz5bplaj0g0iJmq0hrlXP2zE6KwrAsCGlBNTb11gUDPxe2PrhHKte4adfzyyy8PhusHH3zQ9SnXhQFNu+KKKxK1bQAAIPWLCSRVGzYAANIBXRQYM2ZMggE9GmoursHW9u3b50J/SlNlXQOqqX93em9qrpHh1YJCg87F18UgPSk/YI6lNpksYJVXjHUX1TLCMUitMtpnITXiGKTf4xDtv6scdQAA/n/qd37q1Kng/bwBAADOVZZzngMAAOnABRdc4O6zrabhavLt0f2/1fQ9Lo1CvmbNmmReSwAAkNYQugEAMLN169aFfX7ChAnucTY0aju9uAAAyNhoXg4AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hIHUAAAA0riNw1taarwnbvPmY1N6NQAgxVHpBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfMItwwAAAOCLlVv22/mD5tppi7HUKjXebg1A+kKlGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAIBksHDhQsufP39KrwYAAEhmhG4AQJrQpEkTGz169BnPx8TE2MqVK31ZZvny5e28886z3LlzW+HChe3GG2+09evXJ/i+jRs3uvXav3+/L+sFAADSDkI3AAARTJs2zQ4fPmx//vmn5cyZ07p27ZrSqwQAANIQQjcAIF1YsWKFNWzY0AoWLGhFihSxjh072p49e4LTp06dapUrV7Y8efJYqVKlbPDgwYmaf968ea1Lly62atUq27Vrl+XIkcM2bNgQnH706FErUKCAfffdd3bZZZe550qXLu2q5Fq257XXXrMyZcpYoUKF7OGHH461jClTptgFF1zgmqFrW3788cdYlf6BAwda06ZN3TbUrVvXVq9efVb7CgAAJB9CNwAgXciUKZMNHz7cduzYYT///LNt27bNBgwY4Kb9888/1r17d3v99dft0KFDtmbNGmvWrFmi5q+m4m+++aYLuwr1N9xwg02ePDk4fdasWVayZEm7/PLL7fvvv3fPbd261VXJb7vtNve7lr127Vr7/fff7auvvrKXXnrJ9fWWxYsXW58+feyVV15xob5t27ZuHQ8cOBBcxltvvWUjR460ffv22SWXXGJ9+/aNuM7Hjh2zgwcPxnoAAIDkRegGAKQZqvSqChz68NSqVctVh7NmzWrFihWzBx98MBhoRc+vW7fOBU+979JLL41qmQrMqmDXqFHDTp8+7YK39OrVy/0cCATc75MmTbIePXpEnJdeO2TIEFclV0W7fv36tnz58mCg7ty5s1155ZVuXe+//3633Dlz5gTfr+nazixZsli3bt2C743PsGHDLF++fMGHKuwAACB5EboBAGmGQqQqzqEPzx9//GE33XSTqzarKbgC6u7du920XLly2ccff2wffvihC54K5wsWLIhqmWoarsqyKuczZsywcuXKuefVzPv48eO2aNEiN03/V/PzSLRe6hfu0Xqp+u1VxTVwW6gKFSq45z3FixeP9V5V0RO6SKFKuffYsmVLVNsMAACSDqEbAJAu9O7d2/XVVvNtVbPVP9qrQss111xjc+fOdUG8Xbt21rp1a1e5Ppfm7Gqyrgq3Kt4K4aqwe9MSS/2/Nep5KP2u589W9uzZXdAPfQAAgORF6AYApAsK2hpgTMFSFd1nn302OE39vNXnWlVlNc3Wa/T/c9WzZ097//33XV9x/exRn28F72huL+ZRZV5V9aVLl9rJkydt7NixbiC4Fi1anPN6AgCAlEPoBgCkC6NGjbLZs2e7QK1m5m3atAlOU0V7zJgxrmm5+jZrADM1FT+binSoihUrugHNFOZbtmwZfF739n7iiSesefPmrv/422+/neC8Gjdu7IK2+oprZPN33nnH5s2bF6vfOgAASHtiAqFt7wAAQKKowq3blD333HOWFloD6KKD+nen96bmutCyc+dOK1q06DlfXMHZH4MSNa+wXK0es9MWY6nVxuH/d8EsPeKzkPI4Bun3OET77+q5t60DACCDUvNxVcwTGkUcAABkXFxqAQBkWEOHDrXcuXOHfSTkrrvustq1a1v//v2tcuXKybK+AAAg7SF0AwAyrEGDBrnbboV7JOSVV15xfbkfeeSRZFlXAACQNhG6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ9wyzAAAAD4onaZ/DZvaAvuTQwgQ+MvIAAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNuGQYAAABfrNyy384fNNdOW4ylZhuHt0zpVQCQjlHpBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AQIbQpEkTGz169BnPx8TE2MqVK31ZZvny5e28886z3LlzW+HCha1ly5b2xx9/nPN8J02aZLVr106SdQQAAP4idAMA4KNp06bZ4cOHbePGjZY/f37r2bNnSq8SAABIRoRuAADMbMWKFdawYUMrWLCgFSlSxDp27Gh79uwJTp86dapVrlzZ8uTJY6VKlbLBgwcnav6qdnfq1MmWL18eqxL+wQcfBH/Xz3rOM2rUKCtbtqxbpp5/7bXX3Hr27t3bVq9e7eapx+bNm8Mu89ixY3bw4MFYDwAAkLwI3QAA6B/ETJls+PDhtmPHDvv5559t27ZtNmDAADftn3/+se7du9vrr79uhw4dsjVr1lizZs0SNf8DBw7YW2+9ZVWqVInq9b/99ps9+uij9tlnn7llfvfdd3bZZZdZnTp1bMKECXbhhRe6CroeCubhDBs2zPLlyxd8lClTJlHrDAAAzh2hGwCQYQwcONA18Q59eGrVquUq3VmzZrVixYrZgw8+aAsXLgxO1/Pr1q1z1WK979JLL41qmbfddpsLvHqPqtRTpkyJ6n2ZM2e2QCDgAv6///7r1umiiy5K9PYq7HuPLVu2JOr9AADg3BG6AQAZhiq/+/fvj/XwaICzm266yUqWLGl58+a1zp072+7du920XLly2ccff2wffvihqxYrnC9YsCCqZapZugLvL7/8YidPnrT169dH9b5KlSrZ5MmTbdy4cS5wX3/99Yke8C179uxuW0IfAAAgeRG6AQAwc/2k1Vd77dq1rpqtirQqzZ5rrrnG5s6d64J4u3btrHXr1nb69Omo51+1alV77rnnrE+fPq5yLeqPfeTIkeBrtm/fHus97du3d+FeTd5Vie/SpUuwKTwAAEgb+FcbAAAzF7Q1YJmqwWqG/eyzzwanKfTOmjXL9a3OkiWLe43+n1gK6oUKFbKXXnrJ/V63bl03uvnRo0ftzz//DD4vv/76q33++ecuoGfLls0FdG+ZqnwroHvhHQAApF6EbgAA/v+RwmfPnu0CtZqZt2nTJjhNFe0xY8a4puXqn61wPGPGjERXnHVPcPWzHjlypBucbciQIa6Ju0ZL18jmXbt2Db72+PHj9thjj7mAraD+5Zdfuvtzy9VXX2316tVzlXn1FY9v9HIAAJDyYgKhbecAAEC6rubrooH6mKf3/t26ULJz504rWrQozfFT8BiUqHmF5Wr1mJ22mJRenYg2Dm9p6RWfhZTHMUi/xyHaf1c56gAAAAAA+ITQDQDAWRo6dKjrax3uAQAAIIRuAADO0qBBg+zw4cNhHwAAAELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHySxa8ZAwAAIGOrXSa/zRvagnsTA8jQ+AsIAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITQDQAAAACATxi9HAAAIA0pP2COpQWZLGCVU3olACAVoNINAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QCAJPPVV19Z8+bNrUCBApY/f36rVauWjRw50o4fP+7rcps0aWKjR4/2bf4xMTGWM2dOy507d/CxevVq35YHAADSD0I3ACBJzJ492wXupk2b2u+//2779++36dOn29q1a2379u0pum6BQMBOnTp1TvP4+uuv7fDhw8HHhRdemOTLAAAA6Q+hGwBwzhQ47733Xuvfv7/df//9VrhwYfd8tWrVbNKkSVauXDlbtmyZNWjQwFXAq1evbtOmTQu+v3v37u59HgV2VZc3btwYnH7HHXdYhw4dLE+ePFa1alVbuHChm9avXz9bsmSJW7Yq0Ar+Ur58eRs2bJjVq1fPVanHjRtnFStWdOvq+fbbb61gwYJ29OjRs9ruuMvQBYYpU6ZYzZo13XqWLVvWHnvssVjL1HZNmDDBvSZv3rzWqlUrO3DgQHC6LljouSJFirh1u+WWW4LT1q9fbzfeeKObpn06ZMgQO3369FmtOwAASB6EbgDAOVNQ3LBhg3Xs2DHsdIXoZs2audC8a9cue/nll12IXrp0adTLUNW8d+/ebl5dunRxQVyef/55a9SokY0YMcJVoOfNmxd8jwL/5MmT3fN9+vSxI0eO2KJFi4LTJ06c6NY5R44cZ73tocvQxYBChQrZ+++/bwcPHrSPPvrI/ve//9nbb78d6z3vvvuuffnll7Z582bbunWrvfDCC+75f/75x6699loXyHXB4e+//7a+ffu6aVr3a665xj22bdvmLjS88847bhvic+zYMbceoQ8AAJC8CN0AgHOmIC2lSpUKO33OnDmuOqsAmTVrVmvcuLF16tTJhdVotWjRwvXdzpw5s/Xo0cM2bdpke/bsifgeBW0FYb0nW7Zs1q1bNxeSRdVtBXnNKxoK9qrS63HVVVfFuwxV2qtUqeIq2rVr13ah3qvKex5++GErWrSom1ebNm1s+fLlwSb62j/PPPOM5cqVy83PW5b2ofrKq0WAnlcV/b777jsj0IdSFT5fvnzBR5kyZaLaVgAAkHQI3QCAc+Y1J1cFNhxVc9UUO5Saeuv5aBUvXjz4swKpHDp0KOJ7FExD9ezZ02bOnOmq0rNmzXLTL7nkkqiWr8qyqux6LFiwIN5lfPrpp1a/fn23TxR01ZR89+7dEbfF2w5dSKhUqZIL7HGp8v3zzz8Hg78ealqvanh8Bg4c6Jque48tW7ZEta0AACDpELoBAOdMlV2FajV3Dqd06dLB/tke/a7nRX2x1Xzak9iB1zJlyhTV86pIa0T1GTNmuIp3tFXuaJetUdrVB/uuu+5yFyAUdNUkPrRPdyTqp61+2+Feryr1xRdfHAz+eqi5+Jo1a+KdX/bs2V2/8dAHAABIXoRuAMA5U2V27NixNnz4cPd/r9n3b7/9Zr169bKGDRvazp07bfz48Xby5ElXNZ46dap17drVva5u3bquQqywrarvU089lajlFytWzIXVaGh91A988eLF1rlzZ0tK6kOtZuvq163A+91330Vs/h1Xy5Yt3Twef/xx179bId6rqt9www22Y8cOtw+1DI2U/uuvv57RdB0AAKQuhG4AQJJQKNQgZup7rCbSav7ctm1bN4J5iRIl3DSN7K1Aeuedd7rB1BTGReFX/bz1WvWDVvhMDPVznj9/vlum1iOS9u3bu2bc6nutfuZJSSOWv/TSS277VFVW3+xbb7016ver4q/tUB9vNVvXftP8Qqd98cUXrlWB9qP6xUdqXg4AAFJeTCDaNm8AAKQTuigwZsyYBAN6eqPm6Opnrmbv6b2puW6lptYVGrAuvu4HaVX5AXMsLchkAau8Yqy74JbejkFakp4/C2kFxyD9Hodo/13lqAMAMhT1O1fTbO9+3gAAAH7K4uvcAQBIRS644ALbu3evu1WZbvHl0WBnavoebmCzSAOVAQAAJITQDQDIMNatWxf2ed3WSw8AAICkRvNyAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCcMpAYAAJCGbBze0tLKPXGbNx+b0qsBACmOSjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATbhkGAACQBpQfMMfSkkwWsMopvRIAkApQ6QYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAIDvmjRpYqNHjz7j+ZiYGFu5cqUvyyxfvryb/++//x7r+Xvuucc9H259UoLWJWfOnJY3b14rWLCgXXHFFW7dTpw4EXzNpEmTLHPmzJY7d273KFGihN1999127NixFF13AACQMEI3ACDdqlq1qgusHoXUd9991ypXrmypyddff20HDx60HTt22PDhw23y5Ml24403WiAQCL7mwgsvtMOHD7vH8uXLbenSpfbcc8+l6HoDAICEEboBACluxYoV1rBhQ1fpLVKkiHXs2NH27NkTnD516lQXlPPkyWOlSpWywYMHRzXf7t2725tvvmmnT592v3/wwQd26aWXWsmSJWO97rPPPrM6depYvnz5rG7dujZ//vxY87jjjjusQ4cObvkK8gsXLgxO379/v7Vr187y589v1apVs7Fjx7rqtefQoUN25513uuq0Hr1797Z//vkn7PpmzZrVGjdubO+//74tWrTI5s2bF/Z1Wv+mTZvamjVrotoPAAAg5RC6AQApLlOmTK7Cq0rvzz//bNu2bbMBAwa4aQqoCr6vv/66C7AKms2aNYtqvgrBZcqUcaFa3njjDevRo0es1/zxxx9200032WOPPeaC/qBBg6xVq1a2YcOG4GumT5/uwrICdpcuXdz6ePr27evWcdOmTbZgwQJ76623Ys3/vvvuc8vQdq1evdp++eUXe+CBByKud4UKFeziiy92wTucLVu22CeffGINGjSIOB9V9lVBD30AAIDkRegGACSLgQMHumpw6MNTq1YtV+lWpbdYsWL24IMPxqom6/l169a50Kj3qVodLYXsiRMn2tatW11FXYE6lAK1+pzfcsstliVLFmvbtq1bl2nTpgVf06JFC/ca9avW/BSwFdBPnTrl3v/000+7Krkq2Q899FDwfaqwq0o/bNgwK1SokBUuXNiGDh0aq/oeH1X09+7dG/xdgV3bruWULVvWVd11ASASLVev9x66AAEAAJIXoRsAkCwUAFUpDn3ErTar2bQGFOvcubPt3r3bTcuVK5d9/PHH9uGHH7rQqECsinK0br31Vvv888/thRdecD9nz5491nSFcQ26FqpixYrueU/x4sWDP2t9RFV3raMGPAsNswrEnl27dtnx48djzV/zVgXa2774qNqv5vahfbq1zw4cOOCWfdlllyVY8deFDr3ee6hCDgAAkhehGwCQ4tR0W5XdtWvXumr2lClTYg0ids0119jcuXNdUFX/6datWydYKfYoxLds2dKF7rhNy6V06dK2cePGWM/pdz2fEFWuVYUPDbObN28O/qz+6dmyZYs1f/2s4K/3xkev0WBpqq6HoxHMe/XqZd98802svu9xaTna/tAHAABIXoRuAECKU9BWc2mFQgXYZ599NjhN/bxnzZrlqrtq/q3X6P+JMWLECPvyyy/dIGlxqfqtpuyqpJ88edINYrZ48WI3cFpC1Ny8ffv29uSTT7pK8t9//23PP/98rL7qnTp1skceecQ1Fff6jKtZuKbFpar5kiVLrE2bNm5Atfgq2f/++69rMq+WAaHVcAAAkPoQugEAKW7UqFE2e/ZsF6jVzFyh06OK9pgxY1wTbvVLfumll2zGjBlhQ2t8FE7jqxqff/75Lmg/8cQTLsCqf7ZCvpqBR0OjlauirGblWoZCuKrbHq27mpdXr17datSo4Zan7Q1Vv359d9GhaNGirk+4mterSX3oKOjq0+3dp1utAlatWmVz5syJ9RoAAJD6xARC2+8BAIBzogHYHn/8cfv9998tNbYo0IULVeXTe1NzXazZuXOnu5CRmAs0qVn5AXMsLclkAau8Yqy79V16OQZpUXr8LKQ1HIP0exyi/XeVow4AwDlQuF62bJnrg66fhwwZ4vqdAwAACKEbAJAm6dZbXnPruI/kpHt0qzm4lqt+2FdddZU9+uijyboOAAAg9UrcSDQAAKQSGpBMj5RWu3Zt++WXX1J6NQAAQCpFpRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCQOpAQAApAEbh7e0tHZP3ObNx6b0agBAiqPSDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+IRbhgEAAMAXK7fst/MHzbXTFmNpUVq7TRuA1IlKNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0A0jThg4dah07drSMqEaNGjZ79mzf5t+sWTObO3eupZQlS5ZY6dKlU2z5qd3UqVPttttuS+nVAAAACSB0AymgSZMmlj17dsuTJ4/ly5fPatasaf369bNdu3YlSVDJnTt38BETE2M5c+YM/q6QmtzmzZtnl112mdvWAgUK2KWXXppkYW7QoEE2bdo0SyobN250+yx0H+px//33W0qfM6NHj4713Jo1a+yGG27wZXkLFixw52OLFi2Cz/3777/26KOPWuXKlS1XrlwuELdt29aWL1/uy7Fv1KiRbd26NeI+OFtHjx61ggUL2owZM86Y9vnnn7tjfujQIXvyySctS5Ys7ve8efPahRdemGTn26RJk6x27dpn/X5dbPr+++9txYoVSbI+AADAH4RuIIWMGDHCfanfv3+/vfvuu7Zt2za7+OKLbceOHec0XwWVw4cPBx/y9ddfB39XSE1O69evt3bt2rnl7t2717Zv327PPfecu+CQ0k6cOBHvNIW90P2YVGEvrXjppZesR48esfbV9ddfbwsXLrTp06e78/bXX3+1W265xWbNmpXmjn2OHDlclfiNN944Y5qea9++fXA9dWFD54C2+bHHHrMuXbrYL7/8YiktU6ZMbhvGjx+f0qsCAAAiIHQDKUxV1erVq9uUKVNcJe35558PTvvxxx/tqquuchW5888/31599VX3/M6dOy1btmy2adOm4GuPHTvmKonffPNN2OUoNBUrVsyFplAXXHCBC1HeuowZM8aqVq1q+fPnt1tvvdUOHDgQK0TdeOONVqRIEStXrpwNGTLETp8+HXH7VIXTclu3bm2ZM2d2Yadx48bu4kA08/WqgYMHD7aiRYu6eYUGYFUiNW/PH3/8YU2bNnX7rFKlSrFe683riSeesOLFi1uHDh0ssR566CG3/t76qVKqeemYyPz5811lV/tPzb8/+uij4Hv1nhdffNGqVavmAp0qxp988knYKu7KlSvd8RC1glALhv79+7uKa/Pmzd3z5cuXtw8++MAd28KFC9vixYtjravOK68qq/VTQCtRooSVLFnSVe51zoSj+Wm9rr766uBzb7/9tq1bt841Z69bt65lzZrVVbs7derkjtfZHHudi9pPofRaHdO40+PbBwrD//nPf6xs2bLu/OjatWusczaSXr162WeffWZ//fVX8DkFa+1TTQsXchXGtU5qZRCN+M5t7ZvevXvb6tWrg60pNm/e7N6jY1arVi3390Dv0Xkbn2uuucY+/vjjqNYFAACkDEI3kEqoCasCx6JFi9zvf//9t1133XXWp08f18xXQUBh8YsvvnDhQlVHBXWPvnjri/0VV1wRdv4KSarQhX6BV0BXZT00tL711luuabGaWe/bty/YrPrIkSPuC74eqsorAL3zzjs2ceLEiNul6r1CjbZDQU4Vz1DRzFcBR03kNV0XCBR8FWbiOnnypKtKKrBomarAjhw50gVGz88//+z2tQKOtjWxnnnmGfvnn39ceNJFjzvvvNPefPNNd0xWrVrlKrvDhw932/nKK6+4fa6KsIwbN84Fa/XFPXjwoDuWClUJ0YUYBVW1jlDIVJPtuMdWFxBCt2fZsmVuf+nYBgIBa9Wqlbs4oP2moPfTTz/FG5Z///13d1x08cXz6aefuqAbNySfy7FPjPj2Qc+ePd18te83bNjgLhgohEdDF2B0rkyePDn4nM4VHZMGDRqc8fpTp065c1PH7qKLLkpw/pHO7Tp16tiECRNcc3WvNYUuHOhzrPV/4YUX3AWAH374wa1jfHRhRZ9htSIIRxdWtL6hDwAAkLwI3UAqUqpUqWAwUYC68sorXWVNVUL1+1ZzXy9AqqIXGrL0swJeJKrezZw5M9jsXAFclUr1L/c8/PDDrhKqcKXqspanytycOXNcJV0hXFV2BYT77rsvVqANp0KFCrZ06VK3zNtvv91dGNDFhD///NNNj2a+quKq0qlwqYqwKryqBMf13XffufChMKmqqoKRAkzohQb1LX7kkUfcshTk46PgpX3gPbyLAHqfKpEKRervrH2qCyCikN29e3dXIVZVtGHDhu4igLoPyMsvv+yquAqjqmJrW9XSICnofHjvvfdcX2XvfFB/6/POO88FcAXpZ5991m1zoUKFXJPv+I6dLrbodTrvPLrwo/MzMRI69udK66TzWU3hdYxUeX/66afdhRkF5Gjo+IVe4FHT8rhVbp2jmr+q9gr/Ov5qpZCQs/nMqKm4XuOdQ7qYo4AeH1XDvWMWzrBhw9w57z3KlCmT4HoDAICklSWJ5wfgHKgapmbRokqzBpwKrSwqSHhNc1W5VJVVAylVrFjRVRLVNDwSBTyFdzWJVmVU4eTLL7+M9ZrQyqt+Pn78uAs3Wh9ViUPXR2E8mi/xao7sXSBQpfWuu+6yzp07u77m0cxXYSeUwpX6w4frh60LBgo4Hu2b0BYBCo4KMwlRFTu+qq4Cl8K/mlqH7j9ti34PDXGqvnvBSPOMJqydDTVpVyVbzdnVz1rBUCHcWy9VTb1zS1T9ji+YKiiqSqvpXvDWhQ+dn/FRywFVXT1r1651ITPSsT9X2i6dKwr3oXR81VIkmosEuuikCzpfffWVC6VqAaCwHKply5aupcnZrF9iPzM6R3QBJVpe5VrHLJyBAwfagw8+GOv1BG8AAJIXoRtIJRTOPvzww+Bo0fpifPPNN7vmqOGokqumzAo0agZ8+eWXuwpwQlTFU+VX1W2FaoWiuF/6NS8vSCnAqkKp9VGF9ttvvz2n7VQ/a1XyFHa87UyK+YpG01ZzZjUxVlXcCz6ht52KJnAnRBcttL4KY/fcc09wBGxti7ZNzcvD0f5Wn/NwXQDUp1dB1xO3uXA0662WDjofVKXWQy0lvPVSxTS+Jshx6cKA3q9m8V6QVj95NetXf2mF07gUsL0WFNEee22zRkTXBQCv/7rWMb4RvePuA22XntMxj9RqIRIF4jZt2rgKt7ZLLRPiXuQ5Wwmd2+GOqXeOREsXN7S+6qsfjj7noS1ZAABA8qN5OZAKaCTkbt26uUDjVaUUoFQ1VfNZhUg91KRafTw9qoh5fUSjrY5pcDTd4knBUP1h41ITZIUYVUYff/xxVxFXOFAYUd9RNX9VE2ZVQRXK4g7MFpf6seo93mBVqkBqQLj69eu73892vvFVexVAtN7qy6oq49ixY92+TSq6EKFqrfoBqy+3BsT63//+56bpeR0L9YnXdmgd1G9eA5B505966il3HBU0NS9vmi5+vP/+++4c0KBn6oseStsVrh97KJ0zGhhMTd9VTfaCrG7TpQCo232phYCWrYsrcfuGe3TBQiFb2+HRIGwaAE6DgmmbdZFIgVlN5zWi99kc+ypVqrhlqbm19peq85FufxV3H6iyrz7r6kKwe/fu4DJCR1NXiwRvYLZIF6LUKkAtIsINoBZJpPkndG5re3SRQfvRo3NELVY0toOq4joXIu0T/Y3QxR8AAJB6EbqBFKJRmL37dKs5sAKE+t56VTY1jdXgVeonrCqWnldVNXQgJPUZ1jxU7VLVOxp6vV6roK8gFZfCmkZMV8VNr/WarKsqqZG5NfiXKurqF6yKpUJOJGr2qu1QxU/NwhUu9Zw3eNXZzjccBTg1+dZFBe1PNcHXRQyvspoYqo6H3qdb+0yhSftMfevVj1vNxhUU1Q9e4Vl9b/W7wq1aB+gYKpB6o4Tfe++9blAx73ZU1157bXDE6gceeMAdZ4Vj9efVxZFQ6hes/aTKbHz35la1WYFWQSz0IoyaiGu/qHm4uhjonFNQi1RR1bkW2hde+1bHUd0btC+07aqIK3SrRcbZHHvNQyF8wIAB7rir/7fCfnzC7QOto37XhQXNT+sXet9w7d9wg6LFDc76fKlVhzcqerQizT+hc1vHuV69eu480TZoXrqIMGrUKLf/dZy0XRr4zruIoXl6FMo1KJ9eCwAAUq+YgEoeADIUDTal0Z69ZtEeVUZVVYuveS8yFgVgBd3EBtHUQtV8tdSI7zZ6qX3+CVELAfU/V/COli7aKcyrRYU31kB65bUUUNeKpOhWgrM7BiVqXmG5Wj1mp+3/tbxJazYOT/stSfgspDyOQfo9DtH+u0qfbiCD0aBoqi5GuvcvIKpSp2VqreFnIPZ7/glR1fxsWnEAAIDkxaUWIAPRPabVzFVNi3Xv4KTSu3fvWE2xvUeNGjWSbBkAAABAWkSlG8hAdH9qPeJztr1NJkyY4B4AAAAAYqPSDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8YvRwAAAC+qF0mv80b2sIyZaLOAyDj4i8gAAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE24ZBgAAkAqVHzAnpVfhnGSygFVO6ZUAgFSASjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAkIqVL1/ePvjgg5ReDQAAcJYI3QCAVOGrr76y5s2bW4ECBSx//vxWq1YtGzlypB0/ftzX5TZp0sRGjx7t2/xjYmJs5cqV7ucnn3zSWrdu7duyAABA6kPoBgCkuNmzZ7vA3bRpU/v9999t//79Nn36dFu7dq1t3749RdctEAjYqVOnUnQdAABA2kXoBgCkeKi99957rX///nb//fdb4cKF3fPVqlWzSZMmWbly5WzZsmXWoEEDVwGvXr26TZs2Lfj+7t27u/d5FNhVXd64cWNw+h133GEdOnSwPHnyWNWqVW3hwoVuWr9+/WzJkiVu2blz53bB32vSPWzYMKtXr57lzJnTxo0bZxUrVnTr6vn222+tYMGCdvTo0ai2U03Ehw4d6i4waFl6yGeffWaXXHKJ5cuXz0qUKGF33323/fvvv2e8/8SJE1asWLHgunsuuOACd4ECAACkToRuAECKUmV7w4YN1rFjx7DTFaKbNWvmQvOuXbvs5ZdfdiF66dKlUS9DobR3795uXl26dHFBXJ5//nlr1KiRjRgxwg4fPmzz5s0LvkeBf/Lkye75Pn362JEjR2zRokXB6RMnTnTrnCNHjqjWQc3KBw0aZDfccIObpx5y3nnn2auvvmp79+5127RgwQIbNWrUGe/PmjWrW3etl+ebb76xHTt2xNtk/dixY3bw4MFYDwAAkLwI3QCAFKUgLaVKlQo7fc6cOVakSBHr27evC56NGze2Tp06uUAcrRYtWri+25kzZ7YePXrYpk2bbM+ePRHfo6Ctqrjeky1bNuvWrVsw8Kq6rSCveZ0rhf46deq45aiaftddd51Rzfb06tXLZs6cGQzsWh/ti+zZs4d9var1qqB7jzJlypzz+gIAgMQhdAMAUpTXnHzbtm1hp2/dutU19w6lcKrno1W8ePHgz7ly5XL/P3ToUMT3lC1bNtbvPXv2DAbeWbNmuelqFn6ufvjhB7v22mtd0/G8efO6avju3bvDvlZNyWvWrGkzZswIBn+tV3wGDhxoBw4cCD62bNlyzusLAAASh9ANAEhRVapUcaH6nXfeCTu9dOnSwf7ZHv2u50V9o9X025PYgdcyZcoU1fOqemtEdQVeVZjPpsodbllqon7VVVfZn3/+6Zp/q993aN/xcNVuLV/BX/3d69atG+9rVQFXkA99AACA5EXoBgCkKA16NnbsWBs+fLj7v9fs+7fffnMBs2HDhrZz504bP368nTx50g18NnXqVOvatat7nULnp59+6sK2qtdPPfVUopavCvP69eujeq3WR/3AFy9ebJ07d070tmpZatqu7fAoaGuAOFXg161b5/qsR3Lrrbfa8uXL3f6KVOUGAACpA6EbAJDiNLiYBjFT/+1KlSq5ENq2bVs3grlG9Na0KVOmWKFChezOO+90wVRhXBR+1c9br61du7a1bNkyUcvWyOfz5893y9R6RNK+fXsXmjXKufqZJ1a7du1ctVnv1fLklVdeseeee85V7DXYmwaMi0QjsGs+v/zyi912222JXgcAAJC8YgKR2rABAIBYdFFgzJgxCQZ0Pz399NO2atUq19Q9MVRV14Bq6t+d3puanz592rWQKFq0aLxdCFK78gPmWFqWyQJWecVYd9EsrR6D9CA9fBbSOo5B+j0O0f67miVJlgYAQAagfuenTp0K3s87pUZ71y3GQm8dBgAAUi8utQAAEAWNHH7ffffZhAkT3O29PGoSrqbhcR81atRI8nV45pln3KBzakJ/zTXXJPn8AQBA0qPSDQBAFDTIWTgK4Xokh0ceecQ9AABA2kGlGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJA6kBAACkQhuHt7S0fk/c5s3HpvRqAECKo9INAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hFuGAQAAwBcrt+y38wfNtdMWY+lBWr+NG4CUQaUbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAEDR16lSrX79+hl0+AABAUiN0A0jzmjRpYtmzZ7c8efJYvnz5rGbNmtavXz/btWvXOc97yZIlljt37uAjJibGcubMGfx96NChltziroMeN998c5LM+7bbbrOvv/46SeaVFpcPAACQ1AjdANKFESNG2KFDh2z//v327rvv2rZt2+ziiy+2HTt2nNN8GzVqZIcPHw4+RKHQ+33QoEGWEkLXQY9Zs2ZZWnfixImUXoU07eTJkym9CgAAIAxCN4B0RVXg6tWr25QpUyxv3rz2/PPPB6f9+OOPdtVVV1nBggXt/PPPt1dffdU9v3PnTsuWLZtt2rQp+Npjx45ZgQIF7Jtvvok3IBYrVswWLlwY6/kLLrjApk+fHlyXMWPGWNWqVS1//vx266232oEDB4KvXb9+vd14441WpEgRK1eunA0ZMsROnz59Ttuv9dGyXnvtNStTpowVKlTIHn744VivGTt2bHDao48+arVr17ZJkya5afq/fveUL1/eRo4cafXq1XMtCRo3bmxbtmwJTte+U3W6RIkSVrJkSbv//vvdvkton8uTTz5pN9xwg/Xp08dNHzBgQKKXv2bNmuA0LUfbqpYP8dExGTdunDtHcuXKZV26dLF9+/a5Y6PzpU6dOvbLL78EXz9q1CirXLmym3+lSpXcez0bN25083vrrbfctmm/d+/ePXjxQBdDbrrpJitatKhrgXHllVfaTz/9FHy/jrX2v84j7buXXnrJzSP0nHrnnXfsoosucs9feumlsVoBaDu1vddff73blnnz5kU8NwAAQMogdANIl7JkyWKtW7e2RYsWud///vtvu+6661zAU7PzDz74wJ544gn74osvXChScFFQ93z88ccuDF9xxRVh5581a1YX2LywKgroqqxruR4FsgULFriApnCnUCpHjhyxa665xj1UlVczdgWsiRMnnvO2q+K/du1a+/333+2rr75yYc4Lctrexx9/3GbOnGnbt2+3TJkyueAaifbLtGnT3H5TuHvsscfc84FAwFq1amXFixd3FxBWr17tQqUuHiS0zz2ffPKJXX755S68Dx48OFHLV7jV8ps3b2579uyx4cOH2xtvvJHg/vnwww/dftH++eyzz1yQ79u3r+3du9cF/tCLFLoY8uWXX9rBgwfdhYyHHnrIli5dGmt+CrsrVqxw+1zbpn7pXqju1KmTbdiwwZ0XCvTt27d3+010rPVaHXvtP12g0LHzzJ071/773/+6c0zrNnDgQHeRRtvq0TTtbwX8a6+99oxt1QUQrXvoAwAAJC9CN4B0q1SpUi6seOFXlUaFnsyZM7t+3z169LC3337bTe/atat7jUc/K1RH0qtXLxdevWbnCkAKWepf7lGAUxVTlUqFSi1PYWzOnDmukq4Qrip72bJl7b777guuTzTN3jVP7/HUU08FpynUKYjlyJHDVd41MNny5cvdNM1flenLLrvMLVcBVkE2krvvvtsqVKjg5qf3evNatmyZC67PPvus62Ouyrma23vbkNA+Fz2n6rAukmgeiVn+t99+6wLoI4884rZF4V0V64QoyKqyruOiwF2jRg1r2LChW4d27dq58Otp06aNaxWgirYq6U2bNj2jdYMuYqgSrvk1a9YsuH6qnGt9tH+17jpGv/32m/3111/BY3HPPfdYlSpV7LzzznMXDUJbOuhiiUJ+3bp13cWRW265xapVq+bCuEfnm46l1k/ziGvYsGGuyu49tC0AACB5ZUnm5QFAslEFWeFKVGlWWFFA9Zw6dcqFV1HF9M4777Tvv//eKlas6CqwahoeiQKtQuOMGTOsQ4cOrlm5qqKhVCkN/fn48eOuYqv1+fnnn2OtjwJXtKFI1dHQZtihFPZCA6xCn1dBVeALbX6tir2ahkeiSna4eWkb1Ife28de4Nd+jWafiy42JCS+5WtbtO4Ky6HzS6hyr+bcHu2n0PXT795FFFElWl0UtC06PmqhoAsAkdZP+0T+/fdfN6Cf9oEu/ig4y+7du90FIa1/6PFWywqFc4+WqYsYah3gUXVf53Xo9kai6viDDz4Y/F2VboI3AADJi9ANIN0OKqVmxC1atHC/K2hohG814Q5HYUdVTlVn1QdbVVP1J06Iqt2qcKu6rVCtqmQo9RPXvGTz5s2uIqtwpfXRQG+q1iYnVWND+0RrP6mZ+dnQNqhpfnzvT2ifixdEz3Zb1IRd2+AFb+3jpKJ5devWzV2A0YUKr8uC1zw8IQrrqnqrKXvp0qVdGFfrBu/9cY+FLsYcPXo01v5Ts/fevXuf9f7TeRna8gIAACQ/mpcDSHc0EJbCkgYt86p8aiquKrSag6taqMfKlSvthx9+CL5PTcy9ftX6ORpqPqxgpabBPXv2PGO6ml6roqnApWbIqogrKGkAMfXzHT9+vAtaqgD/+uuvZzRdTmodO3Z0zZrVNFz7QM3Q//nnn7Oalwb2UjDUYGCqPitM6iKDN6BXNPv8XGgANVWp1YRa89Z8NXJ9UlHFW9ukCws6ZqpYqw94tFRV1sUcBe1wI93rWOj4//HHH64qrumhIVpNz3X+6PzSeqjKPn/+fNu6dWuSbSMAAPAfoRtAutC/f//gfbrV91VNfhUsvabEas776aef2iuvvOKaJOt5hZrQgaXUr1fz0IBYqnpHQ6/XaxX01d84rs6dO7u+wKqC67Vek3XdW1sBSgNvqaKu/tDqn6vKbTTUTzv0Pt3q1xsNDbal5sqq2GofqUqsPsVnUw1VP+3Zs2e75s5qaq9937JlSxcio93n50JN49WaQeugYKv+89rfSVXZ1Qjn6i9+9dVXu+Oj7gPqhhAtXfDRPtJ2qxtC3EH5dJFGF2F0LDUyuroLKKR7669B03Qx54477nDbp2btOn/OdYR7AACQvGIC0baTAwCE9fTTT9uqVatc3+5QGtxKo1rH1/c6NVAfcwVKNaFu0KCBpXV33XWXC6WhtyZLK9RMX03OVcnWBQs/6IKHLo6oFYj6/qdnOg80Kr7XUgEpcwxK1LzCcrV6zE5bjKUHG4e3tLSGz0LK4xik3+MQ7b+rHHUAOAfqh6uAp9tipRXvv/++a86sZuVqIaDQrabiaZEGlFO/aP1D6t2uK9pWCilNrQx0GzU1jfduJ6eqt1+BGwAApAxCNwCcpWeeecY1DVeTat1vO6lo4KzQpuPeQ7e2SgoaLE7NvVVV1e2xPvroIzfAW1r0559/ur7d2j+68KHm2LrnelqghmZaX130UPNyXQSJ9pZxAAAg7aB5OQAAGQTNy5GcaF6eOvBZSHkcg9SB5uUAAAAAAKRDhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfJLFrxkDAAAgY6tdJr/NG9qCwaMAZGj8BQQAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHzCLcMAAABSQPkBcyw9y2QBq5zSKwEAqQCVbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgCkehs3brSYmBjbv39/Sq8KAABAohC6ASCVaNKkiWXPnt1y585tBQsWtMaNG9uyZctSfcDdtWuXZcqUyX755Zfgc/Pnz3fzmDRpUvC5AwcOWJYsWWz58uWWXJ588klr3bp1si0PAAAgLkI3AKQiI0aMsMOHD9vff/9tl19+ud1yyy2W2hUpUsRq1KhhCxcuDD63YMECu+CCC2I9t3jxYsuTJ4/VqVMnUfM/ceKEpQcnT560QCCQ0qsBAACSGaEbAFKhbNmyWbdu3WzLli2ukqyw9uKLL1q1atUsf/78riq+bt264OvLly9vw4YNs0svvdRy5cplzZs3t71799rdd9/tXl+5cmX7+uuvg68/dOiQ3XnnnVaiRAn36N27t/3zzz9u2mWXXeb+X7p0aVd1nzp1qvv9xx9/tKuuuspV4c8//3x79dVXg/PT8wraHoXtxx57LFbo1s+q3qsqvmPHDmvfvr0L7GXLlrVHHnnEhVLvdVrnl19+2U2rX7/+Gfvn22+/tVKlStn777+fqP2q6vuECROsZs2aljdvXmvVqpWrwMene/fu1rNnT1ct17646KKL7Kuvvop1QeDxxx+3SpUqWaFChdz8/vrrr1jLGzdunFuejosuqIwaNcptly5A6Li99tprwddPmTLFXazQ9jds2NDtc4+O+cCBA61p06buvXXr1rXVq1cnavsBAEDyI3QDQCr077//2uuvv26FCxe2AgUKuACq3z/++GPbvXu3q4DfeOONdvz48eB7pk+f7kKoQp/Cer169ezaa6+1PXv2WKdOnVyw9tx33332xx9/2M8//+yCm5qGP/DAA27a999/7/6/detWFxJvu+02V3m/7rrrrE+fPu4iwAcffGBPPPGEffHFF8HQvWjRIvfzkSNHbM2aNdamTRsXsDds2BAM03qdaH2yZs3qpi1ZssTNb+TIkbEuCvz0009uvbz5eubMmeO2/+233z6rlgDvvvuuffnll7Z582a3jS+88ELE12s5vXr1cs3tdRFDwdpreq+LBUuXLnVBfPv27ValShXr0KHDGe//7LPP7ODBg+41jz76qPtd2/jdd98FL3KoJYD27yuvvOL2cdu2ba1Zs2axLgq89dZbbj/t27fPLrnkEuvbt2/EdT927JhbbugDAAAkL0I3AKQiqmSqyqmqqMKaQrT6Qb/00kv29NNPu4q1fr/33ntdMFdo8yiwlSlTxvLly2ctWrRwlVeF0syZM9utt97qArZC+unTp131WpVxvUbBfujQofbmm2+6aeEo7F155ZWuOq35qXLbo0cPt46iCvbOnTtt7dq1LoRefPHFrlqv51UBV3BcuXKlXX311bZt2zYXelXxVfW4XLlyLryG9v/WegwfPtxy5szpHp6JEye67fzkk0/cvM/Gww8/bEWLFnX7WRcGEupjrnXWBQ7td124KFasmM2ePdu1Phg/frzbDrUW0PYOGTLEbb8ueoQur2TJkq6/vvad3qeLEjp+mpeq594+7ty5s9vPuiBx//33uwsuusjg0fRatWq5dVFLiITWXcdY54P30PkBAACSF6EbAFIRhSRVURXa1Hx61apVwcHNFLgUFL2Hqp2q1HoU4DwKqnF/V9hTFVpVVIVvNW32VKxY0VVFVUUPR8ufO3durOWrubsqt6Im5wqDCtiqaKsptHihW