{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "c68cf9986419f320", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:45.373200Z", "start_time": "2025-10-06T08:13:43.771582Z" } }, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: './stack-overflow-developer-survey-2024/survey_results_public.csv'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mseaborn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msns\u001b[39;00m\n\u001b[32m 5\u001b[39m \u001b[38;5;66;03m# I don't know why it takes so long to load the data\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m./stack-overflow-developer-survey-2024/survey_results_public.csv\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mbasic_info\u001b[39m(column):\n\u001b[32m 10\u001b[39m \u001b[38;5;28mprint\u001b[39m(df[column].head())\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\sam\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 1014\u001b[39m dialect,\n\u001b[32m 1015\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 1022\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 1023\u001b[39m )\n\u001b[32m 1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\sam\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 623\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\sam\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1617\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\sam\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1878\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1879\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1881\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1882\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1883\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1884\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1885\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1886\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1887\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1888\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n", "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\sam\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 869\u001b[39m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m 870\u001b[39m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m 871\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m 872\u001b[39m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 874\u001b[39m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 875\u001b[39m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 876\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 878\u001b[39m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 880\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 881\u001b[39m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m 882\u001b[39m handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n", "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: './stack-overflow-developer-survey-2024/survey_results_public.csv'" ] } ], "source": [ "# load pandas and read the csv file from the url\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "# I don't know why it takes so long to load the data\n", "df = pd.read_csv(\"./stack-overflow-developer-survey-2024/survey_results_public.csv\", )\n", "\n", "\n", "def basic_info(column):\n", " print(df[column].head())\n", " print('-'*40)\n", " print(df[column].tail())\n", " print('-'*40)\n", " print(df[column].isnull().sum())\n", " print('-'*40)\n", " print(df[column].value_counts())\n", " print('-'*40)\n", " print('*'*40)\n", "\n", "# create a value mapping for all categorical data\n", "value_maps = {}\n", "def map_and_fill(column, fill_value=0):\n", " value_map = {v: i for i, v in enumerate(df[column].dropna().unique())}\n", " df[column] = df[column].map(value_map)\n", " df[column] = df[column].fillna(fill_value)\n", " value_maps[column] = value_map\n", " return value_map\n", "\n", "def reverse_map_and_fill(column):\n", " if column in value_maps:\n", " _reverse_map = {v: k for k, v in value_maps[column].items()}\n", " df[column] = df[column].map(_reverse_map)\n", " else:\n", " print(f\"No mapping found for column: {column}\")\n", "\n", "def bin_distribution(column, bins=10,rotate=0):\n", " plt.figure(figsize=(12,6))\n", " sns.histplot(df[column], bins=bins, kde=False)\n", " plt.title(f'{column} Distribution')\n", " plt.xlabel(column)\n", " plt.ylabel('Count')\n", " # rotate x labels\n", " if rotate:\n", " plt.xticks(rotation=rotate)\n", " plt.show()\n", "\n", "# don't overthink this function, it is just a fast way to explode a column with multiple values separated by a delimiter and one-hot encode the result, it is for machine learning data cleaning purpose, not for data analysis\n", "def fast_explode(target_dataframe, target_column, fillna='', split=';', prefix='worked with', tmp_column_name='tmp_c'):\n", " # Step 1: Create a temporary column with split and prefix\n", " _exploded = (\n", " target_dataframe.assign(\n", " **{tmp_column_name: target_dataframe[target_column]\n", " .fillna(fillna)\n", " .str.split(split)\n", " .apply(lambda lst: [f\"{prefix} {lang.strip()}\" for lang in lst if lang])\n", " }\n", " )\n", " .explode(tmp_column_name)\n", " )\n", "\n", " # Step 2: One-hot encode\n", " _one_hot = pd.crosstab(index=_exploded.index, columns=_exploded[tmp_column_name]).astype(bool)\n", "\n", " # Step 3: Combine with original DataFrame\n", " print(target_column, _one_hot.shape[0], target_dataframe.shape[0])\n", " result = pd.concat([target_dataframe.drop(columns=[target_column]), _one_hot], axis=1)\n", " result[_one_hot.columns] = result[_one_hot.columns].fillna(False)\n", "\n", " return result\n", "\n", "\n", "\n", "# let's take a look at the data\n", "print(df.head())\n", "print(df.describe())\n", "print(df.info())\n", "\n", "for c in df.columns:\n", " print(f\"{c}\")" ] }, { "cell_type": "markdown", "id": "99d3e04a255ba7a9", "metadata": {}, "source": [ "we can see null values throughout all columns, few columns have filled with full values." ] }, { "cell_type": "markdown", "id": "31337e624230bc86", "metadata": {}, "source": [ "now we will look into the data and see what we can do to clean it up and make it ready for analysis." ] }, { "cell_type": "code", "execution_count": 2, "id": "b015f782a9e772f", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:45.436975Z", "start_time": "2025-10-06T08:13:45.426772Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 1\n", "1 2\n", "2 3\n", "3 4\n", "4 5\n", "Name: ResponseId, dtype: int64\n", "----------------------------------------\n", "65432 65433\n", "65433 65434\n", "65434 65435\n", "65435 65436\n", "65436 65437\n", "Name: ResponseId, dtype: int64\n", "----------------------------------------\n", "0\n", "----------------------------------------\n", "ResponseId\n", "1 1\n", "2 1\n", "3 1\n", "4 1\n", "5 1\n", " ..\n", "65433 1\n", "65434 1\n", "65435 1\n", "65436 1\n", "65437 1\n", "Name: count, Length: 65437, dtype: int64\n", "----------------------------------------\n", "****************************************\n" ] } ], "source": [ "# start checking..\n", "basic_info(\"ResponseId\")\n", "\n", "# so response_id can be used as index\n", "df.set_index('ResponseId', inplace=True) # setting index" ] }, { "cell_type": "code", "execution_count": 3, "id": "7691ecd2af46dfee", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:45.634201Z", "start_time": "2025-10-06T08:13:45.615724Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ResponseId\n", "1 I am a developer by profession\n", "2 I am a developer by profession\n", "3 I am a developer by profession\n", "4 I am learning to code\n", "5 I am a developer by profession\n", "Name: MainBranch, dtype: object\n", "----------------------------------------\n", "ResponseId\n", "65433 I am a developer by profession\n", "65434 I am a developer by profession\n", "65435 I am a developer by profession\n", "65436 I am a developer by profession\n", "65437 I code primarily as a hobby\n", "Name: MainBranch, dtype: object\n", "----------------------------------------\n", "0\n", "----------------------------------------\n", "MainBranch\n", "I am a developer by profession 50207\n", "I am not primarily a developer, but I write code sometimes as part of my work/studies 6511\n", "I am learning to code 3875\n", "I code primarily as a hobby 3334\n", "I used to be a developer by profession, but no longer am 1510\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'I am a developer by profession': 0, 'I am learning to code': 1, 'I code primarily as a hobby': 2, 'I am not primarily a developer, but I write code sometimes as part of my work/studies': 3, 'I used to be a developer by profession, but no longer am': 4}\n" ] } ], "source": [ "# start checking..\n", "basic_info(\"MainBranch\")\n", "\n", "# main_branch seems like a categorical data, asssign the unique values to the value_maps map to interger\n", "\n", "print(map_and_fill(\"MainBranch\"))\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "fdfb6bf51861d335", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:45.661061Z", "start_time": "2025-10-06T08:13:45.655999Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MainBranch\n", "0 50207\n", "3 6511\n", "1 3875\n", "2 3334\n", "4 1510\n", "Name: count, dtype: int64\n" ] } ], "source": [ "# it seems like a categorical data, let's see the unique values\n", "print(df['MainBranch'].value_counts())\n", "# ok, its categorical data" ] }, { "cell_type": "code", "execution_count": 5, "id": "1228a9be4c390b2a", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:45.816206Z", "start_time": "2025-10-06T08:13:45.794693Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ResponseId\n", "1 Under 18 years old\n", "2 35-44 years old\n", "3 45-54 years old\n", "4 18-24 years old\n", "5 18-24 years old\n", "Name: Age, dtype: object\n", "----------------------------------------\n", "ResponseId\n", "65433 18-24 years old\n", "65434 25-34 years old\n", "65435 25-34 years old\n", "65436 18-24 years old\n", "65437 18-24 years old\n", "Name: Age, dtype: object\n", "----------------------------------------\n", "0\n", "----------------------------------------\n", "Age\n", "25-34 years old 23911\n", "35-44 years old 14942\n", "18-24 years old 14098\n", "45-54 years old 6249\n", "55-64 years old 2575\n", "Under 18 years old 2568\n", "65 years or older 772\n", "Prefer not to say 322\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Under 18 years old': 0, '35-44 years old': 1, '45-54 years old': 2, '18-24 years old': 3, '25-34 years old': 4, '55-64 years old': 5, 'Prefer not to say': 6, '65 years or older': 7}\n" ] } ], "source": [ "basic_info(\"Age\")\n", "# 0 null values\n", "\n", "# mapping the age column\n", "print(map_and_fill(\"Age\"))" ] }, { "cell_type": "markdown", "id": "884781d052dc6ed7", "metadata": {}, "source": [ "age is categorical data, we can keep it as it is. But I assume it represented register age/ working experience, not the actual age of the respondents" ] }, { "cell_type": "code", "execution_count": 6, "id": "6dc187e2d8919e9a", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:45.940528Z", "start_time": "2025-10-06T08:13:45.918772Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ResponseId\n", "1 Remote\n", "2 Remote\n", "3 Remote\n", "4 NaN\n", "5 NaN\n", "Name: RemoteWork, dtype: object\n", "----------------------------------------\n", "ResponseId\n", "65433 Remote\n", "65434 Remote\n", "65435 In-person\n", "65436 Hybrid (some remote, some in-person)\n", "65437 NaN\n", "Name: RemoteWork, dtype: object\n", "----------------------------------------\n", "10631\n", "----------------------------------------\n", "RemoteWork\n", "Hybrid (some remote, some in-person) 23015\n", "Remote 20831\n", "In-person 10960\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Remote': 0, 'In-person': 1, 'Hybrid (some remote, some in-person)': 2}\n", "RemoteWork\n", "0.0 31462\n", "2.0 23015\n", "1.0 10960\n", "Name: count, dtype: int64\n" ] } ], "source": [ "basic_info(\"RemoteWork\")\n", "# mapping the remote_work column\n", "print(map_and_fill(\"RemoteWork\"))\n", "\n", "df['RemoteWork'] = df['RemoteWork'].fillna(0)# filling null values with 0\n", "print(df['RemoteWork'].value_counts())" ] }, { "cell_type": "markdown", "id": "c4d075097ebbae50", "metadata": {}, "source": [ "remote_work is categorical data, we can keep it as it is. And fill the null values with 0, meaning no remote work/experience" ] }, { "cell_type": "code", "execution_count": 7, "id": "403d54cea50102f7", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:46.043963Z", "start_time": "2025-10-06T08:13:46.021402Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ResponseId\n", "1 Primary/elementary school\n", "2 Bachelor’s degree (B.A., B.S., B.Eng., etc.)\n", "3 Master’s degree (M.A., M.S., M.Eng., MBA, etc.)\n", "4 Some college/university study without earning ...\n", "5 Secondary school (e.g. American high school, G...\n", "Name: EdLevel, dtype: object\n", "----------------------------------------\n", "ResponseId\n", "65433 Bachelor’s degree (B.A., B.S., B.Eng., etc.)\n", "65434 NaN\n", "65435 Bachelor’s degree (B.A., B.S., B.Eng., etc.)\n", "65436 Secondary school (e.g. American high school, G...\n", "65437 NaN\n", "Name: EdLevel, dtype: object\n", "----------------------------------------\n", "4653\n", "----------------------------------------\n", "EdLevel\n", "Bachelor’s degree (B.A., B.S., B.Eng., etc.) 24942\n", "Master’s degree (M.A., M.S., M.Eng., MBA, etc.) 15557\n", "Some college/university study without earning a degree 7651\n", "Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.) 5793\n", "Professional degree (JD, MD, Ph.D, Ed.D, etc.) 2970\n", "Associate degree (A.A., A.S., etc.) 1793\n", "Primary/elementary school 1146\n", "Something else 932\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Primary/elementary school': 0, 'Bachelor’s degree (B.A., B.S., B.Eng., etc.)': 1, 'Master’s degree (M.A., M.S., M.Eng., MBA, etc.)': 2, 'Some college/university study without earning a degree': 3, 'Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)': 4, 'Professional degree (JD, MD, Ph.D, Ed.D, etc.)': 5, 'Associate degree (A.A., A.S., etc.)': 6, 'Something else': 7}\n" ] } ], "source": [ "basic_info(\"EdLevel\")\n", "# don't know what it is yet, but it seems categorical data, filling null with 0\n", "print(map_and_fill(\"EdLevel\"))\n", "df['EdLevel'] = df['EdLevel'].fillna(0)" ] }, { "cell_type": "code", "execution_count": 8, "id": "d8a9202090b4e146", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:46.186910Z", "start_time": "2025-10-06T08:13:46.113661Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ResponseId\n", "1 Apples\n", "2 Apples\n", "3 Apples\n", "4 Apples\n", "5 Apples\n", "Name: Check, dtype: object\n", "----------------------------------------\n", "ResponseId\n", "65433 Apples\n", "65434 Apples\n", "65435 Apples\n", "65436 Apples\n", "65437 Apples\n", "Name: Check, dtype: object\n", "----------------------------------------\n", "0\n", "----------------------------------------\n", "Check\n", "Apples 65437\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n" ] } ], "source": [ "basic_info(\"Check\")\n", "# only 1 value drop this column\n", "df.drop(columns=['Check'], inplace=True)" ] }, { "cell_type": "code", "execution_count": 9, "id": "5027a86906866b7e", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:47.863480Z", "start_time": "2025-10-06T08:13:46.203678Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ResponseId\n", "1 Hobby\n", "2 Hobby;Contribute to open-source projects;Other...\n", "3 Hobby;Contribute to open-source projects;Other...\n", "4 NaN\n", "5 NaN\n", "Name: CodingActivities, dtype: object\n", "----------------------------------------\n", "ResponseId\n", "65433 Hobby;School or academic work\n", "65434 Hobby;Contribute to open-source projects\n", "65435 Hobby\n", "65436 Hobby;Contribute to open-source projects;Profe...\n", "65437 NaN\n", "Name: CodingActivities, dtype: object\n", "----------------------------------------\n", "10971\n", "----------------------------------------\n", "CodingActivities\n", "Hobby 9993\n", "I don’t code outside of work 6508\n", "Hobby;Professional development or self-paced learning from online courses 6203\n", "Hobby;Contribute to open-source projects 3732\n", "Professional development or self-paced learning from online courses 3120\n", " ... \n", "Hobby;Contribute to open-source projects;Other (please specify):;School or academic work;Freelance/contract work 1\n", "Other (please specify):;Bootstrapping a business;School or academic work 1\n", "Contribute to open-source projects;Other (please specify):;Bootstrapping a business;School or academic work;Professional development or self-paced learning from online courses 1\n", "Hobby;Contribute to open-source projects;Other (please specify):;Bootstrapping a business;School or academic work 1\n", "Hobby;Other (please specify):;Bootstrapping a business;School or academic work;Professional development or self-paced learning from online courses;Freelance/contract work 1\n", "Name: count, Length: 118, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "CodingActivities 54466 65437\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\qswwq\\AppData\\Local\\Temp\\ipykernel_54308\\4278337842.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " result[_one_hot.columns] = result[_one_hot.columns].fillna(False)\n" ] } ], "source": [ "# CodingActivities\n", "basic_info(\"CodingActivities\")\n", "df = fast_explode(df,target_column='CodingActivities', fillna='', split=';', prefix='coding_activities ')\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "1b418fd9a05f7d52", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:48.130644Z", "start_time": "2025-10-06T08:13:47.880964Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 Employed, full-time\n", "2 Employed, full-time\n", "3 Employed, full-time\n", "4 Student, full-time\n", "5 Student, full-time\n", "Name: Employment, dtype: object\n", "----------------------------------------\n", "65433 Employed, full-time\n", "65434 Employed, full-time\n", "65435 Employed, full-time\n", "65436 Employed, full-time\n", "65437 Student, full-time\n", "Name: Employment, dtype: object\n", "----------------------------------------\n", "0\n", "----------------------------------------\n", "Employment\n", "Employed, full-time 39041\n", "Independent contractor, freelancer, or self-employed 4846\n", "Student, full-time 4709\n", "Employed, full-time;Independent contractor, freelancer, or self-employed 3557\n", "Not employed, but looking for work 2341\n", " ... \n", "Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Employed, part-time 1\n", "Student, full-time;Retired 1\n", "Employed, full-time;Not employed, but looking for work;Student, part-time 1\n", "Not employed, and not looking for work;Student, part-time;Employed, part-time 1\n", "Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Student, part-time;Retired 1\n", "Name: count, Length: 110, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Employed, full-time': 0, 'Student, full-time': 1, 'Student, full-time;Not employed, but looking for work': 2, 'Independent contractor, freelancer, or self-employed': 3, 'Not employed, and not looking for work': 4, 'Employed, full-time;Student, part-time': 5, 'Employed, full-time;Independent contractor, freelancer, or self-employed': 6, 'Employed, full-time;Student, full-time': 7, 'Employed, part-time': 8, 'Student, full-time;Employed, part-time': 9, 'Student, part-time;Employed, part-time': 10, 'I prefer not to say': 11, 'Not employed, but looking for work': 12, 'Student, part-time': 13, 'Employed, full-time;Student, full-time;Independent contractor, freelancer, or self-employed;Employed, part-time': 14, 'Employed, full-time;Independent contractor, freelancer, or self-employed;Student, part-time': 15, 'Independent contractor, freelancer, or self-employed;Employed, part-time': 16, 'Independent contractor, freelancer, or self-employed;Student, part-time;Employed, part-time': 17, 'Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed': 18, 'Student, full-time;Independent contractor, freelancer, or self-employed': 19, 'Employed, full-time;Employed, part-time': 20, 'Not employed, but looking for work;Independent contractor, freelancer, or self-employed': 21, 'Student, full-time;Not employed, and not looking for work': 22, 'Retired': 23, 'Independent contractor, freelancer, or self-employed;Student, part-time': 24, 'Employed, full-time;Independent contractor, freelancer, or self-employed;Employed, part-time': 25, 'Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Student, part-time': 26, 'Not employed, but looking for work;Student, part-time': 27, 'Not employed, but looking for work;Not employed, and not looking for work': 28, 'Independent contractor, freelancer, or self-employed;Retired': 29, 'Not employed, but looking for work;Student, part-time;Employed, part-time': 30, 'Student, full-time;Not employed, but looking for work;Not employed, and not looking for work': 31, 'Employed, full-time;Not employed, but looking for work': 32, 'Student, full-time;Not employed, and not looking for work;Student, part-time': 33, 'Employed, full-time;Retired': 34, 'Employed, full-time;Independent contractor, freelancer, or self-employed;Student, part-time;Employed, part-time': 35, 'Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work': 36, 'Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Employed, part-time': 37, 'Not employed, but looking for work;Employed, part-time': 38, 'Employed, full-time;Student, full-time;Employed, part-time': 39, 'Independent contractor, freelancer, or self-employed;Not employed, and not looking for work': 40, 'Not employed, and not looking for work;Student, part-time': 41, 'Student, full-time;Independent contractor, freelancer, or self-employed;Employed, part-time': 42, 'Student, full-time;Student, part-time': 43, 'Student, full-time;Not employed, but looking for work;Student, part-time': 44, 'Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Retired': 45, 'Employed, full-time;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work': 46, 'Employed, full-time;Student, full-time;Independent contractor, freelancer, or self-employed': 47, 'Employed, full-time;Student, full-time;Student, part-time': 48, 'Not employed, but looking for work;Retired': 49, 'Employed, full-time;Student, full-time;Not employed, but looking for work': 50, 'Not employed, and not looking for work;Retired': 51, 'Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Retired': 52, 'Employed, full-time;Not employed, but looking for work;Employed, part-time': 53, 'Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Student, part-time;Employed, part-time;Retired': 54, 'Employed, full-time;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Employed, part-time': 55, 'Student, full-time;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work': 56, 'Employed, full-time;Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Student, part-time;Employed, part-time;Retired': 57, 'Employed, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed': 58, 'Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Student, part-time': 59, 'Student, full-time;Not employed, but looking for work;Retired': 60, 'Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Student, part-time': 61, 'Student, part-time;Retired': 62, 'Student, full-time;Not employed, but looking for work;Not employed, and not looking for work;Student, part-time': 63, 'Employed, full-time;Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Student, part-time;Employed, part-time': 64, 'Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Retired': 65, 'Employed, full-time;Student, full-time;Student, part-time;Employed, part-time': 66, 'Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Student, part-time;Employed, part-time': 67, 'Student, full-time;Not employed, but looking for work;Employed, part-time': 68, 'Employed, full-time;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Student, part-time': 69, 'Independent contractor, freelancer, or self-employed;Student, part-time;Retired': 70, 'Student, full-time;Independent contractor, freelancer, or self-employed;Student, part-time;Employed, part-time': 71, 'Employed, full-time;Independent contractor, freelancer, or self-employed;Student, part-time;Retired': 72, 'Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work': 73, 'Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Employed, part-time': 74, 'Student, full-time;Independent contractor, freelancer, or self-employed;Student, part-time': 75, 'Independent contractor, freelancer, or self-employed;Employed, part-time;Retired': 76, 'Employed, full-time;Not employed, and not looking for work': 77, 'Employed, full-time;Independent contractor, freelancer, or self-employed;Retired': 78, 'Student, full-time;Student, part-time;Employed, part-time': 79, 'Employed, part-time;Retired': 80, 'Employed, full-time;Independent contractor, freelancer, or self-employed;Employed, part-time;Retired': 81, 'Employed, full-time;Student, part-time;Employed, part-time': 82, 'Employed, full-time;Student, full-time;Independent contractor, freelancer, or self-employed;Student, part-time;Employed, part-time;Retired': 83, 'Student, full-time;Student, part-time;Retired': 84, 'Student, full-time;Not employed, and not looking for work;Employed, part-time': 85, 'Employed, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Employed, part-time': 86, 'Not employed, but looking for work;Not employed, and not looking for work;Student, part-time;Employed, part-time': 87, 'Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Employed, part-time': 88, 'Employed, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Employed, part-time': 89, 'Employed, full-time;Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Student, part-time;Employed, part-time': 90, 'Employed, full-time;Student, full-time;Independent contractor, freelancer, or self-employed;Student, part-time;Employed, part-time': 91, 'Not employed, and not looking for work;Employed, part-time': 92, 'Employed, full-time;Student, full-time;Not employed, but looking for work;Student, part-time': 93, 'Employed, full-time;Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Employed, part-time': 94, 'Employed, full-time;Not employed, but looking for work;Not employed, and not looking for work;Employed, part-time': 95, 'Student, full-time;Independent contractor, freelancer, or self-employed;Employed, part-time;Retired': 96, 'Not employed, but looking for work;Student, part-time;Retired': 97, 'Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Student, part-time;Retired': 98, 'Employed, full-time;Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed': 99, 'Not employed, but looking for work;Not employed, and not looking for work;Student, part-time': 100, 'Employed, full-time;Student, full-time;Independent contractor, freelancer, or self-employed;Student, part-time;Retired': 101, 'Employed, full-time;Student, full-time;Not employed, but looking for work;Student, part-time;Employed, part-time': 102, 'Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Student, part-time': 103, 'Employed, full-time;Student, full-time;Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Student, part-time;Employed, part-time;Retired': 104, 'Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Not employed, and not looking for work;Employed, part-time': 105, 'Student, full-time;Retired': 106, 'Employed, full-time;Not employed, but looking for work;Student, part-time': 107, 'Not employed, and not looking for work;Student, part-time;Employed, part-time': 108, 'Not employed, but looking for work;Independent contractor, freelancer, or self-employed;Student, part-time;Retired': 109}\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basic_info(\"Employment\")\n", "\n", "\n", "# mapping the Employment column\n", "print(map_and_fill(\"Employment\"))\n", "df['Employment'] = df['Employment'].fillna(0)\n", "# plot distribution of Employment column\n", "\n", "bin_distribution(\"Employment\", bins=len(df['Employment'].unique())+1)\n", "\n" ] }, { "cell_type": "markdown", "id": "2e8c21efb62e099b", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 11, "id": "f203fd6e494b12f7", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:56.064196Z", "start_time": "2025-10-06T08:13:48.147525Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 Books / Physical media\n", "2 Books / Physical media;Colleague;On the job tr...