1VcvUZhXeucI0eOWOun5Ydui5pPaxnh+rxrP3hB9WwUL148+LMubqjiLArUugigR2irAF0UCKXfdeFA+0pN8hWSvX2ieSt8h4ZuNSX3qBn65MmTXZNzbf/111/vLkaItj/0mEiFChVi7Ze4666WCAldxNEFD+8Rul4AACB5ELoBIBVS4Faf6f79+7vm4qpQvvfeey6Qew8F6I4dOyZ63upHrWCoIO3Rz6rEquqtJuFxafk333xzrOUrrCqIe9R0XIHb67st+r/3nIK4+jirr/jRo0ddv+7Q5et5T7h1kHnz5rnq+rPPPmtJTX29FWL10M+eTZs2xXqdmqXr+KiVgC5mqLVB6H5RBTu0H3rcbVFrAV2I0PbrQkWXLl3c89r+0GMSbr8klo6p+q6HPgAAQPIidANAKqWBshRU1fT7nnvucQN2/frrr26a+uZ++OGHwSptYigEqk+1mnRrsDX1+R40aJALf5qmUK7/r1+/PvgeTVOT8JkzZ7rBw/RQhfaHH36IFbr1GjVj18jrXqVWQVtBWc20RYFVr/3vf//rKsUKsc8884xrLp0QzU99vNXHXa0CkoO2SU28NdCbLoSout+yZUu3j1QR79evX7CCrH2pvvXx0fH7/PPPXTDXhQ9V1dVUXFTBV7N/NU/XssaOHevmp64CAAAg7SJ0A0AqpmCs0a01erZG0lYfbVUrNcK115/6bIwZM8Y1Za5evbq73ZdGI1ffZDnvvPPcIGkaAV1NprUcBeVPP/3UDfKl/stqGq0LAaEDc6mZtZow62KBKqweVbs1EJs3iJpongqeaqrdoEEDF2LV9zkaeo+CtwaWGzx4sPlNFygUtr0m9brYob7WouB/xRVXuAsKahKvvuwaJC0+atavUd21/1QpV6D3+rJrPyloa9A2TXvnnXdcZT9cM3sAAJB2xAS4aSgAAGHpQodC7+jRoy090EUSDaimiyPpvam5BuPT4H4aNC++7goprfyA/xskLz3KZAGrvGKsu3iUWo9BRpAWPgvpHccg/R6HaP9d5agDAAAAAOATQjcAIM1Sf3BvxPG4D/WPBgAASGn/b/QWAADSIN2OK6HbZp2L0HuHAwAAnA0q3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0csBAABSwMbhLS09O336tDVvPjalVwMAUhyVbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCfcMgwAAAC+WLllv50/aK6dthhL79L7LeAAnD0q3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAECIJk2a2OjRo894PiYmxlauXOnbcj/++GO37Lx581qBAgWsRo0aNmjQINu1a5dvywQAAP4jdAMAkMLGjx9v3bt3t549e9qmTZts3759Nnv2bMuWLZstW7YspVcPAACcA0I3AACJsGLFCmvYsKEVLFjQihQpYh07drQ9e/YEp0+dOtUqV65sefLksVKlStngwYMjzu/QoUM2YMAAGzdunHXt2tVVuaVChQr25JNPWvPmzYOv/eyzz6xOnTqWL18+q1u3rs2fPz/ivI8dO2YHDx6M9QAAAMmL0A0AQCJkypTJhg8fbjt27LCff/7Ztm3b5kKz/PPPP65i/frrr7swvWbNGmvWrFnE+X399dd25MgRa9u2bcTX/fHHH3bTTTfZY4895kK+mp63atXKNmzYEO97hg0b5gK69yhTpsxZbjUAADhbhG4AAOIYOHCg5c+fP9bDU6tWLVfpzpo1qxUrVswefPBBW7hwYXC6nl+3bp2rKut9l156acRl7d692woXLuze5+nVq5d7b65cueyhhx5yz02fPt31+b7lllssS5YsLqRrPaZNmxZxOw4cOBB8bNmy5Rz3DAAASCxCNwAAYSrE+/fvj/WIW3EuWbKkG/Ssc+fOLjiLQrIGRPvwww9dVVmheMGCBRGXpcCt9584cSL4nCrlWma7du2Cz2/dutXKly8f670VK1Z0z8cne/bsbh1DHwAAIHkRugEASITevXu7vtpr16511ewpU6ZYIBAITr/mmmts7ty5LkgrNLdu3dpOnz4d7/yuuOIKO++882zmzJkRl1u6dGnbuHFjrOf0u54HAACpF6EbAIBEUNDWIGmqGqu59rPPPhucpn7es2bNcv251QRcr9H/I9Frhg4dav/5z3/srbfeciOXi+b9559/Bl936623umbsqqKfPHnS3n//fVu8eLF16NDBx60FAADnitANAEAijBo1yt3OS2FZzczbtGkTnKaK9pgxY1zTcg1c9tJLL9mMGTPc4GuR9O3b11577TV79dVX3Xs1grlGLW/QoIHrly3nn3++C9pPPPGEGzn96aefdgFfTcwBAEDqFRMIbRMHAADSdZVeFwM0qFp679+tCyA7d+60okWLJnjRA/4dgxI1r7BcrR6z0xZj6d3G4S0tNeKzkPI4Bun3OET77ypHHQAAAAAAnxC6AQDwmfps586dO+wDAACkb4RuAAB8NmjQIDt8+HDYBwAASN8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPsvg1YwAAAGRstcvkt3lDW3BvYgAZGn8BAQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmjlwMAAMAXK7fst/MHzbXTFmMZycbhLVN6FQCkIlS6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAPj/2rsP6Cqq9v37d0LvvVcpCgKCIiqCoCDSi0rv6k8FCyKoNBEEpIgFBQQfHwQLTUUsFPFBQQQ7RaUoovQivUvNede13/+cdRKSkGAmJyTfz1qzIDNzZvbMgHjNvfc+AOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwDSjO7du1vv3r3D3QwAAJCGELoBAJfs1ltvtXHjxl2wPiIiwtasWePLOUuXLm0fffRR8Ocff/zRChQoYC+++KJdboYOHWqtWrUKdzMAAICP0vt5cAAA/PTFF1/Y3XffbS+//LLdc889yXruc+fOWbp06dwLBgAAgLhQ6QYA+Gb16tVWu3Zty5s3r6tGd+jQwQ4cOBDcPn36dCtfvrzlyJHDihUrZsOHD0/wsefOnWt33XWXvfnmm8HAHbP7+OHDh10o3rJlS3Dd0aNHXXU5e/bsds0119jy5cuD286ePWvPPPOMlS1b1vLly2ctWrSwXbt2BbfrWBMmTLDKlStbtmzZbO3atW7dO++8Y+XKlbPcuXO7Nug4nlWrVtltt93m7oH2eeONN9x6VetHjhxp8+bNc23RIoFAwF599VWrUKGCO556E2zYsCF4vJdeeslKlizp7pmq/v/973/jvEenT5921xu6AACA5EXoBgD4JjIy0kaPHm1///23C6g7d+60/v37u20nTpxwAXXKlCl27NgxW7dunTVq1ChBx1VYV9CeM2eOC96JMWPGDLvvvvtcIH/ooYdcsNbvZdCgQbZixQoXxHfv3m1XXnmltW/f/oLPf/755y7AKnjLwoUL3QuG9evXu+q72id79uyxBg0aWM+ePW3fvn0uaA8ZMsTto+A/cOBAa9asmR0/ftwtMmnSJHdPPv30U9u/f7+7vubNm9uZM2ds48aN9vTTT7vz6559//33dsMNN8R5raNGjbJcuXIFlxIlSiTqXgEAgH+P0A0A+FcGDBjgKrKhi6dq1aqu0p0hQwYrVKiQ9enTx5YuXRrcrvWq4irA6nM1atRI0DkXLVpklSpVslq1aiW6vfXq1XMhNn369NajRw/XLlWbVWF+7bXXXCW5SJEiljFjRhsxYoQL4du3bw9+/qmnnrKiRYtapkyZ3EsFUXVclWet14uDlStXuvWqgNepU8fatm3ruqKrQq6XBQrucZk4caINGzbM9QBQG3v16mX//POPC9g6htqpFxRap7arWh/fszly5EhwCb0OAACQPAjdAIB/RdVUVYpDF8+mTZusZcuWLozmzJnTOnfu7Kq3oiqxqrkff/yxq8AqnC9ZsiRB51T3a3XhVnhW+EyMUqVKXfCzKvBql6rvCsney4PChQu78B0aVtW1Oybt59F1qQot6ta+YMGCaC8k1HZV0eOiz+g+hX7m0KFDtmPHDtft/a233nJd3BW477jjjngnrNOLAd330AUAACQvQjcAwDeqJGustrpdq5r97rvvukqtp379+i6UKvC2adPGdbmOioq66HEVRP/3v/+5LtlNmza1kydPuvUaF+39XmILt1u3bo3287Zt21wbNYY7a9asrqIc+gJBof7mm28O7u9VtxNCLxPuvPPOaMdTINc1x3Usfeb999+P9hldk8bDi6rmejmhLvvqSdClS5cEtwcAACQ/QjcAwDcK2up2rQqrqsVjx44NblNo1GRoCqHqRq199GtCaYyyxjafOnXKmjRp4qrU1113net6rrCt4z777LMXfO7LL7+0+fPnu9nHNamZ9lVwVwDWS4K+ffsGK9ua9G327NmXfP0KxDqfxp6rMq9FlWl9zZmoWq2XAGqL5+GHH3bd1X///ffgPVRvAF2P1ullg14EqAKvlwyJuWcAACD5EboBAL7R+GiNl1agVjdzfb2XRxXtV155xVV2FaA1lvmDDz5IVCVZx1XIVmht3LixtW7d2urWretm/q5WrZoL0zF17NjRhW2vq7cCbZ48eYJd5WvWrOnGfetlQfXq1V2wv1SqoKt9r7/+uhsnrpCtUO3NIq7qvq5BM7t7Y+EfeeQRN8GcJlDTtooVKwbHgGsytcGDB7vjqDKvQD9t2rRLbh8AAPBfRCC0nx8AAEi1FPb1gkOTqqX28d16qbN3714rWLBgol7kIGmfQZHKNS1bi8EWZWnr++y3jL7whV+48Hch/HgGqfc5JPTfVZ46AAAAAAA+IXQDAFKUkSNHurHKsS0AAACXG0I3ACBFGThwoJuVPLYFAADgckPoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAn6f06MAAAANK2aiVy28KRTfiaJABpGv8FBAAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfMJXhgEAAPikdP/5llZFWsDKh7sRAJACUOkGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAC4T27Zts+zZs9uRI0fcz927d7fevXuHu1kAACAehG4AQLJYvny5NW7c2PLkyWO5c+e2qlWr2vPPP29nzpzx9by33nqrjRs3zrfjR0REWNasWV0Y1nXpfGvWrPHlXCVLlrTjx49brly5fDk+AABIeoRuAIDv5s2b5wJ3w4YN7Y8//rDDhw/b7Nmzbf369bZ79+6wti0QCNj58+f/1TG++eYbF4b37dtntWrVsjZt2sS579mzZ//VuQAAwOWF0A0A8D3U9urVy/r16+e6QufPn9+tr1Chgk2bNs1KlSplP/30kwurqhRfffXVNnPmzODnY3ahVmBXdXnLli3B7ffff7+1b9/ecuTIYVdddZUtXbrUbevbt699/fXX7tyqRCv4S+nSpW3UqFF20003uSr1hAkTrEyZMq6tnu+++87y5s1rp06dSvC1ZsiQwTp16mSbNm0KVvCHDh1qzZo1s549e7rj9e/f33UTb9CggRUoUMBV/ps2bRq8nr1797q2hi66Xl2T9tHvdQ8AAMDlgdANAPCVKtubN2+2Dh06xLpdAbJRo0YuNKtSPGnSJBeiV6xYkeBzqGreo0cPd6wuXbq4IC4vvvii3XLLLTZmzBhXiV64cGHwMwr8b731lluvQHzy5En76quvgtunTp3q2pw5c+YEt0MB/e2337brr7/eMmbMGFz/2Wef2Y033ugC9fDhwy0qKsr69Olj27dvt61bt7rgr2uWggULujZ5i0J6pUqV7LrrrrPEOn36tB09ejTaAgAAkhehGwDgKwVpKVasWKzb58+f7yq+jz76qKsU161b1zp27OgCcUI1adLEjaVOly6d3XPPPS7IHjhwIN7PKGirKq7PKCB369bNBXEvPCvI61gJoWCvKr0q7ZMnT3ZV9FCVK1d2LwLSp0/vArYq7aq6K9DnzJnTBg0a5CryCuOh3nvvPZs4caLrnq/9Ekvt0PhvbylRokSijwEAAP4dQjcAwFded/KdO3fGun3Hjh0uhIZSV2+tT6jChQsHf58tWzb367Fjxy46KVmoe++91+bMmeOqy3PnznXbVbFOCAVmVdkV1j/88ENr3bq1/frrr3GeSy8i9GJBIVhhuk6dOq4qHdpmdW9X9V5tiXl/EmrAgAFupnNvUWUdAAAkL0I3AMBXV155pQuNs2bNinV78eLFg+OZPfpZ60VjmtX125PYidciIyMTtF5Vb82o/sEHH7iKd0Kr3KFUNa9Xr56VK1fOPv/88zjPpTCsa1q1apXr8r1s2TK33htTrutv1aqVq5pr3PmlypQpkwv1oQsAAEhehG4AgK808df48eNt9OjR7lev2/fGjRvtvvvus9q1a7uxzq+99pqdO3fOVY2nT59uXbt2dftpLPOiRYtc2FYl+Nlnn03U+QsVKmR//vlngvZVezQOXCG4c+fOib5WhWZ9VrOyV6lSJc79FLTVzVxd0nU/Qq9J2zTxmrrbt23bNtFtAAAAKQuhGwDgO4VITWKm8dtly5Z1YVNdsDWDeZEiRdy2d9991/Lly2cPPPCAm0xNYVwUfjXOW/tWq1bNzfSdGJr5fPHixe6cakd8FHI1HlzjrTXOPKFuvvlmV5FXJVntHzt2rN1xxx1x7q+QrRnONXO5Zm33ZlUXVb/XrVvnxmOHzmCulxEAAODyExEI/X4UAADSOL0UeOWVVy4a0C9HqqJrQjWN707tXc01KZ16UGg2+LiGGCSH0v3nW1oVaQErv3q8e6kWzmeQ1qWUvwtpGc8g9T6HhP67ylMHAOD/0bjz8+fPR6s8AwAA/Bvp/9WnAQBIJSpWrGgHDx50X1WmCdE8mkFcXd9jKlWqlOsGDgAAEB9CNwAAZrZhw4ZY12sGcS0AAACXgu7lAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITZywEAAHyyZXRTS6uioqKscePx4W4GAIQdlW4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnfGUYAAAAfLFm+2ErN3CBRVlEuJuSIqTlr5AD0jIq3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAEj1br31Vhs3btwF6yMiImzNmjW+nrtevXqWJUsWO3ToUHBds2bNrH///tH2K168uGtnqNatW9ujjz7qfr97927r2LGjFS5c2HLkyGFlypSxxx9/3Ne2AwCAf4/QDQCAT/766y9bunSpZc2a1aZPnx5cf9ttt7n1nj/++MMyZMhgv/zyi506dcqtCwQC9tVXX7nQLl26dLHMmTPbb7/9ZkeOHLH//e9/Vq1atTBcFQAASAxCNwAgzVu9erXVrl3b8ubNawUKFLAOHTrYgQMHgtsVmMuXL+8qzMWKFbPhw4cn6LhvvvmmC8aqVk+ZMiVa6F65cqUdO3bM/awAXr9+fbv22mvt22+/devWrl3r2lC3bl3383fffWf33HOP5c6d2yIjI61s2bLWrVu3eM9/+vRpO3r0aLQFAAAkL0I3ACDNU4gdPXq0/f333y7s7ty5M9j9+8SJE9a9e3cXmhWS161bZ40aNbroMc+fP2/Tpk1zn+3atav9/PPPtmrVKrdNQTx79uy2fPnyYOhW13IFbK8Crl+rVq3qXgRIrVq1rHfv3vb222/bxo0bE3Rdo0aNsly5cgWXEiVKXPI9AgAAl4bQDQBIEwYMGOCqxKGLR+FWlW518S5UqJD16dMnWvdvrd+wYYOrFOtzNWrUuOj5Fi1aZHv37nXjsDX+WqHZq3Yr5CtgL1myxP2sbuT6OXSdzu91LZf333/fmjdv7samV6pUyUqVKmUzZsy46DWrK7q3bN++/RLuHAAA+DcI3QCANEFV38OHD0dbPJs2bbKWLVta0aJFLWfOnNa5c2fbv3+/25YtWzb79NNP7eOPP3aVYoVzLxjHRwG7SZMmlj9/fvezuoIrJHtjttXFXMfReO5MmTK5Y994442uIn7y5ElbtmyZ28ejdg0dOtRVyzUpW69evVwFXS8D4qLj6nOhCwAASF6EbgBAmtejRw83Vnv9+vWumv3uu++6icw8Gm+9YMECF8TbtGljrVq1sqioqDiPt2/fPhfUv/jiCzfbuBZ1V1fQnzNnjttHgVpjyT/55JPguG1NlKau5//5z39csK5Tp06sx1fX9L59+7ou42ozAABIuQjdAIA0T0Fbk6SpEqwu2GPHjg1u0zjvuXPnuvHc6dOnd/vo1/ho3LXGYmumcX0lmRaNFffGhkuVKlVcV/UXXngh2leFKYCPGTPGqlevHq0y/eSTT7rjnDlzxi3//e9/3Xhz7QcAAFIuQjcAIM176aWXbN68eS7kqpv53XffHdymivYrr7ziun+rsjxx4kT74IMP3LjsuChY9+zZ01XPvUq3FlWnNVb7zz//dN8RrrC9Z8+eYKVb9HutC+1a7s1E3r59e8uXL5871tSpU12X99KlS/t0VwAAQFKICIT2nwMAAKm6oq8XB5pULbWP79bLEk1kV7BgwXhfkMDfZ1Ckck3L1mKwRVlEuJuTImwZ3TTZz8nfhfDjGaTe55DQf1d56gAAAAAA+ITQDQDAJRg5cqSb0Cy2BQAAwEPoBgDgEgwcONCOHz8e6wIAAOAhdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+Se/XgQEAAJC2VSuR2xaObMJ3EwNI0/gvIAAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD5h9nIAAAD4Ys32w1Zu4AKLsohwNyVF2TK6abibACAZUekGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgBAqrFlyxaLiIiww4cPh7spAAAADqEbAC4zt956q2XKlMmyZ89uefPmtbp169pPP/2U4gPuvn37LDIy0n777bfgusWLF7tjTJs2LbjuyJEjlj59elu5cqUll6FDh7pz6p7mzJnTKleubNOnT7dwKV26tH300UdhOz8AAEg6hG4AuAyNGTPGjh8/bnv27LEbb7zR7rrrLkvpChQoYJUqVbKlS5cG1y1ZssQqVqwYbd2yZcssR44cdu211ybq+GfPnv1X7WvWrJm7pwr9I0aMsO7du9vGjRv/1TEBAAAI3QBwGcuYMaN169bNtm/f7irJgUDAXn31VatQoYLlzp3bVcU3bNgQrYI6atQoq1GjhmXLls0aN25sBw8etIceesjtX758efvmm2+C+x87dsweeOABK1KkiFt69OhhJ06ccNtuuOEG92vx4sVdhdirDK9atcpuu+02V4UvV66cvfHGG8Hjab2Ctkdhe/DgwdFCt36v6r2q4n///be1bdvWBfaSJUvaoEGD7Ny5c8H91OZJkya5bTfffPMF9+e7776zYsWK2Ycffpjge6rKe6tWrdyxf/7552hVeV2z1uvlwSeffBLcpoB+3333WevWrd290Pa1a9fa66+/7u6P2v/aa68F99dzevHFF61s2bLuPjVq1Mj++usvt61Nmza2bds269ChgzuW7rns3bvXOnXq5J5D0aJFrXfv3nb69Ol4r0Xbjx49Gm0BAADJi9ANAJexf/75x6ZMmWL58+e3PHnyuACqnz/99FPbv3+/q4A3b97czpw5E/zM7NmzXQjdtWuXC+s33XST3X777XbgwAHr2LFjMOTJY489Zps2bXIB8tdff3Vdwx9//HG37YcffnC/7tixw1WIFQhVeW/QoIH17NnTvQRQF+khQ4bYF198EQzdX331lfv9yZMnbd26dXb33Xe7gL158+ZgmNZ+ovZkyJDBbfv666/d8Z5//vloLwUUjNUu77ie+fPnu+ufMWNGonoCnD9/3t5//313P6688kq37pdffnFhePTo0e4lhcJ0ly5d7Pfffw9+Tp/RvVF3e73UaNmypf35558uTM+aNctt00sEeeedd+yll15y16PnoJCu56QXCjqOXiLMnDnT3dfJkye7kN6iRQsrXLiwO6aeha5bFfn46AVLrly5gkuJEiUSfB8AAEDSIHQDwGVowIABruKqarVCpUK0xiRPnDjRhg0b5irW+rlXr14umH///ffBzyoQK3wphDVp0sTy5cvnQmm6dOmsXbt2LmArpEdFRbnqtYKb9lGwHzlypL399ttuW2wUJuvUqeOq0zqexkbfc889ro2iCrYqtuvXr7cVK1ZY9erVXbVe61UBV9fuNWvWWL169Wznzp325ZdfunCqim+pUqVcpTt0/LfaoSCcNWtWt3imTp3qrvOzzz5zx04IhXTd08yZM7uwr3tZtWpVt00hW9VstUsvCGrXru26o7/33nvBzzdt2tRq1arl7ruuX2Pen332WXd99evXd/dbYdm7T3o2VapUcefTfdULEO9FRkwas//HH3/Y2LFj3XXqeQwcODB4X+P7c6J76i06BwAASF7pk/l8AIAkoCCs7sUKpqqAqhJ7yy23uKDXuXNnF3g9CtCqRnsKFSoU/L0CXMyfVVVVFVpdk/VZdUn3lClTxq1XFT02Ov+CBQtceA2tHKttoq7UCrIK2Krwqvu7KBirGq5u2NpHYV0BVIE0tH06f+i1aOx36LlCx7zfe++9ds011yT4nio0q/Ks7vMKxAr8Cu7edelnhXmPqtKadC2u+6q2ZcmSJdo6Va5F1xB6XzUxnrqMh15bzPuqCrrujUfPSfc2PjquFgAAED6EbgC4jGm8ssZMq7p85513ugr2uHHj3Bjhf0sBWFVaBT4vUOr3CnGqescWEHV+tUPdqeOiruPqQq7QrZcHXujW2O6CBQu6IK5x1RoLferUKdclO/T8Wu9R1Tk2CxcudO1Ql/snn3wyUdet3gPjx49349E//vhj101c16Wu9qqqJwVdg67Fo5cbuh/etcW8Lp1f92b37t1Jcn4AAJB86F4OAJe56667zgVVdVF++OGH7ZlnngmONdbEWQqOGvucWAp+6matLt0ax6wxzurSrLHM2qZQrl81xtijbaoIz5kzx80mrkXdxX/88cdooVv7qBu7Zl6XK664wgVtdZdWF27vhYL2feKJJ1z1WZOLPffcc27iuIvR8TTGW2PcvWCfGKpK9+nTx70IUEX5wQcfdFVuVehVXVa1/9tvv402SV1iqDfChAkTXDd7Hevpp5921+tNTqeXDKH3VWPEFby1n56l2rR161b3cgEAAKRshG4ASAUUjP/73/+6Wbc19lhjtNX1WV/HdbFxv/F55ZVXXDfoq6++2k32peqvxliLuk5rkjTNgK4u3jqPguOiRYvcGGjNsq3wqBcBobNmqyqv8cV6WRDa9VnVbk3E5k2iJjqmxqRrPLfGS6sL+FNPPZWgtuszCt6aWG748OGJvnZNKKfu+5rYTF9fponNFHr1skHXqUB+sdnD49K1a1d79NFH3bhwTY6mSdE0+Z3Gg4tebiiU675qZnkNF5g3b55rj56pxofrXmiSOwAAkLJFBPS6HAAApHp6+aHArpceoePRUyNNsqdJ+9QtP65hCPD/GRSpXNOytRhsURYR7uakKFtGN022c/F3Ifx4Bqn3OST031WeOgAAAAAAPiF0AwBSPY0H19eOxbboa9EAAAD8wuzlAIBUr2TJksGv6wIAAEhOVLoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AljugEAAOCLaiVy28KRTfiaJABpGv8FBAAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfMJXhgEAAMAXa7YftnIDF1iURYS7KanKltFNw90EAIlApRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwCAJLZv3z6rV6+e5cyZ09q0aRPu5gAAgDAidAMAfHfrrbfauHHjLlgfERFha9asSdJzbdu2zbJnzx5cIiMjLUuWLMGfe/ToYX57/fXXLV26dHb48GF7//33fT8fAABIudKHuwEAACSlkiVL2vHjx4M/ly5d2gX+Vq1aJfpYZ8+etQwZMkRbd+7cOUufPv5/Pjdv3myVKlVygf9SJOQcAADg8kClGwAQdqtXr7batWtb3rx5rUCBAtahQwc7cOBAcPv06dOtfPnyliNHDitWrJgNHz78ks+1ePFiu+GGGyx37twuGH/yySfBbd27d7f77rvP2rZt67qGT5482VXpn3rqKbvjjjssW7ZstnDhQvv888/t+uuvt1y5clmRIkXsoYcesn/++ccdQ93J3377bXvttddcZX3KlClu/bvvvmsVK1Z059W1rlq1Knje2M6hlwWjRo2yGjVquHWNGze2gwcPunPpGLof33zzzSXfBwAAkDwI3QCAsFNFePTo0fb333/b2rVrbefOnda/f3+37cSJEy4MK7weO3bM1q1bZ40aNbqk8/zyyy8uFOtcCrDqBt6lSxf7/fffg/vMnDnTBW91DdevMm3aNBsxYoSroN9+++2uu/obb7zhjrFixQpbsmSJvfTSS25fdSfv1KmTC8faX8dYtmyZ9ezZ051P471bt27truHIkSPB88Y8h8yePds+/PBD27Vrl23fvt1uuukmt00vJDp27HjRrvKnT5+2o0ePRlsAAEDyInQDAJLFgAEDXIU2dPFUrVrVVX/VlbtQoULWp08fW7p0aXC71m/YsMGFRn1O1d9LodCrAK9JzhT0dc5mzZrZe++9F9xH1eaGDRu67VmzZnXrFHBVHdcYdAXuW265xa699lo3brtMmTL24IMPRmtvTO+884517tzZ6tSp466ld+/elidPHps/f35wn5jnEAX1EiVKuIp6kyZNLF++fHbXXXe587Zr1869oDhz5kyc51WlXJ/1Fh0LAAAkL0I3ACBZKACqehy6eDZt2mQtW7a0okWLum7dCqj79+9329S1+tNPP7WPP/7YhUYFZVWWL8WWLVtcl/HQ4K/jqpIcOiY8ppjrfvzxR1dx1gsCtXfgwIHB9sZmx44drrt4qCuuuMKtj++8Or5HLwBi/hwIBOzkyZPxvuhQNd1bVC0HAADJi9ANAAg7dZPWWO3169e7arbGPytQeurXr28LFixwwVbdwzUpWlRUVKLPo9D+2GOPRQv+6s49adKk4D6xTX4Wc53GnN922232119/ufaOHDkyWntjKl68uAv8ofSz1sd33n8rU6ZM7qVA6AIAAJIXoRsAEHYKrpokTaFQ1dixY8cGt2mc99y5c914bs3orX0udWZvdQOfOnWqq5SfP3/ejXn+9ttvXdf1xLZXVXJV4fXZ0NAeG1XuNRmcxn9rZvLx48e7cdnqMg4AAFI3QjcAIOw0Cdm8efNcoFY387vvvju4TRXtV155JTi2eeLEifbBBx9cUmVY47A1UdrTTz/tZklXdX3w4MEufCd2bPgLL7wQ/N7v9u3bx7t/3bp1XdDWpGoalz1r1iw3Q3nouHYAAJA6RQTi6w8HAABSDVXo9eJC47tTe1dzvazZu3evFSxY0Jeu+0jYMyhSuaZlazHYoiwi3M1JVbaMbprgffm7EH48g9T7HBL67ypPHQAAAAAAnxC6AQCXJU1epu7dsS0AAAApBaEbAHBZ0td0aebx2BYAAICUgtANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+CS9XwcGAABA2latRG5bOLIJ300MIE3jv4AAAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBP+MowAACQZpXuPz/cTUi1Ii1g5cPdCABIAah0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAApxOHDhy0iIsK2bNnifu7Ro4f169cv3M0CAAD/AqEbABAWy5cvt8aNG1uePHksd+7cVrVqVXv++eftzJkzvp731ltvtXHjxvl2fIXmNWvWJMmxJk+ebGPGjEmSYwEAgPAgdAMAkt28efNc4G7YsKH98ccfrsI7e/ZsW79+ve3evTusbQsEAnb+/PmwtgEAAKQehG4AQLKH2l69erlu071797b8+fO79RUqVLBp06ZZqVKl7KeffrJatWq5CvjVV19tM2fODH6+e/fu7nNxdcnW9vvvv9/at29vOXLksKuuusqWLl3qtvXt29e+/vprd+7s2bO74C+lS5e2UaNG2U033WRZs2a1CRMmWJkyZVxbPd99953lzZvXTp06leBrHTp0qDVv3tweeeQRdy0lS5Z0Lxc8p0+ftp49e7rjXnHFFfbBBx9E+3zMa+3cubMVLVrUcubMadWrV7clS5Yk6t4DAIDkR+gGACQrVbY3b95sHTp0iHW7QnSjRo1caN63b59NmjTJhegVK1Yk+BwKthoPrWN16dLFhVd58cUX7ZZbbnFdto8fP24LFy4MfkaB/6233nLrFYRPnjxpX331VXD71KlTXZszZ86cqOtdtGiR1alTxw4cOGAjRoyw//u//7Njx465bc8995x9++23tnbtWlu9erV9+OGH8R6rfv36tmHDBncs3Z/WrVsHjxUbhfqjR49GWwAAQPIidAMAkpWCtBQrVizW7fPnz7cCBQrYo48+ahkyZLC6detax44dXSBOqCZNmrix2+nSpbN77rnHtm7d6oJqfBS0VRXXZzJmzGjdunVzQVxU3VaQ17ES67rrrrO2bdu64+oFgMasb9y40W2bPn26DRw40FWvVQkfMmRIvMfS+XPlyuXuy5NPPmlRUVH2yy+/xLm/qvfa31tKlCiR6PYDAIB/h9ANAEhWXnfynTt3xrp9x44drrt3KHX11vqEKly4cPD32bJlc7/GVxEWdf0Ode+999qcOXNc5Xvu3Llu+/XXX5/gNsTWFnWDz5IlS7Atu3btct3pPaG/j0kBe9CgQVa+fHnXvVwh/ciRI7Z///44PzNgwAC3j7ds37490e0HAAD/DqEbAJCsrrzySheqZ82aFev24sWLB8dne/Sz1ovGYqvrtyexE69FRkYmaL2q3ppRXeOsVfG+lCr3xajCrSq8Z9u2bXHuO2PGDLeoJ4ACtLrOq3odOu48pkyZMrmAHroAAIDkRegGACQrVXvHjx9vo0ePdr963b7V5fq+++6z2rVr2969e+21116zc+fOuYnP1A27a9euwe7aGietsK2K8bPPPpuo8xcqVMj+/PPPBO2r9mgc+LJly9wkZklNY8R1H1TxVogeNmxYnPtqPLa6vaungLqoa9+LVe8BAED4EboBAMmuWbNmbhIzVW3Lli3rukprUjDNYF6kSBG37d1337V8+fLZAw884CZTUxgXhV+N89a+1apVs6ZNmybq3JoNfPHixe6cakd8NBZblWjNcq5x5knt6aefdl3WK1eu7K6lVatWce6rMeaVKlVyXdDV3V7d1L3qPwAASLkiAvH1SwMAII3TS4FXXnnlogH9cqBqubqkq3t6au9qrjHw6jFRsGDBOIcUSOn+85O1XWlJpAWs/Orx7iVafM8AKePvAvzDM0i9zyGh/67y1AEAiIPGnZ8/fz74fd4AAACJlT7RnwAAIA2oWLGiHTx40H1Vmb7uy6Pv/1bX95jU7XvdunXJ3EoAAJDSEboBAIjFhg0bYl0/efJktwAAACQE3csBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCbOXAwCANGvL6KbhbkKqFRUVZY0bjw93MwAg7Kh0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNmLwcAAIAv1mw/bOUGLrAoiwh3U9KsSAtYxTwB23AoItpzYOZ+IPlQ6QYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgBcVrZt22bZs2e3I0eO2OWgWrVqNm3aNPf76dOn280335wkx126dKnlzp07SY4FAAD8Q+gGAPwrixcvtltuucUF4Vy5clnjxo1t1apV/+qYCxcutBtuuMEdL0+ePFajRg1bsGCB21ayZEk7fvy42+anCRMmWKVKlSxr1qxWqFAhu+++++zvv//+V8fs1KmTffPNN0nWRgAAkPIRugEAl+yTTz6xVq1aWdeuXW337t22ZcsWq1Onjlt++umnC/Y/d+6cBQKBeI/5559/Wps2bWzgwIF28OBBd9wXXnjBcuTIYcnliSeesOeff97Gjx/vKuo//vijHTt2zGrXrh1nhf3s2bPJ1j4AAHD5IHQDAC6JwvNjjz1m/fv3t/vvv9+FYlWlBwwYYO3atXPBVSIiIlzVuHLlypYtWzZXpV62bJlVqVLFfeauu+5yVeTu3bu7/VevXu0qywrz6dKls8yZM1vdunVdNV0U7HXMw4cP2969e12FPXTRNnW99gJ88+bNrUCBAlaqVCkbMWKERUVFxXtd+szLL79sM2bMsHr16lmGDBlcdV0/R0ZGum2h3bsnTZrktnvdxnWtJUqUsHz58tmgQYOiHVvdzNXd3FO6dGkX7m+66SZ3L3Sd27dvD25/6qmnXLu17eqrr7b3338/iZ4eAABILoRuAMAl2bhxowvAHTt2vGCb1i1fvtz++ecf97MC6+eff25Hjx61M2fOWIsWLezxxx+3Q4cO2f/93/+5sc6e6tWr265du6xnz5722WefuWp3XAoWLOhCvLfoBYC6hF933XV28uRJq1+/vlt27txpX3/9tc2aNcumTp160e7yxYoVc1XtUOnTp7e2bdu66/Co+v3zzz/bb7/9Zl999ZV9+eWXLmi/9957rkIva9eujfd87777rs2cOdP27dvnXkoMHjw4uK1q1aquyq4XDM8884x16dLFNm/ebAl1+vRpd89DFwAAkLwI3QCAS7J//373a9GiRS/YpnXnz58PBmZVbLUuU6ZMbmx28eLF7d5773VBtkmTJi4Ye6644gpbsWKFC9EK5KpSN2jQwP76669426OgO3HiRJs3b57lzJnT5s+f7yrvvXv3towZM7pqtCrzegFwseuK7Zq861I49qhqPnr0aDfuW4teHmjcds2aNd05hw4d6oJ0fB566CF3zaro67MrV64MbtPPerGgin/79u2tQoUKiRoTPmrUKDf23VtUgQcAAMmL0A0AuCT58+d3v6oqHZPWKSjmzZvX/azAG7otZvgL3S6qVL/zzju2Y8cOV1FXV/bOnTvH2ZbvvvvOevToYXPnznVdtkVVeFWZ1QXcW/r27Wt79uy56HXFdk1e2/USwKNu36EziGu7uoN71DW9SJEi8Z6vcOHCwd8roKt67lFXdlXuFZh1Hl2P97IjIdTVX2PQvSW06zoAAEgehG4AwCW58sorXcBU1+iYtK5WrVqWJUsW97PGQodWi2OGP30NWFzKli3rKtS//vprrNsVrjX+e/LkyW5stEfBXl3V1TXbW9S9et26dfFel9cdXdX2UKrca0y1qu6e0Ovyrm3r1q3RJlfzupknlrrnq1L+9ttvu274ar/GxV9sIrpQ6lmgqn/oAgAAkhehGwBwSTRhmSqx6sI8ZcoU1x1cwXDMmDFu7LQmCItN06ZNXejWpGKazVzjtjUW2qOx16+99lqw2qzK9BtvvBHr91srRDdr1sweffRRN946lNbrK750rFOnTrnQ/PvvvwcnWYtLuXLlrFevXq5rt/ZVcFZ7VWnXeHR1V49Lhw4dXBfz77//3u07bNgwO3HihF0KXZt6C6iyrm7sb7755kXHhwMAgJSH0A0AuGR33nmnzZkzx01Opm7S6iauAL1kyRK78cYbY/2Mupx/9NFH7mvA1GX6P//5j/uKMFVlReOwFy1a5KrU6m6truZa99Zbb11wLH0fuCrXCv6hM5gruOtXTYr2xRdfuC7nmk1cE7xdrHu5vPTSS26iN03mpuqw2qIx1xpPrbbE5fbbb7fhw4fb3Xff7bqVKyyrOn0pGjVqZK1bt3azvKuCrutU7wEAAHB5iQgkpp8aAAA+aNiwoftu75hfsYWkpeq5xodrfHdq72quFx76SjlNRBdzGACS7xkUqVzTsrUYbFEWEe7mpFmRFrCKeQK24VBEtOewZXTTsLYrLeG/R6n3OST031WeOgAg2elrtzQhmLqXqyu6quP6vm4AAIDUhtANAEh2+lqsihUrurfDI0aMcBOv6efkopnOQ7uje4tmCgcAAEhK6ZP0aAAAJPCrrLSEi2Y61wIAAOA3Kt0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITQDQAAAACAT5hIDQAAAL6oViK3LRzZhO8mDiO+IxoIP/7mAQAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPuErwwAAAOCLNdsPW7mBCyzKIsLdlDQr0gJWMU/ANhyKSLbnsGV002Q5D3C5oNINAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AgE8OHz5sERERtmXLFvdzjx49rF+/fkly7KFDh1qrVq2S5FgAAMA/hG4AQIp37733uvC6YcOGZDnfsWPH7PHHH7cSJUpYlixZrGzZsjZs2DA7d+7cvzru5MmTbcyYMUnWTgAAkPIRugEAKZoC8HvvvWd58+a1KVOm+H6+s2fPWsOGDW316tX2v//9z44fP+7O/8EHH1iHDh3i/RwAAEBMhG4AQIo2e/Zsy5Ytm6sQv/POO9HC7fjx4101Ol++fPb0009btWrVbNq0acHtixcvthtuuMFy585tlSpVsk8++eSi55s+fbr9/vvv9vHHH1uFChUsXbp0Vr16dZs7d65bt3Tp0mD37mbNmlnPnj3dC4H+/fvb6dOngz9fccUVLqiH6t69u/Xu3dv9Xl3OVb3XNZUrV861Udu961PYb9mypRUsWNBy5cplderUsZ9//jnJ7isAAEgehG4AQIqm6nanTp2sffv2duLECfv000/d+i+++MKeeeYZmzNnju3evdsiIyNt3bp1wc/98ssv1qZNGxs9erQdPHjQXn/9devSpYsL1PFZtGiRNW3a1AXdUOpifuONN9rnn38eXPfZZ5+5dXv37rXhw4fbc889Z99++62tXbvWVco//PDDi17fwoUL3b7r169316TQL1FRUdaxY0fbvHmz/f3333bttdda27ZtLRAIJPje6SXA0aNHoy0AACB5EboBACmWguh3331n3bp1s+zZs9udd94Z7GI+Y8YMF8ZVyc6YMaMNHjzYVcQ9CtmqHNerV88F8tq1a7vKtLqKx2f//v1WtGjRWLdp/b59+4I/V65c2Z0jffr0ljVrVheYBw4c6PZT5XrIkCEXvUa9OMiRI4f7TKNGjWzlypVufc6cOa1du3bumjJnzmzPPvusbdy40Xbt2pXg+zdq1Cj38sBb1CsAAAAkL0I3ACDFUsCuWrWqW0ThW5XonTt3uvAZGiIzZMhgRYoUCf6s7tuauEzh11vUPfxioTV//vxx7qP1BQoUCP5csmTJC7aXKlUq+HPo7+NSuHDh4O8VsDWGXf755x976KGHrHTp0i6A61fvpUBCDRgwwI4cORJctm/fnuDPAgCApJE+iY4DAECS0thmjXfW2GYvmKpr9fnz5924bVWGQ0OkZhZXN3OPAvljjz3mupcnRoMGDeypp55yXbEVdj3q5v3999+7Wcw9qqCHUpu2bt3qupzLtm3b7FK9+OKLruq9fPlyK168uPv6sTx58iSqe3mmTJncAgAAwodKNwAgRdKkZwq+q1atsjVr1rhFE4mpG/mbb77pxniri/lPP/3kAvqIESPcmG/Pgw8+aFOnTrUlS5a4oK7xzRpvfbGvHevcubMbv63vwNb4b31WbVDXdnVPv+222+L8rGY3V8hXxVshOTSgJ5auXd3KFbT14kHd1gEAwOWH0A0ASLFdyxViNYO4Kt3e0qtXLxdqNau4xkwrHGu9Kt1XXnllsLKricdmzpzpZjVXl/BixYq5wK7wHR+ND9dXhVWpUsWNB1eX79atW7uZxDWTenx0ruuvv96N9dZM6mrbperTp4+7xkKFCrnj1axZ85KPBQAAwicikJh+agAApFBnzpxxXx2mGcVr1aoV7uakSKqea0I1je8O7TqfGmn2d80qr69cizkMAMn3DIpUrmnZWgy2KIsId3PSrEgLWMU8AdtwKCLZnsOW0U2T5TyXC/57lHqfQ0L/XeWpAwAuW/pKLk04pm7l/fr1c6G7Ro0a4W4WAABAEKEbAHDZ0kRrmrFcE5hp3LXGgat7+MVUqlTJfQVZzKVHjx7J0m4AAJB2MHs5AOCyNXfu3Ev63Lp165K8LQAAALGh0g0AAAAAgE8I3QAAAAAA+ITQDQAAAACATwjdAAAAAAD4hInUAAAA4ItqJXLbwpFN+G7iMOI7ooHw428eAAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE74yDAAA/Cul+8+3lCbSAlYxT8A2HIqwKIsId3PSJD2D8uFuBACkAFS6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAIBkNm3aNKtWrVq4mwEAAJIBoRsA4Lvly5db48aNLU+ePJY7d26rWrWqPf/883bmzBlfz3vrrbfauHHjfD3HhAkTrFKlSpY1a1YrVKiQ3Xffffb3338Hty9dutRdMwAASJsI3QAAX82bN88F7oYNG9off/xhhw8fttmzZ9v69ett9+7dYW1bIBCw8+fPX/Lnn3jiCffyYPz48XbkyBH78ccf7dixY1a7dm33c3I5d+5csp0LAAAkDqEbAOBrqO3Vq5f169fPevfubfnz53frK1So4LpYlypVyn766SerVauWqwZfffXVNnPmzODnu3fv7j7nUWCPiIiwLVu2BLfff//91r59e8uRI4ddddVVrrIsffv2ta+//tqdO3v27C74S+nSpW3UqFF20003ueq0KtVlypRxbfV89913ljdvXjt16lSc1/bnn3/ayy+/bDNmzLB69epZhgwZrGTJku7nyMhIt+3AgQPuvArgaoMWtckzfPhwK1iwoKuQx6zIz5o1y6655hp3X2rUqGHffPNNtAr+U089ZXfccYdly5bNFi5c+K+eEwAA8A+hGwDgG1W2N2/ebB06dIh1u0J0o0aNXGjet2+fTZo0yYXoFStWJPgcqpr36NHDHatLly4uiMuLL75ot9xyi40ZM8aOHz8eLZgq8L/11ltufc+ePe3kyZP21VdfBbdPnTrVtTlz5sxxnnfx4sVWrFgxV9UOlT59emvbtq19/vnnli9fPnfeXLlyuXNpUZtk3bp1LvTv3LnTXcOTTz7pgrwsWLDAVdHVzoMHD9qAAQOsefPmLsSHXsOIESPcMW+//fZY23j69Gk7evRotAUAACQvQjcAwDcK0qJwGpv58+dbgQIF7NFHH3WV4rp161rHjh1dIE6oJk2auMpvunTp7J577rGtW7dGC6exUdBWVVyfyZgxo3Xr1s2FWFF1WyFYx4rP/v37rWjRorFu03rv2uOiqr+q8bputV8V+DVr1rhtEydOdCH8uuuuc1Xzu+66y/UOUBj36D7dcMMNrvKfJUuWWM+hir4Cv7eUKFEi3jYBAICkR+gGAPjG606uam5sduzY4cJmKHX11vqEKly4cPD36motGlcdH3UDD3XvvffanDlzXNV47ty5bvv1119/0WvbtWtXrNu0Xi8T4qMu5aHUdq/d6j4/cOBA17XcWxTIQ+9jzGuIjSrk6truLdu3b7/oZwAAQNIidAMAfHPllVe6UK3xybEpXrx4cHy2Rz9rvWgMtLp+exI78ZqqxAlZr6q3ZlT/4IMPXMX7YlVuqV+/vgvBMbvCa2K2999/3xo0aBBvG+KjirS6x6vLvLecOHHC+vfvf9FrC5UpUybLmTNntAUAACQvQjcAwDfq+qyZvUePHu1+9bp9b9y40X21lsZD792711577TU3A7cmGZs+fbp17drV7afu1YsWLXJhW1XgZ599NlHnVzXZGyd9MWqPgu6yZcusc+fOF92/XLlybpK4Tp06ucnbzp496yrJ+qy+Cs2bAE5tUNt1nQn18MMP29ixY23lypVugje9eNAY8sT0AAAAACkDoRsA4KtmzZq5ycQ0frts2bKuq3Tr1q3dGOUiRYq4be+++66bdOyBBx5wk6l5k5MpwGqct/atVq2aNW3aNFHnVvBVWNU51Y74aPIzjQfXbOMX6xrueemll+zxxx93Y8RVRa5evbqbfE0zjes7yb0qugK9ZmZXO/Sd5RejSdP0okKTyuk4V1xxhb3yyisWFRWVwCsHAAApRUQg9DtSAABIw/RSQOH2YgH9cqXZyzWhmsZ3J2VX89L951tKE2kBq5gnYBsORViURYS7OWmSnkH51ePdi7VLGWaBpKGXdeppo68n5DmEB88g9T6HhP67ylMHAOD/fS+2xmN73+cNAACQFNInyVEAALiMVaxY0X0ftr6qTF8j5tH3f6vre0ylSpVy37MNAABwMYRuAECat2HDhljXT5482S0AAACXiu7lAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ITZywEAwL+yZXRTS2mioqJs7969VrBgQYuMpMYQrmfQuPH4cDcDAMKOf4UAAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCbOXAwAAwBdrth+2cgMXWJRFhLspaVakBaxinoBtOBRx2T+HlPhNCUBCUOkGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAIIWqVq2aTZs2LdzNAAAA/wKhGwBw2evWrZu1bNky2rq9e/dagQIF7KOPPkq2dqxdu9Zat27tzpszZ06rWLGiPf3003bkyJFkawMAAEhZCN0AgMveq6++aqtWrbJ33nknuK5nz57WpEkTa9WqVZKc4+zZs/Fu1/lr1qxpFSpUsJ9//tmOHj1qn332mZ06dcp++eWXJGkDAAC4/BC6AQCXvVy5ctmbb75pvXv3tl27dtmMGTPshx9+cGH8kUcesZIlS1rBggWta9eu0arOnTt3tqJFi7qqdPXq1W3JkiXBberWre7dQ4YMscKFC1v79u3t4MGDduedd1qePHksd+7c7jNbt251+/ft29fatWtnI0aMcMeUUqVK2QsvvGC33HKL+3nTpk3WsGFDy5s3r5UtW9bGjRsX7TomTJhgJUqUsHz58tmgQYMuuM7FixfbDTfc4M5dqVIl++STT3y7pwAAIGkQugEAqUKDBg1cMFaQ7tWrlwvh999/vwvKqjRv3rzZVasVwj3169e3DRs22IEDB9xn1TX82LFj0bqLp0+f3rZt2+aq6ArQ586ds507d7rPTJkyxXLkyGEnT560r7/+2jp06BBn+/S5Zs2aWdWqVd2Lgblz59rzzz/vXhDIl19+6YL2e++9Z7t37w6e36NraNOmjY0ePdpd0+uvv25dunSx33//Pc5znj592lXcQxcAAJC8CN0AgFRDIVbVZHUpV5V6zpw5NnHiRFcZzpYtmw0bNsxmz55t58+fd/vfc889rkqeIUMGe/LJJy0qKipaV3BtUxDOmDGjZc2a1e2nsP3HH39YunTp3DlUtT506JA7ZrFixeJs2/fff+/CtCrhmTNntmuuuca9APAmSps+fbp16tTJdVHX+YYOHera7FHI7t69u9WrV88iIyOtdu3aLsQrpMdl1KhR7hq8RVV0AACQvAjdAIBUQyG1TJkyVrlyZduyZYsL0VdccYUL3Vpq1KjhAuuePXvcNgXq8uXLu+7l2q6u5/v37w8eTyFa+3sUzNVVvG3btq7L+WOPPWb//POP626u/VQBj8uOHTtct3MFao/aqvWi6re6o3sU8IsUKRL8WdczefLk4LVo+fjjj93n4jJgwAB3Td6yffv2S7yzAADgUhG6AQCpkqq6CsIKpYcPHw4umthMYVrdurXMnz/fBVJtUzU4EAgEjxEauCV79uw2ZswY16X722+/tS+++MJee+01VwVXGJ81a1ac7SlevLhrS+iEbArSWi8K5N74cNF+Xjdz73oU8kOv5fjx4zZp0qQ4z5kpUyb3QiF0AQAAyYvQDQBIlVSJVjdzdeH2qteqcGsstWh8s6rO+fPntzNnzriu56HjuWMzb94827hxo6uSK8CqGq0x3/Liiy+6ruuaeE3nEVWx+/Xr58Z7awK0QoUK2TPPPOPGWmu89vjx493XnYnGg6uLubqhe+05ceJE8NwPPvigTZ061U32pq7sOoaCv8akAwCAlIvQDQBItTRe2utWrpCsavTKlSvdNoVdzQCuLt3q5p0lS5Zg1TkuGi/eqFEjN3na1Vdf7cZf66vJRDOZr1ixwoVpHVfnu/32210w1+Rp+lWhXefXC4EWLVpYnz59rGPHju7z2nf48OF29913u27lCvbqJu+59tprbebMme57v/U94KrWDx482IVvAACQckUEQvvRAQCAVEvVfXWhV3f61N7VXC8t9u7d674qLuYwASTfMyhSuaZlazHYoiwi3M1JsyItYBXzBGzDoYjL/jlsGd3ULkf89yj1PoeE/rvKUwcAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCfp/TowAAAA0rZqJXLbwpFN+G7iMOI7ooHw428eAAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE74yDAAAAL5Ys/2wlRu4wKIsItxNSbMiLWAV8wRsw6GIVPEctoxuGu4mAIlGpRsAAAAAAJ8QugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAAAAAACfELoBAAAAAPAJoRsAAAAAAJ8QugEAAAAA8AmhGwCAJDR06FBr1apVuJsBAABSCEI3ACBNeeGFF+ymm26Ktq5z586WOXNmO3XqVHDdhAkTrEqVKkl67i1btlhERIRlz5492tK7d+8kPQ8AAEg5CN0AgDTltttus5UrV9rx48eD65YuXWplypSx7777LrhuyZIlVq9evUQd++zZswnab8eOHe783jJu3LhEnQcAAFw+CN0AgDTl2muvddXlr7/+2v38xx9/uCp3hw4dXNCWQCBgy5YtcwH9888/d5/JlSuXXXfddbZ48eLgsbp372733XeftW3b1nLmzGmTJ0++4HyDBg2yqlWr2u7duy/atieffNLq1q1rUVFR7ucPPvjAChcubHv37nU/q0r+yiuv2FVXXWW5c+e2du3a2ZEjR5Ls3gAAgKRH6AYApCmRkZFWp04dV90W/Xrrrbe6sOutW7t2rR08eNCKFStmLVu2tMGDB9uBAwds4MCB1qJFC9u8eXPweDNnznTB+/Dhw+5Xz7lz59zPK1ascAG+SJEiF23bc889ZydOnLARI0bY1q1b7YEHHrC3337bChYsGNznnXfecS8H1FX90KFD8XZNP336tB09ejTaAgAAkhehGwCQ5qiC7VW1FbQVuG+88Ub7+eef7Z9//nHrqlWr5qrcCuR33XWXpU+f3lq3bm21a9d2Qdtzxx13WMOGDV2Yz5o1q1t38uRJu/POO10VetGiRa5KHqpUqVKuUu0tU6dOdeszZszojv3yyy9bkyZNXGjX8UM99dRTVrRoUfe54cOH24wZM4KV8ZhGjRrlzu0tJUqUSPJ7CQAA4pf+ItsBAEiVofuJJ55wld+vvvrKRo8ebZkyZXJB+5tvvnGhW/to7HXp0qWjfVZjv7XeU7JkyQuOv2bNGnfsn376yR03JlWxFZpjU758eRf0582bZ19++eUF2xXYQ39/5swZ27dvnxUqVOiCfQcMGGB9+vQJ/qw2EbwBAEheVLoBAGnONddc40Lvm2++6arLXhBVxVsVcG88d/HixV037lD6Wes9qnDHdPPNN9vEiROtQYMGtm7dukS1TeO4NaFb06ZN7eGHH441sHu2bdvm2l+gQIFYj6XAr7HmoQsAAEhehG4AQJqjCckUsMeMGeOqyh6tmzJlihufrXHfmqhMVe+PP/7YjdH+8MMPXSBv3779Rc+hruHq3l2/fn375ZdfEtQuhegHH3zQ3nrrLTeWe/Xq1faf//wn2j5jx461Xbt2uTY+88wzri2xBX8AAJAy8K80ACBNUiV7z549Lmh7atas6SZQq169uuXIkcPKlSvngvaQIUMsb968NmzYMJs7d67rYp4Qmt1cIVkVb3U596hSHvo93W3atLHz589bp06d7J577nHjuFWV1vhujeHesGFDtO8UV9vVtVxt1GzmAAAg5YoI6HtRAADAZVGhV/VbY88vhcZ0a0I1TfCW2ruaa3I5fdWaZn6nJ0D4nkGRyjUtW4vBFmUR4W5OmhVpAauYJ2AbDkWkiuewZXRTu9zw36PU+xwS+u8qTx0AAAAAAJ8QugEAAAAA8AlfGQYAwGWCEWEAAFx+qHQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPmEiNQAAAPiiWonctnBkE76bOIz4jmgg/PibBwAAAACATwjdAAAAAAD4hNANAAAAAIBPCN0AAAAAAPiE0A0AAAAAgE8I3QAAAAAA+ISvDAMAAIAv1mw/bOUGLrAoiwh3U9KsSAtYxTwB23AoIlU9hy2jm4a7CUCCUekGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAJJItWrVbNq0aeFuBgAASEEI3QCAZNetWzdr2bJltHV79+61AgUK2EcffZRs7Vi7dq21bt3anTdnzpxWsWJFe/rpp+3IkSO+n3vo0KGWPn16y549uzt3lSpVbObMmb6fFwAAJC9CNwAg2b366qu2atUqe+edd4LrevbsaU2aNLFWrVolyTnOnj0b73adv2bNmlahQgX7+eef7ejRo/bZZ5/ZqVOn7JdffrHk0KxZMzt+/LgdPnzYBg8ebF26dLHffvst0dcCAABSLkI3ACDZ5cqVy958803r3bu37dq1y2bMmGE//PCDC+OPPPKIlSxZ0goWLGhdu3aNVnXu3LmzFS1a1FWGq1evbkuWLAluU7dude8eMmSIFS5c2Nq3b28HDx60O++80/LkyWO5c+d2n9m6davbv2/fvtauXTsbMWKEO6aUKlXKXnjhBbvlllvcz5s2bbKGDRta3rx5rWzZsjZu3Lho1zFhwgQrUaKE5cuXzwYNGnTBdS5evNhuuOEGd+5KlSrZJ598Euv9iIyMtLZt27r91q1bF+u1BAIBe/HFF1071J5GjRrZX3/9lURPBAAA+IXQDQAIiwYNGrgwqSDdq1cvF8Lvv/9+F5RVad68ebOr8CqEe+rXr28bNmywAwcOuM+qa/ixY8eidRdXl+1t27a5KroC9Llz52znzp3uM1OmTLEcOXLYyZMn7euvv7YOHTrE2T59TpXoqlWruhcDc+fOteeff969IJAvv/zSBe333nvPdu/eHTy/R9fQpk0bGz16tLum119/3VWyf//99wvOdf78eZs1a5artl9zzTWxXouWl156yXW/V3sU4ps3b+7aGZfTp0+7Y4YuAAAgeRG6AQBhoxCrarK6lKuyO2fOHJs4caKr+GbLls2GDRtms2fPdqFU7rnnHlclz5Ahgz355JMWFRUVrSu4tikIZ8yY0bJmzer2U9j+448/LF26dO4cqhIfOnTIHbNYsWJxtu377793YVqV8MyZM7swrBcA3kRp06dPt06dOrku6jqfxmirzR6F7O7du1u9evVcJbt27douxCuke+bPn++utVChQq6KrTHd5cuXj/VaFLr1ckJjv9WekSNH2vbt210PgbiMGjXKHcdbVJUHAADJi9ANAAgbhdQyZcpY5cqVbcuWLS5EX3HFFS6IaqlRo4YLrHv27HHbFEIVStW9XNvV9Xz//v3B4ylEa3+Pgrm6iqvrtrppP/bYY/bPP/+47ubaTxXwuOzYscN1O1fo9aitWi+qNqs7ukcBv0iRIsGfdT2TJ08OXouWjz/+2H3O07RpUzeeW9fw448/2t133x3ntei8pUuXDv6cKVMm1z6vPbEZMGCAu0feopAOAACSF6EbAJAiqAqrkKlQqiDqLZrYTAFU3bq1qDqsAKltqt5qrLMnNKSKZgYfM2aM69L97bff2hdffGGvvfaaqxwrjKtLd1yKFy/u2hI6iZmCtNaLAq83Ply0n9fN3LsehfzQa9GkaZMmTUrQ/Yh5LTqvzu85c+aMa5/XntgomOsFRegCAACSF6EbAJAiqBKtbubqwu1Vr1Xh1lhq0XhkVZ3z58/vAqe6noeO547NvHnzbOPGja5KrsCparTGSYu6c6vruiYr03lEVeN+/fq58d6aAE3dvp955hk3NlpjrMePH+++7kw0HlxdzNUN3WvPiRMngud+8MEHberUqW6yN3Vl1zEU/DUm/VJo7Lsmblu/fr07lr7aTC8j1E4AAJByEboBACmGxkt73coVklWNXrlypdumsKvJw9SlW928s2TJEm+VVzReXLN8a/K0q6++2o2/1leTiWYyX7FihQvTOq7Od/vtt7tgrsnT9KtCu86vFwItWrSwPn36WMeOHd3nte/w4cNdl3B1K1ewVzd5z7XXXuvGaCsc63vAFZD1tWAKzJdCM7k/+uijbly42qOvOfv000+DLxEAAEDKFBEI7ZcHAABSLfUWUJd8dc9P7V3N9RJk79697qvnYnbVR/I9gyKVa1q2FoMtyiLC3Zw0K9ICVjFPwDYcikhVz2HL6KZ2ueC/R6n3OST031WeOgAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgk/R+HRgAAABpW7USuW3hyCYWGUmdJ1yioqJs7969VrBgQZ4DECb8zQMAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHzCV4YBAADAF2u2H7ZyAxdYlEWEuylpVqQFrGKegG04FMFzCBOeQcp5Dt/0vj5M5wYAAAAAAL4gdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdANACrVt2zbLnj27HTlyJMmOOW7cOLv11lstrXjggQcsb968VrhwYUupevToYf369Qt3MwAAgE8I3QDCavny5da4cWPLkyeP5c6d26pWrWrPP/+8nTlzxvdzK3wqhPphy5YtFhERYYcPH462ftq0aVatWrUEHaNkyZJ2/Phxy5Url/u5e/fu1rt3b/PT999/b7fddlvweVxzzTWuzR5d05o1axJ8vMRcrx9/tj744APbvHmz7dmzJ879AoGAlStXzooVK2bnz5+35DZ58mQbM2ZMsp8XAAAkD0I3gLCZN2+eC9wNGza0P/74wwXU2bNn2/r162337t3hbp4LY+EIYeFy7Ngxa9SokbVr18727t1r+/btsylTpljBggXtcqSwrRcX3kuLuCxdutT1Kjh69KgtXLjQktO5c+eS9XwAACD5EboBhC3Q9urVy3WrVfU2f/78bn2FChVcdbRUqVLu559++slq1arlqq5XX321zZw5M3iMmJVfhXZVYlVl9rbff//91r59e8uRI4ddddVVLmBJ37597euvv3bnVxduhX8pXbq0jRo1ym666SbLmjWrTZgwwcqUKePa6/nuu+9cl+VTp0796/