\n", "3 Books / Physical media;Colleague;On the job tr...\n", "4 Other online resources (e.g., videos, blogs, f...\n", "5 Other online resources (e.g., videos, blogs, f...\n", "Name: LearnCode, dtype: object\n", "----------------------------------------\n", "65433 On the job training;School (i.e., University, ...\n", "65434 NaN\n", "65435 Other online resources (e.g., videos, blogs, f...\n", "65436 On the job training;Other online resources (e....\n", "65437 NaN\n", "Name: LearnCode, dtype: object\n", "----------------------------------------\n", "4949\n", "----------------------------------------\n", "LearnCode\n", "Other online resources (e.g., videos, blogs, forum, online community) 3674\n", "Books / Physical media;Other online resources (e.g., videos, blogs, forum, online community) 2493\n", "Other online resources (e.g., videos, blogs, forum, online community);School (i.e., University, College, etc) 2362\n", "Books / Physical media;Other online resources (e.g., videos, blogs, forum, online community);Online Courses or Certification 2286\n", "Other online resources (e.g., videos, blogs, forum, online community);Online Courses or Certification 2182\n", " ... \n", "Books / Physical media;Colleague;On the job training;School (i.e., University, College, etc);Coding Bootcamp;Friend or family member 1\n", "On the job training;Coding Bootcamp;Friend or family member 1\n", "Books / Physical media;On the job training;School (i.e., University, College, etc);Online Courses or Certification;Friend or family member;Other (please specify): 1\n", "Books / Physical media;Coding Bootcamp;Friend or family member;Other (please specify): 1\n", "Books / Physical media;Colleague;School (i.e., University, College, etc);Other (please specify): 1\n", "Name: count, Length: 418, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "1 NaN\n", "2 Technical documentation;Blogs;Books;Written Tu...\n", "3 Technical documentation;Blogs;Books;Written Tu...\n", "4 Stack Overflow;How-to videos;Interactive tutorial\n", "5 Technical documentation;Blogs;Written Tutorial...\n", "Name: LearnCodeOnline, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 Technical documentation;Stack Overflow;Social ...\n", "65436 Technical documentation;Blogs;Written Tutorial...\n", "65437 NaN\n", "Name: LearnCodeOnline, dtype: object\n", "----------------------------------------\n", "16200\n", "----------------------------------------\n", "LearnCodeOnline\n", "Technical documentation;Blogs;Written Tutorials;Stack Overflow 603\n", "Technical documentation;Blogs;Books;Written Tutorials;Stack Overflow 505\n", "Technical documentation;Written Tutorials;Stack Overflow 384\n", "Technical documentation;Blogs;Books;Written Tutorials;Stack Overflow;Social Media 331\n", "Technical documentation;Blogs;Written Tutorials;Stack Overflow;Social Media 314\n", " ... \n", "Written Tutorials;Stack Overflow;Social Media;How-to videos;Interactive tutorial;AI 1\n", "Blogs;Books;Written Tutorials;Stack Overflow;How-to videos;Certification videos;Online challenges (e.g., daily or weekly coding challenges);Programming Games 1\n", "Technical documentation;Blogs;Books;Coding sessions (live or recorded);Video-based Online Courses;Online challenges (e.g., daily or weekly coding challenges) 1\n", "Written Tutorials;Social Media;How-to videos;Video-based Online Courses;AI;Online challenges (e.g., daily or weekly coding challenges) 1\n", "Blogs;Books;Written Tutorials;Stack Overflow;Social Media;How-to videos;Interactive tutorial;Video-based Online Courses;Written-based Online Courses;Certification videos 1\n", "Name: count, Length: 10853, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "1 NaN\n", "2 API document(s) and/or SDK document(s);User gu...\n", "3 API document(s) and/or SDK document(s);User gu...\n", "4 NaN\n", "5 API document(s) and/or SDK document(s);User gu...\n", "Name: TechDoc, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 API document(s) and/or SDK document(s);AI-powe...\n", "65436 API document(s) and/or SDK document(s);User gu...\n", "65437 NaN\n", "Name: TechDoc, dtype: object\n", "----------------------------------------\n", "24540\n", "----------------------------------------\n", "TechDoc\n", "API document(s) and/or SDK document(s);User guides or README files found in the source repository;Traditional public search engine 6947\n", "API document(s) and/or SDK document(s);User guides or README files found in the source repository;First-party knowledge base;Traditional public search engine 5446\n", "API document(s) and/or SDK document(s);User guides or README files found in the source repository 4251\n", "API document(s) and/or SDK document(s);User guides or README files found in the source repository;Traditional public search engine;AI-powered search/dev tool (free) 3079\n", "API document(s) and/or SDK document(s);User guides or README files found in the source repository;First-party knowledge base;Traditional public search engine;AI-powered search/dev tool (free) 2561\n", " ... \n", "Traditional public search engine;AI-powered search/dev tool (free);AI-powered search/dev tool (paid);Other (please specify): 1\n", "Traditional public search engine;AI-powered search/dev tool (paid);Other (please specify): 1\n", "AI-powered search/dev tool (free);AI-powered search/dev tool (paid);Other (please specify): 1\n", "API document(s) and/or SDK document(s);User guides or README files found in the source repository;First-party knowledge base;AI-powered search/dev tool (free);AI-powered search/dev tool (paid);Other (please specify): 1\n", "API document(s) and/or SDK document(s);First-party knowledge base;Traditional public search engine;AI-powered search/dev tool (free);AI-powered search/dev tool (paid);Other (please specify): 1\n", "Name: count, Length: 113, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "LearnCode 60488 65437\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\qswwq\\AppData\\Local\\Temp\\ipykernel_54308\\4278337842.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " result[_one_hot.columns] = result[_one_hot.columns].fillna(False)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "LearnCodeOnline 49237 65437\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\qswwq\\AppData\\Local\\Temp\\ipykernel_54308\\4278337842.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " result[_one_hot.columns] = result[_one_hot.columns].fillna(False)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TechDoc 40897 65437\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\qswwq\\AppData\\Local\\Temp\\ipykernel_54308\\4278337842.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " result[_one_hot.columns] = result[_one_hot.columns].fillna(False)\n" ] } ], "source": [ "# categorical data, a lot null values\n", "basic_info('LearnCode')\n", "basic_info('LearnCodeOnline')\n", "basic_info('TechDoc')\n", "for col in ['LearnCode', 'LearnCodeOnline', 'TechDoc']:\n", " df = fast_explode(df,target_column=col, fillna='', split=';', prefix=f'{col} ')\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "4afc6615f70ec633", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:56.331137Z", "start_time": "2025-10-06T08:13:56.115671Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 NaN\n", "2 20\n", "3 37\n", "4 4\n", "5 9\n", "Name: YearsCode, dtype: object\n", "----------------------------------------\n", "65433 5\n", "65434 NaN\n", "65435 9\n", "65436 5\n", "65437 NaN\n", "Name: YearsCode, dtype: object\n", "----------------------------------------\n", "5568\n", "----------------------------------------\n", "YearsCode\n", "10 4561\n", "5 3723\n", "6 3496\n", "8 3449\n", "7 3333\n", "4 3290\n", "15 2813\n", "20 2636\n", "12 2559\n", "3 2518\n", "9 2251\n", "14 1822\n", "25 1671\n", "2 1642\n", "11 1636\n", "13 1477\n", "30 1461\n", "16 1401\n", "18 1228\n", "17 1038\n", "40 993\n", "24 870\n", "22 842\n", "35 735\n", "1 712\n", "23 634\n", "26 630\n", "Less than 1 year 569\n", "19 561\n", "21 522\n", "28 512\n", "27 504\n", "32 328\n", "34 293\n", "42 289\n", "38 285\n", "29 274\n", "45 257\n", "More than 50 years 254\n", "36 223\n", "33 219\n", "37 216\n", "44 195\n", "43 173\n", "41 147\n", "31 146\n", "39 145\n", "46 98\n", "50 77\n", "48 64\n", "47 63\n", "49 34\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "1 NaN\n", "2 20\n", "3 37\n", "4 4\n", "5 9\n", "Name: YearsCode, dtype: object\n", "----------------------------------------\n", "65433 5\n", "65434 NaN\n", "65435 9\n", "65436 5\n", "65437 NaN\n", "Name: YearsCode, dtype: object\n", "----------------------------------------\n", "5568\n", "----------------------------------------\n", "YearsCode\n", "10 4561\n", "5 3723\n", "6 3496\n", "8 3449\n", "7 3333\n", "4 3290\n", "15 2813\n", "20 2636\n", "12 2559\n", "3 2518\n", "9 2251\n", "14 1822\n", "25 1671\n", "2 1642\n", "11 1636\n", "13 1477\n", "30 1461\n", "16 1401\n", "18 1228\n", "17 1038\n", "40 993\n", "24 870\n", "22 842\n", "35 735\n", "1 712\n", "23 634\n", "26 630\n", "0 569\n", "19 561\n", "21 522\n", "28 512\n", "27 504\n", "32 328\n", "34 293\n", "42 289\n", "38 285\n", "29 274\n", "45 257\n", "51 254\n", "36 223\n", "33 219\n", "37 216\n", "44 195\n", "43 173\n", "41 147\n", "31 146\n", "39 145\n", "46 98\n", "50 77\n", "48 64\n", "47 63\n", "49 34\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basic_info('YearsCode')\n", "# 2 unique string values 'Less than 1 year' and 'More than 50 years'\n", "# convert them to 0 and 51\n", "df['YearsCode'] = df['YearsCode'].replace({'Less than 1 year': 0, 'More than 50 years': 51})\n", "basic_info(\"YearsCode\")\n", "# need to handle the null values\n", "df['YearsCode'] = df['YearsCode'].fillna(-1)\n", "df['YearsCode'] = df['YearsCode'].astype(int)\n", "bin_distribution('YearsCode', bins=52)" ] }, { "cell_type": "code", "execution_count": 13, "id": "892099598df12429", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:56.548162Z", "start_time": "2025-10-06T08:13:56.348761Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 NaN\n", "2 17\n", "3 27\n", "4 NaN\n", "5 NaN\n", "Name: YearsCodePro, dtype: object\n", "----------------------------------------\n", "65433 3\n", "65434 NaN\n", "65435 5\n", "65436 2\n", "65437 NaN\n", "Name: YearsCodePro, dtype: object\n", "----------------------------------------\n", "13827\n", "----------------------------------------\n", "YearsCodePro\n", "2 4168\n", "3 4093\n", "5 3526\n", "10 3251\n", "4 3215\n", "Less than 1 year 2856\n", "6 2843\n", "1 2639\n", "8 2549\n", "7 2517\n", "12 1777\n", "15 1635\n", "20 1549\n", "9 1493\n", "11 1312\n", "13 1127\n", "14 1082\n", "25 998\n", "16 946\n", "18 867\n", "17 814\n", "30 689\n", "24 632\n", "19 516\n", "22 492\n", "23 448\n", "26 426\n", "27 380\n", "21 380\n", "28 342\n", "35 285\n", "29 196\n", "40 194\n", "32 194\n", "34 169\n", "38 134\n", "33 132\n", "36 119\n", "31 106\n", "37 104\n", "45 56\n", "42 55\n", "39 54\n", "41 51\n", "More than 50 years 50\n", "44 42\n", "43 37\n", "46 21\n", "50 14\n", "48 14\n", "49 11\n", "47 10\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "1 NaN\n", "2 17\n", "3 27\n", "4 NaN\n", "5 NaN\n", "Name: YearsCodePro, dtype: object\n", "----------------------------------------\n", "65433 3\n", "65434 NaN\n", "65435 5\n", "65436 2\n", "65437 NaN\n", "Name: YearsCodePro, dtype: object\n", "----------------------------------------\n", "13827\n", "----------------------------------------\n", "YearsCodePro\n", "2 4168\n", "3 4093\n", "5 3526\n", "10 3251\n", "4 3215\n", "0 2856\n", "6 2843\n", "1 2639\n", "8 2549\n", "7 2517\n", "12 1777\n", "15 1635\n", "20 1549\n", "9 1493\n", "11 1312\n", "13 1127\n", "14 1082\n", "25 998\n", "16 946\n", "18 867\n", "17 814\n", "30 689\n", "24 632\n", "19 516\n", "22 492\n", "23 448\n", "26 426\n", "27 380\n", "21 380\n", "28 342\n", "35 285\n", "29 196\n", "40 194\n", "32 194\n", "34 169\n", "38 134\n", "33 132\n", "36 119\n", "31 106\n", "37 104\n", "45 56\n", "42 55\n", "39 54\n", "41 51\n", "51 50\n", "44 42\n", "43 37\n", "46 21\n", "50 14\n", "48 14\n", "49 11\n", "47 10\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basic_info('YearsCodePro')\n", "df['YearsCodePro'] = df['YearsCodePro'].replace({'Less than 1 year': 0, 'More than 50 years': 51})\n", "basic_info(\"YearsCodePro\")\n", "# need to handle the null values\n", "df['YearsCodePro'] = df['YearsCodePro'].fillna(-1)\n", "\n", "df['YearsCodePro'] = df['YearsCodePro'].astype(int)\n", "bin_distribution('YearsCodePro', bins=52)" ] }, { "cell_type": "code", "execution_count": 14, "id": "a2fc5a1ada8fca48", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:56.920552Z", "start_time": "2025-10-06T08:13:56.589900Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 NaN\n", "2 Developer, full-stack\n", "3 Developer Experience\n", "4 Developer, full-stack\n", "5 Developer, full-stack\n", "Name: DevType, dtype: object\n", "----------------------------------------\n", "65433 Blockchain\n", "65434 NaN\n", "65435 Developer, mobile\n", "65436 Developer, back-end\n", "65437 NaN\n", "Name: DevType, dtype: object\n", "----------------------------------------\n", "5992\n", "----------------------------------------\n", "DevType\n", "Developer, full-stack 18260\n", "Developer, back-end 9928\n", "Student 5102\n", "Developer, front-end 3349\n", "Developer, desktop or enterprise applications 2493\n", "Other (please specify): 2458\n", "Developer, mobile 2021\n", "Developer, embedded applications or devices 1623\n", "Engineering manager 1275\n", "Academic researcher 1238\n", "Data engineer 1118\n", "Data scientist or machine learning specialist 1024\n", "DevOps specialist 1019\n", "Research & Development role 943\n", "Senior Executive (C-Suite, VP, etc.) 837\n", "Developer, game or graphics 706\n", "Cloud infrastructure engineer 634\n", "System administrator 552\n", "Developer, AI 543\n", "Developer, QA or test 525\n", "Data or business analyst 523\n", "Project manager 418\n", "Security professional 356\n", "Educator 355\n", "Scientist 332\n", "Engineer, site reliability 310\n", "Product manager 290\n", "Blockchain 235\n", "Developer Experience 224\n", "Hardware Engineer 200\n", "Designer 182\n", "Database administrator 171\n", "Developer Advocate 105\n", "Marketing or sales professional 96\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "{'Developer, full-stack': 0, 'Developer Experience': 1, 'Student': 2, 'Academic researcher': 3, 'Project manager': 4, 'Developer Advocate': 5, 'Developer, back-end': 6, 'Other (please specify):': 7, 'Developer, front-end': 8, 'Database administrator': 9, 'Developer, desktop or enterprise applications': 10, 'Cloud infrastructure engineer': 11, 'Data scientist or machine learning specialist': 12, 'Research & Development role': 13, 'Developer, embedded applications or devices': 14, 'System administrator': 15, 'DevOps specialist': 16, 'Engineering manager': 17, 'Designer': 18, 'Security professional': 19, 'Senior Executive (C-Suite, VP, etc.)': 20, 'Developer, mobile': 21, 'Developer, game or graphics': 22, 'Data or business analyst': 23, 'Educator': 24, 'Developer, QA or test': 25, 'Product manager': 26, 'Developer, AI': 27, 'Scientist': 28, 'Engineer, site reliability': 29, 'Blockchain': 30, 'Marketing or sales professional': 31, 'Hardware Engineer': 32, 'Data engineer': 33}\n" ] } ], "source": [ "basic_info(\"DevType\")\n", "bin_distribution('DevType', bins=df['DevType'].nunique()+1,rotate=90)\n", "print(map_and_fill(\"DevType\"))\n", "df['DevType'] = df['DevType'].fillna(-1)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "id": "7ee137cea177c849", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:57.148428Z", "start_time": "2025-10-06T08:13:56.942894Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: OrgSize, dtype: object\n", "----------------------------------------\n", "65433 2 to 9 employees\n", "65434 NaN\n", "65435 1,000 to 4,999 employees\n", "65436 20 to 99 employees\n", "65437 NaN\n", "Name: OrgSize, dtype: object\n", "----------------------------------------\n", "17957\n", "----------------------------------------\n", "OrgSize\n", "20 to 99 employees 9754\n", "100 to 499 employees 8694\n", "10,000 or more employees 5558\n", "1,000 to 4,999 employees 5353\n", "2 to 9 employees 4833\n", "10 to 19 employees 4084\n", "500 to 999 employees 3183\n", "Just me - I am a freelancer, sole proprietor, etc. 3086\n", "5,000 to 9,999 employees 1867\n", "I don’t know 1068\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "{'100 to 499 employees': 0, '2 to 9 employees': 1, 'Just me - I am a freelancer, sole proprietor, etc.': 2, '10 to 19 employees': 3, '20 to 99 employees': 4, '5,000 to 9,999 employees': 5, '1,000 to 4,999 employees': 6, 'I don’t know': 7, '10,000 or more employees': 8, '500 to 999 employees': 9}\n" ] } ], "source": [ "basic_info(\"OrgSize\")\n", "bin_distribution('OrgSize', bins=df['OrgSize'].nunique()+1,rotate=90)\n", "# categorical data, a lot null values, fill null with -1 meaning no response/don't have an organization?\n", "print(map_and_fill(\"OrgSize\"))\n", "df['OrgSize'] = df['OrgSize'].fillna(-1)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "e2e9bb28961e0f35", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:57.217103Z", "start_time": "2025-10-06T08:13:57.196062Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: PurchaseInfluence, dtype: object\n", "----------------------------------------\n", "65433 I have some influence\n", "65434 NaN\n", "65435 I have little or no influence\n", "65436 I have some influence\n", "65437 NaN\n", "Name: PurchaseInfluence, dtype: object\n", "----------------------------------------\n", "18031\n", "----------------------------------------\n", "PurchaseInfluence\n", "I have some influence 19197\n", "I have little or no influence 17942\n", "I have a great deal of influence 10267\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'I have some influence': 0, 'I have little or no influence': 1, 'I have a great deal of influence': 2}\n" ] } ], "source": [ "basic_info(\"PurchaseInfluence\")\n", "# categorical data, a lot null values, fill null with -1 meaning no response/don't have an influence\n", "print(map_and_fill(\"PurchaseInfluence\"))\n", "df['PurchaseInfluence'] = df['PurchaseInfluence'].fillna(-1)\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "13d9a7ce4849414b", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T08:13:59.095455Z", "start_time": "2025-10-06T08:13:57.271912Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: BuyNewTool, dtype: object\n", "----------------------------------------\n", "65433 Ask developers I know/work with;Ask a generati...\n", "65434 NaN\n", "65435 Ask developers I know/work with;Ask a generati...\n", "65436 Start a free trial;Ask developers I know/work ...\n", "65437 NaN\n", "Name: BuyNewTool, dtype: object\n", "----------------------------------------\n", "20256\n", "----------------------------------------\n", "BuyNewTool\n", "Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow 6619\n", "Start a free trial;Ask developers I know/work with 4927\n", "Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2 Crowd 3007\n", "Start a free trial 2439\n", "Ask developers I know/work with;Visit developer communities like Stack Overflow 2320\n", " ... \n", "Ask a generative AI tool;Research companies that have emailed me 1\n", "Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2 Crowd;Research companies that have emailed me;Other (please specify): 1\n", "Visit developer communities like Stack Overflow;Research companies that have emailed me;Other (please specify): 1\n", "Ask developers I know/work with;Ask a generative AI tool;Research companies that have emailed me;Other (please specify): 1\n", "Start a free trial;Research companies that have advertised on sites I visit;Research companies that have emailed me;Other (please specify): 1\n", "Name: count, Length: 215, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "BuyNewTool 45181 65437\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: BuildvsBuy, dtype: object\n", "----------------------------------------\n", "65433 Is ready-to-go but also customizable for growt...\n", "65434 NaN\n", "65435 NaN\n", "65436 Is ready-to-go but also customizable for growt...\n", "65437 NaN\n", "Name: BuildvsBuy, dtype: object\n", "----------------------------------------\n", "22079\n", "----------------------------------------\n", "BuildvsBuy\n", "Is ready-to-go but also customizable for growth and targeted use cases 25972\n", "Is set up to be customized and needs to be engineered into a usable product 8760\n", "Out-of-the-box is ready to go with little need for customization 8626\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Is ready-to-go but also customizable for growth and targeted use cases': 0, 'Is set up to be customized and needs to be engineered into a usable product': 1, 'Out-of-the-box is ready to go with little need for customization': 2}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\qswwq\\AppData\\Local\\Temp\\ipykernel_54308\\4278337842.py:67: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", " result[_one_hot.columns] = result[_one_hot.columns].fillna(False)\n" ] } ], "source": [ "basic_info(\"BuyNewTool\")# another need to expand categorial data\n", "df = fast_explode(df,target_column='BuyNewTool', fillna='', split=';', prefix='buy_new_tool ')\n", "\n", "basic_info('BuildvsBuy')\n", "# don't plot this for the name is so long\n", "# mapping the BuildvsBuy column\n", "print(map_and_fill(\"BuildvsBuy\"))\n", "# filling null values with -1, meaning?\n", "#TODO meaning?\n", "df['BuildvsBuy'] = df['BuildvsBuy'].fillna(-1)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "77d68a51219472a8", "metadata": { "ExecuteTime": { "start_time": "2025-10-06T08:13:59.125666Z" }, "jupyter": { "is_executing": true } }, "outputs": [], "source": [ "basic_info(\"LanguageHaveWorkedWith\")\n", "drop_columns = []\n", "explode_columns = []\n", "for col in df.columns:\n", " if 'HaveWorkedWith' in col:\n", " explode_columns.append(col)\n", "\n", " elif 'Admired' in col or 'WantTo' in col or 'SO' in col or 'AI' in col:\n", " drop_columns.append(col)\n", "\n", "print(drop_columns)\n", "print(explode_columns)\n", "# drop first\n", "df.drop(columns=drop_columns, inplace=True)\n", "\n", "# expand\n", "for col in explode_columns:\n", " df = fast_explode(df,target_column=col, fillna='', split=';', prefix=f'{col} ')\n", "print(df.isnull().sum())\n", "\n", "# Splits each string in LanguageHaveWorkedWith into a list of languages，Creates a new column called Language in the DataFrame. Converts each list in Language into multiple rows.