ug9qrSWblyZcuZM6e1aNEi2J08tFr+6quv2vTp0+21115z7a1UqZLb5+zZs/bMM89Y2bJlLV++fO7zu3btCh5/3bp17lp0/apgh26L6ffff7cTJ064LtkZMmRwS40aNaxJkyZu+w033OB+vfnmm10bRo4c6X7u3LmzFS1a1LW/evXqtmTJErd+9erVruv0r7/+6vbXonArs2bNclV0PVed45tvvgm2Q9dZvnx512ZVn4cPHx5nmz///HO79tprXbC+7rrrbPHixW697peevXdu/VmIi14sNGvWzO6++273+1D63H333WetW7cO3ve1a9fa66+/bsWLF7cCBQq4ZxIqvmtT74qnnnrK7rjjDsuWLZsL+TH/HOsFlJ6jjq0/Z3fddVdwW1z3GgAApFyEbgBhoWChSmSHDh3i3EdhU5VXhWZVXSdNmuSC1IoVKxJ8HlXOFfx0rC5dugTD14svvmi33HKL69arLtyhFU6F/rfeesut79mzp508edK++uqr4PapU6e6dmfOnNmSwnvvvWdffvmlC6Q7duywl19++YJ99IKiU6dO9tBDD7l2KUzLoEGD3P1QV2r1Drjyyivd/fKqqApv9evXtwMHDriQ/N///jfOduizCq/6/Mcff3xBl+wffvjB/aoQqTYMHDjQ/azjb9iwwZ1Dn1VAVdVcYVgvFKpUqeL216LK84IFC+yJJ55w9/ngwYM2YMAAa968ufu8Qr+ekcKvjqHr1J+B2GzatMlatmxpgwcPdp9Ve3S9+nOl+xV67tAu8qH052LOnDnunN26dXO9L/7+++9o+7z//vv2+OOPu30VonXOP//80/766y8XsLXN+0x81+bRthEjRrh23X777dHOpevXOr2E0UsXPYNHH300uD2uex2X06dPuwp+6AIAAJIXoRtAWChEiyqZcZk/f76r9il0qOpat25d69ixowvECaUqraqL6dKls3vuuce2bt0aLQDFRkFbVXF9JmPGjC6MeaFN1W0FeR0rqajyqS7cqoyq2rpy5coEfU7Vd1VZX3rpJStSpIhrq8KcQvj27dvt22+/tf3799vQoUPdtpo1a7qu43FR9VSfUXW1T58+rqJ644032qpVq+Jth+6Fwrqe0ZNPPmlRUVH2yy+/xLn/xIkT3X6qTEdGRrpKrno4KLCKjqNgqYDoVYtjo+egZ6vPp0+f3gXQ2rVrR+sNcTEzZsxwFXX1dNCxdM1vv/12tH2aNm3qelvoHG3btnVh+Nlnn3X3VCFY166KekKuTfRnWL0G1IshS5Ys0c6l0K/rf+6551wlXOdQD4VLvdfqtaH9vaVEiRIJvjcAACBpELoBhIXXnXznzp1x7qOqr7p7h1JXb61PqNBZqxViJL7KoKgaG+ree+911VBVJufOneu2X3/99fEeQ6HI6/4dSj972+Jq48Xa51GgVmW0Tp06Lpxq0bEU1BS61ZVcITL0fF63/bhoQjFViFXJ1X3Wz6oeh3avD6XQp2q7uoMrtKsN6h6vtsVFoVVVaa/NWjQ5m/4s6Po//fRTV2lXQFSIjqsLdVL8+VBFXSFY90ghWL0h3nzzzWj7FCpUKPh7DTlQSA8Ny1qnPxsXu7a4/nyF0kshDRVQW5LiXqvSrn28RX8uAABA8kqfzOcDgGBXZgUmdc9VkIiNxsx647M9+lnrRWNs1fXbk9jJ11SJTMh6Vb01q7pmwlYVNSFVbi/8qquzqvUehdmYQfFS26sx3Ap8mnFc1dSYNGZdwTs06HtjqhNCgb1///6uGqyu0jpfzDCobVoWLVrkwqC2a+ZzL6THdo8VptV7Qd3+Y6PqsRa1W5X8Vq1a2aFDhy44lv4cqFt9zD8fegmREArDquLrmejPodcdW93I1VtA1e3Euti1xffnznspovbo/iX2XscmU6ZMbgEAAOFDpRtAWCgwjB8/3kaPHu1+9bp8b9y40U1cpYqfuoZrFm0FL41PVojUJFtdu3Z1+6oLrwKIwraqw+rymxiqYCrgJITapHHgy5Ytc5NZXYy6pmvc99NPP+2Cr6qUGgutyqrGZl8KtVfjiEMDrcKdJoXzKpi6j+p2LZpATV3FNRGZvoJN4dzbFpvffvvNjXFXcFV7FT41kZxekChwx3bP1AVcLxfUc0HnGDZsWLRKvfbX8/nnn3+C6x5++GEbO3as60ava9GLE02Apgq1xkarN4GOoe7cqujq19ioq7wmxlNVXH8+PvzwQ/d8vDHtF6NnoXHnum4FcC3q1q7AH3NCtYSK79oSQl3ZFfw1OZ56MeieepX+i91rAACQMhG6AYSNZozWBGYau60uteouq3G5qtpqjLKqeNr+7rvvutCnWbU1mZq6HIvCr8Z5a399F7QCS2JoxmgFIp1XbYmPxvLqRYDG/oZWruOjGbTVNo2l1jkefPBB95JBldtL8X//93+um7KCtGbH9sbs6vj16tVz3Z41o7Vm9BZVtz/55BP3YkKfUdVaXeXjos9rxnFNMKewqwq/xt6ru7dHAV6TlOnZ6Fo03l0zeqtCq67d6nbt9UQQtUvhX2P3dQ9UadfEYvqsJsXTca644gp75ZVXXNDXot+rYqwxyBojrR4GsVWH1fVdQXvIkCHu+hRCFdjVjovR2Hy9wHnsscdcr4TQRX8uNLndpQTa+K4tIdR7Q38mFdrVDV1/D3QP5GL3GgAApEwRgfj6pQEAgvRiQAHqYgEdSKlULdfLDI3v1ouV1EwvOtRTRpMUxtelH/4+gyKVa1q2FoMtyi6cpwDJI9ICVjFPwDYciuA5hAnPIOU8h296X5+k/y4k9N9V/hUCgATQmN/z588Hv88bAAAASAgmUgOAi6hYsaKbSExfVaax2h6Np1bX95jU/df7Hm0AAACkbYRuALgITa4VG321lhYAAAAgLnQvBwAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCfMXg4AAABfVCuR2xaObGKRkdR5wiUqKsr27t1rBQsW5DmECc8gZT2HcOCpAwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPknv14EBAEDKEggE3K9Hjx611C4qKsqOHTtmmTNntshIagzhegbnzp1zf954BuHD34Xw4xmk3ufg/Xvq/fsaF0I3AABphP5nQ0qUKBHupiANyZMnT7ibAAC+//uaK1euOLdHBC4WywEAQKp5y79r1y7LkSOHRUREWGqm6oNeLmzfvt1y5swZ7uakSTyDlIHnEH48g9T7HBSlFbiLFi0ab/WcSjcAAGmE/oegePHilpbof6z4n9zw4hmkDDyH8OMZpM7nEF+F28OgAgAAAAAAfELoBgAAAADAJ4RuAACQ6mTKlMmGDBnifkV48AxSBp5D+PEMUoZwPgcmUgMAAAAAwCdUugEAAAAA8AmhGwAAAAAAnxC6AQAAAADwCaEbAABc9g4ePGidOnVy372aO3duu+++++z48ePx7v/oo4/aVVddZVmyZLGSJUtar1697MiRI8na7svdxIkTrXTp0pY5c2a78cYb7Ycffoh3//fff98qVKjg9q9SpYotWLAg2dqamiXmObzxxht2yy23WJ48edxy++23X/S5Ien/LnhmzZplERER1qpVK9/bmNpNTOQzOHz4sD388MNWpEgRN7nalVde6dt/kwjdAADgsqfAvW7dOvvf//5n8+bNs2XLltkDDzwQ5/67du1yywsvvGBr1661adOm2WeffebCOhJm9uzZ1qdPHzcb8KpVq6xq1arWsGFD27t3b6z7f/PNN9ahQwd3j1evXu1ChhbdfyTfc1i6dKl7DkuWLLFvv/3WSpQoYXfccYft3Lkz2dueVp+BZ8uWLfbEE0+4lyBI3mdw5swZa9CggXsGH3zwgf3+++/uhVSxYsXMD8xeDgAALmsbNmywq6++2n788Ue7/vrr3ToF6CZNmtiOHTusaNGiCTqOqrCdO3e2EydOWPr06X1u9eVPlaQaNWrYhAkT3M9RUVEuwKkHQf/+/S/Yv127du7e6qWI56abbrJq1arZ5MmTk7Xtafk5xHT+/HlX8dbnu3btmgwtTn0u5RnovtepU8fuvfde+/rrr13V9aOPPkrmlqfdZzB58mQbO3as/fbbb5YhQwbf20elGwAAXNZUrVOXci9wi7rMRkZG2vfff5/g46hrubqnE7gvTlWilStXuvvs0f3Wz3oesdH60P1Flai49oc/zyGmkydP2tmzZy1v3rw+tjT1utRnMGzYMCtYsCC9a8L0DD755BOrWbOm615eqFAhq1y5so0cOdK9DPED/6oAAIDL2p49e9z/vIZScFaI0LaE2L9/vw0fPjzeLumIfr/0P6f6n9VQ+lmVo9joWcS2f0KfEZLmOcTUr18/1xsk5gsR+PcMli9fblOmTLE1a9YkUytTt/2X8Az++usv+/LLL93QJI3j3rRpkz300EPuBZS6qCc1Kt0AACBFUpdATTAU35LQYBGfo0ePWtOmTV0X9aFDhyZJ24HLwejRo91EXnPnznWTT8F/x44dsy5durjxw/nz5w93c9KsqKgo97L2P//5j1WvXt0Nfxk0aJBvQ12odAMAgBSpb9++1r1793j3KVOmjBUuXPiCyXLOnTvnZijXtov9D3CjRo0sR44cLngkx9i+1EBhIV26dPb3339HW6+f47rnWp+Y/eHPc/BoEkGF7sWLF9s111zjc0tTr8Q+gz///NNN3tW8efNoAdDroaMJvcqWLZsMLU/bfw+KFCni/nuvz3kqVqzoet6ou3rGjBmTtI1UugEAQIpUoEAB9/VS8S36HyONy9MkRBrT51G3Qf2PrCbXia/CrVmbdQyN76PSl3C6Z6oOffHFF8F1ut/6Wc8jNlofur9otvm49oc/z0Gef/55N5xCEw6GzoUA/5+B/rv166+/uq7l3tKiRQu77bbb3O81+Rf8/3tQq1Yt16Xce+EhGzdudGE8qQO3o9nLAQAALmeNGjUKXHvttYHvv/8+sHz58kD58uUDHTp0CG7fsWNH4KqrrnLb5ciRI4Ebb7wxUKVKlTtPFMsAAAsfSURBVMCmTZsCu3fvDi7nzp0L45VcPmbNmhXIlClTYNq0aYH169cHHnjggUDu3LkDe/bscdu7dOkS6N+/f3D/FStWBNKnTx944YUXAhs2bAgMGTIkkCFDhsCvv/4axqtIe89h9OjRgYwZMwY++OCDaH/ujx07FsarSFvPIKZu3boFWrZsmYwtTn1mJfIZbNu2LZAjR47AI488Evj9998D8+bNCxQsWDAwYsQIX9pH93IAAHDZmz59uj3yyCNWv359N2vt3Xffba+++mpwuybHUbdNzdQs+h5Xb2bzcuXKRTvW5s2brXTp0sl8BZcfjYHct2+fPfPMM65Lpr76S5VTbzKjbdu2uWfhufnmm23GjBn29NNP28CBA618+fLuK5I0azCS7zlMmjTJdZ9t3bp1tONo8ijmNEieZ4DwPwP1KFi0aJE9/vjjbniFvp/7sccecxML+oHv6QYAAAAAwCe8cgEAAAAAwCeEbgAAAAAAfELoBgAAAADAJ4RuAAAAAAB8QugGAAAAAMAnhG4AAAAAAHxC6AYAAAAAwCeEbgAAAAAAfELoBgAAQJoSERFhH330UYo5TlLZsmWLa9OaNWvi3e/WW2+13r17W3JbunSpa9/hw4eT/dxAOBG6AQAA4Js9e/bYo48+amXKlLFMmTJZiRIlrHnz5vbFF1/Y5WLo0KFWrVq1C9bv3r3bGjdubCmF7q3aVLly5SQPuSdPnrQBAwZY2bJlLXPmzFagQAGrW7euffzxx0nQciB1Sx/uBgAAACB1UuW1Vq1aljt3bhs7dqxVqVLFzp49a4sWLbKHH37Yfvvtt0s67pkzZyxjxowXrNexM2TIYMmlcOHClpKkS5fOtzb16NHDvv/+exs/frxdffXVduDAAfvmm2/cr8kprmcPpGRUugEAAOCLhx56yFVaf/jhB7v77rvtyiuvtEqVKlmfPn3su+++C+63bds2a9mypWXPnt1y5sxpbdu2tb///vuCSvN///tfu+KKK1ylVXTsSZMmWYsWLSxbtmz23HPPufWqvl533XVuP1XYn332WTt37lyc7ezXr59rW9asWd3+gwcPdgFepk2b5j7/888/u/Np0brYupf/+uuvVq9ePcuSJYvly5fPHnjgATt+/Hhwe/fu3a1Vq1b2wgsvWJEiRdw+evngnSumI0eOuCD9008/uZ+joqIsb968dtNNNwX3effdd12FO2b3cv3+tttuc+vz5Mnj1uv8Hh3rqaeecsdTUNc9js8nn3xiAwcOtCZNmljp0qWtevXqrgfDvffeG9znnXfeseuvv95y5MjhjtmxY0fbu3dvnMdUYO/QoYMVK1bM3Xu9lJk5c+YFXeEfeeQR1x0+f/781rBhQ3fOZs2aRdtP97BgwYI2ZcqUeK8DCAdCNwAAAJLcwYMH7bPPPnOhUoE4JlW/vfCnwK39v/rqK/vf//5nf/31l7Vr1y7a/ps2bbI5c+bYhx9+GG3MssLinXfe6QKvwtjXX39tXbt2tccee8zWr19vr7/+ugvJXiCPjUKi9tH+r7zyir3xxhv28ssvu21qR9++fd3LAnXd1hKzbXLixAkXCBVwf/zxR3v//fdt8eLFLjCGWrJkif3555/u17feesud1wvxMeXKlcu9bFA3cdE1KjyvXr06GOZ1z9TNOyYFcd0v+f333127dW0enVvPRdXr559/3oYNG+bufVwUohcsWGDHjh2Lcx8F3+HDh7sXFHoZoeAfGvRjOnXqlAvv8+fPt7Vr17qXFF26dHEvaUKprapur1ixwiZPnmz/93//5/5s6Zo88+bNc13gY3s2QNgFAAAAgCT2/fffB/S/mh9++GG8+33++eeBdOnSBbZt2xZct27dOvfZH374wf08ZMiQQIYMGQJ79+6N9lnt07t372jr6tevHxg5cmS0de+8806gSJEi0T43d+7cONs0duzYQPXq1YM/6/xVq1a9YL/Q4/znP/8J5MmTJ3D8+PHg9vnz5wciIyMDe/bscT9369YtUKpUqcC5c+eC+7Rp0ybQrl27ONvSp0+fQNOmTd3vx40b5/ZVWxYuXOjWlStXzp1bNm/e7Nq0evVq9/OSJUvcz4cOHYp2zLp16wZq164dbV2NGjUC/fr1i7MdX331VaB48eLuOVx//fXuvi9fvjwQnx9//NGd/9ixY/G2J5SutW/fvtHaeu21116w39VXXx0YM2ZM8OfmzZsHunfvHm97gHCh0g0AAIAk9/9n0ovbsGGDq8p6XaRFY4ZVCdc2T6lSpdzkXTGpO3MoVVlVtVVXdW+5//77XVVUldDYzJ492409VzVX+z/99NOuy3tiqK1Vq1aNVtXXMVXJV6XZo4q5uox71M08vi7YqmIvX77czp8/76ra6m6tRdXvXbt2uR4A+jmxrrnmmmg/X6wdderUcT0QNAFe69atbd26dXbLLbe4yrZn5cqVbpK8kiVLut4DXgU+rnupa9Ln1a1c3dx17zXeP+b+qobHpGr31KlT3e81FGHhwoXRuroDKQmhGwAAAEmufPnyriv0pU6WFlNsXdRjW69u1xqDrS7o3qJu2X/88UdwLHiob7/91jp16uTGKquLsrpuDxo0yE3Y5YeYE73pHimYxxd21aV71apVtmzZsmihWyG8aNGi7l773Q7vMwraGgP/+eefu5cbCs26V173eo3Jnz59uutiP3fuXPe5uO6lJtdTl3cdT93t9ax0jJj7x/bsNYRALwH0/DSuXWP91TYgJWL2cgAAACQ5VS4VoCZOnGi9evW6IDjpa6xUza5YsaJt377dLV61W2OrtV0V78TSBGqqLJcrVy5B+2sGblXRFbQ9W7dujbaPxhOrKhsfXYfGZit8eteqMciRkZF21VVX2aXSPVJVesKECS70VqhQwU0YprHLekkQ23ju0HbLxdp+qfR8NEGdxmbrpYYmRhs9enTwOXoTwMVF90fj+Tt37ux+VujfuHFjgp67JqHTpHSqdit433PPPUl0VUDSo9INAAAAXyhwK/DdcMMNblIvBTN1w3711VetZs2abp/bb7/ddS9WtVnVXE2ipSqmwmTMruMJ8cwzz9jbb7/tqt3qAq3zzZo1y3UZj42qxOrOrH00wZna5lVoPZqte/Pmza4Su3//fjt9+vQFx1H7VUnv1q2bmxRMlVvN7q2JwQoVKmT/hirbqh57AVsvNBTy1S0+vtCtlwmqYCuc79u3L9pM6pfSBk1Kpy7kmiBNk6ppNnPNkK7qtrqUK+TrK8VUgdZs56Fdz+O695q8TS8+9JwefPDBaLPWX4y6mGuSNX1W9x1IqQjdAAAA8IW+fktBWsFMM4BXrlzZGjRo4MYF66u+RKFQX/GlWb/VlVohXJ9ToLwUqq4rZKr7c40aNdzXa2kmcgXQ2Ojrxh5//HE3y7hmClcA1FeGhdLXnTVq1Mhdh8aVx/xaK9FXXmk8smZh13k17rl+/fquQv1vKVjr5UXo2G39Pua6mPRVXHr50L9/fxf8Y86kntj7qoB7xx13uMCvFwpa995777ntui+q9GvWdlWqVfHWV6PFRy9C1DNBx9F1aEy9qtcJpT8rGouuz6ubPZBSRWg2tXA3AgAAAAASQ5V7vVhQF/O77ror3M0B4sSYbgAAAACXDY39Vjf/F1980Y15V28FICUjdAMAAAC4bGgMvmYrL168uOvSnj49kQYpG93LAQAAAADwCROpAQAAAADgE0I3AAAAAAA+IXQDAAAAAOATQjcAAAAAAD4hdAMAAAAA4BNCNwAAAAAAPiF0AwAAAADgE0I3AAAAAAA+IXQDAAAAAGD++P8Ar1msQrt7L00AAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 1000x1200 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== Checking for Multicollinearity ===\n",
"\n",
"Highly correlated feature pairs (>0.8):\n",
" YearsCodePro <-> WorkExp: 0.929\n",
" YearsCodePro <-> YearsCode: 0.906\n",
" YearsCodePro <-> Age_Ordinal: 0.801\n",
" WorkExp <-> YearsCode: 0.861\n",
" WorkExp <-> Age_Ordinal: 0.827\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1400x1200 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Separate features and target\n",
"X = df_model.drop(columns=['ConvertedCompYearly'])\n",
"y = df_model['ConvertedCompYearly']\n",
"\n",
"print(f\"Features shape: {X.shape}\")\n",
"print(f\"Target shape: {y.shape}\")\n",
"\n",
"# Calculate correlation with target\n",
"correlations = X.corrwith(y).sort_values(ascending=False)\n",
"\n",
"print(\"\\n=== TOP 20 POSITIVE CORRELATIONS WITH SALARY ===\")\n",
"print(correlations.head(20))\n",
"\n",
"print(\"\\n=== TOP 20 NEGATIVE CORRELATIONS WITH SALARY ===\")\n",
"print(correlations.tail(20))\n",
"\n",
"# Visualize top correlations\n",
"plt.figure(figsize=(10, 12))\n",
"\n",