\n", "# df = fast_explode(df,target_column='LanguageHaveWorkedWith', fillna='', split=';', prefix='worked_with ')\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "faf08985cb627fd4", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:38.495139Z", "start_time": "2025-10-06T06:59:38.473677Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 NaN\n", "2 Individual contributor\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: ICorPM, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 Individual contributor\n", "65437 NaN\n", "Name: ICorPM, dtype: object\n", "----------------------------------------\n", "35636\n", "----------------------------------------\n", "ICorPM\n", "Individual contributor 25888\n", "People manager 3913\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Individual contributor': 0, 'People manager': 1}\n" ] } ], "source": [ "basic_info('ICorPM') #Individual Contributor or People Manager\n", "# categorical data, a lot null values, fill null with -1\n", "print(map_and_fill(\"ICorPM\"))\n", "df['ICorPM'] = df['ICorPM'].fillna(-1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "4dcc64ee46092e6d", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:38.858384Z", "start_time": "2025-10-06T06:59:38.581569Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 NaN\n", "2 17.0\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: WorkExp, dtype: float64\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 5.0\n", "65437 NaN\n", "Name: WorkExp, dtype: float64\n", "----------------------------------------\n", "35779\n", "----------------------------------------\n", "WorkExp\n", "3.0 2144\n", "5.0 2050\n", "10.0 2029\n", "2.0 1885\n", "4.0 1767\n", "6.0 1600\n", "7.0 1581\n", "8.0 1554\n", "1.0 1461\n", "15.0 1250\n", "12.0 1249\n", "20.0 1074\n", "9.0 982\n", "11.0 823\n", "25.0 794\n", "13.0 740\n", "14.0 655\n", "16.0 600\n", "17.0 596\n", "18.0 587\n", "30.0 461\n", "24.0 382\n", "19.0 330\n", "22.0 323\n", "23.0 269\n", "26.0 254\n", "21.0 251\n", "27.0 217\n", "35.0 217\n", "28.0 210\n", "0.0 192\n", "40.0 139\n", "29.0 117\n", "32.0 101\n", "33.0 90\n", "36.0 89\n", "34.0 84\n", "31.0 83\n", "37.0 60\n", "38.0 55\n", "50.0 53\n", "42.0 47\n", "45.0 46\n", "39.0 37\n", "41.0 36\n", "43.0 34\n", "44.0 32\n", "46.0 12\n", "48.0 7\n", "47.0 5\n", "49.0 4\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "1 NaN\n", "2 Agree\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Knowledge_1, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Knowledge_1, dtype: object\n", "----------------------------------------\n", "36773\n", "----------------------------------------\n", "Knowledge_1\n", "Agree 13454\n", "Strongly agree 10310\n", "Neither agree nor disagree 2392\n", "Disagree 1813\n", "Strongly disagree 695\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "1 NaN\n", "2 Agree\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Knowledge_1, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Knowledge_1, dtype: object\n", "----------------------------------------\n", "36773\n", "----------------------------------------\n", "Knowledge_1\n", "Agree 13454\n", "Strongly agree 10310\n", "Neither agree nor disagree 2392\n", "Disagree 1813\n", "Strongly disagree 695\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Agree': 0, 'Strongly agree': 1, 'Neither agree nor disagree': 2, 'Disagree': 3, 'Strongly disagree': 4}\n", "1 NaN\n", "2 Disagree\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Knowledge_2, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Knowledge_2, dtype: object\n", "----------------------------------------\n", "37416\n", "----------------------------------------\n", "Knowledge_2\n", "Agree 9060\n", "Neither agree nor disagree 7284\n", "Disagree 5958\n", "Strongly agree 3613\n", "Strongly disagree 2106\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Disagree': 0, 'Agree': 1, 'Neither agree nor disagree': 2, 'Strongly agree': 3, 'Strongly disagree': 4}\n", "1 NaN\n", "2 Agree\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Knowledge_3, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Knowledge_3, dtype: object\n", "----------------------------------------\n", "37342\n", "----------------------------------------\n", "Knowledge_3\n", "Agree 10979\n", "Neither agree nor disagree 7421\n", "Disagree 5522\n", "Strongly agree 2730\n", "Strongly disagree 1443\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Agree': 0, 'Strongly agree': 1, 'Neither agree nor disagree': 2, 'Disagree': 3, 'Strongly disagree': 4}\n", "1 NaN\n", "2 Agree\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Knowledge_4, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Knowledge_4, dtype: object\n", "----------------------------------------\n", "37407\n", "----------------------------------------\n", "Knowledge_4\n", "Agree 12510\n", "Neither agree nor disagree 7216\n", "Disagree 4188\n", "Strongly agree 3257\n", "Strongly disagree 859\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Agree': 0, 'Disagree': 1, 'Strongly agree': 2, 'Neither agree nor disagree': 3, 'Strongly disagree': 4}\n", "1 NaN\n", "2 Agree\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Knowledge_5, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Knowledge_5, dtype: object\n", "----------------------------------------\n", "37557\n", "----------------------------------------\n", "Knowledge_5\n", "Agree 14612\n", "Neither agree nor disagree 5097\n", "Strongly agree 5005\n", "Disagree 2610\n", "Strongly disagree 556\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Agree': 0, 'Neither agree nor disagree': 1, 'Strongly agree': 2, 'Disagree': 3, 'Strongly disagree': 4}\n", "1 NaN\n", "2 Neither agree nor disagree\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Knowledge_6, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Knowledge_6, dtype: object\n", "----------------------------------------\n", "37573\n", "----------------------------------------\n", "Knowledge_6\n", "Agree 9934\n", "Neither agree nor disagree 7391\n", "Disagree 5900\n", "Strongly agree 3673\n", "Strongly disagree 966\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Neither agree nor disagree': 0, 'Strongly agree': 1, 'Strongly disagree': 2, 'Agree': 3, 'Disagree': 4}\n", "1 NaN\n", "2 Disagree\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Knowledge_7, dtype: object\n", 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"Knowledge_8\n", "Agree 10759\n", "Neither agree nor disagree 7046\n", "Disagree 5159\n", "Strongly agree 3067\n", "Strongly disagree 1727\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Agree': 0, 'Neither agree nor disagree': 1, 'Disagree': 2, 'Strongly agree': 3, 'Strongly disagree': 4}\n", "1 NaN\n", "2 Agree\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Knowledge_9, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Knowledge_9, dtype: object\n", "----------------------------------------\n", "37802\n", "----------------------------------------\n", "Knowledge_9\n", "Agree 6657\n", "Strongly disagree 6379\n", "Neither agree nor disagree 6155\n", "Disagree 5890\n", "Strongly agree 2554\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Agree': 0, 'Neither agree nor disagree': 1, 'Strongly disagree': 2, 'Strongly agree': 3, 'Disagree': 4}\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Frequency_1, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Frequency_1, dtype: object\n", "----------------------------------------\n", "37068\n", "----------------------------------------\n", "Frequency_1\n", "1-2 times a week 16989\n", "Never 7607\n", "3-5 times a week 2721\n", "6-10 times a week 569\n", "10+ times a week 483\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'3-5 times a week': 0, '1-2 times a week': 1, 'Never': 2, '6-10 times a week': 3, '10+ times a week': 4}\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Frequency_2, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Frequency_2, dtype: object\n", "----------------------------------------\n", "37073\n", "----------------------------------------\n", "Frequency_2\n", "1-2 times a week 12578\n", "3-5 times a week 6514\n", "10+ times a week 3470\n", "6-10 times a week 3069\n", "Never 2733\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'6-10 times a week': 0, '1-2 times a week': 1, '3-5 times a week': 2, '10+ times a week': 3, 'Never': 4}\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Frequency_3, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Frequency_3, dtype: object\n", "----------------------------------------\n", "37727\n", "----------------------------------------\n", "Frequency_3\n", "1-2 times a week 14426\n", "Never 8206\n", "3-5 times a week 3447\n", "6-10 times a week 863\n", "10+ times a week 768\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'6-10 times a week': 0, '1-2 times a week': 1, '3-5 times a week': 2, 'Never': 3, '10+ times a week': 4}\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: TimeSearching, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: TimeSearching, dtype: object\n", "----------------------------------------\n", "36526\n", "----------------------------------------\n", "TimeSearching\n", "30-60 minutes a day 10951\n", "15-30 minutes a day 7805\n", "60-120 minutes a day 5275\n", "Less than 15 minutes a day 2689\n", "Over 120 minutes a day 2191\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'30-60 minutes a day': 0, '60-120 minutes a day': 1, '15-30 minutes a day': 2, 'Less than 15 minutes a day': 3, 'Over 120 minutes a day': 4}\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: TimeAnswering, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: TimeAnswering, dtype: object\n", "----------------------------------------\n", "36593\n", "----------------------------------------\n", "TimeAnswering\n", "15-30 minutes a day 9341\n", "30-60 minutes a day 8642\n", "Less than 15 minutes a day 5918\n", "60-120 minutes a day 3704\n", "Over 120 minutes a day 1239\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'60-120 minutes a day': 0, '15-30 minutes a day': 1, '30-60 minutes a day': 2, 'Less than 15 minutes a day': 3, 'Over 120 minutes a day': 4}\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Frustration, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Frustration, dtype: object\n", "----------------------------------------\n", "37186\n", "----------------------------------------\n", "Frustration\n", "None of these 2364\n", "Amount of technical debt 2067\n", "Amount of technical debt;Reliability of tools/systems used in work 831\n", "Amount of technical debt;Complexity of tech stack for deployment;Complexity of tech stack for build 666\n", "Amount of technical debt;Tracking my work 556\n", " ... \n", "Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work 1\n", "Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in 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my contributions': 60, 'Amount of technical debt;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 61, 'Number of software tools in use;Complexity of tech stack for deployment;Reliability of tools/systems used in work': 62, 'Complexity of tech stack for build;Reliability of tools/systems used in work': 63, 'Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 64, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 65, 'Amount of technical debt;Complexity of tech stack for 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being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 72, 'Number of software tools in use;Reliability of tools/systems used in work': 73, 'Amount of technical debt;Tracking my work;Complexity of tech stack for build;Reliability of tools/systems used in work': 74, 'Amount of technical debt;Tracking my work;Complexity of tech stack for build;Patching/updating core components;Reliability of tools/systems used in work': 75, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for build;Patching/updating core components': 76, 'Tracking my work;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 77, 'Number of software tools in use': 78, 'Amount of technical debt;Tracking my work;Showing my contributions;Reliability of tools/systems used in work': 79, 'Tracking my work;Reliability of tools/systems used in work': 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my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 90, 'Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Reliability of tools/systems used in work': 91, 'Tracking my work;Maintaining security of code being produced;Patching/updating core components': 92, 'Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work': 93, 'Amount of technical debt;Tracking my work;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 94, 'Maintaining security of code being produced': 95, 'Amount of technical debt;Tracking my 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contributions;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work': 659, 'Number of software tools in use;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 660, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work': 661, 'Number of software tools in use;Showing my contributions;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 662, 'Amount of technical debt;Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 663, 'Amount of technical debt;Number of software tools in use;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 664, 'Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 665, 'Amount of technical debt;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 666, 'Number of software tools in use;Tracking my work;Maintaining security of code being produced;Reliability of tools/systems used in work': 667, 'Amount of technical debt;Number of software tools in use;Complexity of tech stack for build;Maintaining security of code being produced;Reliability of tools/systems used in work': 668, 'Amount of technical debt;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced': 669, 'Amount of technical debt;Number of software tools in use;Tracking my work;Maintaining security of code being produced;Maintaining security of systems/platforms used in work': 670, 'Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 671, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for build;Patching/updating core components;Reliability of tools/systems used in work': 672, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced': 673, 'Amount of technical debt;Number of software tools in use;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 674, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 675, 'Amount of technical debt;Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 676, 'Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Patching/updating core components;Reliability of tools/systems used in work': 677, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Patching/updating core components;Maintaining security of systems/platforms used in work': 678, 'Number of software tools in use;Tracking my work;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 679, 'Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Maintaining security of systems/platforms used in work': 680, 'Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced': 681, 'Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment': 682, 'Amount of technical debt;Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Patching/updating core components;Reliability of tools/systems used in work': 683, 'Tracking my work;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work': 684, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 685, 'Amount of technical debt;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 686, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 687, 'Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 688, 'Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 689, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 690, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Patching/updating core components;Reliability of tools/systems used in work': 691, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 692, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 693, 'Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Patching/updating core components': 694, 'Complexity of tech stack for build;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 695, 'Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 696, 'Number of software tools in use;Tracking my work;Showing my contributions;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 697, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced': 698, 'Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 699, 'Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced': 700, 'Number of software tools in use;Showing my contributions;Patching/updating core components;Reliability of tools/systems used in work': 701, 'Complexity of tech stack for deployment;Patching/updating core components;Maintaining security of systems/platforms used in work': 702, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for build;Reliability of tools/systems used in work': 703, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components': 704, 'Amount of technical debt;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 705, 'Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Patching/updating core components;Maintaining security of systems/platforms used in work': 706, 'Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work;None of these': 707, 'Complexity of tech stack for build;None of these': 708, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components': 709, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 710, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 711, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 712, 'Number of software tools in use;Showing my contributions;Complexity of tech stack for build;Reliability of tools/systems used in work': 713, 'Number of software tools in use;Complexity of tech stack for build;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 714, 'Amount of technical debt;Tracking my work;Showing my contributions;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 715, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work;None of these': 716, 'Number of software tools in use;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components': 717, 'Tracking my work;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 718, 'Number of software tools in use;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 719, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Maintaining security of code being produced': 720, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 721, 'Number of software tools in use;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 722, 'Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 723, 'Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Reliability of tools/systems used in work': 724, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Maintaining security of code being produced;Reliability of tools/systems used in work': 725, 'Amount of technical debt;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 726, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 727, 'Number of software tools in use;Tracking my work;Maintaining security of code being produced;Maintaining security of systems/platforms used in work': 728, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components': 729, 'Number of software tools in use;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 730, 'Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 731, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 732, 'Number of software tools in use;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 733, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 734, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components': 735, 'Number of software tools in use;Tracking my work;Maintaining security of code being produced;Patching/updating core components': 736, 'Amount of technical debt;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 737, 'Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 738, 'Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 739, 'Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work': 740, 'Amount of technical debt;Number of software tools in use;Tracking my work;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 741, 'Amount of technical debt;Number of software tools in use;None of these': 742, 'Number of software tools in use;Maintaining security of code being produced;Patching/updating core components': 743, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Maintaining security of code being produced;Patching/updating core components': 744, 'Tracking my work;Showing my contributions;Complexity of tech stack for build;Patching/updating core components': 745, 'Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components': 746, 'Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 747, 'Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Patching/updating core components;Maintaining security of systems/platforms used in work': 748, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 749, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Reliability of tools/systems used in work': 750, 'Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 751, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Reliability of tools/systems used in work': 752, 'Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Patching/updating core components': 753, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for build;Reliability of tools/systems used in work': 754, 'Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Patching/updating core components;Reliability of tools/systems used in work': 755, 'Amount of technical debt;Number of software tools in use;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 756, 'Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Patching/updating core components;Maintaining security of systems/platforms used in work': 757, 'Number of software tools in use;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work': 758, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced': 759, 'Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Reliability of tools/systems used in work': 760, 'Number of software tools in use;Complexity of tech stack for deployment;Patching/updating core components;Reliability of tools/systems used in work': 761, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Patching/updating core components;Reliability of tools/systems used in work': 762, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Reliability of tools/systems used in work': 763, 'Amount of technical debt;Number of software tools in use;Tracking my work;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 764, 'Showing my contributions;None of these': 765, 'Amount of technical debt;Number of software tools in use;Complexity of tech stack for build;Patching/updating core components;Maintaining security of systems/platforms used in work': 766, 'Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 767, 'Complexity of tech stack for deployment;Complexity of tech stack for build;Patching/updating core components;Maintaining security of systems/platforms used in work': 768, 'Showing my contributions;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 769, 'Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Reliability of tools/systems used in work': 770, 'Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Reliability of tools/systems used in work': 771, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 772, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 773, 'Amount of technical debt;Tracking my work;Complexity of tech stack for deployment;Patching/updating core components;Reliability of tools/systems used in work': 774, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 775, 'Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment': 776, 'Amount of technical debt;Number of software tools in use;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work': 777, 'Amount of technical debt;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 778, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 779, 'Amount of technical debt;Number of software tools in use;Tracking my work;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 780, 'Amount of technical debt;Number of software tools in use;Maintaining security of code being produced;None of these': 781, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 782, 'Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 783, 'Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for build;Patching/updating core components;Reliability of tools/systems used in work': 784, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 785, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced': 786, 'Amount of technical debt;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Reliability of tools/systems used in work': 787, 'Amount of technical debt;Number of software tools in use;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 788, 'Amount of technical debt;Complexity of tech stack for deployment;Complexity of tech stack for build;Reliability of tools/systems used in work;None of these': 789, 'Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 790, 'Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for build;Reliability of tools/systems used in work': 791, 'Complexity of tech stack for deployment;Complexity of tech stack for build;None of these': 792, 'Amount of technical debt;Number of software tools in use;Complexity of tech stack for deployment;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 793, 'Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 794, 'Tracking my work;Showing my contributions;Patching/updating core components;Maintaining security of systems/platforms used in work': 795, 'Amount of technical debt;Number of software tools in use;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 796, 'Number of software tools in use;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced': 797, 'Number of software tools in use;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 798, 'Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work': 799, 'Number of software tools in use;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work': 800, 'Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 801, 'Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 802, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 803, 'Amount of technical debt;Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 804, 'Amount of technical debt;Number of software tools in use;Showing my contributions;Reliability of tools/systems used in work;None of these': 805, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 806, 'Amount of technical debt;Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 807, 'Number of software tools in use;Tracking my work;Complexity of tech stack for build;Patching/updating core components;Reliability of tools/systems used in work': 808, 'Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Reliability of tools/systems used in work': 809, 'Tracking my work;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 810, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 811, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 812, 'Tracking my work;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 813, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for build;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 814, 'Amount of technical debt;Complexity of tech stack for build;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 815, 'Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 816, 'Amount of technical debt;Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Patching/updating core components': 817, 'Tracking my work;Showing my contributions;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 818, 'Amount of technical debt;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Patching/updating core components;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 819, 'Tracking my work;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of code being produced;Maintaining security of systems/platforms used in work': 820, 'Showing my contributions;Complexity of tech stack for deployment;Maintaining security of systems/platforms used in work': 821, 'Amount of technical debt;Number of software tools in use;Complexity of tech stack for deployment;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 822, 'Number of software tools in use;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 823, 'Amount of technical debt;Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 824, 'Amount of technical debt;Reliability of tools/systems used in 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my contributions;Maintaining security of code being produced;Patching/updating core components;Reliability of tools/systems used in work': 847, 'Maintaining security of systems/platforms used in work;None of these': 848, 'Amount of technical debt;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work;Reliability of tools/systems used in work': 849, 'Number of software tools in use;Tracking my work;Complexity of tech stack for deployment;Maintaining security of code being produced;Patching/updating core components;Maintaining security of systems/platforms used in work': 850, 'Amount of technical debt;Number of software tools in use;Tracking my work;Showing my contributions;Complexity of tech stack for deployment;Complexity of tech stack for build;Maintaining security of systems/platforms used in work': 851, 'Number of software tools in use;Tracking my work;Showing my 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(more often) continuous delivery;Innersource initiative': 502, 'Automated testing;Observability tools;AI-assisted technology tool(s);Developer portal or other central places to find tools/services': 503, 'Automated testing;Observability tools;Developer portal or other central places to find tools/services;None of these': 504, 'Automated testing;Observability tools;None of these': 505, 'Knowledge sharing community;AI-assisted technology tool(s);Microservices;Developer portal or other central places to find tools/services;Innersource initiative': 506, 'DevOps function;Observability tools;AI-assisted technology tool(s);Developer portal or other central places to find tools/services': 507, 'Knowledge sharing community;Microservices;None of these': 508, 'DevOps function;Automated testing;Observability tools;Knowledge sharing community;AI-assisted technology tool(s);None of these': 509, 'Knowledge sharing community;AI-assisted technology tool(s);Microservices;Developer portal or other central places to find tools/services;Continuous integration (CI) and (more often) continuous delivery;Innersource initiative': 510, 'Automated testing;AI-assisted technology tool(s);Microservices;Developer portal or other central places to find tools/services;Innersource initiative': 511, 'DevOps function;Automated testing;AI-assisted technology tool(s);Microservices;Developer portal or other central places to find tools/services;Innersource initiative': 512, 'DevOps function;Automated testing;Observability tools;Developer portal or other central places to find tools/services;Innersource initiative': 513}\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: ProfessionalCloud, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: ProfessionalCloud, dtype: object\n", "----------------------------------------\n", "36946\n", "----------------------------------------\n", "ProfessionalCloud\n", "Hybrid (on-prem and cloud) 13230\n", "Cloud only (single or multi-cloud) 11256\n", "On-prem 4005\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Cloud only (single or multi-cloud)': 0, 'Hybrid (on-prem and cloud)': 1, 'On-prem': 2}\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: ProfessionalQuestion, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: ProfessionalQuestion, dtype: object\n", "----------------------------------------\n", "36630\n", "----------------------------------------\n", "ProfessionalQuestion\n", "Traditional public search engine 15813\n", "A coworker 5324\n", "AI-powered search (free) 2251\n", "AI-powered search (paid) 1947\n", "Slack search 1053\n", "Do search of internal share drives/storage locations for documentation (i.e., not a structured knowledge base) 937\n", "Internal Developer portal 777\n", "Other: 559\n", "Microsoft Teams search 146\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Internal Developer portal': 0, 'A coworker': 1, 'Traditional public search engine': 2, 'Slack search': 3, 'Other:': 4, 'AI-powered search (paid)': 5, 'AI-powered search (free)': 6, 'Do search of internal share drives/storage locations for documentation (i.e., not a structured knowledge base)': 7, 'Microsoft Teams search': 8}\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Industry, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: Industry, dtype: object\n", "----------------------------------------\n", "36579\n", "----------------------------------------\n", "Industry\n", "Software Development 11918\n", "Other: 3077\n", "Fintech 1641\n", "Internet, Telecomm or Information Services 1629\n", "Banking/Financial Services 1371\n", "Healthcare 1277\n", "Manufacturing 1265\n", "Retail and Consumer Services 1264\n", "Government 962\n", "Media & Advertising Services 894\n", "Higher Education 890\n", "Transportation, or Supply Chain 859\n", "Computer Systems Design and Services 844\n", "Energy 578\n", "Insurance 389\n", "Name: count, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "{'Healthcare': 0, 'Software Development': 1, 'Banking/Financial Services': 2, 'Other:': 3, 'Media & Advertising Services': 4, 'Insurance': 5, 'Internet, Telecomm or Information Services': 6, 'Higher Education': 7, 'Energy': 8, 'Fintech': 9, 'Retail and Consumer Services': 10, 'Manufacturing': 11, 'Government': 12, 'Computer Systems Design and Services': 13, 'Transportation, or Supply Chain': 14}\n" ] } ], "source": [ "basic_info('WorkExp')\n", "# numerical categorical data, a lot null values, fill null with -1\n", "df['WorkExp'] = df['WorkExp'].fillna(-1)\n", "\n", "basic_info('Knowledge_1')\n", "# from Knowledge_1 ~9 check the survey question, it is the long question about different things\n", "for i in range(1,10):\n", " basic_info(f'Knowledge_{i}')\n", " print(map_and_fill(f'Knowledge_{i}'))\n", " df[f'Knowledge_{i}'] = df[f'Knowledge_{i}'].fillna(-1)\n", "\n", "# Frequency_1 ~ 3 also\n", "\n", "for i in range(1,4):\n", " basic_info(f'Frequency_{i}')\n", " print(map_and_fill(f'Frequency_{i}'))\n", " df[f'Frequency_{i}'] = df[f'Frequency_{i}'].fillna(-1)\n", "\n", "basic_info('TimeSearching')\n", "print(map_and_fill(\"TimeSearching\"))\n", "df['TimeSearching'] = df['TimeSearching'].fillna(-1)\n", "\n", "basic_info('TimeAnswering')\n", "print(map_and_fill(\"TimeAnswering\"))\n", "df['TimeAnswering'] = df['TimeAnswering'].fillna(-1)\n", "\n", "basic_info('Frustration')\n", "# change it's name to Challenge_Frustration\n", "df.rename(columns={'Frustration': 'Challenge_Frustration'}, inplace=True)\n", "print(map_and_fill(\"Challenge_Frustration\"))\n", "df['Challenge_Frustration'] = df['Challenge_Frustration'].fillna(-1)\n", "\n", "\n", "basic_info('ProfessionalTech')\n", "# change name to Company_ProfessionalTech\n", "df.rename(columns={'ProfessionalTech': 'Company_ProfessionalTech'}, inplace=True)\n", "print(map_and_fill(\"Company_ProfessionalTech\"))\n", "df['Company_ProfessionalTech'] = df['Company_ProfessionalTech'].fillna(-1)\n", "\n", "basic_info('ProfessionalCloud')\n", "print(map_and_fill(\"ProfessionalCloud\"))\n", "df['ProfessionalCloud'] = df['ProfessionalCloud'].fillna(-1)\n", "\n", "\n", "basic_info('ProfessionalQuestion')\n", "# change name to FirstAnswer(er)_ProfessionalQuestion\n", "df.rename(columns={'ProfessionalQuestion': 'FirstAnswerer_ProfessionalQuestion'}, inplace=True)\n", "print(map_and_fill(\"FirstAnswerer_ProfessionalQuestion\"))\n", "df['FirstAnswerer_ProfessionalQuestion'] = df['FirstAnswerer_ProfessionalQuestion'].fillna(-1)\n", "\n", "\n", "basic_info('Industry')\n", "print(map_and_fill(\"Industry\"))\n", "df['Industry'] = df['Industry'].fillna(-1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "e192d8d9a341f631", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:40.535282Z", "start_time": "2025-10-06T06:59:38.875733Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: TechEndorse, dtype: object\n", "----------------------------------------\n", "65433 APIs;Integrated developer community;Quality su...\n", "65434 NaN\n", "65435 NaN\n", "65436 APIs;Customization;Connected to an open source...\n", "65437 NaN\n", "Name: TechEndorse, dtype: object\n", "----------------------------------------\n", "21769\n", "----------------------------------------\n", "TechEndorse\n", "APIs;Customization;Reputation for quality and excellence 1499\n", "APIs;Customization;Integrated developer community;Quality support system;Reputation for quality and excellence;Frequent updates to features;Connected to an open source project 1227\n", "APIs;Customization;Reputation for quality and excellence;Connected to an open source project 1206\n", "APIs;Customization;Quality support system;Reputation for quality and excellence 1144\n", "APIs;Customization;Reputation for quality and excellence;Frequent updates to features 1040\n", " ... \n", "Integrated developer community;Quality support system;Frequent updates to features;Connected to an open source project;Other: 1\n", "Integrated developer community;AI tool integration;Reputation for quality and excellence;Other: 1\n", "APIs;Customization;Integrated developer community;AI tool integration;Frequent updates to features;Other: 1\n", "Customization;Integrated developer community;Quality support system;Reputation for quality and excellence;Other: 1\n", "APIs;Customization;Integrated developer community;Quality support system;AI tool integration;Frequent updates to features;Connected to an open source project;Other: 1\n", "Name: count, Length: 386, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "1 United States of America\n", "2 United Kingdom of Great Britain and Northern I...\n", "3 United Kingdom of Great Britain and Northern I...\n", "4 Canada\n", "5 Norway\n", "Name: Country, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 Germany\n", "65437 NaN\n", "Name: Country, dtype: object\n", "----------------------------------------\n", "6507\n", "----------------------------------------\n", "Country\n", "United States of America 11095\n", "Germany 4947\n", "India 4231\n", "United Kingdom of Great Britain and Northern Ireland 3224\n", "Ukraine 2672\n", " ... \n", "Micronesia, Federated States of... 1\n", "Nauru 1\n", "Chad 1\n", "Djibouti 1\n", "Solomon Islands 1\n", "Name: count, Length: 185, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: Currency, dtype: object\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 EUR European Euro\n", "65437 NaN\n", "Name: Currency, dtype: object\n", "----------------------------------------\n", "18753\n", "----------------------------------------\n", "Currency\n", "EUR European Euro 12730\n", "USD\\tUnited States dollar 10939\n", "INR\\tIndian rupee 2905\n", "GBP\\tPound sterling 2614\n", "CAD\\tCanadian dollar 1639\n", " ... \n", "SZL\\tSwazi lilangeni 1\n", "none\\tCook Islands dollar 1\n", "AWG\\tAruban florin 1\n", "BBD\\tBarbadian dollar 1\n", "BMD\\tBermudian dollar 1\n", "Name: count, Length: 142, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 NaN\n", "5 NaN\n", "Name: CompTotal, dtype: float64\n", "----------------------------------------\n", "65433 NaN\n", "65434 NaN\n", "65435 NaN\n", "65436 NaN\n", "65437 NaN\n", "Name: CompTotal, dtype: float64\n", "----------------------------------------\n", "31697\n", "----------------------------------------\n", "CompTotal\n", "100000.0 939\n", "60000.0 839\n", "120000.0 793\n", "80000.0 728\n", "50000.0 705\n", " ... \n", "83363.0 1\n", "4848000.0 1\n", "42720.0 1\n", "6770000.0 1\n", "578000.0 1\n", "Name: count, Length: 3337, dtype: int64\n", "----------------------------------------\n", "****************************************\n", "[nan 'PKR\\tPakistani rupee' 'EUR European Euro'\n", " 'USD\\tUnited States dollar' 'BRL\\tBrazilian real' 'GBP\\tPound sterling'\n", " 'RON\\tRomanian leu' 'INR\\tIndian rupee' 'CHF\\tSwiss franc'\n", " 'TRY\\tTurkish lira' 'RUB\\tRussian ruble' 'ZAR\\tSouth African rand'\n", " 'CZK\\tCzech koruna' 'CAD\\tCanadian dollar' 'IRR\\tIranian rial'\n", " 'MXN\\tMexican peso' 'UAH\\tUkrainian hryvnia' 'DOP\\tDominican peso'\n", " 'KMF\\tComorian franc' 'RSD\\tSerbian dinar' 'PEN\\tPeruvian sol'\n", " 'MAD\\tMoroccan dirham' 'GEL\\tGeorgian lari' 'PLN\\tPolish zloty'\n", " 'SAR\\tSaudi Arabian riyal' 'SEK\\tSwedish krona' 'BGN\\tBulgarian lev'\n", " 'KZT\\tKazakhstani tenge' 'SGD\\tSingapore dollar' 'JOD\\tJordanian dinar'\n", " 'JPY\\tJapanese yen' 'NOK\\tNorwegian krone' 'ILS\\tIsraeli new shekel'\n", " 'DKK\\tDanish krone' 'THB\\tThai baht' 'RWF\\tRwandan franc'\n", " 'HUF\\tHungarian forint' 'BDT\\tBangladeshi taka' 'IDR\\tIndonesian rupiah'\n", " 'BAM\\tBosnia and Herzegovina convertible mark' 'PHP\\tPhilippine peso'\n", " 'XOF\\tWest African CFA franc' 'DZD\\tAlgerian dinar' 'TND\\tTunisian dinar'\n", " 'MYR\\tMalaysian ringgit' 'BHD\\tBahraini dinar' 'ARS\\tArgentine peso'\n", " 'NIO\\tNicaraguan cordoba' 'AFN\\tAfghan afghani' 'UYU\\tUruguayan peso'\n", " 'BYN\\tBelarusian ruble' 'COP\\tColombian peso' 'ALL\\tAlbanian lek'\n", " 'AUD\\tAustralian dollar' 'UZS\\tUzbekistani som' 'NZD\\tNew Zealand dollar'\n", " 'MVR\\tMaldivian rufiyaa' 'GHS\\tGhanaian cedi'\n", " 'AED United Arab Emirates dirham' 'NGN\\tNigerian naira'\n", " 'FJD\\tFijian dollar' 'GTQ\\tGuatemalan quetzal' 'UGX\\tUgandan shilling'\n", " 'CRC\\tCosta Rican colon' 'MUR\\tMauritian rupee' 'KES\\tKenyan shilling'\n", " 'EGP\\tEgyptian pound' 'TWD\\tNew Taiwan dollar' 'AMD\\tArmenian dram'\n", " 'KRW\\tSouth Korean won' 'CLP\\tChilean peso' 'ISK\\tIcelandic krona'\n", " 'HNL\\tHonduran lempira' 'HKD\\tHong Kong dollar'\n", " 'CNY\\tChinese Yuan Renminbi' 'VND\\tVietnamese dong'\n", " 'BSD\\tBahamian dollar' 'LKR\\tSri Lankan rupee' 'BTN\\tBhutanese ngultrum'\n", " 'MNT\\tMongolian tugrik' 'KHR\\tCambodian riel' 'NPR\\tNepalese rupee'\n", " 'BOB\\tBolivian boliviano' 'ETB\\tEthiopian birr' 'AOA\\tAngolan kwanza'\n", " 'MKD\\tMacedonian denar' 'SYP\\tSyrian pound' 'NAD\\tNamibian dollar'\n", " 'ANG Netherlands Antillean guilder' 'TJS\\tTajikistani somoni'\n", " 'BIF\\tBurundi franc' 'JMD\\tJamaican dollar'\n", " 'TTD\\tTrinidad and Tobago dollar' 'SLL\\tSierra Leonean leone'\n", " 'SRD\\tSurinamese dollar' 'GYD\\tGuyanese dollar' 'KGS\\tKyrgyzstani som'\n", " 'ZMW Zambian kwacha' 'MDL\\tMoldovan leu' 'OMR\\tOmani rial'\n", " 'CUP\\tCuban peso' 'XPF\\tCFP franc' 'KYD\\tCayman Islands dollar'\n", " 'TZS\\tTanzanian shilling' 'KWD\\tKuwaiti dinar' 'TMT\\tTurkmen manat'\n", " 'QAR\\tQatari riyal' 'YER\\tYemeni rial' 'MWK\\tMalawian kwacha'\n", " 'IQD\\tIraqi dinar' 'IMP\\tManx pound' 'KPW\\tNorth Korean won'\n", " 'XAF\\tCentral African CFA franc' 'MGA\\tMalagasy ariary'\n", " 'PYG\\tParaguayan guarani' 'ERN\\tEritrean nakfa' 'MMK\\tMyanmar kyat'\n", " 'SHP\\tSaint Helena pound' 'MZN\\tMozambican metical' 'none\\tFaroese krona'\n", " 'AZN\\tAzerbaijan manat' 'LYD\\tLibyan dinar' 'MOP\\tMacanese pataca'\n", " 'LBP\\tLebanese pound' 'BND\\tBrunei dollar' 'VES\\tVenezuelan bolivar'\n", " 'SOS\\tSomali shilling' 'CDF\\tCongolese franc'\n", " 'XDR\\tSDR (Special Drawing Right)' 'MRU\\tMauritanian ouguiya'\n", " 'WST\\tSamoan tala' 'SDG\\tSudanese pound' 'XCD\\tEast Caribbean dollar'\n", " 'FKP\\tFalkland Islands pound' 'BWP\\tBotswana pula' 'GGP\\tGuernsey Pound'\n", " 'CVE\\tCape Verdean escudo' 'GIP\\tGibraltar pound' 'SZL\\tSwazi lilangeni'\n", " 'none\\tCook Islands dollar' 'AWG\\tAruban florin' 'BBD\\tBarbadian dollar'\n", " 'BMD\\tBermudian dollar']\n", "Currency\n", "EUR 12730\n", "USD 10939\n", "INR 2905\n", "GBP 2614\n", "CAD 1639\n", " ... \n", "GIP 1\n", "SZL 1\n", "AWG 1\n", "BBD 1\n", "BMD 1\n", "Name: count, Length: 141, dtype: int64\n", " CompTotal\n", "count 6.543700e+04\n", "mean 1.528187e+145\n", "std 3.909204e+147\n", "min 0.000000e+00\n", "25% 0.000000e+00\n", "50% 1.000000e+04\n", "75% 1.140000e+05\n", "max 1.000000e+150\n", "1.0000000000000002e+150\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": 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"title": { "standoff": 15 }, "zerolinecolor": "#283442", "zerolinewidth": 2 } } }, "title": { "text": "Salary Distribution Histogram (log scale)" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "Salary Amount" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "sum of Number of People" }, "type": "log" } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "basic_info(\"TechEndorse\")\n", "basic_info(\"Country\") # do keep this for later analysis\n", "basic_info(\"Currency\") # their salary is in this currency\n", "basic_info(\"CompTotal\") # salary! for a year?!\n", "# CompTotal means total compensation, including bonus, etc, not just base salary\n", "# so it is a numerical data\n", "df['CompTotal'] = df['CompTotal'].fillna(0)\n", "\n", "print(df['Currency'].unique())\n", "# keep first 3 letters of the currency that is not null\n", "\n", "df['Currency'] = df['Currency'].str[:3]\n", "print(df['Currency'].value_counts())\n", "# using gpt to get the currency to usd exchange rate\n", "usd_avg_rates_2024 = {\n", " \"AUD\": 1.5158, # Australian dollar\n", " \"BRL\": 5.3872, # Brazilian real\n", " \"CAD\": 1.3699, # Canadian dollar\n", " \"CNY\": 7.1957, # Chinese Yuan Renminbi\n", " \"DKK\": 6.8955, # Danish krone\n", " \"EUR\": 1.0820, # European Euro\n", " \"HKD\": 7.8030, # Hong Kong dollar\n", " \"INR\": 83.6566, # Indian rupee\n", " \"JPY\": 151.4551, # Japanese yen\n", " \"MYR\": 4.5747, # Malaysian ringgit\n", " \"MXN\": 18.3062, # Mexican peso\n", " \"NZD\": 1.6529, # New Zealand dollar\n", " \"NOK\": 10.7574, # Norwegian krone\n", " \"SGD\": 1.3363, # Singapore dollar\n", " \"ZAR\": 18.3346, # South African rand\n", " \"KRW\": 1363.4381, # South Korean won\n", " \"LKR\": 301.6752, # Sri Lankan rupee\n", " \"SEK\": 10.5744, # Swedish krona\n", " \"CHF\": 0.8808, # Swiss franc\n", " \"TWD\": 32.1064, # New Taiwan dollar\n", " \"THB\": 35.2845, # Thai baht\n", " \"GBP\": 1.2781, # Pound sterling\n", " \"VES\": 38.3314 # Venezuelan bolivar\n", "}\n", "# convert all salary to usd\n", "print(df[['CompTotal']].describe())\n", "print(df['CompTotal'].max())\n", "\n", "# there are some outliers, let's see how many are above 300000\n", "# remove these outliers using quantile\n", "#TODO apply currency conversion\n", "\n", "df = df.loc[(df['CompTotal'] < df['CompTotal'].quantile(0.99)) & (df['CompTotal'] > df['CompTotal'].quantile(0.01))]\n", "#remove salaries below 1000 usd per year ,remove larger than 300000 usd per year\n", "\n", "\n", "\n", "# Example: df['CompTotal'].value_counts()\n", "salary_counts = df['CompTotal'].value_counts()\n", "import plotly.express as px\n", "# Convert to DataFrame\n", "df_salary = salary_counts.reset_index()\n", "df_salary.columns = ['Salary', 'Count']\n", "fig = px.histogram(\n", " df_salary,\n", " x='Salary',\n", " y='Count',\n", " nbins=100, # Adjust bins for granularity\n", " title='Salary Distribution Histogram',\n", " labels={'Salary': 'Salary Amount', 'Count': 'Number of People'},\n", " log_y=False # Log scale for better visibility\n", ")\n", "\n", "fig.show()\n", "# Plot histogram (weighted by Count)\n", "fig = px.histogram(\n", " df_salary,\n", " x='Salary',\n", " y='Count',\n", " nbins=100, # Adjust bins for granularity\n", " title='Salary Distribution Histogram (log scale)',\n", " labels={'Salary': 'Salary Amount', 'Count': 'Number of People'},\n", " log_y=True # Log scale for better visibility\n", ")\n", "\n", "fig.show()\n" ] }, { "cell_type": "code", "execution_count": 22, "id": "13c35bdcc84199a5", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:40.606135Z", "start_time": "2025-10-06T06:59:40.566523Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " CompTotal\n", "count 27425.000000\n", "mean 117201.911431\n", "std 96985.286454\n", "min 1000.000000\n", "25% 52000.000000\n", "50% 90000.000000\n", "75% 150000.000000\n", "max 500000.000000\n" ] } ], "source": [ "# limit salary to 1000 to 1.5M\n", "df = df.loc[(df['CompTotal'] >= 1000) & (df['CompTotal'] <= 500000)]\n", "print(df[['CompTotal']].describe())\n" ] }, { "cell_type": "code", "execution_count": 23, "id": "776c86687fd3c650", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:40.694833Z", "start_time": "2025-10-06T06:59:40.662168Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "375 NaN\n", "380 0.0\n", "386 NaN\n", "390 10.0\n", "393 40.0\n", "Name: JobSatPoints_1, dtype: float64\n", "----------------------------------------\n", "65397 NaN\n", "65402 NaN\n", "65409 NaN\n", "65413 NaN\n", "65432 NaN\n", "Name: JobSatPoints_1, dtype: float64\n", "----------------------------------------\n", "9315\n", "----------------------------------------\n", "JobSatPoints_1\n", "0.0 6609\n", "10.0 2721\n", "20.0 2006\n", "5.0 1045\n", "30.0 891\n", " ... \n", "47.0 1\n", "38.0 1\n", "7.5 1\n", "27.0 1\n", "37.0 1\n", "Name: count, Length: 74, dtype: int64\n", "----------------------------------------\n", "****************************************\n", " 100.0 11991\n", "-9.0 9220\n", " 0.0 2699\n", " 99.0 131\n", " 90.0 79\n", " ... \n", " 393.0 1\n", " 273.0 1\n", " 677.0 1\n", " 649.0 1\n", " 271.0 1\n", "Name: count, Length: 417, dtype: int64\n", "2369\n" ] } ], "source": [ "basic_info('JobSatPoints_1')\n", "# JobSatPoints_1~11 need to fillna\n", "\n", "# it is an over all job satisfaction score from 1 to 100. total score should be 100 lets check\n", "# !! there is no jsp_2 and jsp_3\n", "# fillna with -1 first\n", "for i in range(1, 12):\n", " if i in [2,3]:\n", " continue\n", " df[f'JobSatPoints_{i}'] = df[f'JobSatPoints_{i}'].fillna(-1)\n", "\n", "total_score = df[[f'JobSatPoints_{i}' for i in range(1, 12) if i not in [2,3]]].sum(axis=1)\n", "print(total_score.value_counts())\n", "# ok there are some value larger than 100, let's check total number\n", "print((total_score > 100).sum())\n", "# let try to normalize these values to 100, reverse the mistake, round to integer incase accuracy problem\n", "df.loc[total_score > 100, [f'JobSatPoints_{i}' for i in range(1, 12) if i not in [2,3]]] = df.loc[total_score > 100, [f'JobSatPoints_{i}' for i in range(1, 12) if i not in [2,3]]].div(total_score[total_score > 100], axis=0).multiply(100)\n", "df[[f'JobSatPoints_{i}' for i in range(1, 12) if i not in [2,3]]] = df[[f'JobSatPoints_{i}' for i in range(1, 12) if i not in [2,3]]].round().astype(int)\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "f8a803aeb54a906e", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:40.770632Z", "start_time": "2025-10-06T06:59:40.759927Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 100 12884\n", "-9 9220\n", " 0 2699\n", " 99 705\n", " 101 483\n", " ... \n", " 32 1\n", " 88 1\n", " 23 1\n", " 84 1\n", "-5 1\n", "Name: count, Length: 108, dtype: int64\n", "691\n" ] } ], "source": [ "total_score = df[[f'JobSatPoints_{i}' for i in range(1, 12) if i not in [2,3]]].sum(axis=1)\n", "print(total_score.value_counts())\n", "# ok there are some value larger than 100, let's check total number\n", "print((total_score > 100).sum())" ] }, { "cell_type": "code", "execution_count": 25, "id": "d6f0796d4ebf54dc", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:40.873557Z", "start_time": "2025-10-06T06:59:40.845975Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TechEndorse 1582\n", "OpSysPersonal use 506\n", "OpSysProfessional use 1877\n", "TBranch 1516\n", "SurveyLength 971\n", "SurveyEase 930\n", "ConvertedCompYearly 8347\n", "JobSat 9338\n", "dtype: int64\n" ] } ], "source": [ "# check null values again\n", "null_counts = df.isnull().sum()\n", "print(null_counts[null_counts > 0])" ] }, { "cell_type": "code", "execution_count": 26, "id": "d3b18c9e8671604a", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:42.462285Z", "start_time": "2025-10-06T06:59:40.973373Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OpSysPersonal use 26919 27425\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\qswwq\\AppData\\Local\\Temp\\ipykernel_49188\\4278337842.py:67: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "OpSysProfessional use 25548 27425\n", "{'Austria': 0, 'Turkey': 1, 'France': 2, 'United States of America': 3, 'United Kingdom of Great Britain and Northern Ireland': 4, 'Bulgaria': 5, 'Greece': 6, 'Brazil': 7, 'Germany': 8, 'Italy': 9, 'Ukraine': 10, 'Canada': 11, 'Switzerland': 12, 'Belgium': 13, 'Peru': 14, 'Bolivia': 15, 'Morocco': 16, 'Luxembourg': 17, 'Georgia': 18, 'Saudi Arabia': 19, 'Ireland': 20, 'Romania': 21, 'Spain': 22, 'India': 23, 'Cyprus': 24, 'Paraguay': 25, 'Lithuania': 26, 'Netherlands': 27, 'Slovenia': 28, 'Singapore': 29, 'Pakistan': 30, 'Venezuela, Bolivarian Republic of...': 31, 'Latvia': 32, 'Costa Rica': 33, 'Poland': 34, 'Portugal': 35, 'Finland': 36, 'Israel': 37, 'Nicaragua': 38, 'Czech Republic': 39, 'Serbia': 40, 'Croatia': 41, 'Iran, Islamic Republic of...': 42, 'Bosnia and Herzegovina': 43, 'South Africa': 44, 'Bangladesh': 45, 'Thailand': 46, 'Slovakia': 47, 'El Salvador': 48, 'Ecuador': 49, 'Argentina': 50, 'Malaysia': 51, 'Zimbabwe': 52, 'Afghanistan': 53, 'Mexico': 54, 'Malta': 55, 'Russian Federation': 56, 'Belarus': 57, 'Egypt': 58, 'Denmark': 59, 'Montenegro': 60, 'Australia': 61, 'Isle of Man': 62, 'New Zealand': 63, 'Palestine': 64, 'Armenia': 65, 'Maldives': 66, 'United Arab Emirates': 67, 'Nigeria': 68, 'Fiji': 69, 'Guatemala': 70, 'Dominican Republic': 71, 'Turkmenistan': 72, 'Estonia': 73, 'Philippines': 74, 'South Korea': 75, 'Uruguay': 76, 'China': 77, 'Ghana': 78, 'Colombia': 79, 'Hong Kong (S.A.R.)': 80, 'Viet Nam': 81, 'Japan': 82, 'Sweden': 83, 'Taiwan': 84, 'Hungary': 85, 'Lebanon': 86, 'Democratic Republic of the Congo': 87, 'Syrian Arab Republic': 88, 'Iraq': 89, 'Chile': 90, 'Kyrgyzstan': 91, \"Lao People's Democratic Republic\": 92, 'Indonesia': 93, 'Norway': 94, 'Tunisia': 95, 'Burundi': 96, 'Kazakhstan': 97, 'Rwanda': 98, 'Mauritania': 99, 'Sierra Leone': 100, 'Panama': 101, 'Cuba': 102, 'Guyana': 103, 'Uzbekistan': 104, 'Sri Lanka': 105, 'Zambia': 106, 'Algeria': 107, 'Ethiopia': 108, 'Republic of Moldova': 109, 'Jamaica': 110, 'Kenya': 111, 'Nomadic': 112, 'Andorra': 113, 'Kuwait': 114, 'Republic of North Macedonia': 115, 'Togo': 116, 'Qatar': 117, 'Tajikistan': 118, 'Jordan': 119, 'Albania': 120, 'Sudan': 121, 'Namibia': 122, 'Uganda': 123, 'Kosovo': 124, 'Burkina Faso': 125, \"Côte d'Ivoire\": 126, 'Nepal': 127, 'Mauritius': 128, 'Cameroon': 129, 'Yemen': 130, 'Oman': 131, 'Benin': 132, 'Azerbaijan': 133, 'Central African Republic': 134, 'Somalia': 135, 'Suriname': 136, 'Libyan Arab Jamahiriya': 137, 'Cape Verde': 138, 'Bahrain': 139, 'Bhutan': 140, 'Trinidad and Tobago': 141, 'Antigua and Barbuda': 142, 'Honduras': 143, 'Liechtenstein': 144, 'Senegal': 145, 'Congo, Republic of the...': 146, 'Mozambique': 147, 'Angola': 148, 'Samoa': 149, 'Brunei Darussalam': 150, 'Lesotho': 151, 'Myanmar': 152, 'Cambodia': 153, 'Botswana': 154, 'Iceland': 155, 'Haiti': 156, 'Mongolia': 157, 'Swaziland': 158, 'Chad': 159, 'United Republic of Tanzania': 160, 'Monaco': 161, 'Republic of Korea': 162, 'Malawi': 163}\n", "{'EUR': 0, 'USD': 1, 'GBP': 2, 'CAD': 3, 'BRL': 4, 'UAH': 5, 'CHF': 6, 'PEN': 7, 'MAD': 8, 'TRY': 9, 'GEL': 10, 'SAR': 11, 'RON': 12, 'INR': 13, 'SGD': 14, 'PLN': 15, 'ILS': 16, 'CZK': 17, 'RSD': 18, 'BAM': 19, 'ZAR': 20, 'BGN': 21, 'MYR': 22, 'BDT': 23, 'BHD': 24, 'MXN': 25, 'NIO': 26, 'RUB': 27, 'BYN': 28, 'DKK': 29, 'PKR': 30, 'AUD': 31, 'UZS': 32, 'NZD': 33, 'MVR': 34, 'AED': 35, 'NGN': 36, 'FJD': 37, 'GTQ': 38, 'DOP': 39, 'KRW': 40, 'UYU': 41, 'CNY': 42, 'GHS': 43, 'BSD': 44, 'HKD': 45, 'PHP': 46, 'BOB': 47, 'IRR': 48, 'COP': 49, 'SEK': 50, 'SYP': 51, 'CLP': 52, 'EGP': 53, 'NOK': 54, 'TND': 55, 'BIF': 56, 'ARS': 57, 'SLL': 58, 'GYD': 59, 'ZMW': 60, 'ETB': 61, 'MDL': 62, 'CUP': 63, 'CRC': 64, 'KES': 65, 'AFN': 66, 'THB': 67, 'KWD': 68, 'TMT': 69, 'MKD': 70, 'KZT': 71, 'VND': 72, 'XOF': 73, 'QAR': 74, 'TJS': 75, 'JOD': 76, 'ALL': 77, 'KGS': 78, 'UGX': 79, 'IMP': 80, 'LKR': 81, 'HUF': 82, 'MUR': 83, 'XAF': 84, 'DZD': 85, 'NPR': 86, 'OMR': 87, 'AZN': 88, 'LYD': 89, 'BTN': 90, 'TTD': 91, 'TWD': 92, 'PYG': 93, 'AMD': 94, 'HNL': 95, 'LBP': 96, 'XDR': 97, 'MZN': 98, 'AOA': 99, 'MRU': 100, 'WST': 101, 'BND': 102, 'MMK': 103, 'FKP': 104, 'IDR': 105, 'GGP': 106, 'YER': 107, 'IQD': 108, 'MNT': 109, 'SRD': 110, 'SZL': 111, 'TZS': 112, 'MWK': 113}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\qswwq\\AppData\\Local\\Temp\\ipykernel_49188\\4278337842.py:67: FutureWarning:\n", "\n", "Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n" ] } ], "source": [ "drop_columns = ['TechEndorse','SurveyLength','SurveyEase','ConvertedCompYearly']\n", "explode_columns = ['OpSysPersonal use','OpSysProfessional use']\n", "for col in explode_columns:\n", " df = fast_explode(df,target_column=col, fillna='', split=';', prefix=f'{col} ')\n", "df.drop(columns=drop_columns, inplace=True)\n", "\n", "df['TBranch'] = df['TBranch'].map({'Yes': 1, 'No': 0})\n", "df['TBranch'] = df['TBranch'].fillna(-1)\n", "\n", "print(map_and_fill('Country'))\n", "print(map_and_fill('Currency'))\n" ] }, { "cell_type": "code", "execution_count": 27, "id": "f837985519d64fc7", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:42.528304Z", "start_time": "2025-10-06T06:59:42.487763Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JobSat 9338\n", "dtype: int64\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " MainBranch Age Employment RemoteWork EdLevel YearsCode \\\n", "375 3 4 0 2.0 5.0 12 \n", "380 0 1 0 0.0 2.0 15 \n", "386 0 1 3 0.0 2.0 27 \n", "390 0 4 5 0.0 3.0 7 \n", "393 0 1 0 2.0 5.0 32 \n", "... ... ... ... ... ... ... \n", "65397 0 3 6 2.0 4.0 3 \n", "65402 0 4 0 2.0 1.0 10 \n", "65409 0 4 0 2.0 2.0 13 \n", "65413 0 1 0 0.0 1.0 20 \n", "65432 0 2 0 2.0 1.0 38 \n", "\n", " YearsCodePro DevType OrgSize PurchaseInfluence ... \\\n", "375 6 3.0 0.0 1.0 ... \n", "380 6 12.0 4.0 2.0 ... \n", "386 17 6.0 2.0 2.0 ... \n", "390 7 2.0 3.0 0.0 ... \n", "393 18 17.0 6.0 1.0 ... \n", "... ... ... ... ... ... \n", "65397 3 0.0 6.0 1.0 ... \n", "65402 7 0.0 4.0 2.0 ... \n", "65409 9 0.0 0.0 1.0 ... \n", "65413 18 6.0 0.0 1.0 ... \n", "65432 24 0.0 4.0 0.0 ... \n", "\n", " OpSysProfessional use MacOS \\\n", "375 False \n", "380 True \n", "386 True \n", "390 False \n", "393 True \n", "... ... \n", "65397 False \n", "65402 False \n", "65409 False \n", "65413 True \n", "65432 True \n", "\n", " OpSysProfessional use Other (please specify): \\\n", "375 False \n", "380 False \n", "386 False \n", "390 False \n", "393 False \n", "... ... \n", "65397 False \n", "65402 False \n", "65409 False \n", "65413 False \n", "65432 False \n", "\n", " OpSysProfessional use Other Linux-based \\\n", "375 False \n", "380 False \n", "386 False \n", "390 False \n", "393 False \n", "... ... \n", "65397 False \n", "65402 True \n", "65409 False \n", "65413 False \n", "65432 True \n", "\n", " OpSysProfessional use Red Hat OpSysProfessional use Solaris \\\n", "375 False False \n", "380 False False \n", "386 False False \n", "390 False False \n", "393 False False \n", "... ... ... \n", "65397 False False \n", "65402 False False \n", "65409 False False \n", "65413 False False \n", "65432 False False \n", "\n", " OpSysProfessional use Ubuntu OpSysProfessional use Windows \\\n", "375 True True \n", "380 False False \n", "386 False False \n", "390 False False \n", "393 False False \n", "... ... ... \n", "65397 False False \n", "65402 False True \n", "65409 False False \n", "65413 False False \n", "65432 True True \n", "\n", " OpSysProfessional use Windows Subsystem for Linux (WSL) \\\n", "375 False \n", "380 False \n", "386 False \n", "390 False \n", "393 False \n", "... ... \n", "65397 False \n", "65402 False \n", "65409 False \n", "65413 False \n", "65432 True \n", "\n", " OpSysProfessional use iOS OpSysProfessional use iPadOS \n", "375 False False \n", "380 False False \n", "386 False False \n", "390 False False \n", "393 False False \n", "... ... ... \n", "65397 False False \n", "65402 False False \n", "65409 False False \n", "65413 False False \n", "65432 True True \n", "\n", "[27425 rows x 477 columns]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "null_counts = df.isnull().sum()\n", "print(null_counts[null_counts > 0])\n", "df" ] }, { "cell_type": "code", "execution_count": 28, "id": "e07580d8aa7c1ee3", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:44.709562Z", "start_time": "2025-10-06T06:59:42.663271Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\qswwq\\AppData\\Local\\Temp\\ipykernel_49188\\2350222389.py:1: FutureWarning:\n", "\n", "Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", "\n" ] } ], "source": [ "df.replace({True: 1, False: 0}, inplace=True)\n", "# df" ] }, { "cell_type": "code", "execution_count": 29, "id": "102a5e911707cf4f", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:45.335376Z", "start_time": "2025-10-06T06:59:44.817252Z" } }, "outputs": [], "source": [ "# start model prediction using xgboost or lightgbm\n", "from sklearn.model_selection import train_test_split, cross_val_score, KFold\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", "import numpy as np\n", "# --- Candidate models ---\n", "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, HistGradientBoostingRegressor\n", "from xgboost import XGBRegressor\n", "from lightgbm import LGBMRegressor\n", "\n", "# ahh.. standard scaler\n", "# ========== 1. Data preparation ==========\n", "# Filter and clean\n", "# using country and currency as grouping then separate the data into train_df\n", "\n", "# train_df = df.loc[df[\"CompTotal\"] >= 1000].copy()\n", "# train_df.drop(columns=[\"JobSat\"], errors=\"ignore\", inplace=True)\n", "#\n", "# # Split features and target\n", "# X = train_df.drop(columns=[\"CompTotal\"])\n", "# y = train_df[\"CompTotal\"]\n", "#\n", "# # Split into training / testing\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", "# # ========== 2. Preprocessing pipeline ==========\n", "# # Identify numeric and categorical columns\n", "# num_cols = X_train.select_dtypes(include=[\"number\"]).columns\n", "# cat_cols = X_train.select_dtypes(exclude=[\"number\"]).columns\n", "#\n", "# # Numeric pipeline\n", "# numeric_pipeline = Pipeline(steps=[\n", "#\n", "# (\"scaler\", StandardScaler())\n", "# ])\n", "#\n", "#\n", "#\n", "# # Combine into column transformer\n", "# preprocessor = ColumnTransformer(\n", "# transformers=[\n", "# (\"num\", numeric_pipeline, num_cols),\n", "#\n", "# ]\n", "# )\n", "#\n", "# # ========== 3. Define models ==========\n", "# models = {\n", "# \"GradientBoosting\": GradientBoostingRegressor(\n", "# n_estimators=200, learning_rate=0.05, max_depth=3, random_state=42\n", "# ),\n", "# \"RandomForest\": RandomForestRegressor(\n", "# n_estimators=400, random_state=42, n_jobs=-1\n", "# ),\n", "# \"HistGradientBoosting\": HistGradientBoostingRegressor(\n", "# learning_rate=0.05, random_state=42\n", "# )\n", "# }\n", "#\n", "#\n", "# models[\"XGBoost\"] = XGBRegressor(\n", "# n_estimators=600, learning_rate=0.05, max_depth=6,\n", "# subsample=0.8, colsample_bytree=0.8, n_jobs=-1, random_state=42\n", "# )\n", "#\n", "#\n", "# models[\"LightGBM\"] = LGBMRegressor(\n", "# n_estimators=600, learning_rate=0.05, num_leaves=31,\n", "# subsample=0.8, colsample_bytree=0.8, n_jobs=-1, random_state=42\n", "# )\n", "#\n", "# # ========== 4. Evaluate each model with cross-validation ==========\n", "# cv = KFold(n_splits=5, shuffle=True, random_state=42)\n", "# results = []\n", "#\n", "# for name, model in models.items():\n", "# pipe = Pipeline(steps=[(\"preprocessor\", preprocessor),\n", "# (\"model\", model)])\n", "# scores = cross_val_score(\n", "# pipe, X_train, y_train,\n", "# scoring=\"neg_root_mean_squared_error\",\n", "# cv=cv, n_jobs=-1,error_score='raise'\n", "# )\n", "# rmse = -scores.mean()\n", "# print(f\"{name:>20s} | CV RMSE: {rmse:.2f}\")\n", "# results.append((name, rmse, scores.std()))\n", "#\n", "# # Sort and select best\n", "# results_df = pd.DataFrame(results, columns=[\"Model\", \"CV_RMSE\", \"Std\"]).sort_values(\"CV_RMSE\")\n", "# best_model_name = results_df.iloc[0][\"Model\"]\n", "# print(\"\\nBest model:\", best_model_name)\n", "#\n", "# # ========== 5. Fit best model pipeline and evaluate on test set ==========\n", "# best_model = models[best_model_name]\n", "# best_pipeline = Pipeline(steps=[\n", "# (\"preprocessor\", preprocessor),\n", "# (\"model\", best_model)\n", "# ])\n", "#\n", "# best_pipeline.fit(X_train, y_train)\n", "# y_pred = best_pipeline.predict(X_test)\n", "#\n", "# rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", "# mae = mean_absolute_error(y_test, y_pred)\n", "# r2 = r2_score(y_test, y_pred)\n", "#\n", "# print(f\"\\n--- Test Performance ({best_model_name}) ---\")\n", "# print(f\"RMSE: {rmse:.2f}\")\n", "# print(f\"MAE : {mae:.2f}\")\n", "# print(f\"R² : {r2:.4f}\")\n", "#\n", "# # Optional: show model comparison table\n", "# print(\"\\nModel comparison (lower RMSE better):\")\n", "# print(results_df.to_string(index=False))\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "cb89a1aca5743cbb", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:45.753180Z", "start_time": "2025-10-06T06:59:45.749122Z" } }, "outputs": [], "source": [ "# lightbgm seems to be the best model here, check feature importance and plot the top 20 features\n", "\n", "# feature_names_num = num_cols.tolist()\n", "#\n", "# feature_names = feature_names_num\n", "#\n", "# importances = best_model.feature_importances_\n", "# feature_importance_df = pd.DataFrame({\n", "# 'Feature': feature_names,\n", "# 'Importance': importances\n", "# }).sort_values(by='Importance', ascending=False)\n", "#\n", "# print(\"\\nTop 20 Feature Importances:\")\n", "# print(feature_importance_df.head(20))\n", "#\n", "# # Plot top 20 features\n", "# plt.figure(figsize=(10, 6))\n", "# sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(20))\n", "# plt.title('Top 20 Feature Importances')\n", "# plt.tight_layout()\n", "# plt.show()" ] }, { "attachments": { "2f8352b5-06b3-4ccd-870c-c3ffc3318a1f.png": { "image/png": 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} }, "cell_type": "markdown", "id": "76d6758b74778fcf", "metadata": {}, "source": [ "![image.png](attachment:2f8352b5-06b3-4ccd-870c-c3ffc3318a1f.png)\n", "# first modelling is terrible, we need a deeper analysis of the data.\n", "# we can see that the country and currency are important features, we can try to group the data by country and currency and see what we can find out." ] }, { "cell_type": "code", "execution_count": 31, "id": "86e4174942fdbb93", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:45.953116Z", "start_time": "2025-10-06T06:59:45.897255Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(np.int64(0), np.int64(0)) (488, 477)\n", "(np.int64(0), np.int64(1)) (2, 477)\n", "(np.int64(0), np.int64(10)) (1, 477)\n", "(np.int64(1), np.int64(0)) (11, 477)\n", "(np.int64(1), np.int64(1)) (39, 477)\n", "(np.int64(1), np.int64(2)) (1, 477)\n", "(np.int64(1), np.int64(9)) (78, 477)\n", "(np.int64(1), np.int64(31)) (1, 477)\n", "(np.int64(2), 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"(np.int64(129), np.int64(84)) (2, 477)\n", "(np.int64(130), np.int64(1)) (1, 477)\n", "(np.int64(130), np.int64(11)) (2, 477)\n", "(np.int64(130), np.int64(107)) (1, 477)\n", "(np.int64(131), np.int64(87)) (3, 477)\n", "(np.int64(132), np.int64(73)) (1, 477)\n", "(np.int64(133), np.int64(1)) (2, 477)\n", "(np.int64(133), np.int64(88)) (11, 477)\n", "(np.int64(134), np.int64(73)) (1, 477)\n", "(np.int64(135), np.int64(1)) (2, 477)\n", "(np.int64(136), np.int64(1)) (3, 477)\n", "(np.int64(136), np.int64(110)) (1, 477)\n", "(np.int64(137), np.int64(89)) (1, 477)\n", "(np.int64(138), np.int64(0)) (2, 477)\n", "(np.int64(139), np.int64(24)) (5, 477)\n", "(np.int64(140), np.int64(13)) (1, 477)\n", "(np.int64(140), np.int64(90)) (1, 477)\n", "(np.int64(141), np.int64(1)) (1, 477)\n", "(np.int64(141), np.int64(91)) (3, 477)\n", "(np.int64(142), np.int64(31)) (1, 477)\n", "(np.int64(143), np.int64(1)) (4, 477)\n", "(np.int64(143), np.int64(95)) (6, 477)\n", "(np.int64(144), np.int64(1)) (1, 477)\n", "(np.int64(144), np.int64(6)) (1, 477)\n", "(np.int64(145), np.int64(8)) (1, 477)\n", "(np.int64(146), np.int64(1)) (2, 477)\n", "(np.int64(147), np.int64(98)) (1, 477)\n", "(np.int64(148), np.int64(1)) (1, 477)\n", "(np.int64(148), np.int64(99)) (1, 477)\n", "(np.int64(149), np.int64(101)) (1, 477)\n", "(np.int64(150), np.int64(102)) (1, 477)\n", "(np.int64(151), np.int64(1)) (1, 477)\n", "(np.int64(152), np.int64(103)) (1, 477)\n", "(np.int64(153), np.int64(1)) (3, 477)\n", "(np.int64(154), np.int64(4)) (1, 477)\n", "(np.int64(155), np.int64(1)) (2, 477)\n", "(np.int64(156), np.int64(1)) (1, 477)\n", "(np.int64(157), np.int64(1)) (1, 477)\n", "(np.int64(157), np.int64(109)) (1, 477)\n", "(np.int64(158), np.int64(111)) (1, 477)\n", "(np.int64(159), np.int64(29)) (1, 477)\n", "(np.int64(160), np.int64(1)) (1, 477)\n", "(np.int64(160), np.int64(112)) (1, 477)\n", "(np.int64(161), np.int64(0)) (1, 477)\n", "(np.int64(162), np.int64(40)) (1, 477)\n", "(np.int64(163), np.int64(113)) (1, 477)\n" ] } ], "source": [ "for train_df in df.groupby(['Country', 'Currency']):\n", " print(train_df[0], train_df[1].shape)" ] }, { "cell_type": "markdown", "id": "1d86264972a1ea48", "metadata": {}, "source": [ "# we can see that some country and currency have very few data, we can filter out these data and only keep the country and currency with more than 1000 data points.\n" ] }, { "cell_type": "code", "execution_count": 32, "id": "555b3d2c73b6c793", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T06:59:46.072316Z", "start_time": "2025-10-06T06:59:46.023518Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(np.int64(2), np.int64(0)) (1304, 477)\n", "France\n", "EUR\n", "(np.int64(3), np.int64(1)) (6880, 477)\n", "United States of America\n", "USD\n", "(np.int64(4), np.int64(2)) (1978, 477)\n", "United Kingdom of Great Britain and Northern Ireland\n", "GBP\n", "(np.int64(8), np.int64(0)) (2972, 477)\n", "Germany\n", "EUR\n", "(np.int64(11), np.int64(3)) (1237, 477)\n", "Canada\n", "CAD\n" ] } ], "source": [ "# finding the country and currency with more than 1000 data points\n", "for train_df in df.groupby(['Country', 'Currency']):\n", " if train_df[1].shape[0]>1000:\n", " print(train_df[0], train_df[1].shape)\n", " for x in value_maps['Country'].keys() :\n", " if value_maps['Country'][x]==train_df[0][0]:\n", " print(x)\n", "\n", " for x in value_maps['Currency'].keys() :\n", " if value_maps['Currency'][x]==train_df[0][1]:\n", " print(x)\n", "# and it's USA and USD, France and EUR, UK and GBP, Canada and CAD, Germany and EUR" ] }, { "cell_type": "code", "execution_count": 38, "id": "5d4f761217d2fff6", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T07:16:13.125597Z", "start_time": "2025-10-06T07:16:05.285582Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Components ExplainedVariance\n", "0 1 0.034639\n", "1 2 0.058752\n", "2 3 0.080626\n", "3 4 0.100431\n", "4 5 0.114995\n", ".. ... ...\n", "94 95 0.470120\n", "95 96 0.472636\n", "96 97 0.475141\n", "97 98 0.477626\n", "98 99 0.480088\n", "\n", "[99 rows x 2 columns]\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# select these country and currency ,3 and 1\n", "df_cleaned = df.loc[df[\"CompTotal\"] >= 1000].copy()\n", "df_cleaned.drop(columns=[\"JobSat\"], errors=\"ignore\", inplace=True)\n", "df_cleaned = df_cleaned[((df_cleaned['Country']==value_maps['Country']['United States of America']) & (df_cleaned['Currency']==value_maps['Currency']['USD']))\n", " ]\n", "\n", "from sklearn.decomposition import PCA\n", "\n", "results = []\n", "for group in df_cleaned.groupby(['Country', 'Currency']):\n", " train_df = group[1]\n", " if train_df.shape[0]<1000:\n", " continue\n", "# --- 1️⃣ Prepare data ---\n", "# Assuming df_cleaned is your cleaned DataFrame with only numeric columns\n", " X = train_df.select_dtypes(include=['number'])\n", "\n", " # --- 2️⃣ Standardize the features ---\n", " scaler = StandardScaler()\n", " X_scaled = scaler.fit_transform(X)\n", "\n", " # --- 3️⃣ Apply PCA ---\n", " # Let's start with 2 principal components for visualization\n", " for n in range(1, 100):\n", " pca = PCA(n_components=n)\n", " pca.fit(X_scaled)\n", " results.append({'Components': n, 'ExplainedVariance': pca.explained_variance_ratio_.sum()})\n", "\n", " pca_summary = pd.DataFrame(results)\n", " print(pca_summary)\n", " plt.plot(pca_summary['Components'], pca_summary['ExplainedVariance'], marker='o')\n", " plt.xlabel('Number of Components')\n", " plt.ylabel('Total Explained Variance')\n", " plt.title('Explained Variance by Number of PCA Components')\n", " plt.grid(True)\n", " plt.show()\n" ] }, { "cell_type": "code", "execution_count": 39, "id": "1b198ad8b70c2b55", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T07:23:42.670315Z", "start_time": "2025-10-06T07:20:44.601124Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " GradientBoosting | CV RMSE: 25496.22\n", " RandomForest | CV RMSE: 25170.80\n", "HistGradientBoosting | CV RMSE: 25254.36\n", " XGBoost | CV RMSE: 25147.70\n", " LightGBM | CV RMSE: 25736.40\n", "\n", "Best model: XGBoost\n", "\n", "--- Test Performance (XGBoost) ---\n", "RMSE: 27999.24\n", "MAE : 17544.87\n", "R² : 0.2137\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " XGBoost 25147.704785 2924.025317\n", " RandomForest 25170.802974 3036.293508\n", "HistGradientBoosting 25254.355634 2648.286957\n", " GradientBoosting 25496.223737 3139.568396\n", " LightGBM 25736.398139 2455.650891\n", "EUR\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances:\n", " Feature Importance\n", "394 OfficeStackSyncHaveWorkedWith IRC 0.020897\n", "246 EmbeddedHaveWorkedWith Micronaut 0.020615\n", "417 AISearchDevHaveWorkedWith Claude 0.018561\n", "157 DatabaseHaveWorkedWith MariaDB 0.018521\n", "271 MiscTechHaveWorkedWith JAX 0.017854\n", "188 PlatformHaveWorkedWith Netlify 0.015353\n", "186 PlatformHaveWorkedWith Managed Hosting 0.014557\n", "213 WebframeHaveWorkedWith Fastify 0.013727\n", "289 MiscTechHaveWorkedWith Tauri 0.012357\n", "184 PlatformHaveWorkedWith IBM Cloud Or Watson 0.012197\n", "371 OfficeStackAsyncHaveWorkedWith GitHub Discuss... 0.011741\n", "463 OpSysProfessional use Fedora 0.011012\n", "321 ToolsTechHaveWorkedWith Unity 3D 0.010669\n", "141 DatabaseHaveWorkedWith Cloud Firestore 0.010394\n", "181 PlatformHaveWorkedWith Google Cloud 0.010187\n", "446 OpSysPersonal use MacOS 0.009957\n", "6 YearsCodePro 0.009949\n", "212 WebframeHaveWorkedWith FastAPI 0.009757\n", "195 PlatformHaveWorkedWith Scaleway 0.009595\n", "235 WebframeHaveWorkedWith jQuery 0.009208\n", "\n", "--- Test Performance (XGBoost) ---\n", "RMSE: 27999.24\n", "MAE : 17544.87\n", "R² : 0.2137\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " XGBoost 25147.704785 2924.025317\n", " RandomForest 25170.802974 3036.293508\n", "HistGradientBoosting 25254.355634 2648.286957\n", " GradientBoosting 25496.223737 3139.568396\n", " LightGBM 25736.398139 2455.650891\n", "EUR\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances (all features):\n", " Feature Importance\n", "394 OfficeStackSyncHaveWorkedWith IRC 0.020897\n", "246 EmbeddedHaveWorkedWith Micronaut 0.020615\n", "417 AISearchDevHaveWorkedWith Claude 0.018561\n", "157 DatabaseHaveWorkedWith MariaDB 0.018521\n", "271 MiscTechHaveWorkedWith JAX 0.017854\n", "188 PlatformHaveWorkedWith Netlify 0.015353\n", "186 PlatformHaveWorkedWith Managed Hosting 0.014557\n", "213 WebframeHaveWorkedWith Fastify 0.013727\n", "289 MiscTechHaveWorkedWith Tauri 0.012357\n", "184 PlatformHaveWorkedWith IBM Cloud Or Watson 0.012197\n", "371 OfficeStackAsyncHaveWorkedWith GitHub Discuss... 0.011741\n", "463 OpSysProfessional use Fedora 0.011012\n", "321 ToolsTechHaveWorkedWith Unity 3D 0.010669\n", "141 DatabaseHaveWorkedWith Cloud Firestore 0.010394\n", "181 PlatformHaveWorkedWith Google Cloud 0.010187\n", "446 OpSysPersonal use MacOS 0.009957\n", "6 YearsCodePro 0.009949\n", "212 WebframeHaveWorkedWith FastAPI 0.009757\n", "195 PlatformHaveWorkedWith Scaleway 0.009595\n", "235 WebframeHaveWorkedWith jQuery 0.009208\n", "\n", "=== Top-50 Feature Refit (XGBoost) in France [EUR] ===\n", "BEFORE -> RMSE: 27999.24 | MAE: 17544.87 | R²: 0.2137 (all 475 numeric features)\n", "AFTER -> RMSE: 27794.53 | MAE: 17620.20 | R²: 0.2252 (top 50 features)\n", "ΔRMSE (top-50 - all): -204.71\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " GradientBoosting | CV RMSE: 44468.45\n", " RandomForest | CV RMSE: 45618.97\n", "HistGradientBoosting | CV RMSE: 43766.51\n", " XGBoost | CV RMSE: 43491.01\n", " LightGBM | CV RMSE: 43218.98\n", "\n", "Best model: LightGBM\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004035 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 2434\n", "[LightGBM] [Info] Number of data points in the train set: 5256, number of used features: 445\n", "[LightGBM] [Info] Start training from score 146266.471081\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "E:\\Projects\\AAU_Projects\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning:\n", "\n", "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "--- Test Performance (LightGBM) ---\n", "RMSE: 43805.74\n", "MAE : 33321.31\n", "R² : 0.4599\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " LightGBM 43218.976453 856.461970\n", " XGBoost 43491.011284 949.970241\n", "HistGradientBoosting 43766.512776 701.416182\n", " GradientBoosting 44468.454404 679.251521\n", " RandomForest 45618.965385 476.208844\n", "USD\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances:\n", " Feature Importance\n", "6 YearsCodePro 689\n", "8 OrgSize 574\n", "5 YearsCode 569\n", "31 Company_ProfessionalTech 483\n", "7 DevType 481\n", "30 Challenge_Frustration 398\n", "34 Industry 324\n", "15 WorkExp 261\n", "4 EdLevel 244\n", "1 Age 239\n", "9 PurchaseInfluence 231\n", "35 JobSatPoints_1 226\n", "38 JobSatPoints_6 223\n", "398 OfficeStackSyncHaveWorkedWith Microsoft Teams 212\n", "2 Employment 209\n", "3 RemoteWork 186\n", "40 JobSatPoints_8 183\n", "39 JobSatPoints_7 180\n", "41 JobSatPoints_9 162\n", "24 Knowledge_9 158\n", "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005494 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 2434\n", "[LightGBM] [Info] Number of data points in the train set: 5256, number of used features: 445\n", "[LightGBM] [Info] Start training from score 146266.471081\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "E:\\Projects\\AAU_Projects\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning:\n", "\n", "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "--- Test Performance (LightGBM) ---\n", "RMSE: 43805.74\n", "MAE : 33321.31\n", "R² : 0.4599\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " LightGBM 43218.976453 856.461970\n", " XGBoost 43491.011284 949.970241\n", "HistGradientBoosting 43766.512776 701.416182\n", " GradientBoosting 44468.454404 679.251521\n", " RandomForest 45618.965385 476.208844\n", "USD\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances (all features):\n", " Feature Importance\n", "6 YearsCodePro 689\n", "8 OrgSize 574\n", "5 YearsCode 569\n", "31 Company_ProfessionalTech 483\n", "7 DevType 481\n", "30 Challenge_Frustration 398\n", "34 Industry 324\n", "15 WorkExp 261\n", "4 EdLevel 244\n", "1 Age 239\n", "9 PurchaseInfluence 231\n", "35 JobSatPoints_1 226\n", "38 JobSatPoints_6 223\n", "398 OfficeStackSyncHaveWorkedWith Microsoft Teams 212\n", "2 Employment 209\n", "3 RemoteWork 186\n", "40 JobSatPoints_8 183\n", "39 JobSatPoints_7 180\n", "41 JobSatPoints_9 162\n", "24 Knowledge_9 158\n", "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000874 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 1176\n", "[LightGBM] [Info] Number of data points in the train set: 5256, number of used features: 50\n", "[LightGBM] [Info] Start training from score 146266.471081\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "E:\\Projects\\AAU_Projects\\.venv\\Lib\\site-packages\\sklearn\\utils\\validation.py:2749: UserWarning:\n", "\n", "X does not have valid feature names, but LGBMRegressor was fitted with feature names\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "=== Top-50 Feature Refit (LightGBM) in United States of America [USD] ===\n", "BEFORE -> RMSE: 43805.74 | MAE: 33321.31 | R²: 0.4599 (all 475 numeric features)\n", "AFTER -> RMSE: 44944.17 | MAE: 34548.96 | R²: 0.4315 (top 50 features)\n", "ΔRMSE (top-50 - all): +1138.43\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " GradientBoosting | CV RMSE: 33684.22\n", " RandomForest | CV RMSE: 34334.85\n", "HistGradientBoosting | CV RMSE: 33086.39\n", " XGBoost | CV RMSE: 32503.95\n", " LightGBM | CV RMSE: 32633.06\n", "\n", "Best model: XGBoost\n", "\n", "--- Test Performance (XGBoost) ---\n", "RMSE: 30376.02\n", "MAE : 21626.69\n", "R² : 0.4336\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " XGBoost 32503.952249 666.753464\n", " LightGBM 32633.062433 793.076592\n", "HistGradientBoosting 33086.394733 961.407344\n", " GradientBoosting 33684.224973 1169.760418\n", " RandomForest 34334.847963 759.452971\n", "GBP\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances:\n", " Feature Importance\n", "145 DatabaseHaveWorkedWith Couchbase 0.019253\n", "423 AISearchDevHaveWorkedWith Meta AI 0.018048\n", "404 OfficeStackSyncHaveWorkedWith Symphony 0.014252\n", "245 EmbeddedHaveWorkedWith Meson 0.014246\n", "373 OfficeStackAsyncHaveWorkedWith Linear 0.013896\n", "399 OfficeStackSyncHaveWorkedWith Ringcentral 0.013709\n", "465 OpSysProfessional use MacOS 0.012186\n", "165 DatabaseHaveWorkedWith Presto 0.010981\n", "398 OfficeStackSyncHaveWorkedWith Microsoft Teams 0.010376\n", "237 EmbeddedHaveWorkedWith Boost.Test 0.010324\n", "449 OpSysPersonal use Red Hat 0.010158\n", "471 OpSysProfessional use Windows 0.010029\n", "305 ToolsTechHaveWorkedWith Google Test 0.009765\n", "354 NEWCollabToolsHaveWorkedWith RubyMine 0.009620\n", "131 LanguageHaveWorkedWith Solidity 0.009368\n", "400 OfficeStackSyncHaveWorkedWith Rocketchat 0.009177\n", "107 LanguageHaveWorkedWith Groovy 0.009039\n", "243 EmbeddedHaveWorkedWith LLVM's Clang 0.008962\n", "318 ToolsTechHaveWorkedWith Pulumi 0.008922\n", "169 DatabaseHaveWorkedWith Snowflake 0.008597\n", "\n", "--- Test Performance (XGBoost) ---\n", "RMSE: 30376.02\n", "MAE : 21626.69\n", "R² : 0.4336\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " XGBoost 32503.952249 666.753464\n", " LightGBM 32633.062433 793.076592\n", "HistGradientBoosting 33086.394733 961.407344\n", " GradientBoosting 33684.224973 1169.760418\n", " RandomForest 34334.847963 759.452971\n", "GBP\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances (all features):\n", " Feature Importance\n", "145 DatabaseHaveWorkedWith Couchbase 0.019253\n", "423 AISearchDevHaveWorkedWith Meta AI 0.018048\n", "404 OfficeStackSyncHaveWorkedWith Symphony 0.014252\n", "245 EmbeddedHaveWorkedWith Meson 0.014246\n", "373 OfficeStackAsyncHaveWorkedWith Linear 0.013896\n", "399 OfficeStackSyncHaveWorkedWith Ringcentral 0.013709\n", "465 OpSysProfessional use MacOS 0.012186\n", "165 DatabaseHaveWorkedWith Presto 0.010981\n", "398 OfficeStackSyncHaveWorkedWith Microsoft Teams 0.010376\n", "237 EmbeddedHaveWorkedWith Boost.Test 0.010324\n", "449 OpSysPersonal use Red Hat 0.010158\n", "471 OpSysProfessional use Windows 0.010029\n", "305 ToolsTechHaveWorkedWith Google Test 0.009765\n", "354 NEWCollabToolsHaveWorkedWith RubyMine 0.009620\n", "131 LanguageHaveWorkedWith Solidity 0.009368\n", "400 OfficeStackSyncHaveWorkedWith Rocketchat 0.009177\n", "107 LanguageHaveWorkedWith Groovy 0.009039\n", "243 EmbeddedHaveWorkedWith LLVM's Clang 0.008962\n", "318 ToolsTechHaveWorkedWith Pulumi 0.008922\n", "169 DatabaseHaveWorkedWith Snowflake 0.008597\n", "\n", "=== Top-50 Feature Refit (XGBoost) in United Kingdom of Great Britain and Northern Ireland [GBP] ===\n", "BEFORE -> RMSE: 30376.02 | MAE: 21626.69 | R²: 0.4336 (all 475 numeric features)\n", "AFTER -> RMSE: 34802.56 | MAE: 24260.03 | R²: 0.2565 (top 50 features)\n", "ΔRMSE (top-50 - all): +4426.54\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " GradientBoosting | CV RMSE: 27036.35\n", " RandomForest | CV RMSE: 27340.84\n", "HistGradientBoosting | CV RMSE: 26299.64\n", " XGBoost | CV RMSE: 26050.19\n", " LightGBM | CV RMSE: 26372.26\n", "\n", "Best model: XGBoost\n", "\n", "--- Test Performance (XGBoost) ---\n", "RMSE: 22574.87\n", "MAE : 15064.41\n", "R² : 0.4762\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " XGBoost 26050.186903 1441.041835\n", "HistGradientBoosting 26299.643373 1600.015139\n", " LightGBM 26372.263883 1349.049433\n", " GradientBoosting 27036.347568 1322.608622\n", " RandomForest 27340.837374 1460.906098\n", "EUR\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances:\n", " Feature Importance\n", "434 AISearchDevHaveWorkedWith Whispr AI 0.028386\n", "440 OpSysPersonal use BSD 0.028385\n", "199 PlatformHaveWorkedWith Vultr 0.026693\n", "124 LanguageHaveWorkedWith Prolog 0.017655\n", "320 ToolsTechHaveWorkedWith Terraform 0.013332\n", "131 LanguageHaveWorkedWith Solidity 0.012844\n", "185 PlatformHaveWorkedWith Linode, now Akamai 0.012813\n", "189 PlatformHaveWorkedWith OVH 0.012552\n", "148 DatabaseHaveWorkedWith DuckDB 0.012274\n", "385 OfficeStackAsyncHaveWorkedWith Stack Overflow... 0.012065\n", "6 YearsCodePro 0.011430\n", "404 OfficeStackSyncHaveWorkedWith Symphony 0.011309\n", "403 OfficeStackSyncHaveWorkedWith Slack 0.010139\n", "2 Employment 0.010090\n", "169 DatabaseHaveWorkedWith Snowflake 0.009543\n", "264 MiscTechHaveWorkedWith DirectX 0.009310\n", "222 WebframeHaveWorkedWith Phoenix 0.008670\n", "138 DatabaseHaveWorkedWith BigQuery 0.008645\n", "90 LanguageHaveWorkedWith Apex 0.008375\n", "319 ToolsTechHaveWorkedWith Puppet 0.008281\n", "\n", "--- Test Performance (XGBoost) ---\n", "RMSE: 22574.87\n", "MAE : 15064.41\n", "R² : 0.4762\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " XGBoost 26050.186903 1441.041835\n", "HistGradientBoosting 26299.643373 1600.015139\n", " LightGBM 26372.263883 1349.049433\n", " GradientBoosting 27036.347568 1322.608622\n", " RandomForest 27340.837374 1460.906098\n", "EUR\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances (all features):\n", " Feature Importance\n", "434 AISearchDevHaveWorkedWith Whispr AI 0.028386\n", "440 OpSysPersonal use BSD 0.028385\n", "199 PlatformHaveWorkedWith Vultr 0.026693\n", "124 LanguageHaveWorkedWith Prolog 0.017655\n", "320 ToolsTechHaveWorkedWith Terraform 0.013332\n", "131 LanguageHaveWorkedWith Solidity 0.012844\n", "185 PlatformHaveWorkedWith Linode, now Akamai 0.012813\n", "189 PlatformHaveWorkedWith OVH 0.012552\n", "148 DatabaseHaveWorkedWith DuckDB 0.012274\n", "385 OfficeStackAsyncHaveWorkedWith Stack Overflow... 0.012065\n", "6 YearsCodePro 0.011430\n", "404 OfficeStackSyncHaveWorkedWith Symphony 0.011309\n", "403 OfficeStackSyncHaveWorkedWith Slack 0.010139\n", "2 Employment 0.010090\n", "169 DatabaseHaveWorkedWith Snowflake 0.009543\n", "264 MiscTechHaveWorkedWith DirectX 0.009310\n", "222 WebframeHaveWorkedWith Phoenix 0.008670\n", "138 DatabaseHaveWorkedWith BigQuery 0.008645\n", "90 LanguageHaveWorkedWith Apex 0.008375\n", "319 ToolsTechHaveWorkedWith Puppet 0.008281\n", "\n", "=== Top-50 Feature Refit (XGBoost) in Germany [EUR] ===\n", "BEFORE -> RMSE: 22574.87 | MAE: 15064.41 | R²: 0.4762 (all 475 numeric features)\n", "AFTER -> RMSE: 25590.00 | MAE: 18173.97 | R²: 0.3269 (top 50 features)\n", "ΔRMSE (top-50 - all): +3015.14\n" ] }, { "data": { "image/png": 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8zBHiFBeZrAXg3NwSjfFl3YagDruPI/6Aa5Pqzzpmkz4CJ2Rw4MgWVj5RWhB4QmATDCD1mvaSAcBeCmSHEBCI08qPPfZYnwuCA+y2z3wh1ulD2CW+NOHzx/gxxgQ5CI6wiRyfW8oyKNEg+FQcGHPGWW63KClVlhc310IIIYQQYhUBlwuXkDTkbNUei7KHOmOEHQ60ENmArAPq1AnIFLY5nhAFIcdbCCGEEEIIIdLA/hBsoIerj9st0S1KijZXE0IIIYQQQog0UGLAfhDcPo5d7YUoKUo1F0IIIYQQQgghyhA53kIIIYQQQgghRBki4S2EEEIIIYQQQpQhEt5CCCGEEEIIIUQZIuEthBBCCCGEEEKUIRLeQgghhBBCCCFEGaL7eAshhMg55sxZYPn55d2KykOVKmZ16lS32bMXmO51kh005uWDxj37aMzLB417dsc5EyS8hRBC5BxVquRZ1arl3YrKR16eEuGyjca8fNC4Zx+NeflQWcc9P3+5P3IJ3cdbCCGEEEIIIUSFYdmyfJs3b1GZi28c77p15XgLIYRYRek7YpxNnDGnvJshhBBCiFWMxvVqWr9OrSwvr0pOud4S3kIIIXKOqbPm28TpEt5CCCGEqBhUzqR/IYQQQgghhBAiS0h4CyGEEEIIIYQQZYiEtyg3nn32Wdt6663tqaeeSnp+4MCBdtJJJyX+/u+///y5/fff37bffnvbZ599rH///vb333+XQ6vN9ttvP297Ya/TLx5NmjSxZs2aWceOHW3s2LGl1gbO/dFHH63wfOrYlQUjR460HXbYweclwO/0k77H/P77797WCRMmFPs6pdkXxop2wBlnnGG9e/dOev3ll1/217lmzODBg+3II49cYcxnz57t41DUfBQF5+G9n3zySdLzXbp08eenTZuW9PyBBx5oDz/8sK+/1LGOufTSS/2RLRg32vvBBx+Ue1uEEEIIIXIRCW9Rbrzyyiu26aab2gsvvFDocTfffLO9/vrr1q9fPxs1apSL7vfff98uvPBCy1Uuu+wye++99+ydd96xJ5980nbeeWfr1q2bjRs3zlZ1dt11V1uyZIlNnDgx8dzXX39ta6+9tgvtX3/9NfH8+PHjrWbNmgnRmyvtp70xiOZ69eqtIJ6/+OIL22233fx35pPgQliTzO3KUqdOHWvcuLF99dVXSUGMzz//fIX2zJkzx3755Rfbfffdizzv5Zdf7o9sQeCCz/Lzzz+ftWsKIYQQQqxKSHiLcgGnD3fs7LPPtk8//TRJrKXy3HPP2XnnnWd77rmnbbLJJv7z6quvtjFjxtjMmTMtF6levbqtv/76tsEGG9hWW21lF198sbVp08aDBqs69Kthw4ZJYhGByLxss802SWIR4brLLrvk1D0kac9PP/1kCxcuTDxHm3GZae8///yTeP7LL79MCG/6vcYaa/jvpXkXRgIB8Vjye7Vq1axt27ZJY0kQo0aNGhkFMVh/PLLBt99+6wGBM8880wNk8bgKIYQQQoj/kTvfhkWlAucaYXDEEUe4s1eY612lShX78MMPLT8/P/EcziOOea1atfxvHFgccdxAHrjh8+bNSxz/2Wef2fHHH2877rij7bTTTnb66acnRDtpu6SCEwRAlL344ou2dOlSu/XWW61ly5b+XI8ePWzu3LmJ8/3444/+HlKu27Vrl1Eq9XHHHWc//PCDTZ061f+eP3++XXTRRe6Gc52+ffsmRF+HDh3szjvvTHo/1yP1uTi8+eab3j7aicA7//zzXRgtWLDAn2NcA6Tu8xyBEBg9erQddthhPmbHHHOMffzxx4ljOdc333yT+BuBiEDlEZ8zFq7//vuv3XTTTda6dWufg+7du9tvv/3mr5FSjaAcNGiQNW/e3K699tqkfvBe5u+0007zuYYnnnjC061ZC6Skf//990l9oa+8dvDBByc53PRx9dVXd8EIuPQzZsywY4891tckbjNMmTLF/vrrL+9rnE5OWjXBIB5xujfjdvjhh/v5TzzxRJs+fXqJHHiuwRpm3GLhzVjGQQzEP23hWM5x4403pk3vZp2de+65fgxjy2cjlGlwDJ8b5qJp06a+VkL/Q5/vuOMOvwbHFOR2U1LBOOPWI76FEEIIIUQyEt6iXEA0U6uNiEC8kKJakIt48skn22OPPebHXXXVVfbaa6+5QN1iiy1cQAEiGSF433332aOPPurCApccEJmkee+1114uEh544AF36O69994kN5HzjRgxwkUwYgNhdf3113uqOA491w48/fTT1rVrVxfppFLHrxXE5ptv7j8nTZrkP0kFpm2PP/64C2rEVxCcCF6Eb+CPP/5wNxbXPFPoI2PQqVMnr0e+/fbbPdWdPiIwW7VqlXSNt99+22rXru3ijjTySy65xF1M+kiAhGBFCBrELi1CmPELwjuIRZ5H3AbhzRhxPQQiopngxllnnZUUUEH0PfPMMz7nAV5HRPPzrrvuctf5rbfe8t/79Onj80SbeQ9COVxr8uTJNnToULviiivsoYceSpyP9xNMCO0nUMDeAbjMCNPQfsZ7yy23TAR3Aoj/Qw891B+sgwB7FXAtnqMdpKNnAmOJSCeVPA5i8Pyff/5pP//8sz/PGMdp5gQLCA4wlqwb+vjuu++ucH4COJyHdcZng7mNAzi8n7XPONJ/auBDW4DMEt6brrSDzyxra9999/XxI+uB8wghhBBCiGQkvEXWweVEYB1wwAH+90EHHeSp5rjS6cCJxindcMMNXTTiPiMaEWiwePFiF1jXXHONu3a4dAMGDHCHFhcUkY7A4zwNGjRwkcY1ca1jVx2RiThGaHGdXr162d577+2ihHMjwgK4r7Sf+lzc1rjeuSBC6i+OM6L4jTfe8H7RXtqN441oQYwj6hDoQXThIm677bae4h1ACOPoxo8hQ4YkXkeoIgRxz0nRJ6DQokWLRL8R8QjhEPAgoMF1GQuCE7wPB5drImoZCwQYINAQtgQ4cGJJgWYsEIsEKRCEZAGsueaann6OECWr4corr7Q99tjDHVKEKcdRrx845ZRTvFa4UaNGiecYFwQ/fVtnnXX8ufvvv9+DKQg+ju3Zs6fVr1/fgwSMH2KQvm+33Xa+Vpj/mDhwEBxmiAMHcX13DAJzrbXW8geBigDrh/Mwn2QIZLImgLnZaKONvD0hiMF5WC+ME+1ZtmyZB5YY9wBBJ9xqxp1ADcemuyainjZzHeaCoFL79u0Tr7O+EdWsfTadI5D06quvJmVqbLbZZn5cKnxm+TzHn2U+d5m6/UIIIYQQlYXVyrsBonK63QgyhCAgbviyj+gMab2p4LjyIN2bTa4Q2jjGiBwcTFJcScWOQXgiXDmGFFp2g0YMImgR5KR4x5tcIaSAa5CmjmgLIDpI1w0g4AMIJFKhiyKk96677rpeY0z7ELOpbUZk4sAyFghuHEh+Iq5iEF04tzFkBoSUawQpY3P33Xe72OZB38Mu3YhWxhDhzBix6zqOKNA+xCtuf4AxDnNG/6l5RgySYh0EahCLBFYQwCE1mnmgb3F711tvPReNXIufgHiOQYSy4zfvY40EeA9BCzIdAswB10HMI1RpR4D07xjGNmwEhrBF3AP9uOGGG1wAI7wR05lCwKC4ayLAOCG8CSwwLiHwgABnLAnMMI4I53jNhkBEuGZIw48haELgATeaBynhBFQC8eeAaxDgYXwDqXOS+lnmdd4D3HmA4ApBltRghxBCCCFEZUbCW2QdvqzjQiM2Aggl6r5JHY7BwUMghXpV3GhEA+IBd400YVLIYfjw4UlCJIgT0rRx+BDSOL44uaRVIzgDBAICq61W9MeiatWqxe53EMQ45/yOUAqufQwbsgFCm7Rl2o74QhCmHhc74BCLU8YOZ54UfYRm586d7ZFHHkm8zlghvnG6GaO6deu6wAvzgaNOwCImBCfiOm8cTjYCCyAWEa04+8Ghjcc3huvEqeapx+HUklKOu81YUIcd3sfO8QjJGIIa6dzWsClagOwAavxJ7+dnEJ/MDfOC2CdIkc7xLoiV2UCOcSJ9nnPErnYIBIT67njdpVuD6co1GCN2YKfen3WPMCZ4FVLhU9c7Yxv3pbC54zNLoCoIb2A+JbyFEEIIIZJRqrnIKriR3333nacBI6jD47bbbnNHOK45Dl/uqV3lPalCKqT64r4iQnCpEaI8EGDsIE7aM+dEkJKqTCozgpHU9oJqykmbRuDHabs45bjT8Y7XxQWRjfinvTi8OMKkdYc2c25S5INrSXABgU7tMI5tYc5jOhA/iLhbbrnF67wR1bjpcb9JN0eUkfYeO+q0jw3PQtt44H7HNcQIQWq4Ea9x7TFikU3k4lRt+ozA47kAgo32BLc7HewITx9wnulH2DCP97ApWty+e+65x89PWjRp2PGGZanrh6AD7jF9Ymy5FRowH1yPDfdwneNU8hiOK01Yk6wxBDap+PHzBBJC3XdJINODeTrqqKM8zZzPRbwBWrwxIJ831n0mO6dzVwJqwakhjz/LBMnIPIg3aRNCCCGEqOxIeIusu92k0lI3iqgKD0Qf6dyp9wFGqLIJG+7ZSy+95GIQccXmWQhUXG9ENk4otxhDoOBUcvsuRB11rVyPjagQCghuNlVDeKRLyw1Qt41IwVEnRfu6667znbhjx7cwENVsaIWbinjm/dTNBueeelpqj6mtJcUYYUR97aJFi1z4A6IPQUvAgNrr4kK/uTbnJ+CBc4oYjftNMIE2pgpv3HHaS+o59eiINx5x7TUCFQeV9sbOO2IR4R2n6+NcM0ekdDNHiDt2dKduP2QsFAYBE4InIbX81FNPdfee9UL7SDsnNZ5xZT2QTs+1ELJcD9c8FdrPekwVtPyNOxw7z6kg1BHEZAqUBrSbVP44bR9w3/l8sMFZSYU3AQo2X+NzgyAmwyF2qMlYePDBB71mn3XKngmHHHJIkedl7MgQ4DMYf5YJ8rD2dE9vIYQQQoj/Q8JbZBW+rJMqnpr6C6RFs+t2qphhN26EFOIJAUraMe44dd6ILEDQklLLxmukkuOuIrBxwnkP9eG8Rto2Qowdu6ljLUh8U1eNoGDTLtqFQAx1wJnAbujUQyNsEYkIX4RrLJ5wtwkMIHI5Bhc3rlkOjjROeEmEN8EDggWcHzFE8IEN5mL3l3lgYyz6F9dE8z7aR/o+gpzN5nCcYzGK6OL9qQIVsUhfSN+O06EZc1L9mQfGlBRmxiTdWkiFYwhM4P4TPKBNbH6H20qaO0EVatlDYICSBdLJGVfWBrf3SgXHnkBH7NYDc4T4LEzosh6ZU9ZVadzTGwed9qQGMYD28XoslosDO9szF2QN0G76TKAiQCkCASbKClgbZJiE4E9B8Lkhk4RN5FJhXo8++mgPhBSnzl0IIYQQoiJTZXlpfGsUQgixyhEyMFL3D8gFug4eZeOnzCzvZgghhBBiFaNJ/do2rGdbmzt3oS1d+n97CZUFVB/Wrfu/OxcVhRxvIYQQQgghhBCiDJHwFkIIIYQQQgghyhDdTkwIISopuZhiHmhYt4YtXrK0vJshhBBCiFWMxvX+7/a6uYRqvIUQQgghhBBCVBiWLcu3efMWWX7+8pyp8ZbjLYQQIudgQxSRXWrVqqZxzzIa8/JB4559NOblQ2Ue9/z85WUuuouLhLcQQoicIz8/3/LLdiNSkRKxDw6B8uCyg8a8fNC4Zx+Nefmgcc89tLmaEEIIIYQQQghRhsjxFkIIkXPk5eVZnkLDWadqVQ16ttGYlw8a9+yjMS8fKuu45+dgqrk2VxNCCCGEEEIIUWFYps3VhBBCiKLpO2KcTZwxp7ybIYQQQohV8HZi/Tq1sry8Kjnlekt4CyGEyDmmzppvE6dLeAshhBCiYlA5k/6FEEIIIYQQQogsIeEthBBCCCGEEEKUIRLeQlRSZs6caX369LGWLVta06ZNrU2bNvbAAw/Y0qVLi3We999/3zp27Gg77rij7bLLLta1a1f75ptvVrp9zz77rG299daJR5MmTWznnXe2Hj162E8//WSrGieddJINHDjQcgnaE4/xDjvsYEceeaS98847ScfNmjXLevfubXvuuacf07ZtW3vssceSjtlvv/2S5qpZs2a+LsaOHZvlXgkhhBBC5B4S3kJUQn777Tc79thjbdq0aXb77bfbK6+8YmeffbYNGzbMzjzzTMvPz8/oPAjss846yw4//HB78cUX7fHHH7eNN97YTj75ZD/3yrLhhhvae++95493333XnnjiCZs7d26x2igKB4Ecxph1gKg+99xzE/PHjS/OOOMMW7hwod1///326quv+t+smwcffDDpXJdddpmfB+H+5JNPeqCkW7duNm7cuHLqnRBCCCFEbiDhLUQlpG/fvtagQQMXUrvuuqv/fthhh9nQoUPt008/dQGdCS+99JLttddedsIJJ1jDhg1tq622smuuucbWX399F2grS9WqVf1cPOrVq+fnv+CCC2zq1Kn2/fffr/T5hdnqq6+eGONNN93UTj/9dA+evPXWW/464/ztt99av379bLvttvO1csQRR1iXLl1sxIgRSeeqXr26n2eDDTbwubr44os9k6J///7l1DshhBBCiNxAwluISgZpw4gqBBbCNgbBdfTRRycEFenexx9/vN18883ujO6zzz721FNPJY7Py8tzYTZ79uzEc1WqVHEntEOHDvbPP/+46/n6668nXv/vv/9s9913tw8++MDmz5/v7iriv3nz5nbhhRfa33//XWj7Q5sRjPDDDz94Gjfp8gcffLC79nEqNY48gYHddtvNPv74Y78u6dSkTO+///7uogf++usvT79v0aKFp81fdNFF/hx89NFHnk49fPhwa9Wqle20007++pIlSxLO8D333OPHbL/99p7Cf9ddd5UoDR23mZTt4DoTxKBvtJkAyRtvvJGUvdC9e3dP9efaXHPZsmW2MqyzzjpJcxxKCmJOPPFEu++++4o813HHHedzRLBECCGEEKKyIuEtRCUD9xKRiIhLB4Jz4sSJCUH59ddf24QJEzx1+JxzznFHm3RiOOaYY2zOnDm27777evo3db+//PKL1a9f39Zbbz1ba6217IADDrDXXnstcX7SjldbbTUXwnfeeaf9+eef7rA/+uijft3BgwcX2PY//vjD7rjjDttss82scePGLuwJINBmUt0vueQSf//zzz+feM+bb77p6dOPPPKIC+KePXvaIYccYiNHjrTzzjvP+zNp0iQ/lv7RVwT0Qw895LXkl156aVJdPH0hUwChTEAhXIufXOO6666zUaNGeeo+xzDeKwNBDZxjUrY5b/v27e3888+3efPm+TzS5jp16thzzz3nzjJZCLS/JHA+RP3PP/9sBx54oD+Hc73HHnv4uB111FF26623ehCiWrVq7n4Xxeabb+4/wxgLIYQQQlRGdB9vISoZwcGtUaNG2tfD8wi74GAPGDDAxR0i7JNPPnFHHEcXUYUDjtB7++233UknJRlhe8MNN9jaa6/tqca9evWyf//919Zcc00Xj7yOcz19+nQXcJtssokfi6iOmTFjhjvtgIvLObbZZhsXf7wfR552IQqhUaNGfk5EfLt27fy5unXrumsf+sSD57gmD1LYSY9G9OOI0z5EPdx0003uME+ePDnh1l9xxRW25ZZbuiON801gAnd/o402cuHLBmTANQcNGmQ//vijp2iXFIINXJd6dwIap512ml+bsfzwww99jJgDnGkCEgQf2AgN4Z8JlBaEMSbYwuZ61OjTn8C9997rG++98MILNmTIEH8gum+55RZ32guD9HOgRlwIIYQQorIi4S1EJaNmzZqJlHPEXCq4uoBjDdRuI24DuMZxevYWW2zhqegItvHjx/sGXQhzxCwilRrwNdZYw3e3bt26tTuqwZFF4JEKjljlQTo1G7UFEMVh92yEJW2PAwYIYgRzEI5BoMcp9IjVAH1CENMunHGcehxkzksqNecOohsILPAa1wkCkvEIrLvuuold4HGFv/zySxejOOU457j5K7sJHIEGUvxPPfVUbxvp8WyMR6CC6xBIwPEPcD0yAdiErlatWkWen/lk/gCBT7sJntBv3HRA5DNPPMhoGDNmjJcTkOXA77xeEKF0gLESQgghhKisSHgLUckgxRxhyo7k6YQ3z+OoIpaBtPAYhG2o+73xxhu9XprbR3Ecddo8EFkIsvB+BDUp2tRl8xp134DYZgds0sFxzK+88kpPYw9CkPfGQjcVRC/n4H0FkSoKr776aq/5JgDAgxR6RHjobyr0N66ZTj2O9GzAdb7++utdFB900EHuPBNYKAnx9cg4wGH+6quvfJxGjx7tdeY86D8ud7r0/BAoKArKAeIxJpCCi46wRngzb6S7d+rUyV9nA7ZTTjnFMx7IBqDGn/r6ggib4JElIIQQQghRWVGNtxCVjNq1a3vdNWItdRMuNup6+umnPXU6wKZYcZowwpyUc0AkP/PMMytcA+eY6wRwsbkdGKnopJkjJuHhhx/2Gmhqh0kzJ1U73oitKHCAp0yZ4injiEceX3zxxQr3mA7gQFPTzXG4tbQdp5p2cS42ewtp5aEuGcc2dsELgjp10ru5pRZp7rjNCNYgzAsDMR+P8a+//pr4HVebAAfilpR9MgpIAyeDgHYhkhnr0H82ZKN2PoxxSaDNwann/KwVXPSYkHkQz3M6GOOwG7oQQgghRGVFwluISsjll1/utd5sTEaNL+IKJxWHlk3PgrsJixYtsquuusoFICnk1ECH10k95hZkONQ4m4hWhDubj3Xu3DlxDlKhSY1mAzBqvgO///67XXvttS6W2dALd3XbbbfNuB/c1gpBiONN+3DP2dwsTo2PIX2afuJMkzJNvTqp6lyTtPK9997bnWrcZR78joMfAg2FgdBmx3QCAQQnEMmkbodN6opK92azt3BdhHMscBH1iF8EOZkB1LHTZlxnUunZXZ3xZy7ZlZ2xTt2xviBoIwEJHpQZIOipkT/00EP9dYIiZB5QW07/EPZskEf/cPYJegQWLFiQOA/tYS7YkT3eoE4IIYQQojKiVHMhKiHcZxkRjZjjFl7sTI4j2bFjR08jDqnkgLtKvTY7mPOTDcdCTTHiDLeWtGTEISKONHWELbXIAdxXnG6cZURmgF3FEWu4zwh8RC7nzxTS1rmlFdfDZaaGmzRydgBPB22lzxyPaGdjN/pFejjgLFPfTNAA4Uof2KgsE3C6eZB6j/BnbBDA1EwXBfXb3HKLW3QxNwRGQh8Yc3ZHJ7hBbTznZldzRDfcfffdfl92shS4DRjjTMAgU6jLD+di3rkefejRo4c/x5iS1n777be7wA+b05HFkLqBG+PKg/nGCSc4QFYDt4sTQgghhKjMVFmeSR6kEKJSwq7h3BcawbyyXHDBBZ4KHQSdEIXRdfAoGz/lfxv9CSGEEEJkSpP6tW1Yz7Y2d+5CW7p05Ta5LQoq++rWzWxfHTneQogyhTRy6rjZGOzll18u7+YIIYQQQgiRdSS8hRBlCjXDpKJTExzXAwshhBBCCFFZUKq5EEKInEOp5kIIIYQoCUo1F0IIITKkYd0atnjJ0vJuhhBCCCFWMRrXq2m5iBxvIYQQQgghhBAVhmXL8m3evEWWn1+2UleOtxBCiFUa0sNEdqlVq5rGPctozMsHjXv20ZiXD5V53PPzl5e56C4uEt5CCCFyjvz8fMsv27IskRKxDw6B8uCyg8a8fNC4Zx+Nefmgcc898sq7AUIIIYQQQgghREVGjrcQQoicIy8vz/IUGs46Vatq0LONxrx80LhnH4155Rv3XEz3Lk+0uZoQQgghhBBCiFVyg7PyRJurCSGEWKXpO2KcTZwxp7ybIYQQQogS3tKrX6dWlpdXpUIL7+Ig4S2EECLnmDprvk2cLuEthBBCiIqBii2EEEIIIYQQQogyRMJbiDJmv/32s6233jrx2G677eyQQw6xhx9+OHHMSSedZAMHDszofLNnz7aRI0cm/v7iiy/soIMOsh122MGeeuopy0Z/nn322RWe5zleK0u++uorH8MZM2YkPX/wwQd7///999/Ec//99581bdrU3njjjWJfpzT7Mm3aNG8zP6+55ho7+eSTk14fP368v37ppZcmPf/CCy/Ybrvt5rfVisf877//tueff77I+SgKzrPjjjvaiBEj0r5+xRVX2Omnn17keQprmxBCCCGE+B8S3kJkgcsuu8zee+89fyAEu3XrZgMGDCiRSLn55pvtnXfeSfx977332qabbupi/NBDD7WKzLbbbmvrrLOOC/DAH3/8YdOnT7dq1aq5iA1MmDDBlixZYrvuuqvlCrTl22+/dTEd+Oijj6xevXr+M4aACsezu/fTTz9thx12mD9PwOaZZ55Z6basu+66ts8++9jrr7++wmtLly610aNHW9u2bYt1ztJqmxBCCCFERUPCW4gsUL16dVt//fX9sdFGG9lRRx1le+65Z1rRUxSpNyJYsGCBO7ubbLKJi6mKzGqrrWY77bRTkvBGsG6//fbuDsfiFeG61VZb2XrrrWe5AkIaV3jy5MmJ52jzKaec4gGEX3/9Nan99Alq165ta621lv9emjeiQFh/+OGHvoZiPvjgA88eOOCAA4p1Pt0kQwghhBAiPRLeQpSjiFx99dVXeB6Xtn///taqVStPSyeV98knn/TXSEd/7rnn/MHzpKh//PHHNmjQIE9Xhr/++sv69OljLVq0sF122cUuuugify6IPN531VVX+Wu45aQ433TTTdazZ09PPcZZ/e677+y2225zobj33nsnpbZnymeffWbHH3+8nxOxTNryzJkz3e2lb7EzimDjOqRXw6effmpHH320BxQOP/xwe+211xLH0qavv/468Td9QqDyQEQGvvzyy4Rw5Zr333+/7b///n5Oxu37779PHMvY3XHHHbb77rtb9+7dk/rBe3v06GFHHnmkzZ8/35/DDWac6NsxxxzjcxCnuPft2zcxdnF2wgYbbGANGjRItJ+5/vzzz71dTZo0SbR/8eLF9sMPP1jz5s2T0rl53HXXXX69MN/w448/WseOHT3dvl27du72Z0Lr1q1d0L/11ltJzzPf++67r2cRsD5S0+C5dqpDn65ttJu11bJlS2+XhLkQQgghKisS3kJkGYQZTvf777/vgisVxPDbb7/tInvUqFEuWBBys2bNstNOO83TyXmQfswxzZo18+dJY4dzzjnHhdc999xjDz30kP30009Jwom0bAQfQimkEj/yyCMuUl988UV3iHFgqSVH8AehHqdHFwUOKun0e+21l7388sv2wAMP2C+//OJ9I3WaGnfEa+zuzps3z8fjzz//9PcivF966SXr2rWrtx8xni5dOxbeCNpFixb586SdB+FNYOLBBx/0lH+CFvXr1/fzhmNhzJgx9vjjj9uFF16Y1Jfrr7/eJk6c6H2oUaOG/37JJZfYmWee6eN1xBFHeFBh6tSpfjxzwrnuvvtuF/OPPvpo0vlof3Ds+ck5Gzdu7KI/CG/6sfbaa9s222yT9F7EPnPNnIf5BtYC/aE9NWvW9PnKhDXWWMMOPPDApMwL1uebb75Z7DTzgtrGHDJ2N9xwg1XhZpdCCCGEEJUQCW8hsgBCCEHCA8cV4Ya4RbSlgvN53XXXuUuMO4oDixj6+eef3YHEoeRB+jEiGdecumfS2BGFOI64jFyHB7/jaMbpzYi0hg0b2sYbb+x/k6rdqVMnfw7BhePK5lqbb765u8M45gj/dP0Jj1js/fPPP3bWWWfZ2Wef7X3AXWcDOJxZaNOmjQceSLsGHG3cV1Llhw0b5m79iSee6O3BaT7uuOM8OACMC4EDAgpssvb777/bzjvvbFtuuaWn9OMg01ZeQ+Tisg4dOtTOO+88F/b0iUBG1apVXagGuMZmm21mW2yxReK5++67z4MfCMe6dev6c/zeoUMHd+JpH5ul4Wwj2rkWG9zhkONWMy6I/YKEN0I7BAf4GZzzuL47hnlnrplz5jtAZgFp4Qh45ot1kCn0A6EcghDjxo3zn/SpOBTUNtY4DjjrWgghhBCisqL7eAuRBRBiCE9Yc801XZgg/NKBgEKU4hAilkn7hmXLlhV5HY4PDmoAoYkLymsIU6AePCb+GwGFyAw1xbQXELvp+hPANUV8Av3DqWezLdz3SZMmeWo3AjmIZ44hDRsRzntJiQ99wDFGtAYIPIQ+0R4CBYhXhCm/I/gAsYvwRvgjoAlOIMJx00kLDyAOeR/iPYALHkNaPOn2G264YZKQ5D2kYof0/9A+0qnnzp1rc+bMSXKqSf+OQVBfffXVPp649Qjf8DxZBtR5I7xDmnkmENwIMMfx7u5FgdPOe959913PRCDQwC7x6cogSkLquAohhBBCVEYkvIXIAnXq1HF3NBMQe7impFojXnGSM721FanD6UC0x8I9iOm43jwm1WnNpD88F2CjsPbt23uNOu41DjHp89Rdx6nJON2cB8HKDtthR23EaGqtddxGROk333zjAhvhGMA1xt1HCAfhmtrXeEzi9PnU40iLxt3GsSZtvFevXon3kVrO3MSEQAXEtcypArZRo0aeqUA6OeOB+w6IX1xhRDdBhdT+F0ZBQZxM3xtS/8kIYNd9UvPjcYj7w/wUh4LGXwghhBCiMqFUcyFyjCeeeMI3R6PWGHFK2jcE8VNYnSyuMBuAxWnluM2kdMcueFmDiMNlHzJkiKfU4+bi5MYCLqSbI74JLFDTHPpAvTSCPDyoOaZWOMD5cNIRrqnCm03J4h3BEbQ4+DwXQJhTJ17YmOBys/M8Tjz14aGGm/dwT+64fbjfOMa1atXya8Wbv4WMhRhS76k1R4AjxAP0hQAFad8ELdJRFnXSlBeQfUCaOdkDsdtO4GDhwoWJv+Od17PRNiGEEEKIioCEtxA5BmKMVGsEDhuKXXzxxUmp3ghUNkjDVU6FtHJqc6khxzXlwe8IKW6tlc0+UGPNbanoB5uqkU4ep6uTjs39q6m/ju8/Tq05bjbOP3XtCO5bb701UY8OpKyTus6545R06rxxpBG+sXjs3Lmz3Xnnne6GkypOYIN07HBv7MLgGFLjgzPNuV599VXfNI0N40in54GARniecMIJfi1ELO1gh/pUaNsrr7ySCA4E+JsgA8K8IBeb+ScNHvFfWtA/5owxp7+xgCZVngAJc0lQ49prry0wDb0s2iaEEEIIURGQ8BYix2AXbdxcHOHevXt7GjCbpIVbRLHZ2JQpU3zTqnS3Z7rxxhu95heB2KVLFxejcepwNkBI0z5qwUk5p5aZAACiNxbfiDwEZryRFzXB7Mg+duxYd2Jvv/1239U83ogOFxunmTrt4JQDghFRy73Sw2ZowG7bxx57rAtuUvjZkO2xxx7zGvBMuPzyy11IEzxApA4YMMCGDx/u7R8xYoTdcsstCaFPijhp6KSmszs7100Fxx5XO3brw/OMT2H13exCToo864Oa8NKC87HGQs15gPVGzTeb5bEpH3NCwCSbbRNCCCGEWNWpslw3VhVCCJFjdB08ysZPmVnezRBCCCFECWhSv7YN69nW5s5daEuXZn5L2lUNkgTr1v3f5sVFIcdbCCGEEEIIIYQoQyS8hRBCCCGEEEKIMkTCWwghhBBCCCGEKEN0H28hhBA5R8O6NWzxkuLdM1wIIYQQuUHjejXLuwk5hzZXE0IIIYQQQghRqixblm/z5i2y/PyKKzeLs7maHG8hhBA5B7ugiuxSq1Y1jXuW0ZiXDxr37KMxr5zjjuCuyKK7uEh4CyGEyDm4H3h+xb37SE5G7IM7oTy47KAxLx807tlHY14+aNxzD22uJoQQQgghhBBClCFyvIUQQuQceXl5lqfQcNapWlWDnm005uXDqj7uSuEVYtVDwlsIIURO1qWJ7KNxzz4a8/JhVR/3yrBplRAVDQlvIYQQOUffEeNs4ow55d0MIYTIyds09evUyvLyqkh4C7EKIeEthBAi55g6a75NnC7hLYQQQoiKwapd4CKEEEIIIYQQQuQ4Et5CCCGEEEIIIUQZIuEtxP9n6623to8++sgqGwX1e+DAgXbSSSeV6bVHjhxpO+ywg/3333+J5/i9WbNmtt9++yUd+/vvv3tbJ0yYUOzrlGZfGCvaAWeccYb17t076fWXX37ZX+eaMYMHD7YjjzxyhTGfPXu2j0NprMPQtvjBWHbp0sW++OILKymMXWp/hBBCCCFE5kh4CyHKjV133dWWLFliEydOTDz39ddf29prr+1C+9dff008P378eKtZs2ZC9OZK+2lvqvitV6/eCuIZ4bvbbrv57++9954LYrj55pvtnXfeKdV2cf7wePbZZ6169eoeJFiwYEGpXkcIIYQQQmSGhLcQotxYf/31rWHDhvbVV18lnkOw7rnnnrbNNtskiVeE6y677OL3d84VaM9PP/1kCxcuTDxHm4PD/M8//ySe//LLLxPCm36vscYa/vvy5aW/Iy3nD4/GjRvb5Zdfbn/99VelzOgQQgghhMgFcucbrBA5DOLonnvu8fTn7bff3lq2bGl33XVXUiru3Xff7YKradOmdvDBB9vYsWMTr8+dO9fOOeccdzn3339/e/zxxxPObZy6HLj00kv9kcm18/Pz3TXdfffd/UFK84EHHpgQWfPnz7eLLrrIdt55Z39v3759kwRhprz55pvWrl07Tw3H6T3//PNdcOKi8tyHH36YOPbvv//25z799FP/e/To0XbYYYfZjjvuaMccc4x9/PHHiWM51zfffJP4m3YjUHnE54yF67///ms33XSTtW7d2nbaaSfr3r27/fbbb/7atGnTfDwHDRpkzZs3t2uvvTapH7z3+OOPt9NOO83ddnjiiSd8fJkf5vL7779P6gt95TXmNXa46ePqq69u3377rf+NSz9jxgw79thj3WX+/PPP/fkpU6a48KWvcTo56dvPPfecP+LUesbt8MMP9/OfeOKJNn36dFsZqlat6j9pa0HjM2bMGDvqqKN8/TJXr7/+eoHnw0U/9NBD/dijjz7aPvnkk8RrrC2EPkGJVq1a2VNPPWXbbrutX1cIIYQQorIi4S1EBjz//PP2yCOP2HXXXWejRo2ys88+20VTEFyAOG7Tpo3X+DZp0sT69OnjohgQbnPmzHHBfeWVV7roKa1rDxkyxI+55ZZb7KGHHrK33347KUUbEYQ45tqIcoRjqhgtil9++cXOO+8869Spk9cj33777TZu3DgbMWKEC0wEFuI6QBtq167t4os08ksuucTOPPNMe/HFF+2II46w008/3aZOnerHIkaD440QJqU8CO8QPOB5+huE91VXXeXXu/HGG100L1261M4666zEeAOi95lnnrGTTz458RyvMxf8JHiB6/zWW2/578wXApg28x6EcrjW5MmTbejQoXbFFVf4GAd4P8GE0H4CBQRHqlWr5qI2tB/3e8stt7RatWoljSviHwHL4+mnn048j1jlWjxHOwislBSCPgMGDPBrh/T21PH54IMP7Nxzz/Ua9BdeeMEDB7169UoKiMSim+BNt27dfN21aNHC09j/+OMPf71fv34+hw888IDddtttdv/999uyZctK3H4hhBBCiIqAhLcQGbDRRhtZ//79PQV6k002cceUNN4ff/wxcQzuK+7fpptu6iITB/bPP/90txORikhEkHMc7ndpXXv48OHWs2dPd7NxFm+44YZE+jKC+Y033nB3GJcThxLRhMCM630Rwoiy+IGgDyBUEYIdOnTwNnAtBFdoAwEHhHC47muvveZiskqVKi7AeB8OLmnlCL29997bAwGAQEXY4izjateoUcPToxHkbDzG+LGh2pprrunp5whRxCEBjD322MPHFGHKce+//36izaeccorPRaNGjRLP0XcEP31bZ511/DmEISJy33339WMZy/r163uQgDEi0EDft9tuOw8wIPBj4sABQpusA4gDB3F9dwwCfa211vIHgYoA64fzMGdkCMQ18JkQ5pCgAGOEyEYEM7bpxmfYsGHu5nfu3NnH/tRTT7WDDjrIHnzwwRXO/dhjj3lWANkPm222mV144YW21VZbeWCCDAjEOEEMMhEYG8ZOCCGEEKKys1p5N0CIVQHEC6IQV5maXoQgojp2WGOBt+666/pPnFjSltdbbz1r0KBB4nVESWlcGxd95syZnpIcQAyxCRlwPMchdGN4DgGKOxtcSkRaqsAKKdf0DXeXdHrENo9JkyYldulGtOKs007EImn2jz76aKINiNcnn3wyaedyxDswLgQScFdJsQ4CFScdUY1oRACH+u6ff/7Z2x+3l/FFMHItfgLiOQYXlpRo3hfGJ7SPwMStt96alI7OdRDzuLW0IxCPNSAuEZuA0EbcA/0gCIJbj/BGTGcKgjjAONCe4hDaw3ixFlOd9tTxYQw6duyY9DrCHUc8FY4l6yKG9czzBFCY23iMYpddCCGEEKKyIuEtRAaQ+nv99dd7Ci5OIKnTcQpzqJ9NBQd4tdVWK3QDLVzhVBDsvK+oa4djUs8f/kY0ItzSCagNNtgg6Xfc6JhYnOK44rRTh4zQxBkl/T2Ae4z4xukm5bhu3bruroc24KjjkMbg8qbWeVP73bZt28TzuL6IVpxUnHHA+U4H14kDIanH4S6TUo67TQo34xned9lll3lGQQyCNV1tddgULRaWBD9I4ecntfRAajljj9gnSJHO8S6Ild1ALnUu0xGPT7oxZSzj8Szs2DD2YT3GlMXmcUIIIYQQqxpKNRciA0iLxuVDoCEgcRBJg85EVGy++eaeHh3XXce1s0Gwk2odiDeiKuzapA5z66q41pzrsKEa4P7iFiPuEWM82PyKmt+wsVgmkNqN8MV1p84bUY1jHvefdHNui0VqO5tzBWgD/QnX54H7/e677yaOwc2mD4jXkKoNiNUffvghKVUbhxyBF9+Xmjpm2hPc7nSQDk0fcJ7px7x58xLtY1O0uH3U63N+sgeYn3hDte+++y7pvAQdSIGnTzi93AoNGHOuR000GQNxKnlRgZdswxiQrZCaIZBuPNMdy988j1PPeMXrO12duBBCCCFEZUPCW4gIanURhPFj8eLFLnbZgIrUY4QEG0+RUpuJeEWQkFaNcMY5pg75zjvvTLyOM4r7i9hDNFNzHIu7oq5NvS3n4xjO37t374SgQ/RTl0wdLn1D3PL6okWLkup9i4JUbtLOOQftIIUaMRr3n3R2HN9U4Y07/uqrr3rqOTXnDz/8sD/i1HwEKhuy0abYrcUJR3gjkqmxDs41bjUp3aR202d2bd9www1tr732KrIv1Dbj5ofUcuqZce9Jz6Z9pJ2TGs/Y4XqTTs+1EJdcL95RPm7/K6+8soKrzd/sBh/c+nQg1HHWw+Zk5QFzRLYC40CKPfNDzT5ZDumOpZ6b8WItUF/PHFCLztywzwEbATJeBC/4PVcCDEIIIYQQ5YVSzYWISLd7NLdVQjTzQITVqVPHNw5DMFFvnQlsjsaGU2wyRlo34gSBDYg7hB2bX1FXza3ATjjhBHdxoahrszM2gpddqbltFDtMUysdnHTcbWq4EUw4xQjx4m54hbgnGMA5SDVGSOLCIzbjFOwDDjjABVdcE039L21gJ3Z+4oriOMdilOAD708VqKRqE7jALQ63xALS7dmsrkePHi7+2egNsZiaBp4OjiH4gPONgCdIMGvWLA9e8HOLLbbwWvYQGGDemB8EOoKdseDaMTj2bEQWu/VBeBO4KSzNnHllLNntPb59Wjah7j3MEYEHxpyd61PT7yEeL/YawO2n7wQqwtywEzxrhbXNWmZtpyvFEEIIIYSoLFRZrgI8IcoUhBe7muMIB/GBo4rA4VZWKwuuPJukhVRmNlxDMOG0sgO5ENmEjAfWH+43kCVBeQKp68UR310Hj7LxU2aWYUuFEGLVpEn92jasZ1ubO3ehLV264l4cuQYJT3XrVrdZsxaYVEf20Lhnd5wzQY63EGUMDjGONWm77du3d7eQ+3hz+6bSgNpibilGOjnpvHfccYfXGkt0i/KAVPwxY8Z45gWb4hFgYlM+Od5CCCGEqMyoxluIMoYdqhHauN7s2M09vEn3pla7NOB+1lyD20GRys7u0lxPiPIq12AzPTYCJD2fAFCo8xZCCCGEqKwo1VwIIUTO0XfEOJs4Y055N0MIIXKOxvVqWr9OrZRqLgpF454dlGouhBBilaZPhxbl3QQhhMhZli3Lt/x8qSkhViUkvIUQQuQcODkiu9SqVU3jnmU05uVDRRh3RLeEtxCrFhLeQgghcg72KsjP/QzKCkO4zToumlISs4PGvHzQuAshygttriaEEEIIIYQQQpQhcryFEELkHOzUn6fQcNapWlWDnm0q65grVVoIUdmQ8BZCCJGTNZgi+2jcs09lHXNSvefNWyTxLYSoNEh4CyGEyDl0OzEhKv7tsPLyqkh4CyEqDRLeQgghco6ps+bbxOkS3kIIIYSoGFTOwiIhhBBCCCGEECJLSHgLIYQQQgghhBBliIS3WGW59NJLbeutty7w8dFHH63UuXkUxrRp0wq9/kknnVTi6xfV/oJeHzhw4EpdNxNGjhxpO+ywg/3333+J5/i9WbNmtt9++yUd+/vvv3tbJ0yYUOzrlGZfGCvaAWeccYb17t076fWXX37ZX+eaMYMHD7YjjzxyhTGfPXu2j0OgpOsttKugR1FrsDT44osv7KCDDvI5feqpp8r8ekIIIYQQlRHVeItVlssvv9wuuOAC//3VV1+1Bx980J5++unE6zVr1izT62+00Ub23nvvJf4+5phj7LTTTrPDDjvM/1599dWtIrLrrrvakiVLbOLEiS7W4Ouvv7a1117bhfavv/5qDRo08OfHjx/v8xBEb660/8UXX1xBANerV89/nnvuuUmidLfddvPfmeuwpm6++WZbvny5HXrooSvVFoIV8Rpq2bKli3+eh7XWWsvKmnvvvdc23XRT//yst956ZX49IYQQQojKiBxvscpSvXp1W3/99f3B71WrVk38zWONNdYo0+unXo+/4zZVVBFD3xo2bGhfffVV4jkE65577mnbbLNNkvOLcN1ll138nsy5Au356aefbOHChYnnaHOXLl28vf/880/i+S+//DIhvOM1heguDThfvIYAcR+v67JmwYIF1rRpU9tkk01s3XXXLfPrCSGEEEJURnLn27AQpQzu63nnnefCaffdd7d+/fq5UxvAjT3++ONtp5128hTpxx9/PO155s+f7y4oTmnz5s3twgsvtL///jujNjzxxBN+bhxM0qa///77xGuLFi2yK6+80tvGo0+fPvbvv/8mXv/000/t8MMPd1f5xBNPtOnTpxd7DN58801r166dn4P2n3/++S44EVs89+GHHyaOpU88x3Vh9OjR7t7vuOOO7uZ//PHHiWM51zfffJMkXBlnHvE5Y+FK32666SZr3bq1j3n37t3tt99+S0rbHzRokI/xtddem9QP3stckVEQ5rCwsaUv9JXXDj74YHfkA/SRbIRvv/02sU5mzJhhxx57rAvdzz//3J+fMmWK/fXXX97XOJ0cR/q5557zR5xaXxrzlQoCnzHBCacdjBltDdCmO+64w9cPrz377LPWsWNHO/vssz3AgLPPWJBaT2Bk++23t0MOOcTeeOMNfz/jxrxyjZCVQJ9Ziy1atPBzXHTRRf5cmGf6fNVVV/lruOWkwzOvPXv29LXCmvnuu+/stttu8zbvvffeSWn5QgghhBCVEQlvUSFBnJ1yyim2ePFie+yxx+z222+3t99+2wYMGOCv43jyOiIPsYKwvvHGG11spnLnnXfan3/+6cL80Ucf9RRran+L4q233rK77rrLRQwiDaFy8sknJ0TMFVdcYZ999pmfizRffqedAeptOYb0ed5DenNx+OWXXzzw0KlTJxc+nHvcuHE2YsQIF5itWrVK6i/jU7t2bW8nfbzkkkvszDPPdPF2xBFH2Omnn25Tp071YxFUwfFmrAliBOEdHG+eR9wG4Y1Y43qMM6J56dKldtZZZ1l+fn6iDYjeZ555xscpwOuIaH4ynrjERY0t15o8ebINHTrUx/Chhx5KnI/3IxBD+wkUIEirVavm6yG0H/d7yy23tFq1aiWNK+KfFHMecWnDys5XOmj/Sy+9ZLfccos9+eSTVqdOHb9+XF8/ZswYX5sEhIC52GKLLXyeEezXXXedBxFYY9SyM3eUaTA/Ia2dc4aU93POOcdr8u+55x4fNz4rca05AQXey+embdu2/twjjzzi88xaIdODzxZ18LQ5CPV4noUQQgghKhuq8RYVkrFjx9off/zh4iPU5eIuIyR79erlz2+77bYu6GCzzTZzgXH//ffbgQcemHQuhAaijFRc6phxGDOBc3Xr1s323Xdf/xtH8N13300I2VGjRrmwQTQCLm+8CRltxckEHGfEagxCmPT2mLDJGSB0EIIdOnTwv2k/LuaPP/7of7dp08ZFMMdUqVLFXnvtNReT/P7AAw/4+3BwAVH7ySefuMBDhCFQEW+4qbS5Ro0a1rhxY6tbt64LLoQemQJrrrmmp58jRF944QW77777bI899vBzIkz32Wcfe//99/29gGCj3jimb9++LvgRoeuss06RY4vDT6CBIMl2223nryPwYxc9DhwgtMM4Ix4Rp6n13TGshVB7TaAi0/kqCfQT0RrOSx8Q06zv4LYfd9xxvn6BPjF/tCW0kbk69dRTbauttvK/EdkECZgn9inA/WdcSW0n4IIDztoMc4KbjYtNICPQtWtXLzcIELggwAOI8euvv97XFW3AVWfdzJo1y+vohRBCCCEqIxLeokKCiG7UqFHSBms777yzu6w4wbxOXWsMgjWdWEJ0ItxI1eVB6nIQpEW1AdFy6623JqVM//zzzy4kly1blhCGQQyGtGaIBSgOdZyGDqTO49zG4O6HlGv6j7t79913u9jmMWnSpMQu3YhWxDPp4KQZI+YQq6HtiFccy1jUI/qAzdMQaqSbk2IdBCrtbNKkiTvXpLOH+m76TCAgbi/OKOKOawWRV79+/aT+4N4i+HlfPJeFjS2in7GlHYGwCVw81s8//3xCeCPugX7ccMMN7ugivBGwmVLUfBUXSgJIgydQFNfIU4NOPwOpY4YrHm/KRiCC1HKCTYjnkGLPGKXC6yGIEth888197Hkt1JwTxImJ/+baBGBCGwi+QFzmIYQQQghR2ZDwFhWS8GU/JggNfqZ7HWGYTowgtt955x2vlyYdG+ectNyiUok512WXXebvj2EDq5kzZxbZh6I2JNtggw2SXEeIxSnuJXXROKMIzc6dO3tKcACXE/GN0012AGIpBCNoO446oi0mFnShzhuHNKQcA+4sohXhiNsK6cY7XCdOQU49DneZlHLcbVK4qcMuamzT1VanbrRHkIU5oPabnwRlgNRyxCVinyBFOse7IEp7A7mwFsmwiIVw6jynjlnq3xdffLEHMAi4sB4ImOCSp6OgDQlpS/zZSL3Gaqsl/1eSS5vpCSGEEELkAvp2JCokCBVcwXnz5iWeQwwiEHAmeR2nNwZxkipw4OGHH3aX8KijjnIR1L9/f3v99dczagOOJeI4PKibpR04xqSJI44DuJJco7QgtRvhS30wacCIapz2eEdu0s0JKnDtcBu00HY2PIvbjvtNOncAN5txQbyGVGhArP7www9Jqdr0l7HnucDcuXO9PenGPEB6NH3AeaYfYT4LG1vSrkmfjjdUY7OvGIIOpMDTJ9xwSgiANO1Q90/GQJxKHsNxZQ3OM+41+wuEPpIajtOPq58JlAKQOs9GZz169PAyilAHn25ndsaVEoE4rZwABOcpbJ6EEEIIIUThSHiLCslee+3lYg+3j9RrNtAinRhnFkGDEKU2mVRlRAwbdA0fPtxOOOGEFc6FwKO2FlGHmMchpj68KKirxWEmpZn0dgQT6duk7uLM4iaz8RV1uYhExFGofy4NSOWm75yfPpJCzXXilF92nMbxTRXeuOPcG53Uc9pO8IEHYjSAQCUDgPGMnXeccIQ3Ijmk0uNc41YzB6R2E3Bgt+wNN9zQ56ooqP3G5Q2p5UWNLe4u1yK4wvVwzVOh/a+88soKrjZ/k90Q3Pp0INRx1skUKEuYBzbFYzM51h5106Txh5ruosDBpq0EigikUE4Qat3TpX4zfqwJNtZj3fDgd8Yi1IgLIYQQQojiI+EtKiS4yWHncTYJYxO1/fffPyE6Nt54YxsyZIgLEeq1qYNm07D27duvcC52BicVGdcVQcdtwBB6RYGQpT6XXdER/B988IFfJ4hXUqWpQ0ZEktaNa8zxpQWbWnHbLsQbgQZuQ8VtpmL3F2F2wAEHuACOa6J5HzvAE4ygH9QH4zjHYpS0bN6fKlBJ1cYdZczizd8QcGzuhvNKyjPpyoj5TO63zjHcEotNwQgeFDW27HZOOjljy7xye69UcOyZy9itD8Kb3fALSzNnHRDMYJO80rqndzq4tzgbtVHeQKCGOWTjuzjVvKhxY60SLCK7geAL65h083gjvxg23CNoxbrh+swztxsTQgghhBAlp8rysvzWKIQQQpSAroNH2fgpRe+FIIRY9WhSv7YN69nW5s5daEuXZvdWg1QK1a1b3WbNWmD6BpwdNOblg8Y9u+OcCXK8hRBCCCGEEEKIMkTCWwghhBBCCCGEKEN0OzEhhBA5R8O6NWzxkqXl3QwhRBnQuF5m+1QIIURFQsJbCCFEztGnQ4vyboIQogxZtizf8vNVeCqEqDxIeAshhMg52HRJZJdatapp3LNMZR5zRLeEtxCiMiHhLYQQIufIz8cNK+9WVK5dWYMLqd1vs4PGXAghKhfaXE0IIYQQQgghhChDJLyFEEIIIYQQQogyRKnmQgghco68vDzLU2g461StqkHPNhVtzFW7LYQQ6ZHwFkIIkZObTonso3HPPhVtzKlZnzdvkcS3EEKkIOEthBAi5+g7YpxNnDGnvJshhCjm/bn7dWpleXlVJLyFECIFCW8hhBA5x9RZ823idAlvIYQQQlQMKlZhkRBCCCGEEEIIkWNIeItKy9Zbb20fffSRVTYK6vfAgQPtpJNOKtNrjxw50nbYYQf777//Es/xe7NmzWy//fZLOvb333/3tk6YMKHY1ynNvjBWtAPOOOMM6927d9LrL7/8sr/ONWMGDx5sRx555ApjPnv2bB+H0lqHU6dOtXPPPdeaN29uO+64o7Vv397bVJKxevbZZ1eYh5hLL73UH6nvA/pE34QQQgghxIpIeAshssauu+5qS5YssYkTJyae+/rrr23ttdd2of3rr78mnh8/frzVrFkzIXpzpf20NwbRXK9evRXE8xdffGG77bab//7ee+95cAFuvvlme+edd0qlPYsXL7aTTz7Z6tSpY8OGDbMXX3zRjj76aLvkkkvstddeK/b5DjvsMHv66aczOva0005LBBumT59uPXv29PYIIYQQQogVkfAWQmSN9ddf3xo2bGhfffVV4jkE65577mnbbLNNknhFuO6yyy5+W6lcgfb89NNPtnDhwsRztLlLly7e3n/++Sfx/JdffpkQ3vR7jTXW8N+XLy+9DYfGjRtnixYtsquvvtq22morH9sTTjjB2rVrZyNGjCj2+dZaay2rXbt2RsdWq1bN1ltvvVLvkxBCCCFERSR3vtEKkUMgJO655x5Pu91+++2tZcuWdtdddyVeJ8X27rvvdsHVtGlTO/jgg23s2LGJ1+fOnWvnnHOOu5z777+/Pf744wnnNk5dTpfCW9S18/Pz3TXdfffd/UFK84EHHpgQrfPnz7eLLrrIdt55Z39v3759kwRhprz55psu4EgNx+k9//zzXXAuWLDAn/vwww8Tx/7999/+3Keffup/jx492t1TUp+POeYY+/jjjxPHcq5vvvkm8TftRqDyiM8ZC9d///3XbrrpJmvdurXttNNO1r17d/vtt9/8tWnTpvl4Dho0yNOtr7322qR+8N7jjz/eHVrcdnjiiSd8fJkf5vL7779P6gt95TXmNXa46ePqq69u3377rf+NSz9jxgw79thjrXr16vb555/781OmTLG//vrL+xqnk+MQP/fcc/6IU7oZt8MPP9zPf+KJJ7qDnAkEJZgTRH/MBRdcYP369Uv8TbCAtcqaaNWqla8n1lEqqanmtIs1wBo/77zzkhztONWcNR5+Dh8+3K/z+uuvJ5UTsFY/+OCDjPolhBBCCFHRkPAWIg3PP/+8PfLII3bdddfZqFGj7Oyzz3ahEQQXII7btGnj9bRNmjSxPn36JMQMwm3OnDkuuK+88koXhaV17SFDhvgxt9xyiz300EP29ttvJ6VoX3755S6OuTaiHOGYKkaL4pdffnGh1alTJ6/dvf32291dxUVFYCLeENcB2oBTiiNMGjmpzmeeeaanPh9xxBF2+umney0yIEaD440QJqU8CO8QPOB5+huE91VXXeXXu/HGG100L1261M4666wk8YjofeaZZzz1OsDrzAU/EZu4zm+99Zb/znwhgGkz70Eoh2tNnjzZhg4daldccYWPcYD3E0wI7SdQQHAE9xfRH9qPEN5yyy2tVq1aSeOK+D/00EP9Ead0P/XUU34tnqMdBFYyoUWLFta4cWPr2LGjBxfoFwEL5mKjjTbyY1iHzCPp8FyH/tG3Rx99tNBz875u3br5NVhvW2yxha/HdHDe8JNU9wMOOCAp1Z21s9pqqyXmUwghhBCisiHhLUQaEC39+/f3FOhNNtnERQ3pwj/++GPiGNxXRMamm27qIhMH9s8//3S3E6GBSESQcxzud2ldG0eRelrc7G233dZuuOGGRKovgvmNN95wdxiXFacSxxuBiRgPIIRxdOMHgj6AUEUIdujQwdvAtRBgoQ0EHBDC4bqILMRklSpV7IEHHvD34eCS+oyo3XvvvT0QAAhUhC3OMiKxRo0aLh4R5GzOxfixodqaa67p6ecI0RdeeMEDGHvssYePKcKU495///1Em0855RSfi0aNGiWeo+8Ifvq2zjrr+HP333+/C8p9993Xj2Us69ev70ECxohAA33fbrvtPMCAwI+JAwcIbZxciAMHcX13DAKddO7UlG7WD+dhzsgQiGvgC4MxYj2ceuqp7r4ToGHsjzrqKPv555/9GAJD1NAzFptvvrmLYoIqjENhMA60keyJzTbbzDdww5FPR+gLP+kb62PMmDGebQAI9kMOOcSqVq2aUb+EEEIIISoauo+3EGlA4CEKcZVJ00UIIqpjhzUWeOuuu67/xIklbZna1wYNGiReJz26NK6NCzlz5swkAYQoYhMy4HiOQ+jG8BwCFHcWSEPGuY157LHHEinX9A13l3R6xDaPSZMmJXbpRrTirNNOxCJp9sFBpQ2ItieffDIp1RjxDowLgQTSzUllDgIVJx1RjXONAA713QhI2h+3l/FFrHMtfgLiOQYn/ZNPPvH3hfEJ7SMwceuttyaeQyByHcT8smXLvB2BVLGJ8MYBBoQ2ghboB0EQ3HqEN2I6UwgYBBiHIFgzgb6RYcDjhx9+8BIBXPoePXp4MIH+EkTAcQ4QaGFNUZZQEMw340AwJR6LTDZQ22uvvXz9sC4IPBEMIkNECCGEEKKyIuEtRBpImb3++uu9dveggw5yUROnMAO1vqngACNwCttsKhYyAQR7EEaFXTsck3r+8DeiEeFGynUqG2ywQdLvuNExsTjFccVpp94Xodm5c2dPfw/gHiO+cbr/+OMPq1u3rrvroQ046tQGx+CEptZ5U/vdtm3bxPO4vohW6pZxxoOrmw6uEwdCUo/DXSb1GnebFG7GM7zvsssu84yCGIIn6Wqrw6ZosWgl+EEKPz+pZwZSyxl7xD6itThp1SXdQI7Uf9pNPT2wwRoPhDZzQKAm3fiFcWMsCiN1nbHmMxHerFPq41kfvIc2hnESQgghhKiMKNVciDSQFk1tNQINAUmtLmnQmezeTDov6dFx3XW8mVgQ7KRaB9ggLJNrk5ZNrW5ca851gnOJ+4tbjLhHWPNgY7UBAwYkNhbLBFK7Eb647tQHI6pxzOP+k07MbbFwM4PwC22gP+H6PHC/33333cQxuNn0AfEaUrUBsYprG6dq45Aj5OINxNi8jvYEtzsdCFD6gPNMP+bNm5doH2nZcftwYzk/2QPMT7yh2nfffZd0XoIOpMDTJxxg0riBMed6bFBGxkBBu4OnC7yUFMbqvvvuW2GjNNYJAQMEL/1lrON7p5MNQPvCruTpIJBA32NxXtA91dP1iVID5pyaetLMS7PfQgghhBCrGhLeolJDrS7iIH7g6CF22YGZ1GNEc69evVy4ZCJeETqkVSOccY6pQ77zzjuTBA3uL2IP0UytbSzuiro2O0lzPo7h/L179/bnETaIfuqSL7zwQu8bgovXueUUYixTEGSknXMO2kEKNWI07j/p7Di+qcIbd/zVV1/11HNqzh9++GF/xKn5CFQ2ZKNNsfOOE46YRCTj2gbnGrealG5Su+kzdccbbrihpzQXBbXfuPkhtZx6aNx70sVpH2nnpMYzdghV0um5Fmn0XC/eUT5u/yuvvLKCq83fpHoHtz4dCHWcdTIFVhYyIVhD7CHw2Wef+e/UVrNxHLcVQ3wjgJk3auRJO2e+qAUno6EwMUxghc8Cm/xRk8865RoF9QmYm3CrNYIrPM/+ApxLCCGEEKIyI+EtKjVs0kVKbvxATCKacaQRYWwqRR0zt+wqyPFLhc3RcEbZ6Ip7LLMJW3C6EXcIO4QbadaIFURSoKhrszM2f/MaopKUbwRUOD/uNhuiIYARmQQC4nrmTEDcU5fOOXC8uWUWLnwcIEDUsVEXAjiuieZ9tIFNvxDkpEPjOMdilOAD708VqKRq017SkuONuEi3Z3M36pYRjKRPI+ZT08DTwTEEH0jhJ3hAmwhmELxg/AlgUMseAgOIVtLJGTtu8cbtvVJBVBLMiN36ILwRq4WlmTOvBDPY7X1l739NbTgZEqSqI77Z4I61h9gmOBHWG6KZIAMZFKw91k1RG/4RrOB9jBltZsPAUOOfCu45/WGjurDDOWsSp5v1EfYWEEIIIYSorFRZvrLf/IQQSSC8ECk4wkEM46jirJJ2u7LgyiNkQiozdbzUK+O0IriFyBW4nzgZDQRMikvXwaNs/JSZZdIuIUTZ0KR+bRvWs63NnbvQli5NLoHJFUj0qVu3us2atcD0DTg7aMzLB417dsc5E7S5mhClDG4srjXObPv27W3WrFl+H282myoNqC3GTSadHFfxjjvu8FpjiW6RK1AvT5kDwSBuZyaEEEIIUdlRqrkQpQxpvwhtXG9SmUnppe6a9ObSgFpdrtGxY0dPZWdjLa4nRK7AbcQo42DNKyAkhBBCCKFUcyGEEDmIUs2FWPVQqrlIh8a8fNC4ZwelmgshhFilaVi3hi1esrS8myGEKAaN69Us7yYIIUTOIuEthBAi5+jToUV5N0EIUQKWLcu3/HzZa0IIkYqEtxBCiJyDVFWRXWrVqqZxzzIVccwR3RLeQgixIhLeQgghcg42DczPzRLRClujFtxK1QJmB425EEJULrSruRBCCCGEEEIIUYbI8RZCCJFzcMu8PIWGs07Vqhr0bFPRxlyp5kIIkR4JbyGEEDlZ+yqyj8Y9+1S0MSd1ft68RRLfQgiRgoS3EEKInKPviHE2ccac8m6GEKKYtxPr16mV5eVVkfAWQogUJLyFEELkHFNnzbeJ0yW8hRBCCFExqFiFRUIIIYQQQgghRI4h4S2EEEIIIYQQQpQhEt6VgP3228+23nrrxGO77bazQw45xB5++OHEMSeddJINHDgwo/PNnj3bRo4cmfj7iy++sIMOOsh22GEHe+qppywb/Xn22WdXeJ7neK0s+eqrr3wMZ8yYkfT8wQcf7P3/999/E8/9999/1rRpU3vjjTeKfZ3S7Mu0adO8zfy85ppr7OSTT056ffz48f76pZdemvT8Cy+8YLvttpvfTzke87///tuef/75IucjEz766CO/dkEUZ10WF87L+Vd16Adj+MEHH6zwGs8zxuVJvP6EEEIIISorqvGuJFx22WV22GGH+e9Lly61Dz/80C6//HJbb731rF27dsU6180332zLly+3Qw891P++9957bdNNN7UHH3zQz1eR2XbbbW2dddZxAb7xxhv7c3/88YdNnz7d1l13XRexe+yxhz8/YcIEW7Jkie26666WK9CWF1980cU0t2sChFm9evVWEGgEVDie455++mnvNxCw4djirpuSisrVV1+9zK+zKvPyyy/7549gyJ577mm5xkYbbWTvvfee1a5du7ybIoQQQghRbsjxriRUr17d1l9/fX/wRfioo47yL+mvv/56sc+F6I5ZsGCBO7ubbLKJi8+KzGqrrWY77bSTC+8AInT77bd3dzgWrwjXrbbaKqeCEQhpHOvJkycnnqPNp5xyigcQfv3116T20ydANK211lpp578sYeyqVatYt9opTb799lv75Zdf7Mwzz/TP8sKFCy3XqFq1qv+7w08hhBBCiMqKhHclBhGZzk3Epe3fv7+1atXK09JJJX7yyScTDuRzzz3nD54nVffjjz+2QYMGJVKG//rrL+vTp4+1aNHCdtllF7vooov8uSDyeN9VV13lr+GWk+J80003Wc+ePW3HHXd0Z/67776z2267zYXi3nvvnZTanimfffaZHX/88X5OxPLpp59uM2fOdLeXvj3zzDOJYxGTXIf0avj000/t6KOP9oDC4Ycfbq+99lriWNr09ddfJ/6mTwhUHmQSBL788suEcOWa999/v+2///5+Tsbt+++/TxzL2N1xxx22++67W/fu3ZP6wXt79OhhRx55pM2fP9+fGz16tI8TfTvmmGN8DuIU9759+ybG7p133km8tsEGG1iDBg0S7WeuP//8c29XkyZNEu1fvHix/fDDD9a8efOkdHIed911l18vThH/8ccfrWPHjp5ujxOO218axKnmrBPWZVgnrVu3Tkp5J82fdcTzzDfj+NtvvyVenzRpUmI9kG4/d+7cpGsVNudFwfgMGzbMOnTo4GPAXH3zzTeJ13///Xc777zzfD0wx/369fOxpw3bbLONj3WYO9p+5513Jt57wQUX+GehILebeaPUgfcWFkij5IG+jR07ttDPBzDPjP3dd9/ta2CvvfbysR41apTtu+++vrYY6wBBG9YoxxKEIrDH+UGp5kIIIYQQEt6VkvAF/f3333fBlQpi+O2333bBwxdthBRCbtasWXbaaad5ijkP0o85plmzZv486aRwzjnnuPC655577KGHHrKffvopqX6YtGxEB1/u27Zt68898sgjLkpIg8blxIGllhzBH4Q6AjRTcOG7devmggFx8sADD7gzSN9InabGHfEau7vz5s3z8fjzzz/9vYiwl156ybp27ertR5gBogOnMbQnFt4I2kWLFvnzpJ0H4U1gglR8Uv4JWtSvX9/PG46FMWPG2OOPP24XXnhhUl+uv/56mzhxovehRo0a/vsll1ziLifjdcQRR7homjp1qh/PnHAuRBNi/tFHH006H+0Pjj0/OWfjxo1dEAbhTT/WXnttF4UxiH3mmjkP8w2sBfpDe2rWrOnzVRYgbgkGMafsK8B1mGvgd+b0xhtvtCeeeMJLKs466yyfJ9bbGWec4UEH1h1CNQSToKg5zwTGnWswBmSYIK6Ba7OeCWY89thjdvvtt/vna8CAAVarVi3vTwicMO7//POPB0NCQIjabQJFqfAaASmEMFkBZLCwttLB+QiA3XDDDX6uwj4fAdYvGRDMbZs2bezqq6/2tcS6YmwIJBEgA9bssmXLfNwR6AR4OF4IIYQQQvwPCe9KAqIEscQD1wvhhhhAtKWCg3bddde5C4ZQwTlErP/888/+BZ+UYx6kHyOScc2p/yWdFFGIiMAN4zo8+P2tt95KSm9G2DRs2DBRJ41L1qlTJ38OMY5IueKKK2zzzTd35w3HHOGfrj/hEYs9xAui6+yzz/Y+4K4j1HBmASFB4IG0a8DdxCklVR5xh1t/4oknentwL4877jgPDgDjgpgioMAma7iZO++8s2255ZYuuBA5tJXXELkIpKFDh7rjibCnTwQySL1FpAW4xmabbWZbbLFF4rn77rvPgx8Io7p16/pz/I6ziitL+3BvcbYR7VyLDe6C+8i4IPYLEt4I7RAc4GcQgHF9dwzzzlwz58x3AOf0gAMOcAHPfLEOygKcU4IMzCnjyTwzp6wPshWuvPJKr7FnDbMXwZQpU3yex40b54EVxCDjf8IJJ3h7A0XNeSbg8oYxOPXUUxOONw4zjjCfA9qPQKadzBep4YjfMO4IfeaSbAmELFkRrDXWXCo4yjj6oR+sb85DYCuGzx1BGj7zYZ+Hoj4fwFriM8h4MBZ8Js8991wfW7Is6tSp4+fmONpAlgtjy/plfMkwEEIIIYQQ/0Obq1USEGJ8sYY111yz0JpLvkQjVnDH+GIdXC2EQFFwfHBQA3wZxwXlNYQpUA8eE/+NuENkhppi2gsIkHT9CeDiI2aA/uHUsxEY7jsiABGDQAaEDMeQho0I5704gqEPOMaI1gCBh9An2kOgAPGKMOX3sPEYYhfhjbBBgBCcQIQj+kjpDSBceR/iPYALHkPaLynGG264YZLI5T04nbFjS/tatmzpqctz5sxJcqpJfY5BUCNAGU/cegR8eJ4sA1xOhHdIM88ExFuAOY53dy9NGjVqlPg97CeAs01QCGc7HmOCQswZ48UxvDfMUxiXkIZf1JyXpG28H7g+r/EZCLAOaRMuMw70iBEjXMB+8skn1r59exferFuENAEBykJSeeWVV3zNsOEfENRB0BOAQFQHCKJxLfZ2CBT1+QCEdRiv8BlM/ZyyhqpUqeKBl1dffdXXPsEOgg7FyVARQgghhKjoSHhXEvgSjXOVCYg9XFPSbvlyjpOc6a2t1lhjjbTPI9pj4R6+yAdShUWq05pJf3gugMOIgCGNF+GCQ0x6L4ImgPuH0815EKz77LOPP49IQYym1lrHbUSUIi4Q2KRoB3CNcfcRXUG4pvY1HpNYnKQeh6DB3caxJr23V69eiffh+qbuKh4CFakboKXW8SMCEaWkNTMeuO9BMONmIroJKqT2vzCytXFWuj0J6GsmY5y6KVx8rkzmvCRtg3RtC58FfhIEIlCB8EW4UseOAOZ30sxTA0zhfWRCsG6D8Ab6miq8qb2nbaS+47bzGc3k85Gu76zJVLgm5QfsP8Bnin8rWP+UnAghhBBCiP+hVHOxAtRpkjZK3SZfpEkxjYVLui/fARxCvoDHaeW4aaR0F8c9XFmo9cVhHDJkiKfU4+bi5MbiK6SbI74RC9Q0hz5QL40gD48333zTa38DnA+nEKGSKrzZKCveERxBi4PPcwGECXXihY0JriRCCSee+vBQw8172Kgqbh/u97vvvus1w1wr3vwtZCzEkFpMPTACPHZq6QsCjNpzRFk6Cpv/8gLHHaEYjzGilDFjvCgDwBUP9eAQbwCXyZyXFM7Ntcl6CNBO2sttwPhJejzp7swdD9YXohsHPF19N6+R2cAmbNRUhwe111wr1IjDgQce6CnlfI5DDXcmn49M4fNNO3HPCVwQwAqbtGVzB3whhBBCiFxGwlusAGKMtFu+iFNzevHFFyeleiNQqSPFNUuFtHJqVKknxTXlwe+4v9xaK5t9oMYagUI/EBykk8fp6qRjc/9q6q/DPcmBWnPcbJx/RAzi69Zbb03UowOOJA4l547TkxF4uJEI3zhVu3Pnzi6ScMNJPSawgcsZam4Lg2NwRYMzzblI62WjK1KVETw8ENCIYupruRZ1zbQDBzUV2kaqcggOBPgbwYkwL8jFZv4RVqW5SzVBg/iRek/xomDvgWOPPdbHiPdSY07AgjR9aqhxdUm15t71jD8brDGGmc4564YN2DIpt0iF6xMY4HPEmqGunnaylwFlGeEYAiEh1RshzGeQ1G76kApzx1rDDedzFR70g7Uf7/YeUt/PP/983zOAecvk85Ep9IEMFdrEvws48WEn+pKcTwghhBCiIiLhLVaAXbRxA3GEe/fu7TuAs0lacAjZeIo6TjZmS+dosas0QgOB2KVLFxcI7OqdTRDStI9acFJqEWMEABBdsRhA1CIwCRYEqJtlR3Y2xUIcsQs1TmK8ER0uNq4oddrBKQeEL6IWkRc2QwNScRGGCG5S+NmQjR2uqQHPBAQjQhpxhAhnR+zhw4d7+6kPvuWWWxJCH9eRNHRS09m5muumgrDD1Y7d+vA841NYfTcOKunFrA9qwksDUufjR6i3Lw7MLwKbOafmmBRvAhKkVpNqjbvLJmxsgsZeAAQoMp1zdvimhj6+PVmmsL4GDx7sv5PSjQCmHvvaa69NHIOrTRYEAQ8gfZzSgXRuN/ODY80GZ6nQZ9YXewCk1tnTb8Q5KeeZfj4ygcAAewYg6hk7RDybsuHkp8u2EEIIIYSojFRZrlxAIYQoEoQp5RfxRnei7Og6eJSNn/K/lHUhxKpBk/q1bVjPtjZ37kJbujQ3N1ikWqpu3eo2a9YC0zfg7KAxLx807tkd50yQ4y2EEEVASj811RLdQgghhBCiJEh4CyFEEVA6wc7yQgghhBBClATdTkwIIYqA2v3i3FpMrDwN69awxUuWlnczhBDFoHG9muXdBCGEyFn0TVIIIUTO0adDi/JughCiBCxblm/5+SooFUKIVCS8hRBC5BxsziSyS61a1TTuWaYijjmiW8JbCCFWRMJbCCFEzsEt6/Jzc1PkCrsra3ArtfttdtCYCyFE5UKbqwkhhBBCCCGEEGWIHG8hhBA5R15enuUpNJx1qlbVoGebVXnMlVYuhBCZI+EthBAiJ2tfRfbRuGefVXnMSZOfN2+RxLcQQmSAhLcQQoico++IcTZxxpzyboYQopBbh/Xr1Mry8qpIeAshRAZIeAshhMg5ps6abxOnS3gLIYQQomKw6hYWCSGEEEIIIYQQqwAS3kIIIYQQQgghRBki4S1EGbHffvvZ1ltvnXhst912dsghh9jDDz+cOOakk06ygQMHZnS+2bNn28iRIxN/f/HFF3bQQQfZDjvsYE899ZRloz/PPvvsCs/zHK+VJV999ZWP4YwZM5KeP/jgg73///77b+K5//77z5o2bWpvvPFGsa9Tmn2ZNm2at5mf11xzjZ188slJr48fP95fv/TSS5Oef+GFF2y33Xbz+1jHY/7333/b888/X+R8FIevv/7aunXrZrvuuqvtvPPOdvzxxyeN2wcffOBt/Pnnn9O+n/G/7777kvqaCv1L7aMQQgghRGVDwluIMuSyyy6z9957zx8IGkTOgAEDkgRUptx88832zjvvJP6+9957bdNNN3Uxfuihh1pFZtttt7V11lnHBXjgjz/+sOnTp1u1atVcxAYmTJhgS5YscTGZK9CWb7/91sV04KOPPrJ69er5zxgCKhzP7bSefvppO+yww/x5AjbPPPNMqbVp7Nix1qlTJ9tkk01s6NChfu4DDzzQLrjgArvnnnv8GAIA66+/vr3++usrvP+7776zqVOnWtu2bUutTUIIIYQQFRUJbyHKkOrVq7tw4bHRRhvZUUcdZXvuuWdaIVMUy5cn7xq7YMECd3YRTuuuu65VZFZbbTXbaaedkoQ3gnX77bd3cRiLV4TrVlttZeutt57lCghpHOvJkycnnqPNp5xyigcQfv3116T20yeoXbu2rbXWWmnnf2UgQwAX+rTTTrM+ffpYkyZNrHHjxv73TTfdZHfeeadNnDjRqlat6lka6dYrAZ9ddtnF17UQQgghhCgcCW8hykFErr766is8j0vbv39/a9Wqlaelk0r85JNP+mukoz/33HP+4HlS1D/++GMbNGiQp/jCX3/95SKqRYsWLoguuugify6IPN531VVX+Wu45QgvRFbPnj1txx13dGcVF/O2225zobj33nsnpbZnymeffeYpy5wTsXz66afbzJkz3e2lb7Fri5jkOqRXw6effmpHH320BxQOP/xwe+211xLH0iZSowP0CYHK48MPP0w8/+WXXyaEK9e8//77bf/99/dzMm7ff/994ljG7o477rDdd9/dunfvntQP3tujRw878sgjbf78+f7c6NGjfZzo2zHHHONzEKe49+3bNzF2cXbCBhtsYA0aNEi0n7n+/PPPvV2I3tD+xYsX2w8//GDNmzdPSifncdddd/n1wnzDjz/+aB07dvR0+3bt2rnbnwlvvfWWzZs3z7p27brCa5QvbL755ol5Yh6++eYb++2335KOGzVqlNxuIYQQQogMkfAWIksgzHAO33//fRdcqSCG3377bRfZiBqEFEJu1qxZ7kSSTs6D9GOOadasmT9PGjucc845LrxIE37ooYfsp59+SqqtJS0bwYeIC4LpkUcecZH64osvukOMA0stOYI/CPU4PboocOFJp99rr73s5ZdftgceeMB++eUX7xup07iniNfY3UUAMh5//vmnvxfh/dJLL7kopP2I8XTp2rHwRtAuWrTInyftPAhvAhMPPvigp/wTtKhfv76fNxwLY8aMsccff9wuvPDCpL5cf/317vrShxo1avjvl1xyiZ155pk+XkcccYQHFUi3BuaEc919990u5h999NGk89H+4Njzk3PiMiP6g/CmH2uvvbZts802Se9F7DPXzHmYb2At0B/aU7NmTZ+vTEBIN2rUyDMy0kG9dwgSEGQgqyJ2vYMQZz6FEEIIIUTR6D7eQpQhCCHEM/zzzz+eNoy4RbSlgvO5xx57uEsMOLAIRza2QrSFlGPSjwHXnLpn0tgRhbihCHbEHOBmI9ji9GZEWsOGDRN/k6pNnS8gxhGbV1xxhV8LdxhBivCnFjm1P4GlS5d6G0IfzzrrLDv11FOtSpUq7vLioAbB2aZNGz8vadekx+Not27d2n/HmcatP/HEE/1Y2kkggeAA/WdcCBwQUKCu+/fff3eBiFBFQOIgM4ZswMbxuOnULp9//vmJQAdtp44ZoYpTDMcdd5xtttlm/ntoJxuGMZb0v27duv4cArxDhw7uAAObpX3yySd+DIKcDe74GdxqxP4ZZ5yRGCfaxLGA0A7BAX5eeeWVK9R3xzAfzDVzHsYayCw44IAD/HfGlb5mApkQCP+CQMTPnTs38TfzRsCEtQtkQrRs2dJq1aqV9D7WEPMew5yFMRNCCCGEqKxIeAtRhpCqjPCENddc00UTdbPpQEDhht9www0ulkn7hmXLlhV5HY4PDmqAdGEEFK8FZxPnMib+G3GHyAwCn/YG4ZSuPwGc0CAo6R9OPRuBIZonTZrkqd0IZEA8cwxp2Ig53ktKfOgDjjGubpwlEPpEewgUII4RpvyOGAXELsIb4b/FFlt4cIKAAW46jm0A4cr7EO8BXPAY0uJJt99www2TRC7vQXCG9P/QPgQoInXOnDlJTjXp3zEI6quvvtrHE7c+iFGeJ8uAOm+EdxDumUBgI8Acx7u7FwbrgvEpCMYgFtUIarIWaGedOnU8KNGrV68V3scxpNWnbgoohBBCCFHZkfAWogxBpMQOc2Eg9nBNSbVGvOIuZ3prqzXWWCPt84j2WLgHMR3Xm8ekOq2Z9IfnAmwU1r59e69Rx73GISZ9nrrrAC48TjfnQbDus88+CeccMZpaax23EVFKmjMCmxTtAK4xdcsI4SBcU/saj0mcPp96HI4t7jaONWnjQWDyPlLLmZuYEKhI3QAttY6f1G7S+UnhZjxC5gCCGace0U1QIbX/hVFQEKcoCEYQHGH8U11rIKU/Ht8tt9zSH+zMT3CBIEO6comNN954heAO2QlCCCGEEJUd1XgLkSM88cQTvjkatcaIUzbaisVcagpvDK4wG4DFaeW4zaR0xy54WUM6Mm7qkCFDPC0ZNxcnNxakON04+4hvAgukioc+UC+NIA+PN9980+u9A5wPJx3hmiq82ZQs3hEcQYuDz3MBhDmisrAxweVm53mceOrDQw037+E+1XH7cL/fffddF69cK978LWQsxLCxHbXmCHCEeIC+EKCg9pygRToKm//iwuZv9HPw4MErvIabjbtPACUG15v5QHzH8yaEEEIIIYpGwluIHAExRqo1QpUNxS6++OKkVG+EDhuk4SqnQlo5YooaY1xTHqHemFtrZbMP1Fh/8MEH3g9Sj0knj9PVcUypGaf+Or7/OLXmuNk4/9S1I7hvvfVWd1EDpKyTus6545R03FgcaYRvnKrduXNnvzUWbjhiksAG6djh3tiFwTGkxgdnmnO9+uqrvmkaG8bhGPNAQCOKTzjhBL/WuHHjvB3sUJ8KbXvllVcSwYEAfyNqEeYFudjMPyngiP+VBZee9pFh0a9fv8SY0rfevXt7SUHqBm8ETNhHgPlUzbYQQgghRPGQ8BYiR2BjM9xcBA7ihx2juQVWuEUUt7WaMmWKb8yW7p7ON954o9f8IhC7dOniYpTN2bIJQpr2IdxwTKllJgCA6I3FN6IWgUmwIK61Zkf2sWPHurt6++23+67m8UZ0uNg4zdRpx44rwhdRyz2lw2ZowE7gxx57rAtuUvjZkO2xxx5LbFBXFJdffrkLacQmInzAgAE2fPhwb/+IESPslltuSQh9UsRJQyc1nd3ZuW4qOPa42rFbH55nfAqr72ZTOFLkWR/UWq8suPrU5jMmYcM/3G425UuX7s78kBJPejq71gshhBBCiMypsjzdN3ghhBCiHOk6eJSNnzKzvJshhCiAJvVr27CebW3u3IW2dGnmt50sb6jaqVu3us2atcD0DTg7aMzLB417dsc5E+R4CyGEEEIIIYQQZYiEtxBCCCGEEEIIUYbodmJCCCFyjoZ1a9jiJUvLuxlCiAJoXK9meTdBCCFWKSS8hRBC5Bx9OrQo7yYIIYpg2bJ8y89X8agQQmSChLcQQoicgw2bRHapVauaxj3LrOpjjuiW8BZCiMyQ8BZCCJFzcOu0/FVno+QKsStrcDC1+2120JgLIUTlQpurCSGEEEIIIYQQZYgcbyGEEDlHXl6e5Sk0nHWqVtWgZ5tVZcyVVi6EECuHhLcQQoicrH0V2Ufjnn1WlTEnJX7evEUS30IIUUIkvIUQQuQcfUeMs4kz5pR3M4QQ///WYf06tbK8vCoS3kIIUUIkvIUQQuQcU2fNt4nTJbyFEEIIUTFYNQqLhBBCCCGEEEKIVRQJbyGEEEIIIYQQogyR8BaVhv3228+23nprfzRp0sSaNWtmHTt2tLFjxxbrPB988IH99NNPGR176aWX+iPbfPTRR97PdJx00kk2cODAMr3+NddcYyeffHLSc+PHj/c2pY7HCy+8YLvttpvft7m4lGZfOA/nW7Zsme288872/PPPJ71+5ZVXevsZ25jOnTvbddddt8KYT5gwwT7//PMi5yPTtoW1y2P77be3/fff3+644w7777//rDRI17fifnbi43jsuuuu1qNHD5s9e3aptFEIIYQQYlVFwltUKi677DJ777337J133rEnn3zSBVa3bt1s3LhxGZ8DoTVr1qwybeeqDoLr22+/TRLTiLp69eqtIO6++OILP57bR+UCVatWdWH51VdfJT2frv30j+N23313fw9rK3D22Wfbzz//XGrtCufnMXLkSLvgggtsxIgR1qdPH8ulz0447t1337XHHnvM/vrrL7vkkkuy0kYhhBBCiFwlN77pCpElqlevbuuvv75tsMEGttVWW9nFF19sbdq0sf79+5d30yoUCOm///7bJk+enHgOwXrKKafYH3/8Yb/++muS8MbxzrX2x8KbNk+fPt0d8Vh4//DDD7Zo0SLbZZddbI011vC1VVasvvrqfn4eDRo0sMMOO8xuvvlme+655+ybb76xXPnsxMdts8021qtXL3fGFyxYUOZtFEIIIYTIVSS8RaXnuOOOcwE1depU/3vSpEnWpUsXdxh32GEH69SpUyK1nFRaII06pDg/9dRTdsghh3j6L84nadakKwcQoGeddZaf6/DDD7cPP/wwSdCRitu8eXN//1FHHWWfffZZ4vVHH33U9t13X3/v0UcfbZ9++mniNdqMEGzatKkdfPDBNmzYsGL3ffny5XbPPfd4v7h+y5Yt7a677vLXcCx33HFHW7x4ceJ4nEyczn/++cffO2jQIH8PQrV79+42Y8YMPw7RhTj8+uuv/e8lS5Z42jXp0aQqhzHg3PSD/sPvv/9u5513ngtxxrJfv37+Xnj22Wc9vRknGaH74osvJvXll19+sRYtWtidd96ZuCbv5zw8LrzwQps3b17ieOb5+OOP9z4yn3Pnzk28RnsmTpyYuDbtZXz22Wcf+/LLLxNjwu9bbrml1apVKymdnHlBqPfu3Tsptf7xxx+3Vq1a+dritXD+krLnnnvapptuaqNHjy6wtCFOISdIQMp8GBPc8n///XeF877xxhu+rooqw0j97KRj7bXXtipVqpSwh0IIIYQQFQMJb1Hp2XzzzRNCjNRhBGT9+vW99viJJ55wEX3TTTf5MU8//bT/RHSfdtpp9vHHH7u4O//8823UqFEuujnmzTffTJwfUYRDSM3wXnvtZeecc07C/UMMcn6uw+sI1quvvtpf++6772zAgAF21VVXeWox4rZnz57eRoTv6aefnhCgpPIOHjx4hbrkouD4Rx55xGuUaT+ilr6RJo6IRTQhwAOvv/66i/S11lrLhg4dai+99JLdcsstnnpcp04dH5NQcxy7xvysUaOGNW7c2AVfEN4Ic66BM4oIxRFH1JKifPvtt9vbb7/tYxDXiW+xxRaeYo3gD8yZM8eDJYceeqgHMuDWW291J/i+++7zAAYBEEQ9cK0zzjjDgwMIegIX9CGA6EQsIr4B4UowAJGNoxtqt3Hr6U8qjOGGG27oadeXX3554vnXXnvNHnjgAQ9uMN7PPPOMlcb6zXTPgSuuuMIDO6yVBx980H9nnGPo20UXXWQ33HCDBwmKunb47KRj4cKFdv/993vAgnETQgghhKisSHiLSk8QBIgEBC2uKq4hTuJ2223nLnQQFrVr1/afNWvWtGrVqtk666zjovWggw6yTTbZxJ3vbbfd1n788cfE+XFKEcyIFNJz11tvPXv55ZfdMT7ggAPcdeQ1BOUJJ5yQuBaOKeJv44039nNzDgIACG8EL0KX5xo1auRimIABAjMGZzX1EbvmG220kacK45xyDRxg0oRp/2qrreb9QmwDAQKcUFKcAUFFfxCetP/aa6/1et7gksbCG6Ed0sn5ScAitb6b95EBQB9xaWkT7iwuMXMDjMeZZ57p1wtzgYuLiEYsIywB8U5ggEAIz3M+BDzX/f77770uGfebIAfnYtyZiwBp47wvOPYIb/rJ9XHDg4NM+4NbH8McUyvO2ooFJ0EUgjAEYAhsBGG/Mqy77rqJ8SkM5gaxz5gSsGFtM2esrwClAYwvgZwwz5l+duI+ss522mknvw7XpBZcCCGEEKIys1p5N0CI8gYnNAgYhDTiEycYtxQhgvNct27dtO9FVOP+kt6MYEbUkXYbu7EIuAACE3cXhxIRx7VeffVVdxmnTJni1wwbknEORBrp6Yh50rSPPfZYF8S0C9GGwAkgjBF7MekccFz2wB577OHp0rjWtImduP/8889EG6jhJU0ehxi3GTebdiG0SAunfjfeFI3ARdhQDEGNsOW9CFX6EZ5nl2vqvGPhyvUJIhDUCJDWvnTpUk8jB4INjHcM7jjHBGEMnJu2EkSJoV+0j9e5FvMdIJ2fjcMCIXCAW0tAgLaEwAFiEiHLudIJ74IgmBOL1pVNNQ/rl7VbFKxL1giCO+4jjwBBJMaSgEym14b4+mQcELCB+fPne5CITAiyFMgYEEIIIYSojEh4i0oPYhkQBQjKY445xmt2cZHbtm3rIpe03HTg0pKe3a5dO0/L5Xdc1phUMYz4Y6MsfiJIECe4i1wPsUgqOpCCTf04Lu2YMWM8JRr3l5+Io+AIF0bDhg1XeC4Wrpz/+uuvd0GPWMLpjG8DhqhEnOIQ01dcYdxgBDZwOyvSx2OCcEbY4vziGiPu+/btmxCc1HkjuhG2OPWw5pprrtDWUCsffqY7BiHJTvOkR1MHj4Mdjh8+fHiSuA7indR+Mg5imJMYBCnp1p988okHWJiPILxx5QlEbLbZZgnnPRNS10JqG0oCNdasPyDwEJ+TdVJQ/9JBoILjKJ9gfTHXmX524vGN110IaLButbu5EEIIISorSjUXlR7qbBFv1PsicmfOnOkp2127dvV0YDYMK0ggIVzbt2/vKbuIV0Qf7mx8fBAnQQjhoCPYcMgRdQ8//LCLT5xVrg28H2E3ZMgQd6XZiAuXlY2wqMtF7OKQkx6OyOGBkMX9LQ4IeYIF1CIj3gg44EaH9uNmkz5PrTV16zjgQL02Agt3PFwflxRBSrsCpBqz6zYCHCEewJ3mnKSJBweWPuEgxxug0Scc/tgpTgUHntpuhCLzAMwlIpdzhfbhypJWT/8Qilwr3mkbtz+GbALmMqSZB3gvYh4nt7x3Y+ee8pQkUKMOiOY47TvePT6MSZzeTukApRSBAw880NcDqfr33ntvsT47RRFvOCiEEEIIUdmQ8BaVCoQWYhGBiyAmtZZU77ATNAIRMYggmTZtmgtrdguPU4IRXdRAcy6ORyBzLp7jPJw/Pp6a6rvvvttTqXEScbVx0hGvCNtXXnnFxRPCOuyUzvtxptk1nDbQFo6jbdQrH3HEEe4643hzXhxF+oIYLg4IbcRbSHMndZz2xe1HbLPRHKKfIEAAl5mNud566y0XsdRXkzJPUCF2zGl3qkDlb4Q8wjy4wNQ9I+CoG2c8qQvHJQ9jVRQEDwhKcD1ENoEQUt0RzgQ5OC/p1gQrCKgQKGDjM8YPN5Z1EMM5ENnMSyy8Q5037S8szZx1QrZEHEhYGZgX1hYPBDVlBJQN0M+wmzru8vvvv+9zihNOICI43fSH4ArrhEwDMhFuu+22pDkNx7FZIJvSse4y/eykHseD97OmGXcCOEIIIYQQlRWlmotKBWnVPBBPpAhTO43jHOpccTlDujhCE0GDuEWgUefLruPcKoqNunBDSQvHjea2SgiW1q1be9127J4idhDfiGhqtnGxSVvmgTDkeXbgxvFFvJKOiytOWxA37EAdNsHCUQ47SSOM6AvnJwDABmHF3cQKscrjyCOPdNGOc0y74vazSRYCnVR63OcAu4jjrjI+1PqSjs2O3XGNNuNKsCB152+eR9zHwhUBTl8R2x06dPDN66gLRwRmAuPH3JAezjwgCG+88UavOUa0ci1cXK7Dg3lgvHF8mWfGL/V+2LQTARvX0ofAATuUFya8WQfcZ5ugBO1aWQjwhL0DEPUEENjZPj4380jwg7p8UvrZxT2+1RdzzZo69dRTXZBT4kCwJRXGhGwIAkXcbi6Tz04gHBdKA9g0kABNqJEXQgghhKiMVFleGkWGQgghRCnSdfAoGz/lf6UXQojypUn92jasZ1ubO3ehLV36v803V1XYg7Nu3eo2a9YC0zfg7KAxLx807tkd50xQqrkQQgghhBBCCFGGSHgLIYQQQgghhBBliIS3EEIIIYQQQghRhmhzNSGEEDlHw7o1bPGS/7sPuRCi/Ghc7/82zRRCCFEyJLyFEELkHH06tCjvJgghIpYty7f8fO3QJIQQJUXCWwghRM7B7skiu9SqVU3jnmVWpTFHdEt4CyFEyZHwFkIIkXPk5+OulXcrKtftUIKrqdvOZAeNuRBCVC60uZoQQgghhBBCCFGGyPEWQgiRc+Tl5VmeQsNZp2pVDXq2ydUxV2q5EEKULhLeQgghcrL2VWQfjXv2ydUxJwV+3rxFEt9CCFFKSHgLIYTIOfqOGGcTZ8wp72YIUWlvH9avUyvLy6si4S2EELkgvP/++2/75ZdfbIsttrAlS5bYuuuuW1rtEkIIUYmZOmu+TZwu4S2EEEKIikGJCov+/fdfu+KKK2y33XazY445xv744w+79NJLrUuXLvbXX3+VfiuFEEIIIYQQQojKJLxvuukmmzRpkj333HO25ppr+nPnnnuuzZ071/r161esc82cOdP69OljLVu2tKZNm1qbNm3sgQcesKVLlyYdN3DgQNtll11s1113daf9rLPO8uNPOukkF/08SoOXX37ZjjrqKNthhx1s9913t/POO8+mTp2aeB1nf8SIESt9nWnTptnWW2/tPwuDfnPcBx98YOXNRx995G1JB/NAW8uSa665xk4++eSk58aPH+9tSp3/F154wQND3JKouJRmXzgP51u2bJntvPPO9vzzzye9fuWVV3r7GduYzp0723XXXbfCmE+YMME+//zzIucjU95//33r2LGj7bjjjv756tq1q33zzTeJ15cvX27Dhg2z0iBdPwvj559/tvPPP98/hzvttJMdffTR9vTTT1tZ8uabb9ree+/t4/HEE09k9BldWVL/bVsZ9ttvP29zQQ8hhBBCCLEKCe/XX3/dLr/88qQvcvzet29fe/fddzM+z2+//WbHHnusf7G9/fbb7ZVXXrGzzz7bv+ifeeaZCdGEi37XXXfZJZdc4oLqww8/tLFjx9rw4cPtlltu8bbwWFneeOMNu+qqq9y5f/XVV+3BBx90wXTiiScmvhDTxnvuuceyBYGATTfddAXBVhlBmHz77bdJYhohV69evRUE3RdffOHHszNyLlC1alVr1qyZffXVV0nPp2s//eM4BCfvee+99xKv8flAkJYGCGwCWIcffri9+OKL9vjjj9vGG2/swY0gNj/55BO79tprLdsQYODfBrjvvvu8fccff7wH/QhWlBV33nmnBwH5/Ddv3tzKmtR/21a2XIfABOuFx6GHHuqP8He8joQQQgghRHYpkSpZuHChrb322is8j2BAqGYKQr1BgwZ2//33u0ji98MOO8yGDh1qn376qQsBCKJ3zz33tPr169uCBQusbt26tv3227toqV69uj9WFsQtrlrbtm29Ldttt50Le67/zjvvJBzAbIHIpIaeIATBDsa9MhMcwcmTJyeeQ7CecsopXu7w66+/JglvHO9ca38svGnz9OnT3RGPhfcPP/xgixYtchd0jTXWsPXXX79M2vPSSy/ZXnvtZSeccII1bNjQttpqK88q4HoIz2yv9xgyGFq3bm233nqrZ7YQfEKI82/FU089ZW+//XaZXJd/Wxh3/p0J2TxlSeq/bStL7dq1ff54rLXWWv4If5fVOhJCCCGEEGUkvElnvO2225LSIhE9pJnzZTkTZs2aZW+99Zadfvrp7gbG4LohgEnpxnnjenDAAQf473wpnzFjhrvszz777Aqp5jhHhxxyiKeLkkb73XffJV4jfZRz4CQieL7//vv/G4y8PPvyyy+TBC5fvhHk9Atx1Lt3bxdLIQWVMeA5vjgTCOC6OOeB2bNnW8+ePT3NGJGDkEgnZh577DEXZjh9sdvdpEkTO/jgg+2///5z8R2DOOI10uIJWITrUn/fvXv3FYIcF110USLFnXMxnry3W7duNm/evMSxZC2Qbs/4HXHEEcVOc6d/ZAUwzowJDiKuXjg35128eHHieJw4xueff/7x9w4aNMjfw3jQD+YaNthgAw+IfP3114m0f9Ku999/fx8nMiGAcyNeg2P5+++/e8kAQhwXmXXKe4H1wxrBSUZw4azGEPho0aKFO6Hhmryf8/C48MILk8aOEgycWfqIc0z5RYD2TJw4MXFt2sv47LPPPr7uwpjw+5Zbbmm1atVKSidnvbL2WG/xeidA1apVK1/TvBbOXxSsd9Y/azRQpUoVz/To0KGDr5WQ2h/SxDl3//79/XoEppjjJ598MvF+AgY40mF8KCNhT4hUWKsIajJXUiE4wTilrmFgvfJZ5N8GRDJ/h3kHPo88R+AORo8e7Z8N5oP9KD7++OPEsYwnnwvWD3NAexnfyy67LPFvTqo7TX9YD6wVPk88R8CRtTVmzJjEsQcddJC72AE+96yVmNR/28KcUj7BGiK9ntdDABLCv3V8Lvk3p7jZD4wL/7Yy9mQ6vPbaa4nXippb/sZRb9++vb//tNNO8/GizIjxPfLII+3HH3/0Y/n3in+HQtYGc0mgSQghhBCislIi4c0Xa76082UTscAXMb5o1qhRw7+YZurmIrL4kpwOvtjy5RtHG4cL+Imo5ovxhhtu6IKNL9UxfJEn7RwXFBGFsEFY8qUSoY8ApI3Up3MNhEXYEK5Tp06efkuNZ69evfx6fFnEDSQFlC+Q8bU32mgjr8OdMmWKixWEMmKR6wfxg6D7888/3cUnnR6hl1ozO2rUKP9ijljdZptt/DnGZuTIkbbvvvtatWrV/Es2bQ4gli6++GLvG+9nDqiHRQRSJ0/tbgiMIAz4gs3zAa7FNWkXQvahhx7y5/nijMN+4IEH+ljj/pOOTB8yhUDFI4884mND2xgD6liZc0QL2RJxSQJBAL7U487RHpxYMg340l+nTh3/gs8X+VTXmJ+sucaNG/sX/CDA6A/XYCyZB9YC65TgBnOAWzpgwIDE9RE67MyPmEPwB+bMmeNlB6Tr9ujRw59jzFgjpD8/+uijPsaIeuBaZ5xxhgcHmGeCIrFwQawgbFnXgJDlM4TIJmMj1G7j1tOfVBhD1h5rMC6tYG7ZF4G1zXg/88wzGc0TQpQ+ssaYc8aHQAPO63rrrefrO9S6s95Z//fee6+PH89zrXbt2rl4JZAGiK3PPvvMBg8e7J8JfmfMY+gnovWGG25wkZcK47vOOuvY5ptvnrbdBGmYe8aM9yOuA7QN1zf8+4H4pW/8W4BYJdAX79nAPJG+ztjxeQvjm66W/JxzzvHAGJ8dPi8//fSTi2D+LeTzGUQ9/2YwjmE+gc9jal8Z3/jfNuaUc7JeCdLQNkTtjTfemNRHPpcE84YMGWKNGjWyTOEzzL8XCG8+Y9Tz0/4QpChqboG5vOCCC7zMh4AmATo+04wXnzk+H8C/cZQpsAZ4jWDm9ddfn3FbhRBCCCEqGiW6nRguHl/OcLn5oshGaIifgr4opyOIXYRTOsLzCEm+SAM/Q1o5Lnm61EmEDmIRxwgQp6uvvrpfjzRVvngiNIAvrwhAvpTjfu2xxx7+hZHjEOk4ylwHRxRBQdpv6rX5gnzqqad6mi4gEvkSjTDmmog63D3EGFx99dXuCgb40kt6LxkEiMoAgoUaeJwwILBBwACHCWHEl3vEKEKBv7kuriQOPaKtZs2a3gfEBtfgWBz34DohJBGCgPMVXGS+JCNsENuAkKS98+fPT7QNAZYKbnVI7UZQ4JwhRoC5wMVG1OOk0RfENsKU0gTGJ2zKx9hTZx+EJ/XFiGECKohzxig4gAjtcE1+htrfuL6b99FnRDVjAhyHGCO4Aohh/kb4B+gzfWeMmHtAvBMYQNgGFxoBT1txjpkv1itzHIQjYgxxC6wfzsdY8xPhTf+4PuuIv5kj2s/aTAUxzNpLLa1gvPj8sQYRQUHYFwXtY60iJBFcrBfmgawNRDFCKoxZWO9kFvA5wY0FnEzmFueVzxmCDVGK8A3zF2dxUCaAeEMQpwbNAnxu6B/jkg7aFLIMCCYhTJkjjicIQaCE3wlG4NyzvoEgG2KQ9RPcZZxu1nsgjC//1sSfU8aUuaR/jDUg2OkDfWKNhnXJ5415ZH0iWhkX3p8qvLlW6r9tBAC23XZbD6LBZptt5v/G8rkgGAYEK9M58kXBv22sD/asAAKKzA1BMj4vhc0tpT2AaOccwLGI+fBvLf/WcK7g5vNvUQjisJ7izBAhhBBCiMpGiYQ3X7RwW3CTg6AsLuELPV9MEY/pdjsHvrSF3zMB9xmhHEDshJRPvsDyZTm4MkAabJyuiajkyybP80Ub95YvrNSYstN0KrhCCEeEHV/AcXUBQUlbaH88RkFIh82rEIEci1iNYRM3vrTyJRxIh+VY3C5EMW4uogHRjxDgdWpgQ+094gORwJdhnDy+tCMAAnzpDuDmB0eZNiOOY4IIDM5Xuo3e4jRavpCTLo1rzZjz5Z4v6GFTNMQSfcAhJjDBtREuuGKkhSOI403REPVhjhAICFvei1ANoornCXYQDEK4hjRzro8rGNYbILQIFuFKAq56LLoB95djENVBAHJu2hqvL6BftI/XuRaiO4BICvsDhHbi1jJ3BASC6CNwwHwhOjlXcTb2Ym0GEG+ZppoDTv/NN9/sfWUuWHesZYR2CDjEsH5xbxFSrPdQxsEaxknmZ7x+6G8cUCILgmulrvcY5opgBeOabnM8/j3gcwUE0XCKWW8EQwi0kIkQ5p61H2cdhLUWyLSumr6G7Io4cEFbg/AmAEL6O+Ie4U2AkgAa0LYgsguDNoeAWPxvEiUyxW1zuj6QDh8HzhiP0KfC5jYQ/1vGZyZuC3+Hf0eOO+44X0uMC2ubcyPahRBCCCEqKyUS3rgfcV1oSUCQ4PiQVppOePM8X1YRzsVhtdUK7hJfIEkjDU5sLDwRfQhFXE7ag1uDQ8UDATBu3Li0whtHHcFCfSMBCQQLXzohFroFgbNFSirOYEhBp52IML64B+ENtCMIb8QgwQ9EHLdAIhWV9E8eiHJcf1x8UqF5jYBDTEFtK2z80on2QCxccVFJKyUQEGpd49uAISoRp4wpQokv5cwzAhvuuOOOJIEDQTgjbBFduMaILVJhg+DEsUN0MyahPjjdBllBSISf6Y5BPDLfpEQjGBBZ4XjGOBbXQbwjjlLr91PHGRGKsEGcEbgKgRLECXPEWsLlzESkBVL3SMh0QzScYtYt48a8My88+DzE9coxZGYwv4wJQSfEZnBfM1nvBC04Dmedz2G6zzf1wgg46vRpW7p/G0KJCvOA+MbpJpDBv01BuDJfpJbTzoLWaqYbqBX07xDXCIEzPhe43TxIwWZvAj7bBPHSpdSnI117UjetLOmmbwQ8CFSl1s6Hz3xhc1vQWivorgGUT5BBQSYFD4KdlOLwb1xBmQxCCCGEEBWZEtV4IwYRf3yJpt6PDZ3iRyYgLBBc1IKm7oROyi4pz6SJFhe+/MaptpybL484T4g5HFWOCQ/SbBFrfBmn7hHBm0pIPYX4SyOili+TfGEldRtXOaTQI344P+mV9CeAGxfSuIExQJgiJoKTzGZmOH5s6MVz4UF6LG4oX+ZxxhBOiAwcYtwlvvyHzaoQL2xGRi0ybcl0h+/U8QPmmfNnCim31HUT5OALPJuEEagJgpAv66Qz84WcoEGoPcdRRMDijof5oU8IUpz4AGnM1LsjwOMaV9xpzkmKcHBdmXPGLE5zZb4RG7FTnApOHVkDiMNwOy3cPoQH5wrtQ6SSVk//EBtcC9czEKdZA24jTjtufVzHzXsRkazBbO3GTt12unpw5iHdegeCC5Q8kOFAmnXYEI65DeMTrx+yQRChAT4jrA3eR01xOpg7ghJhQ7sYgipkEIRbjQHrh+e4Vpy+ztyTWRJ/3nG/i3PLw/hclFvEO+qzkR7/BoQgEWuGNlAOwr+RBFn4d4dxzlR4cy4CSjEEY1IDUSWBc5CVEI8Hnz/WXFFzW1z494rgDZ8h/p0iVZ6xWNmArRBCCCHEqkqJb3JMCjNf5IrrSMeQIopQxZXCJcIhwp3FHUV8sNlZccHlpWYbYcaXTEQRXxz5Mk9aNjWIfClE/CDoSEXFzUQw4AThzCAI+IKN4/bwww/7F9NQF4lDGdKB6Tt/U6/MF3xEbxBppPsipki7pp/UACO2ODdpqDGka7LREe1BtCFyeS9uMXW74cF4IDZpP+IIgUvgghRnBGf4wh/gyzP1tojcVKeqIHDtmQvex/jhqlObHacLFwVCm+ABYpmAAoEBHMw4BRqxhHuPG8gYBXCZqQHGLWOMSXcm0IALHMCVZYxSBSp/IyQQ5qG/jDWCkMwE5oC6W1xyMgIK2l8ghuABgoHrIbIRfKS6M5cIL87LOG2yySZe+0qgIGySxeZY4bZcAc7B3BLgiYV3qPOm/YWlmSPOWZulUS9LAIiadVLNGRvOS8ALkRSyO4IjzzwyV6w/BBVrjnVC/4G5pW8EWkgnRyCTlUBQKp7fMAZkehAUCiUXqfC5Zd4J7HEejuMzzWeUOYidWDZDJP08VXjTB8afYBefdz7LPIqzIVmAfyO4DkEy+saD35mrsL8Dwps1jRuPqx/uO89chbrpouAzTrCGf4f4/NBnMiy45dvKEjaPZE74bPHvGtfhLhJQ2NwWF/4dYx3w7wDn41pkEvFvgxBCCCFEZaREqeZ8KS4NcGSpJ0U84rLg8iKScFjZ2begNMbC4IswKZLUaeOc4pzhauNo86WcOmWcNH5S33r33XcnvoizgzUpzQhangfej0DgJyAicIpI2eQLMWIZR4eaYMQXm3QhHPnyzJd1XmfzNNLPERz85AswIjmG4ANCDRFE8IEdlFMhxZQ0UMQR4o4N7jie/uEUI2bi+lX6y2sFbWKVDlxgzkvaPV/KEYmcg7nK9NZFiFUepDHTLlwvBFzs/iJE+BKOExintzMHpP1Tz46byLizSVZco42gwdVO3fmb5xEJsXBFgLO+ENtkULBDPHMXNq8qCoJLBHNID+c2VmQdMN9kOBBM4FoEU7gODwIVBAtweSmVQDAhdlLbSVAndZM6AgekTBcmvAmMMOfMBe1aGZgXgkfsPM2apz+0mTIB9gwA/iZ4wWeS9cBrBB4InLAmEMH0m7lFmDLvCC6CXIhP1l7YxC6G8eGapJyzvlJBzJL2zGZjfKZYCwRfOFfsdgN9IHMEpzhOTWeNsfkd65mfrG3WdXHq52OYd9qLoKfPjFGc4cP8EUAJG8uR9s41GcNMSjgAEcwaor3MC3+z5rhrwcpCgI+xZv3wmWL+wq3JoKi5LQ6se7KLwi3X+Bzzb2qmAUAhhBBCiIpGleUlyCMM92QuiHSiUWQfNkoidRQXVXWVQohVia6DR9n4KZlvrCmEKD2a1K9tw3q2tblzF9rSpf/bGLWiwNehunWr26xZC6wElTSiBGjMyweNe3bHucwcb9JsY6ijJhWUGkhuESXKF9JuSY/GOeNezRLdQgghhBBCCFF+lEh4k1adDlIVJfLKH+orSfkl1ZaUXyGEEEIIIYQQq+Dmaumg5pQ6ZVG+UFvOTshskBY2xxJCCCGEEEIIsQo53gXBLX1Keo9ZIYQQItCwbg1bvGRpeTdDiEpJ43r/t6GpEEKIchTe3MonNaWcnajZvZZb7AghhBArQ58OLcq7CUJUapYty7f8fO3IJIQQ5Sq8zz333KS/EeHcOohbxnCrLSGEEGJlYDdlkV1q1aqmcc8yuTzmiG4JbyGEKGfhzT2oud9yav0w99rlfsfcG1YIIYQoKfn5uG3l3YrKQ0hiw+XUbWeyg8ZcCCEqFxkL78mTJ9vs2bP990GDBlmTJk2sZs3kGqAffvjBnnjiCQlvIYQQQgghhBCiuMKbe0N37tw58fc555yzwjE44KecckqmpxRCCCHSkpeXZ3mlet8NkQlVq2rQK+qYK3VcCCHKlyrLlxc/wYnN1Z5++mmrXbt22bRKCCGEEEKUGqS0z5u3qNKLb1L869atbrNmLVCKf5bQmJcPGvfsjnOZ1Xi/9dZbhTrj9erVK8lphRBCCKfviHE2ccac8m6GEBXm9mD9OrWyvLwqlV54CyFEeVEi4U29980332yTJk2yZcuW+XMY50uWLLE5c+bYd999V9rtFEIIUYmYOmu+TZwu4S2EEEKIikGJCov69OnjApudzWfNmmWnnXaaHXLIIb6r+XXXXVf6rRRCCCGEEEIIISqT4/3111/bk08+adtss409//zzttlmm9kJJ5xgjRs39trvo446qvRbKoQQQgghhBBCVBbHe7XVVrPq1f9XRI7onjBhgv/eokUL+/7770u3hUIIUcacf/751rp1a1u8ePEKr5166qnWsWNHL6fJFn/99ZfdcMMNvpHljjvuaIceeqg9/PDDfm/rksJtHjO91ePAgQNt6623TjwIsu6+++7Wu3dv38dDCCGEEEJkQXg3a9bMHnjgAfvnn39s++23983W+FL6zTff2JprrlmSUwohRLlxySWX2IIFC+yee+5Jev7111+3Tz75xK699lqrwraVWWDu3Ll27LHH+r+nlO68/PLLdu6559qQIUOyWsrDv/PvvfeeP9555x27//77PdvpwgsvzFobhBBCCCEqdao5rseZZ55pDRo0cCfo0Ucftd12280WLVpkZ511Vum3UgghypANNtjAxe1tt91mxxxzjP/bRmAR1xnHe6uttspaW2655RZbY401PLgZApm0Z6211vJ/X0888UQv6ylrVl99dVt//fUTf3O3Cv7dJzsAR75mzZpl3gYhhBBCiErteG+xxRbuBB1//PG2zjrr2DPPPGM33XSTDRs2zM4+++zSb6UQQpQxJ510kjVs2ND/LQMc3ry8PP837bfffrPu3bt72jfp33fddVfijg7w1FNP+QaTZACRkn3NNdckXg8p3kcccYTtueee9vPPP9urr75qBx98sO2www522GGH2RtvvOHHcmeIV155xffMSM0e2nfffT3dvH79+v434peNLinx2WWXXeyiiy7y5wKffvqptWvXzpo2bWrnnXfeCmn0o0eP9mvTJ4INH3/8cZFjVLVqVXf+EeXPPvusB14ZH67/4osveio847b//vv7dRlTlR8JIYQQQpRQeANfsD766CP/Irh06VKrVauWbb755qXbOiGEyBLsXXHllVd6UBEhjON81VVXuQA+55xzrE6dOvbcc89Z//797aWXXkqkpSNY+/Xr507wqFGjXHSzyeSbb76ZOPcLL7xgPXv29HRx9se4+OKLrVu3bn58+/bt/b3z5s2zX375xTOHEOSpIHj32GMPd8OBNrG/Bu146KGH7KeffkrUcHPXCc6PKGcDTIKlXCswceJET6/HwUYwExQ4/fTTberUqQWODwGDe++914MHBFxh/Pjxfu4RI0ZYy5YtbdCgQfbggw/aZZdd5mNFkKBr167eJyGEEEKIykyJUs1xf7iFGO4KD9wNXA6+hPFllc14hBBiVaN58+Z2+OGHu0OMI92qVSv74IMPbMaMGe5q44CzoSSilZIb3F5EKLXXBx10kJ9jk002cSH8448/Jp5DSOOUw3fffWf//fefbbjhhi5M+beUfzMR+PPnz/djwuaVBYFwRvAjpkPaOU49DvbkyZO9zbVr13YXHMFOGj112gH+ne7QoYP3FU4++WSvZX/88ccT4h3HnDpvoL0EWHfddVcPMgQ4N+KdNHj2+Rg6dKgHEfg/Afr27WsHHnigi3vccSGEEEKIykqJhDcbDfEF7Oqrr/afcOutt9rll1/uX8oee+yx0m6nEEJkBVLKEYqhbAYnGTeadOo444cacDZCI70c4XnnnXfapEmTPLUa5xgHOBDSw4EdwvfZZx+vHUc0I1LZTG3ttde29dZbz4+JU8bTgbiuUaNGUq03GUfUXfMa7WjSpEnShnCI/5BuTp9Gjhzpt4UMIK7jNtOvm2++2X8n4ICQr1atWlI7yAKg7zB79mwfJ1LXA6Skcx6uJ4QQQghRmSmR8MYJIbWQer/4CxYb/+ge3kKIVZlQWx1+4vTicg8ePHiFY3Gmx44d6yKdemoccn4n3TzdOQExTMr5V1995eno1FoPHz7cH1tuuaWf89tvv/Ua6VRwl6mbDunmqVBXHmrLU29/xr/RQXhzDKnltDkmiOjwOzXvmYxV6u+pbVqZ26AJIYQQQlTaGm++kOFupDJlyhRbd911S6NdQgiRE+Aqk2qO44sQ5TFt2jR3uBHRpKBTp00mEM41zjO12gXd9xv398Ybb3Rh3atXL99MbaONNnIBT5056eJsVMlGazHctpEHu4vTJtLScbcDuNx///23v4aAJ6U93gCOevC4T/Qh9IcH7ve7775b4nEiYFC3bl374osvklx0ggjZ2IVdCCGEEKLCCW9q9diE6O23304IbnY2Z4dddscVQoiKAunXpIpTL00aORk//FtHajhZP6SHs78Fr1HXTY30n3/+uYJwDpAiTi01Dvqvv/7q/45Onz7dtt12W3+demwEdJcuXbyOGxGPuOe81GKzmRnifu+99/Zac5xzHvxOjTq3PmvTpo2729SeI87Zg+Ozzz5LtKFz586+szq3guT8bJLJo1GjRis1VpyXgAQBAgIMjNO///7rwQQhhBBCiMpMiVLNSaXkyyM13ny5O+OMM7zWjy9dfFkUQoiKAuL67rvv9o3C2JCMzdS4dRhCN+wuzkZrxx13nGf8tG7d2m+1GDvMMdwbe+DAgV4/zY7k/NvJhmShvprXEeYcc+GFF3rd9Kabbmo9evTw8wZwzdlTg393aSO14rQDqPVGbPNv9JFHHumCnJ/Bhd9pp51swIABfg1+cn7uH85xKwMbxRE0QHDzk83Z2PODbAEhhBBCiMpMleUF5UOmwH1l+fKJ4A4guqnd41HULrxCCCFEpnQdPMrGT5lZ3s0QokLQpH5tG9azrc2du9CWLq3cey6w52TdutVt1qwFltk3YLGyaMzLB417dse5VFPNSVOkXi9mr7328vvFSnQLIYQQQgghhBClWOMdyNAsF0IIIYQQQgghKi0lqvEWQgghypKGdWvY4iVLy7sZQlQIGterWd5NEEKISo+EtxBCiJyjT4cW5d0EISoUy5axJ48yFYUQYpUQ3iNHjky6Tzebqo0ePXqFHWvbtWtXei0UQghR6WATKJFdatWqpnGvwGOO6JbwFkKIVUB4b7zxxvbggw8mPcdtcIYOHZr0XJUqVSS8hRBCrBT/u2NGebeicu3KGlxRbd+SHTTmQghRuchYeL/11ltl2xIhhBBCCCGEEKICohpvIYQQOUdeXp7lrdR9N0RJqFpVg17Rxlwp5kIIkRtIeAshhMjJ2leRfTTuFW/MSWWfN2+RxLcQQpQzEt5CCCFyjr4jxtnEGXPKuxlCrPK3EevXqZXl5VWR8BZCiHJGwlsIIUTOMXXWfJs4XcJbCCGEEBUDFXMJIYQQQgghhBBliIS3EEIIIYQQQghRhkh4iwQzZ860Pn36WMuWLa1p06bWpk0be+CBB2zp0qVJxw0cONB22WUX23XXXe3vv/+2s846y48/6aST7NJLL/VHafDyyy/bUUcdZTvssIPtvvvudt5559nUqVMTry9ZssRGjBix0teZNm2abb311v4zHfTxuuuus7333tu23357O+igg+yuu+7y65clhbWrNMe5IO6//37bf//9V1gjtIm5jvn000+tSZMmNm/evGJfpzT78uyzz9p+++3nv7N+maeYIUOGePs5Lubyyy/3dZw65r/++qu98847Ga2TTPjmm2+sS5cu1qxZM3+ccMIJ9v7771u2+eijj7wvQgghhBAiO0h4C+e3336zY4891kXF7bffbq+88oqdffbZNmzYMDvzzDMtPz/fj/vrr79czFxyySX2wgsv2Icffmhjx4614cOH2y233OIChsfK8sYbb9hVV13lIuXVV1+1Bx980JYtW2YnnniiC2Ggjffcc4+VNYjCCRMm+LiMGjXKLr74Yhf8N954o1VkCKywHubMmZMk2OrVq2dffPGF/fvvv4nn+Rsht95661muQHDo66+/TnoutJ+fMbR/t912s4022sjee+89/wmXXXaZffXVV6XSnt9//91OOeUUF9xPP/20PfPMM7bHHnvYGWecYV9++WWpXEMIIYQQQuQmEt7C6du3rzVo0MBdTgQXvx922GE2dOhQdzMff/xxPy6I3j333NPq169vCxYssLp167oTjKCpXr26P1aW559/3o4++mhr27att2W77bZzYc/1gwO5fHnZ79BK/wgCEEzYeeedbZNNNrEDDjjALrjgAhdPISBREWHM11577STximBt166drbvuuvb5558nCdfmzZtbLsE6jttOhgJtJpgTC2/m+KeffnLhXbVqVVt//fX9Z2nz+uuv+/o555xzbPPNN7fNNtvMzj33XB83RLgQQgghhKi4SHgLmzVrlr311lt2+umnryA4Nt54YxfAOLy4nyGNF/HJ77jBM2bMSKTvpqYN44ofcsghtuOOO1rHjh3tu+++S7z2xBNP+DlwAEld/v777xOv5eXluQu4cOHCxHNrrrmmC/LWrVu7cOrdu7dNnz49kf6LKOc5ggIEArguojkwe/Zs69mzpwvovfbay2699da04v2xxx5z0YbLXaVKFX+MGzcu6RjSzZ977jl/7e6777bDDz886XUc+k6dOvnvtI9xIIhAu3ieFOYAjurxxx/vY3TwwQe7k19cnnrqKe8v5yct/5prrvEMAQQl14+v9/PPP3taOFkOhc3D6quv7m1KFd6cH7FItkOqYxyyIihZaNGihbvOF110kT8X3s+1yGbgtXvvvTepH7jrjAHzyNzwGDRokJc/MCfdu3f39Rb4448/rGvXrrbTTjt5WcIvv/ySeI02MueskTDOBIXat2/vKfOhbIF1xvOMSZxOzjr++OOPPcMjTq1nTbH+GRvaE/pWFKxp2hKXSwCZEz169Eisq4ceeijpddYW88vni3aw3ugba5jPA1kY++67r4/PTTfdlHgf4/zwww/7+xkfnPU///wz6dwE1Fq1auVzz5jH5RNjxozxMaWMhCAcgYNAaAdBDF5nzsh8gaI+D0IIIYQQlREJb2HffvutCxxqqdOBQJo4caI72ggA4CdiklTcDTfc0NNz+XIewxdxnGLSa1988UUXhd26dfMv9wh9BA0CDQHLNU4++eSEiOFLOvWw1FX36tXLr4fIatiwobutCIX42qQGU4c9ZcoU/5JPfThChOsHMUHqPMIDF5+0cYQMqfQxiBgEOSns22yzjV8L8TFgwAAXtv3793fHnQAFjiXCm1riH374wa8dGDlypD8f18XTFq45d+5cvz4gDE877TS/FuPA+JDGz3hnCuKwX79+dv7553v7Ed248W+++aY7qwjK0aNHJ45/7bXXfPwYs6LmgTEMqdYIdR4ELhDZwTVGTBK8CY43ji5BC8YQEYn4j4MxHM+cMBYEIwKLFy/2sgbaTH8YW+bqpZde8myHJ5980urUqePj9d9///l7qPsn64D1QeDokUceSZyP/pGVEdpPe2l3ENmh/QQN6DfCOIb5Ypy4HvMXYJxYI48++qh/du67776M5unQQw+1tdZayz8nnJPsEtbNBhts4FkjwJphfgKMHesKQQ7jx4/3IArzy7FXX321twOxyxhzzji4RbsJTDB2jC8OewzXYh8H1gBrJzjvH3zwgR975JFH+uecMhQ+h3wmA8wvbeCzxniyhpiLTD4PQgghhBCVDQlvkRBZNWrUSPt6eJ6Ns2rXru2/8zOklYf0XERFDF/2EVa4uQhmaqP5m+shEBCZOHWNGjVyJxqRhEAHal8RxfxEHF5xxRV+7LXXXutf7tdYY42ka/MT4cfriFjOibihzYhbhCyi5YYbbrBtt93Wj0W0xH0mpR7X77bbbnPBGUAEcn36h4OIc0hb3n77bX990003ddcP4RKEJeIHoR449dRT3YnfaqutfDyCgMHdrlmzpp8fIU92AWns//zzT+K9jFnYjCs8EKOBddZZx4MOiDNSmbkuffzxxx/9dQRP7FYitkKQpKh5iNO1EaoET7geApY+LFq0yB3jLbfc0mrVquXjTCAA55Ux4cHvzOHkyZMTbUAMsibIqADceYQd80pQImRe0D7WDS47gpz5Zf0Q1KF/zCnzw/XpE2MbEwcOglsP/IyFd3DrY1hfuP70N65dx8GnXzjeiOlMgyQEDRDMOO4EJhgXnGECU6zRMNe0h3rwIFhx+1kjQICMtcLYHXfccQkxjfA95phj/BrxOHMtxDMu/vXXX+/jhSgOkHnAmsQ9J0Mh9IXPHi52586drXHjxr5+WV8EtQJknrBeWf8ETAjKENjK5PMghBBCCFHZkPAWiS/1uJbpIC0XirtxFo4XdcIBRBVuLkIZJw/hEYtJvvSTBh3gOdKMEXIIMEQOggCHLx3UHvN+hBiiO4gwRB1tof3UiwdIFz7iiCMSf1955ZXuxIaNtQI4oaTWktb77rvv+vkZM9KDceGDuA1CA7GEkEMEBRBKAVz04NjSLkRy7LYickgNDpCOzbXjR0j5B8QwwuvOO+/0NiGYEMOh/hxByt+0lRRqxjmIoKLmgXZQA43LGgtXhC7CFFGLmAtuN6KPYAZiLYBgZrxiQUiAIIYxI5DBmLFOgDIDBCiCPLQNIU0whfZNmjTJ5zSId0jN2giBA+Y1Ftj8pN4bIUsfilOfjrAMMAbxJnNFQYYGwQN2MkeE49LTLsR0GCtEcryW4kwSxodAQCi9SB1LgkNxujjZCQHWPuPFnBfUl/BejkE8xzD+8XsJ1MRrGsIdEIr6PAghhBBCVDZWK+8GiPIHsYLDiIOJMEiF5xEDQRBlymqrFby8EMOkiuMCx/AFHsFFajHOMu1BYFCHygMxSb01TlwqOKOIQBw+RDcCH1cQcC6LglRtxBjCKKSgIzZ5DkcPSAsm7fbAAw90x4/XCAggjqjVpX4XR7lDhw5J5y7o+oWNUQBhmSpUq1Wrlvgd95c0egIPjBG/k24e4L3MMbXJiETEKGNT1DwAm6sRPGENEABhbIA0cMQqohHhGuajoDXCdXgEgmgMEOygzTjhzC/uazj+jjvuSBLygJAnHTq1Rj91nOkr5QGsC0RnEIs8TyCC+eU6BD8yJTUlPVMIoDAPjDXn4HceZBjEO+SHDAXmkkBJfEu3dOuFuSiI1OPpa9z+1D0dwnimzg/w2Ys3E0y3psP7i/o8CCGEEEJUNuR4C08bx/0dPHhwkjgC0kdx5kryxRmXN07D5dw4tZ999pkLKdxMjgkPakYRcrh2pFIHxywGVy6ku8eCg43VqDUlTRzXF2EcUugRA5wfpzRsKAY459y7OcAY4MgjMnGVgXMwLqmbUuE6IlpCW6h/x9WjRpY+h5rcokAIsplZLCBJ98bhzxTqm0kpRhQTFMA1ZZOx+JwIIRxlxHdca1vYPASof6aOHqEaO6j0lxpn0oiDk8z55s+fn+Ru40wzP6niOYZrILZZZ+ywT0YAzjkuKWMf2oZAx6EnU4AUaeYn3qyMFO4Y0vcJHrCe4nTyUOfN2HHtstjFPBWCNGzclwr9DOsopJuToRA2EoyDLMUl/vwxTmQvZHL/buYq9RZnBC8Km8OYkn4ehBBCCCEqKhLeIrGRFCKG1Fdqndk5mg252GiLL9Al2ZGY9GxqhdmMii/9OI+IQRxU0qnZCAtxgUhETJGSimhEBLFbNBtY4RIi4qhLpb4aAcW9vAFBRZtJO8Zp5W+cQlxCXODgzpI+S2o09eL0E6GL08m5qW2NwX3EdaU9iBRqn2kT7aVOmXMzPtRhI0LiFGUEE23knCF9vyio8SUgwOZt9IMNx9gULbVdhYGTiyiiX9Q9s8kWYjVOOcaVp90EFWIRVNg8BOgj91LHnWWMA6wLxpkU5pBGzPvYEI8ABk44D37nHAjloiDowM7mYWdvnHRqvhl7xoeUbAQsgppr4R7j2CPuCCqwGVsquNvU0oc0+QB/M9aFpZkTYOG6oQZ7ZSCDg1IF1iDzwGeCcWXMmYc4w4E0b+ZlZTckI7hEHxkfxol1FaeIFwTjjlNNG+g/65p/D1Jr6AujJJ8HIYQQQoiKioS3SKRQc8swxOSFF17oNcAIHm4BhgNakvRaBA2bN1GnTS112Ok67OxM7S51yXxBJ22YnZmDKOA2RdRc8+UfN5eUcQQEO0hT0wwIaVxQxCuiEwHD8YgVNlEjPZyU6uCC8jrCkXMhnPmZLqBA8AEhT4oz6bSIB0QmTizjwk7aOJTsBh2PC4IWVz91d/fCwO0cMmSIi2LGgf6RZs8GcZnCLuIIX/qDgCNNGIEUu7/ML+NGP9gELVDUPAAuN5u9pW5ARjCD8UwVrqQYI8YRb8wjx7EGMg0ikLFAG8hO4P1sGsZaIJWegBDjHoQcGQ70h3VKoCa+7VcAR5tN4FLbz99sTpZuY7UAGQQEFwjGrCyMI2uJDAP2IGC86SelAantZl5IE99nn31W6prsyM+4hNILxisT2DiOYBC3G6OdONf8e5BaklAYJfk8CCGEEEJUVKosT3cjYyFEscEZRByycdbKpAcLgUBGoMe138WFsg6CMuw8vip+HroOHmXjp/xvY0chRMloUr+2DevZ1ubOXWhLl/7fHg2VGarU6tatbrNmLTB9A84OGvPyQeOe3XHOBG2uJsRKQv0yNdDcPg23XaJblBRSwslUGD58uLvhqyL6PAghhBBCrIhSzYUoBag9pt6ctG0hSgq13+zuTop7fC/5VQ19HoQQQgghkpHjLcRKwq23qNEWYmWhnp1HacCGdOWBPg9CCCGEECsi4S2EECLnaFi3hi1esrS8myHEKk3jerqjgBBC5AoS3kIIIXKOPh1alHcThKgQLFuWb/n52llJCCHKGwlvIYQQOQe7MIvsUqtWNY17BRxzRLeEtxBClD8S3kIIIXKO/HxcuvJuReW6HUpwR3XbmeygMRdCiMqFdjUXQgghhBBCCCHKEDneQgghco68vDzLU2g461StqkEvrzFXSrgQQlRsJLyFEELkZO2ryD4a9/Ibc1LO581bJPEthBAVFAlvIYQQOUffEeNs4ow55d0MIbJ2269+nVpZXl4VCW8hhKigSHgLIYTIOabOmm8Tp0t4CyGEEKJioGIuIYQQQgghhBCiDJHwFjnDzJkzrU+fPtayZUtr2rSptWnTxh544AFbunRp0nEDBw60XXbZxXbddVf7+++/7ayzzvLjTzrpJLv00kv9URq8/PLLdtRRR9kOO+xgu+++u5133nk2derUxOtLliyxESNGrPR1pk2bZltvvbX/TAd9vO6662zvvfe27bff3g466CC76667/PplSWHtKs1xLoj777/f9t9//xXWCG1irmM+/fRTa9Kkic2bN6/Y1ynNvjz77LO23377+e+sX+YpZsiQId5+jou5/PLLfR2njvmvv/5q77zzTkbrJBO++eYb69KlizVr1swfJ5xwgr3//vtp278yZGN9CCGEEEKsSkh4i5zgt99+s2OPPdZFxe23326vvPKKnX322TZs2DA788wz/Z6+8Ndff7mYueSSS+yFF16wDz/80MaOHWvDhw+3W265xQUMj5XljTfesKuuuspFyquvvmoPPvigLVu2zE488UQXwkAb77nnHitrEDATJkzwcRk1apRdfPHFLvhvvPFGq8gQWGE9zJnzf+nGH330kdWrV8+++OIL+/fffxPP8zeidL311rNcgeDQ119/nfRcaD8/Y2j/brvtZhtttJG99957/hMuu+wy++qrr0qlPb///rudcsopLriffvppe+aZZ2yPPfawM844w7788stSuYYQQgghhEiPhLfICfr27WsNGjRwlxPBxe+HHXaYDR061N3Mxx9/3I8LonfPPfe0+vXr24IFC6xu3bruBCNoqlev7o+V5fnnn7ejjz7a2rZt623ZbrvtXNhz/eBALl9e9hvg0D+CAAQTdt55Z9tkk03sgAMOsAsuuMDFUwhIVEQY87XXXjtJvCJY27VrZ+uuu659/vnnScK1efPmlkuwjuO2k6FAmwnmxMKbOf7pp59ceFetWtXWX399/1navP76675+zjnnHNt8881ts802s3PPPdfHDREuhBBCCCHKDglvUe7MmjXL3nrrLTv99NNXEBwbb7yxC2AcXtzPkAaL+OR33OAZM2Yk0ndTU1xxxQ855BDbcccdrWPHjvbdd98lXnviiSf8HDiApC5///33SfcQxgVcuHBh4rk111zTBXnr1q1dOPXu3dumT5+eSP9FlPMcQQECAVwX0RyYPXu29ezZ0wX0XnvtZbfeemta8f7YY4+5aMPlrlKlij/GjRuXdAzp5s8995y/dvfdd9vhhx+e9DoOfadOnfx32sc4EESgXTxPCnMAR/X444/3MTr44IPdyS8uTz31lPeX85OWf80113iGAIKS68fX+/nnnz0tnCyHwuZh9dVX9zalCm/Oj1gk2yHVMQ5ZEZQstGjRwl3niy66yJ8L7+daZDPw2r333pvUD9x1xoB5ZG54DBo0yMsfmJPu3bv7egv88ccf1rVrV9tpp528LOGXX35JvEYbmXPWSBhngkLt27f3lPlQtsA643nGJE4nZx1//PHHnuERp9azplj/jA3tCX0rCtY0bYnLJYDMiR49eqR9z5tvvumBDsot6P/555+f9Jko7PNV0JgKIYQQQlRGJLxFufPtt9/6F3K+3KcDgTRx4kR3tBF4wE++9JOKu+GGG3p6Lg55DCnoOMWk17744osuCrt16+bOI0IfQYNAQ8ByjZNPPjkhYhCn1MNSV92rVy+/HiKrYcOG7rYiEuNrkxpMHfaUKVNc9FIfjlDh+qEWm9T5P//801180sYJFJBKH0MqOYKcFPZtttnGr4WgGzBggAuc/v37u+NOgALHEuFNLfEPP/zg1w6MHDnSn4/r4mkL15w7d65fHxCGp512ml+LcWB8SONnvDMFcdivXz8XZbQf0Y0bj2jDWUVQjh49OnH8a6+95uPHmBU1D4xhSLVGqPMgcIHIDq4xYpLgTXC8cXQJWjCGDz30kIv/OBjD8cwJY0EwIrB48WIva6DN9IexZa5eeuklz3Z48sknrU6dOj5e//33n7+Hun+yDlgfBI4eeeSRxPnoH1kZof20l3YHkR3aT9CAfiOMY5gvxonrMX8Bxok18uijj/pn57777stong499FBba621/HPCOckuYd1ssMEGnjWSCkEE+sdngfXEmiEAFPY1KOzzVdiYCiGEEEJURnQ7MVHuBJFVo0aNtK+H59k4q3bt2v47P0NaeUjPTQWhhLDCzQVqo3FRuR6iA5Gw7777+ms40e+++64LCNxFal8RxRyHOKTOm+vg6l1xxRW2xhprrHBthN+pp55qW221lf+NuEGQIW655vjx492tJHUdrr76alu0aFGivaTUI1pvu+02F5wBBAvCmHTghx9+2B8IJYT+PvvsY5tuuqlvLofoReQgLHEe4/pz2oUTD4xHEPy42zVr1vQ+IfwQ87T1n3/+cdEPjGGqYEJcBZd9nXXW8bbgwgPpzAjeH3/80Z8jAECaM+MRhDfBBChqHhgH6vcBoYq443oI2BtuuMHHD8d4yy23tFq1annAgEAAY9G4cWN/30033eRic/LkyYn241ITRAngzhNgYV4RmCHzgvbhjuOyw7XXXuvuN6KTeWROx4wZ45kZtIFgDdcOhMABopf2B6HP+fi7Q4cOLrxx51NhfbFe6S+166HMAgef+QbOm2mQhKABAZHBgwd7IIRN1Rgb1jpCntdjCCiwLmhjmFfaybwW9fkqbEyFEEIIISojEt6i3EH4Aa4lDnIqpOUC4iP8ngk4wAjlAAIANxdwQREdCI4Am3WRBh3AbSTNmOcRc6SZI1gRup07d17heqTkIqxxBBF5uJFBgNAW2h9EN5AuDGGX6iuvvNKPDRtrBRDEiFAeuO4IU4Qt6cEIKBxLxC1OKMIbdxJhGgupWGQiqINjS7u23XbbJLcVkR63i3RsrhFz8803J35HDOOk3nnnnTZp0iRPFSedGYEKiF6CCbSd6yIUce8zmQdSuKmBJlU9pJkDIhdhiqhF/Aa3m3EnUBNEN+C2ssZ4LdT/IyJjGDN2z6ddrBMgpZoNyRCP8fgQlKB9tJM5RXQHyNpIFd4EEQhUILAR7sD88DuZHvQBZzlTWH8B+hNvMlcUfL64LkEf1idBEEobENiULMQ0atTIx4LnEds8mN8jjzyyyM9XQWMqhBBCCFFZkfAW5Q5i5f+1dybwNlXtH39cMmSKpEFCKlRCRBmjVGTKVDSXUkk0KCkaSANppom3iYqKRpI0lwZJCZUhFWUIkfm69//5rve/9rvPcUfuOfe45/f9fPZ779l7n73XWnt5u7/1e55n44bhFmYkvNlP3mtu/3gvUiTz6Y3AJVTcu8BhUYrgIrSYas+0h9zuZs2auQ0XkHDbjIQ3jh8iEGGCC4gTfvbZZ7tjOIHZQag2xbcQRt6RRmyyD0ENCGCqv7du3drlmnMM1xNxS64ughcx5V1KT2b3z2qMPAjLaKFasmTJ4HfcX8LoWXhgjPgd597Dd3nGLEogEhGjPkogq+cAFFejyBpzgMUPL1xx4BHbiFmEq38emc0R7sPm4ZmGYbGDNuOE83xxdv35Dz30UISQB4T8F198sUvOcvQ401fSA5gXiHTErN/PQgTPl/uw+JFTokPScwoLKDwHxppr8Dsb4fAZVchngYR5TE487WWMw6H02c2djMZUCCGEECJZUY63yHcIG8f9JQQ2LI6AnF7CY6OFZE7A5Q2H4XJtRMTs2bOdkMLN5By/EZqNkMO9Ja837FyGHUYf7h4OvyYMmLxunF2caISxD7lFnHF9QuV9QTEgR5d3N3sYAxxDRCbuOnANxoXc8DCEH7NY4dtC/jsuKuHo9NmHfWcHQhCHOiwgCfcmxDqnEE5PwTBEMYsCOMzkB4evycLAhx9+6MR3OPc8q+fgIf+ZPHqEKvndHvqLa0tYvS+sxvU2bNgQEVaOS8vziRbPYbgHwpB5RoV9nHmcc6IGGHvfNsQkDj1uLykFPJ9wsTJyy8MQus/iAfPJtxF8njdjx73jEYbNIg3udjT008+jMNRQYHGDRSjyvAlvp6/+uWb17yuzMRVCCCGESFYkvEVCQJEmRAwFqsh1pnI0YdQU2kKw+ArduYHQbMJ8CcFGMOA8IhpwUAmnxr1D4CISEVOExiIaEUFUiyb8GZcQEUcRKnKrEVC8yxsQVLSZsGOcVj6Ty0yINi6wd2cJMyY0mlxa+onQxenk2lQ3D4P7iENIewixJveZNtFecs25NuPD68QQkuFXaJFvSxu5pg/fzw7ytFkQoHgb/aDgGEXRotuVFTi5OLr0i3BkCpkhVsNFtnDlaTeLCuFFgayeg4c+kmOPO8sYe5gXPtfah9XzPQrisYCBE87G71zD595nBYsOVOEmlB9weclPZuwZH0KyEbAIau6Fe4xjjwBlUYFibNHgFpNL78PkPXxmrLN6DRoLLNyXOgF7ChEcpCkwB3kO/JtgXBlzn14Q/Vx5powhCw3k1FNh3j/XrP59ZTWmQgghhBDJiIS3SAgIoSY3GjF5ww03uLxQBA85pDiguxNei6ChMBZ52h06dAgqXfvKzuTukpeMYCVsmFxWHwrMu5bJuSZsGzeXkHFEEhWkyWkGhDSuH+IVgYKA4XwcXUQK4eGEVHsXlOMIR66FcOZnRgsKLD4g5AlxJnQZMY3IxDVkXMgHxqEcO3ZsxLggaHEdo6u7ZwVu5xNPPOFEMeNA/3A4KeaWU6gijvClPwg4wrgJUQ67vzxfxo1+UATNk91zAFxu8qrDjjGwmMF4RgtXwqYR44hmniPnMQdyAmKTiAXaQHQC3+/ataubC4TSsyDEuPuFDSIc6A/zlIWa8Gu/PDi/FIGLbj+fqfodvT8MEQQsLrAYs6cwjswlIgwodMd4009SAzJqN/vIsWccmaf0nXP9K8Oy+veV1ZgKIYQQQiQjhdL1YlUhCgQ4o4hDqlWHc7CF2BvpNXqazVma82KKQuzN1KxU3sb3b2fr1m2y1NS0/G5OgYYssQoVStuaNRtNfwHHB415/qBxj+845wQVVxNiL4f8ZXKgeb0TbrtEtxBCCCGEEImFQs2FKACQe0y+OWHbQgghhBBCiMRCjrcQezm8eoscbSGEEEIIIURiIuEthBAi4ahSoYxt2Z6a380QIi5Uq5izN1EIIYTYe5HwFkIIkXAM7t44v5sgRFzZuTPN0tJUAUkIIQoqEt5CCCESDqo7i/hSrlxJjXs+jjmiW8JbCCEKLhLeQgghEo60NNy//G5Fcr0Oxbuueu1MfNCYCyFEcqGq5kIIIYQQQgghRAyR4y2EECLhSElJsRQtDcedwoU16Pkx5gozF0KIgo+EtxBCiITMfRXxR+OeP2NOuPn69ZslvoUQogAj4S2EECLhGDrxc1u4Ym1+N0OIuLxKbFjPZpaSUkjCWwghCjAS3kIIIRKOZWs22MLlEt5CCCGEKBgomUsIIYQQQgghhIghEt5CCCGEEEIIIUQMUai5EELsJq1atbLly5dneOy5556zRo0a5dm9zj//fGvYsKH17dvX9hb+/vtv++qrr6xNmzb53RQhhBBCiHxFwlsIIfaAQYMGWdu2bXfZX7ZsWUt2Ro4caenp6RLeQgghhEh6JLyFEGIPKF26tB1wwAH53YyEBNEthBBCCCGU4y2EEDENRX/llVesS5cudtxxx9kll1ziQtMJF69Tp4517NjRfvnlF3fua6+9Zj169HAucb169ezkk0+2SZMmZXptzsdJ5rqdO3e2r7/+2u1/4403XIh7ampqcO67777rrocQzk2b4JtvvnHX59z27du7a3kGDhxod999t/Xv3999t0WLFjZlyhR37JFHHrHJkye7jXsKIYQQQiQzEt5CCBFDHnzwQbv++uttwoQJNn/+fDvrrLOscePGTvyWKFHCRo0aFZz7ww8/2IIFC+zll1+2q6++2u644w779NNPMxTdQ4cOtd69ezuhy/Uuv/xyW7lypZ1yyim2detWmzVrVnD+1KlTnUgvVKhQrtq0evVqdw+E95tvvmm9evVyYhsx7hk/frwdc8wx9tZbb9lpp51mt912m23cuNEJeu7JxnWFEEIIIZIZCW8hhNgDEJo41OHtzDPPDI4jWhG1xx57rJ144ol25JFHOmebnx06dLAlS5YE5yKM77vvPjvqqKOsa9eu7joTJ07c5Z7PP/+8K7bWqVMnO/zww+2GG25w33nhhResZMmS1rJlS5s2bZo7d8uWLfbRRx/tVpsQ1Zx33nnnWZUqVZwbfvbZZ9uzzz4bXKtGjRp22WWXWeXKla1fv35O9OOY047ixYu7rXz58jEbfyGEEEKIvQHleAshxB5wzTXXOKc3TJEi//u/VgSpBxFaqVKliM87duwIPiNu999//+Azwvill17a5Z6LFy+2Pn36ROyrW7eu2w/t2rWzW2+91W6//Xb78MMPrWLFiu5auW0TAvyDDz5wiwkejlWrVi34XLVq1eD3UqVKuZ/hMHchhBBCCCHhLYQQewRCGcGcGYULF474nJKSeaBRWLDDzp07Mzy/WLFiu+zj3LS0NPd78+bN3WfyvsnJjq4qntM2IaDJ677iiisybec+++yzy/dUVE0IIYQQIhKFmgshRIKwbNky27RpU/B53rx5LoQ8GhznuXPnRuzjs3eiixYtaq1bt7b33nvPPvvss4gw89zA9WgTCwt+e//9912+d07wOeVCCCGEEMmOhLcQQuwBFBKjCFn0tnnz5lxfi++QM07IOLnd5Gn37Nlzl/Muuugil89NYbWlS5e6SugLFy50eeEews0panbQQQe53O3dgXsj/h944AH79ddfneCm8NohhxySo+9TqI2K6RR9E0IIIYRIZhRqLoQQe8Dw4cPdFg2FxnLLwQcf7N4JjoDm54gRI6x+/fq7nNe2bVtbs2aNPfzww07k16pVy8aNG2fVq1cPzuGVYhQ449zdhdzvxx9/3An7sWPH2oEHHuiqmlOALSdQjI1cdM6nyroccCGEEEIkK4XSlYwnhBD5Dq8Ie/TRR23mzJl5cr1///3XmjRp4l7zFS6mtrfQa/Q0m7N0VX43Q4iYU7NSeRvfv52tW7fJUlP/W6dBxA7W/ypUKG1r1mw0/QUcHzTm+YPGPb7jnBPkeAshRAGCtVQKqk2fPt1VI98bRbcQQgghREFDwlsIIQoQhHMTok7l8jFjxuR3c4QQQgghhIS3EEIkBp07d3ZbXkDl8b2dKhXK2Jbteh+4KPhUq1g2v5sghBAiDkh4CyGESDgGd2+c300QIm7s3JlmaWlKwhRCiIKMhLcQQoiEg0JTIr6UK1dS455PY47olvAWQoiCjYS3EEKIhCMtDQcwv1uRPPg3veG8qvptfNCYCyFEcpGS3w0QQgghhBBCCCEKMnK8hRBCJBwpKSmWoqXhuFO4sAY9L1EIuRBCCI+EtxBCiITMfRXxR+OetxBGvn79ZolvIYQQEt5CCCESj6ETP7eFK9bmdzOE2KPXhA3r2cxSUgpJeAshhJDwFkIIkXgsW7PBFi6X8BZCCCFEwUDJXEIIIYQQQgghRAyR8BZCCCGEEEIIIWKIhLfYK2nVqpXVqFHDbTVr1rR69erZOeecY5988kmurvPFF1/Y4sWLc3TuwIED3RZvvvzyS9fPjDj//PPtkUceien977jjDrvgggsi9s2ZM8e1KXo8Xn/9dWvYsKF7B3Nuycu+cB2ut3PnTjv++ONtypQpEceHDBni2s/Yhrnooovsrrvu2mXMFyxYYN9++222zyOnfPbZZ26+1qlTx+rXr2+9evWyefPmWbz57rvv7LTTTrPatWvbpEmTMhyTjPjjjz/cufwUQgghhBDZI+Et9loGDRpkn376qX300Uf28ssvO4HVu3dv+/zzz3N8DYTWmjVrYtrOvZ0GDRrYjz/+GCGmEWcVK1bcRaQh5DifV0ElAoULF3aLMt9//33E/ozaT/84r1GjRu47zC1Pnz597Ndff82TNiGwr7rqKmvfvr298cYb9uKLL9ohhxziFjfiLWSffPJJO+yww2zq1KnWpk2buN5bCCGEECKZSIy/joXYDUqXLm0HHHCAHXjggXbUUUfZjTfeaGeeeabdfffd+d20AgVC+t9//7UlS5YE+xCsF154oa1cudJ+//33COGN451o7Q8Lb9q8fPly54iHhffPP/9smzdvdg500aJF3dyKBW+++aY1adLEzj33XKtSpYqbu0QVcL933nnH4snGjRvtuOOOs0MPPdRKlSoV13sLIYQQQiQTEt6iQHH22Wc7AbVs2TL3edGiRXbppZc6B5Nw2p49ewah5YSrA06jD3Em3PaMM86wY4891jmfCCLClT0IUNxKroVjOWvWrAhBd80119gJJ5zgvn/WWWfZ7Nmzg+PPPfectWzZ0n23c+fO9s033wTHaDNCEBF0+umn2/jx43Pd9/T0dHv88cddv7h/06ZN7dFHH3XHPv74YxfWvGXLluB8HF2iBLZu3eq++9hjj7nvIFSvuOIKW7FihTuPhY3KlSvbDz/84D5v377dhV2fcsopLszfjwHXph/0H/766y/r16+fE+KM5bBhw9x34bXXXnOh1jjJCF2c3zC//fabNW7c2B5++OHgnnyf67DdcMMNtn79+uB8nnOPHj1cH3me69atC47RnoULFwb3pr2Mz8knn2xz584NxoTfjzzySCtXrlxEODnPBaF+8803R4TW41Q3a9bMzS2O+etnB9EAP/30k/3999/BvkKFCtm4ceOse/fu7jPz8frrr7fbbrvNPaOTTjrJnnrqqQh3/umnn3bPgDlDG7kmXHnllXbvvfcG5956661u3oWfe4sWLdx3vvrqK/fcMwqdz24+h3n++efdvCEkP6/msxBCCCFEQULCWxQoqlevHggxxAkCslKlSi73+KWXXnIiesSIEe6cV155JRA5l1xyiRMhiLvrrrvOpk2b5kQ357z//vvB9d977z3nUJIzjGt59dVXO9cQEINcn/twHMF6++23u2Pz58+3++67zwkpwnoRKf3793dtRPhedtllgQC96aabbPTo0bvkJWcH5z/77LMuR5n2I2rpG2HiiNgSJUo4Ae6ZPn26E+nFixe3F154wTmx999/vwvb33///d2Y7NixYxfXmJ9lypSxatWqORHshTfCnHvUqlXLiVAccUQtouzBBx+0Dz/80I1BOE/8iCOOsIkTJzrB71m7dq1bLCH0GeEHo0aNciHaiE8WMFgAQdQD97r88svd4gCCHqFHHzyIP4Qt4hsQ1SwGILKJmvC527j19CcaxvCggw5yqQ233HJLsP/dd9+1sWPHusUNxvvVV1/N0XPq2rWr6yNiGJHM+LDQwDzdb7/9Iq5frFgxmzx5shuPkSNH2tKlS90xxDJCnTZxnO+SJ45jz1iGnfyvv/7a/vzzT7cQ4vPLOYd+sWjAcw6H1Xuyms9h6DvPh0Ufnn1ezWchhBBCiIKEhLcoUCCkYNOmTU4A4KriUpLHeswxxzjXDlEO5cuXdz/Lli1rJUuWtH333deJVopNEXqL83300UfbL7/8Elwf5w/BjMAntB2h9NZbbznH+NRTT7XBgwe7YwhKQon9vXBMEX/k8nJtrsECAMIbwYvQZV/VqlWdGGbBAIEZBpEUvYVd84MPPtiF2eOOcg8cYMKXaX+RIkVcvxDbgKCaMWOGtW3b1n3GPaU/CE/af+edd9o///wTFKsLC2+Etg8n5ycLFtH53XwPx5Q+4qbSJgqa4RLzbIDxQHhyP/8sEI6IaMQyTi0g3lkYYCGE/VwPAc99cXnJ6cf9RhRyLcadZ+EhbJzvecceUUo/uT9urheptN+79WF4xuSKM7f8/AIWUViEYQGGhQ0v7LODNhJZwfNAFLPY07p1a7eQEI5I4L6IVsLREdV8ZvGBucZ4cD6ON9cbOnSoayNCF1FNW1gQWr16tRsbIgH8AgMFBXHqud4+++zj5n10WH1289nD/MPtf+CBB9yzh5zOZyGEEEKIZKJIfjdAiLwEJxTIV0VQID5x2hAs5CjjPFeoUCHD7yKqcX8Jb0ZgIOoIWQ+7sQg4DwITh4/QdUQc9yJHF4GDM8k9fUEyroFIIzwdMY9g6tatmxPEtAuhhJD2IIwRUmEycgxxJT0nnniiC5fGtaZNhP0ivHwbyH8nTB6HGLcZN5t2IYRxQ6+99tqIomgsXPiCYogqhC3fRajSD7+fkGnyvMPClfsjuljU8BAynZqa6txdQJwx3mFwfznHC2Pg2rSVRZQw9Iv2cZx78bw9hPNTdM/jFw4IL2dBgLb4hQMcWxYZuFZGwjszWMzxIMhzGmoOCFkcbPrKs3j77bed848A9gsOLJ6E5wCLQ5zPeHsx7UFAM38Zd8aJBR5EMUKeecX4ECbOHGFus1CQFdnNZw+LKcxVFn08OZ3PQgghhBDJhIS3KFD4PFfCiBGUhPWSs4vr1q5dOycKCNHNCFxawrM7derkHEF+x2UNEy0eECKIHn4SsrthwwbnInM/xCKh6EAINi4nLu0HH3zgQqJxf/mJmPKOcFbgfEYTFq5cf/jw4U7Q46biloZfA4aoRJziENNXHE3cYAQ2PPTQQy58PIwXzgg3HFJcY8Q9DqsXnOR5I7oRtjibQIh0ND5X3v/M6ByiEqg0P2DAAJcHj9vqz58wYUKEuPbinVBoHNowPJMwCO977rnHOcwIVJ6HF9648ojfww8/PHDec0L0XIhuQ2aQf92xY0c3biy88FzYWCxibmTWB3+PjMYNGCcvjHHhmWvbtm1ziww8V8K9iVZgUYJUgazIbj57SMtAmBMh4fO4czqfhRBCCCGSCYWaiwIFebaIN/J9ER6rVq1yIa6E6uLyUTAsM4GEcO3SpYsTEYhXRB/ubPh8L+y9wMBBR7DhIiLqnnnmGSc+cVa5N/B9hN0TTzzhHEdCc3FZEUW4kIgiHEUcTsQ1G0IW9zc3IORZLCDvl8UDFhxwR337cbMJnyfXmrx1HHBAhCFgccf9/XEwEaQ+pxjI2SWfGAGOEPfgTnNNwsQZe6BPOMjhAmj0CaEZdoqjwYEntxvhxnMAniUil2v59iFSCaunfyyycC+faw++yJcH95Vn6cPMPXwXMU94dLyqsZNPnVE+OM8hJ8KfxQ6iNhhPD6KYXH6/cMLCEfMfUcyiA8+OgmfkjXMsO7Kbzx4Wb1jgwQ33ERl5NZ+FEEIIIQoSEt5ir8XnsCIIEMTkZxMa6ytPIxARg+Qy835khDWuXDgkGNFFDjTX4nwEMtdiH9fh+uHzCd8dM2aMC+klNxfBg5OOaELYEjJMPjfC2ldK5/s40xTEog20hfNoG/nKHTp0cK4zDiHXJUSaviCGcwNCm/xdHxZM6DjtC7cfsU2hOUQ/iwAeXGYKoM2cOdOJWMKdEW0sKnhwZWl3tEDlM0IeceddYBxXBDN544wnTisuuR+r7GDxgEUJ7ofIZiGEUHeEM6KQ65IGgLhjQYWFAgqfMX5EEUS/lotrILJ5LmHh7fO8aX9WYebME6IlwgsJuwvh/uRoE2rO2HBdiviRZ89zyAmcR0oEz4s+k4vNM/U5+zxbX90fhx9Bz4JHToV3dvM5jC/sxkIN/47yaj4LIYQQQhQkJLzFXgth1TikzZs3t4svvtgJThw6LwxxOX24OGIAQYYYwCUlzxd45RGFuhAVhNEiDnglGdcjpJc817B7ipOM+CZUmNBqXGzClql6jTCk6jbi8sknn3TiFYcXV5xccMQH4gpHlwrQCBVcdUQh30Pwcn2+RyGr3r1752o8EKvkuNO2vn37OlFP0a5w++vWresEOqHotM1D1WzC8hkf2kBkABW7wznaOKcsFkRX/mY/YiwsXBHghDYDr8giJJm8du9iZweuKc+G8HD6xCIILjhVzrkebWeMuQ8h2TwH8rQpnofzz/hFQztZiAjnHgPzhVzorIQ384BFG59/vSfw/KmEziIPr7djvKnCznxmjHICYeAsRiC4CcknRx9H2TvmzClCyqknQDqB7z/PHiGeHdnN52ioYs59SFfIq/kshBBCCFGQKJSe08REIYQQIk70Gj3N5iz9b3i7EHsjNSuVt/H929m6dZssNTWyMCFQP7JChdK2Zs1G019i8UPjHn805vmDxj2+45wT5HgLIYQQQgghhBAxRMJbCCGEEEIIIYSIIRLeQgghhBBCCCFEDNF7vIUQQiQcVSqUsS3bU/O7GULsNtUq/q84pRBCCCHhLYQQIuEY3L1xfjdBiD1m5840S0tTVSMhhBAS3kIIIRIQKkGL+FKuXEmNex6D6JbwFkIIARLeQgghEo60NJzC/G5Fcr0OxTu0eu2MEEIIkfeouJoQQgghhBBCCBFD5HgLIYRIOFJSUixFS8Nxp3BhDfruorByIYQQWSHhLYQQIiHzjUX80bjvPoTpr1+/WeJbCCFEhkh4CyGESDiGTvzcFq5Ym9/NECLHrw4b1rOZpaQUkvAWQgiRIRLeQgghEo5lazbYwuUS3kIIIYQoGCiZSwghhBBCCCGEiCES3kKYWY0aNdy2YsWKXY69+OKL7tgjjzziPg8cONBtu8uXX34Z3C+jbXev/ccff7jv8zO3x/e0Tznh6aeftlNOOSVi36pVq1ybzj///Ij933zzjdWsWdPWr1+f6/vkZV9ee+01a9Wqlfv9zDPPtEcffTTi+BNPPOHaz3lhbrnlFrvqqqt2GfPff//dPvrooxw9r5y0LTxvjjnmGGvevLkNGzbM/v333+A85m30+OYVCxYssG+//db9Tr9ox6effhpxzt9//20nnXSSPffcczFpgxBCCCHE3oCEtxD/zz777GMzZ87cZf+MGTOskH/J7f+LKrbdpV69ek6c+M2LI/95T66dyDRo0MCJzLVr10YsQlSsWNG+++4727