"# Get top 30 features by absolute correlation\n",
"top_features = correlations.abs().sort_values(ascending=False).head(30)\n",
"top_correlations = correlations[top_features.index]\n",
"\n",
"plt.barh(range(len(top_correlations)), top_correlations.values)\n",
"plt.yticks(range(len(top_correlations)), top_correlations.index, fontsize=9)\n",
"plt.xlabel('Correlation with Salary')\n",
"plt.title('Top 30 Features by Correlation with Salary')\n",
"plt.axvline(x=0, color='black', linestyle='-', linewidth=0.5)\n",
"plt.grid(axis='x', alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# Check for multicollinearity among top features\n",
"print(\"\\n=== Checking for Multicollinearity ===\")\n",
"top_20_features = correlations.abs().sort_values(ascending=False).head(20).index.tolist()\n",
"\n",
"# Correlation matrix of top features\n",
"feature_corr = X[top_20_features].corr()\n",
"\n",
"# Find highly correlated feature pairs (>0.8)\n",
"high_corr_pairs = []\n",
"for i in range(len(feature_corr.columns)):\n",
" for j in range(i+1, len(feature_corr.columns)):\n",
" if abs(feature_corr.iloc[i, j]) > 0.8:\n",
" high_corr_pairs.append((feature_corr.columns[i], feature_corr.columns[j], feature_corr.iloc[i, j]))\n",
"\n",
"if high_corr_pairs:\n",
" print(\"\\nHighly correlated feature pairs (>0.8):\")\n",
" for feat1, feat2, corr in high_corr_pairs:\n",
" print(f\" {feat1} <-> {feat2}: {corr:.3f}\")\n",
"else:\n",
" print(\"\\nNo highly correlated feature pairs found (threshold: 0.8)\")\n",
"\n",
"# Optional: Heatmap of top features\n",
"plt.figure(figsize=(14, 12))\n",
"sns.heatmap(feature_corr, annot=False, cmap='coolwarm', center=0, \n",
" square=True, linewidths=0.5, cbar_kws={\"shrink\": 0.8})\n",
"plt.title('Correlation Matrix of Top 20 Features')\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "bfa67b69",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape after dropping multicollinear features: (14924, 72)\n",
"\n",
"=== TOP 20 CORRELATIONS (After Removing Multicollinearity) ===\n",
"Country_United States of America 0.600111\n",
"YearsCodePro 0.310453\n",
"OrgSize_Ordinal 0.211776\n",
"Has_Terraform 0.175568\n",
"RemoteWork_Remote 0.142808\n",
"Has_AWS 0.140094\n",
"Has_Kubernetes 0.126078\n",
"Has_Go 0.121580\n",
"DevType_Engineering manager 0.106681\n",
"DevType_Senior Executive (C-Suite, VP, etc.) 0.097640\n",
"Has_Rust 0.085012\n",
"Has_Python 0.083086\n",
"Has_React 0.063394\n",
"Has_Docker 0.061214\n",
"Industry_Financial Services 0.060895\n",
"Has_Redis 0.058601\n",
"Country_Switzerland 0.057682\n",
"EdLevel_Ordinal 0.055231\n",
"Has_Elasticsearch 0.050139\n",
"DevType_Other (please specify): 0.048299\n",
"dtype: float64\n",
"\n",
"=== Features with correlation < 0.01: 4 ===\n",
"['Has_SQL', 'Industry_Retail and Consumer Services', 'DevType_DevOps specialist', 'Industry_Legal Services']\n",
"\n",
"=== FINAL FEATURE SET ===\n",
"Features: 68\n",
"Rows: 14924\n",
"\n",
"Final features ranked by correlation:\n",
"Country_United States of America 0.600111\n",
"YearsCodePro 0.310453\n",
"OrgSize_Ordinal 0.211776\n",
"Has_Terraform 0.175568\n",
"RemoteWork_Remote 0.142808\n",
" ... \n",
"Country_Italy -0.100666\n",
"RemoteWork_In-person -0.117706\n",
"Country_Brazil -0.132113\n",
"Country_India -0.181109\n",
"Country_Other -0.277522\n",
"Length: 68, dtype: float64\n"
]
}
],
"source": [
"# Drop redundant experience variables\n",
"multicollinear_drops = ['YearsCode', 'WorkExp', 'Age_Ordinal']\n",
"\n",
"X_reduced = X.drop(columns=multicollinear_drops)\n",
"\n",
"print(f\"Shape after dropping multicollinear features: {X_reduced.shape}\")\n",
"\n",
"# Recalculate correlations\n",
"correlations_reduced = X_reduced.corrwith(y).sort_values(ascending=False)\n",
"\n",
"print(\"\\n=== TOP 20 CORRELATIONS (After Removing Multicollinearity) ===\")\n",
"print(correlations_reduced.head(20))\n",
"\n",
"# Now drop very weak predictors (|correlation| < 0.01)\n",
"weak_features = correlations_reduced[abs(correlations_reduced) < 0.01].index.tolist()\n",
"print(f\"\\n=== Features with correlation < 0.01: {len(weak_features)} ===\")\n",
"print(weak_features[:20]) # Show first 20\n",
"\n",
"# Drop weak features\n",
"X_final = X_reduced.drop(columns=weak_features)\n",
"\n",
"print(f\"\\n=== FINAL FEATURE SET ===\")\n",
"print(f\"Features: {X_final.shape[1]}\")\n",
"print(f\"Rows: {X_final.shape[0]}\")\n",
"\n",
"# Show final feature list\n",
"final_correlations = X_final.corrwith(y).sort_values(ascending=False)\n",
"print(\"\\nFinal features ranked by correlation:\")\n",
"print(final_correlations)\n",
"\n",
"# Create final modeling dataset\n",
"df_final = pd.concat([X_final, y], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "f7039b96",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training set: (11939, 68)\n",
"Test set: (2985, 68)\n",
"\n",
"=== MODEL PERFORMANCE ===\n",
"\n",
"Training Set:\n",
"R² Score: 0.5531\n",
"MAE: $29,834.58\n",
"\n",
"Test Set:\n",
"R² Score: 0.5647\n",
"MAE: $30,881.92\n",
"\n",
"=== TOP 10 MOST IMPACTFUL FEATURES ===\n",
" Feature Coefficient\n",
" Country_India -61208.113364\n",
" Country_Brazil -57285.162776\n",
" Country_United States of America 56311.330286\n",
" Country_Italy -45739.032413\n",
" Country_Portugal -39461.936394\n",
" Country_Spain -37908.859947\n",
" Country_Poland -37272.735270\n",
"DevType_Senior Executive (C-Suite, VP, etc.) 35917.362070\n",
" Country_Sweden -35394.571760\n",
" Country_France -35248.270299\n"
]
}
],
"source": [
"# Separate features and target\n",
"X = df_final.drop(columns=['ConvertedCompYearly'])\n",
"y = df_final['ConvertedCompYearly']\n",
"\n",
"# Train-test split\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42\n",
")\n",
"\n",
"print(f\"Training set: {X_train.shape}\")\n",
"print(f\"Test set: {X_test.shape}\")\n",
"\n",
"# Train linear regression model\n",
"model = LinearRegression()\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Make predictions\n",
"y_pred_train = model.predict(X_train)\n",
"y_pred_test = model.predict(X_test)\n",
"\n",
"# Evaluate\n",
"print(\"\\n=== MODEL PERFORMANCE ===\")\n",
"print(\"\\nTraining Set:\")\n",
"print(f\"R² Score: {r2_score(y_train, y_pred_train):.4f}\")\n",
"# print(f\"RMSE: ${mean_squared_error(y_train, y_pred_train, squared=False):,.2f}\")\n",
"print(f\"MAE: ${mean_absolute_error(y_train, y_pred_train):,.2f}\")\n",
"\n",
"print(\"\\nTest Set:\")\n",
"print(f\"R² Score: {r2_score(y_test, y_pred_test):.4f}\")\n",
"# print(f\"RMSE: ${mean_squared_error(y_test, y_pred_test, squared=False):,.2f}\")\n",
"print(f\"MAE: ${mean_absolute_error(y_test, y_pred_test):,.2f}\")\n",
"\n",
"# Top 10 most important features (by coefficient magnitude)\n",
"coefficients = pd.DataFrame({\n",
" 'Feature': X.columns,\n",
" 'Coefficient': model.coef_\n",
"})\n",
"coefficients['Abs_Coefficient'] = abs(coefficients['Coefficient'])\n",
"top_features = coefficients.nlargest(10, 'Abs_Coefficient')\n",
"\n",
"print(\"\\n=== TOP 10 MOST IMPACTFUL FEATURES ===\")\n",
"print(top_features[['Feature', 'Coefficient']].to_string(index=False))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "7db97790",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== TOP 20 POSITIVE COEFFICIENTS (Increase Salary) ===\n",
" Feature Coefficient\n",
" Country_United States of America 56311.330286\n",
"DevType_Senior Executive (C-Suite, VP, etc.) 35917.362070\n",
" Country_Switzerland 29827.786692\n",
" DevType_Engineering manager 24117.744900\n",
" DevType_Other (please specify): 13231.239000\n",
" DevType_Developer, mobile 12666.293949\n",
" Has_Terraform 8671.813906\n",
" Industry_Financial Services 7478.518615\n",
" Has_Rust 6580.300282\n",
" Has_Kubernetes 6533.803807\n",
" DevType_Other 6352.495143\n",
" Has_TypeScript 6326.088629\n",
" Has_Go 6004.289804\n",
" Has_React 5754.080078\n",
" DevType_Developer, back-end 5077.897084\n",
" Has_AWS 4984.284478\n",
" Has_Kotlin 4632.116174\n",
" OrgSize_Ordinal 4471.811309\n",
" Has_Redis 3408.064819\n",
" RemoteWork_Remote 3080.409892\n",
"\n",
"=== TOP 20 NEGATIVE COEFFICIENTS (Decrease Salary) ===\n",
" Feature Coefficient\n",
" Industry_Insurance -6709.806111\n",
" Has_CSharp -7105.670401\n",
" Has_SpringBoot -7286.369380\n",
"Country_United Kingdom of Great Britain and Northern Ireland -9496.742854\n",
" Employment_Simple_Full-time + Freelance -12568.844850\n",
" Industry_Wholesale -16399.784485\n",
" Country_Germany -17270.165382\n",
" Employment_Simple_Full-time -18322.343972\n",
" Country_Netherlands -19257.820041\n",
" Industry_Higher Education -22416.839984\n",
" Employment_Simple_Part-time -30028.069466\n",
" Country_Other -31984.032445\n",
" Country_France -35248.270299\n",
" Country_Sweden -35394.571760\n",
" Country_Poland -37272.735270\n",
" Country_Spain -37908.859947\n",
" Country_Portugal -39461.936394\n",
" Country_Italy -45739.032413\n",
" Country_Brazil -57285.162776\n",
" Country_India -61208.113364\n",
"\n",
"=== COEFFICIENT BREAKDOWN BY CATEGORY ===\n",
"\n",
"Country features - Average absolute impact: $34,265.48\n",
"Skill features - Average absolute impact: $3,994.12\n",
"DevType features - Average absolute impact: $11,667.53\n",
"Industry features - Average absolute impact: $7,721.10\n",
"\n",
"=== SAMPLE PREDICTIONS ===\n",
" Actual Predicted Difference\n",
" 42167.0 65642.953943 -23475.953943\n",
" 87813.0 92422.205473 -4609.205473\n",
"190000.0 198285.695497 -8285.695497\n",
"107090.0 91056.166442 16033.833558\n",
"300000.0 241048.205597 58951.794403\n"
]
}
],
"source": [
"# 1. Residual plot - check for patterns\n",
"plt.figure(figsize=(12, 5))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.scatter(y_test, y_pred_test, alpha=0.5)\n",
"plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
"plt.xlabel('Actual Salary ($)')\n",
"plt.ylabel('Predicted Salary ($)')\n",
"plt.title('Predicted vs Actual Salaries (Test Set)')\n",
"plt.grid(alpha=0.3)\n",
"\n",
"plt.subplot(1, 2, 2)\n",
"residuals = y_test - y_pred_test\n",
"plt.scatter(y_pred_test, residuals, alpha=0.5)\n",
"plt.axhline(y=0, color='r', linestyle='--', lw=2)\n",
"plt.xlabel('Predicted Salary ($)')\n",
"plt.ylabel('Residuals ($)')\n",
"plt.title('Residual Plot')\n",
"plt.grid(alpha=0.3)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# 2. Show ALL significant coefficients\n",
"coefficients_sorted = coefficients.sort_values('Coefficient', ascending=False)\n",
"\n",
"print(\"\\n=== TOP 20 POSITIVE COEFFICIENTS (Increase Salary) ===\")\n",
"print(coefficients_sorted.head(20)[['Feature', 'Coefficient']].to_string(index=False))\n",
"\n",
"print(\"\\n=== TOP 20 NEGATIVE COEFFICIENTS (Decrease Salary) ===\")\n",
"print(coefficients_sorted.tail(20)[['Feature', 'Coefficient']].to_string(index=False))\n",
"\n",
"# 3. Feature categories breakdown\n",
"print(\"\\n=== COEFFICIENT BREAKDOWN BY CATEGORY ===\")\n",
"\n",
"# Country features\n",
"country_coefs = coefficients[coefficients['Feature'].str.startswith('Country_')]\n",
"print(f\"\\nCountry features - Average absolute impact: ${country_coefs['Abs_Coefficient'].mean():,.2f}\")\n",
"\n",
"# Skills (Has_*)\n",
"skill_coefs = coefficients[coefficients['Feature'].str.startswith('Has_')]\n",
"print(f\"Skill features - Average absolute impact: ${skill_coefs['Abs_Coefficient'].mean():,.2f}\")\n",
"\n",
"# DevType features\n",
"devtype_coefs = coefficients[coefficients['Feature'].str.startswith('DevType_')]\n",
"print(f\"DevType features - Average absolute impact: ${devtype_coefs['Abs_Coefficient'].mean():,.2f}\")\n",
"\n",
"# Industry features\n",
"industry_coefs = coefficients[coefficients['Feature'].str.startswith('Industry_')]\n",
"print(f\"Industry features - Average absolute impact: ${industry_coefs['Abs_Coefficient'].mean():,.2f}\")\n",
"\n",
"# 4. Prediction examples\n",
"print(\"\\n=== SAMPLE PREDICTIONS ===\")\n",
"sample_idx = [0, 100, 500, 1000, 2000]\n",
"comparison = pd.DataFrame({\n",
" 'Actual': y_test.iloc[sample_idx].values,\n",
" 'Predicted': y_pred_test[sample_idx],\n",
" 'Difference': y_test.iloc[sample_idx].values - y_pred_test[sample_idx]\n",
"})\n",
"print(comparison.to_string(index=False))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "7428d9ca",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=== ERROR BY SALARY RANGE ===\n",
" mean median count\n",
"Salary_Range \n",
"<$50k 23094.124364 19159.462266 743\n",
"$50-100k 22427.082209 16700.085894 1099\n",
"$100-150k 30620.128394 26940.220831 593\n",
"$150-200k 27475.614914 20436.613767 318\n",
">$200k 101212.327040 84484.124508 232\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\annas\\AppData\\Local\\Temp\\ipykernel_33440\\2462263129.py:13: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" error_by_range = salary_ranges.groupby('Salary_Range')['Error'].agg(['mean', 'median', 'count'])\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1200x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Check if model struggles with high earners\n",
"salary_ranges = pd.DataFrame({\n",
" 'Actual': y_test,\n",
" 'Predicted': y_pred_test,\n",
" 'Error': abs(y_test - y_pred_test)\n",
"})\n",
"\n",
"salary_ranges['Salary_Range'] = pd.cut(salary_ranges['Actual'], \n",
" bins=[0, 50000, 100000, 150000, 200000, 500000],\n",
" labels=['<$50k', '$50-100k', '$100-150k', '$150-200k', '>$200k'])\n",
"\n",
"print(\"=== ERROR BY SALARY RANGE ===\")\n",
"error_by_range = salary_ranges.groupby('Salary_Range')['Error'].agg(['mean', 'median', 'count'])\n",
"print(error_by_range)\n",
"\n",
"# Check US vs non-US prediction accuracy\n",
"X_test_with_pred = X_test.copy()\n",
"X_test_with_pred['Actual'] = y_test.values\n",
"X_test_with_pred['Predicted'] = y_pred_test\n",
"X_test_with_pred['Error'] = abs(y_test.values - y_pred_test)\n",
"\n",
"# Feature importance visualization\n",
"plt.figure(figsize=(12, 8))\n",
"top_20_coef = pd.concat([coefficients_sorted.head(10), coefficients_sorted.tail(10)]) # Use concat instead of append\n",
"colors = ['green' if x > 0 else 'red' for x in top_20_coef['Coefficient']]\n",
"\n",
"plt.barh(range(len(top_20_coef)), top_20_coef['Coefficient'], color=colors, alpha=0.7)\n",
"plt.yticks(range(len(top_20_coef)), top_20_coef['Feature'], fontsize=9)\n",
"plt.xlabel('Coefficient (Impact on Salary in $)')\n",
"plt.title('Top 10 Positive & Negative Feature Impacts on Salary')\n",
"plt.axvline(x=0, color='black', linestyle='-', linewidth=1)\n",
"plt.grid(axis='x', alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "7a1f4487",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=== FACTORS YOU CAN CONTROL ===\n",
"\n",
"Top 15 ways to increase your salary:\n",
" Feature Coefficient\n",
"DevType_Senior Executive (C-Suite, VP, etc.) 35917.362070\n",
" DevType_Engineering manager 24117.744900\n",
" DevType_Other (please specify): 13231.239000\n",
" DevType_Developer, mobile 12666.293949\n",
" Has_Terraform 8671.813906\n",
" Has_Rust 6580.300282\n",
" Has_Kubernetes 6533.803807\n",
" DevType_Other 6352.495143\n",
" Has_TypeScript 6326.088629\n",
" Has_Go 6004.289804\n",
" Has_React 5754.080078\n",
" DevType_Developer, back-end 5077.897084\n",
" Has_AWS 4984.284478\n",
" Has_Kotlin 4632.116174\n",
" OrgSize_Ordinal 4471.811309\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1000x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Show the skills/factors you CAN control\n",
"controllable_features = coefficients[\n",
" (coefficients['Feature'].str.startswith('Has_')) | \n",
" (coefficients['Feature'].str.startswith('DevType_')) |\n",
" (coefficients['Feature'] == 'YearsCodePro') |\n",
" (coefficients['Feature'] == 'OrgSize_Ordinal') |\n",
" (coefficients['Feature'].str.startswith('RemoteWork_'))\n",
"].copy()\n",
"\n",
"controllable_features = controllable_features.sort_values('Coefficient', ascending=False)\n",
"\n",
"print(\"\\n=== FACTORS YOU CAN CONTROL ===\")\n",
"print(\"\\nTop 15 ways to increase your salary:\")\n",
"print(controllable_features.head(15)[['Feature', 'Coefficient']].to_string(index=False))\n",
"\n",
"# Visualize it\n",
"plt.figure(figsize=(10, 8))\n",
"top_controllable = controllable_features.head(15)\n",
"colors = ['green' if x > 0 else 'red' for x in top_controllable['Coefficient']]\n",
"\n",
"plt.barh(range(len(top_controllable)), top_controllable['Coefficient'], color=colors, alpha=0.7)\n",
"plt.yticks(range(len(top_controllable)), top_controllable['Feature'], fontsize=10)\n",
"plt.xlabel('Impact on Salary ($)')\n",
"plt.title('Skills & Factors YOU Can Control to Increase Salary')\n",
"plt.grid(axis='x', alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ee32b480",
"metadata": {},
"source": [
"## Key Insights\n",
"\n",
"### 1. Model Struggles with High Earners\n",
"- **<$50k-$200k**: Mean error ~$22k-$31k (reasonable)\n",
"- **>$200k**: Mean error **$101k** (huge!)\n",
" - The model significantly **underestimates high salaries**\n",
" - Only 232 people in this range (7.8% of test set) - limited training data\n",
"\n",
"### 2. US Predictions Are LESS Accurate\n",
"- **US**: Mean error $46,758 (median $34,254)\n",
"- **Non-US**: Mean error $24,949 (median $18,559)\n",
"\n",
"This is counterintuitive! Possible reasons:\n",
"- **US has wider salary variance** (more $200k+ earners)\n",
"- **More diverse tech hubs** (Silicon Valley vs. Austin vs. NYC)\n",
"- The single `Country_USA` feature can't capture regional US differences\n",
"\n",
"### 3. The Model is Better at Mid-Range Salaries\n",
"The sweet spot is **$50k-$150k** where errors are ~$22k-$31k.\n",
"\n",
"## Interpretation\n",
"\n",
"The linear regression model is doing well for \"typical\" developer salaries but hits limitations:\n",
"\n",
"1. **Linear relationships** struggle with extreme values\n",
"2. **Geographic granularity** - \"USA\" is too broad (no state/city info)\n",
"3. **High earners have unique patterns** - probably FAANG, startups with equity, or very senior roles that aren't fully captured\n",
"\n",
"## Final Model Summary\n",
"\n",
"**Strengths:**\n",
"- Explains 56.5% of variance (solid for real-world data)\n",
"- No overfitting\n",
"- Accurate for typical salaries ($50k-$150k)\n",
"- Clear interpretable coefficients\n",
"\n",
"**Weaknesses:**\n",
"- Underestimates high earners (>$200k)\n",
"- Less accurate for US (wide variance)\n",
"- Geography dominates (might mask other patterns)\n",
"\n",
"### Major Limitations\n",
"\n",
"1. **66% Data Loss** \n",
" - We dropped anyone with ANY missing values\n",
" - Creates potential selection bias\n",
" - **Fix**: Imputation strategies, missing indicators\n",
"\n",
"2. **$500k Salary Cap**\n",
" - We artificially limited the upper range\n",
" - Model can't learn patterns above $500k\n",
" - **Fix**: Remove cap, use robust regression, or separate models by range\n",
"\n",
"3. **Linear Assumptions**\n",
" - Assumes linear relationships (may not be true for salaries)\n",
" - Can't capture interactions (e.g., Python + ML + PhD together)\n",
" - **Fix**: Try polynomial features, Random Forest\n",
"\n",
"4. **Geographic Oversimplification**\n",
" - \"USA\" includes Silicon Valley ($300k+) AND rural areas ($70k)\n",
" - Can't capture cost-of-living differences\n",
"\n",
"### Next Steps for Better Predictions\n",
"\n",
"1. **Try Random Forest Regressor**\n",
" - Can capture non-linear relationships\n",
" - Handles feature interactions automatically\n",
" - Typically improves R² by 10-15 percentage points\n",
"\n",
"2. **Feature Engineering**\n",
" - Interaction terms: Has_Python × DevType_ML_Specialist\n",
" - Career progression: YearsCode - YearsCodePro (hobby time)\n",
" - Skill combinations: Count of cloud + DevOps skills\n",
"\n",
"3. **Separate Models by Salary Range**\n",
" - <$150k: Use log-transformed linear regression\n",
" - \\>$150k: Use Random Forest with equity/company size features\n",
"\n",
"4. **Collect More Data**\n",
" - Under-sampled: High earners, certain countries\n",
" - Missing: City/region, company size tier, equity compensation\n"
]
},
{
"cell_type": "markdown",
"id": "e9aa19f4",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment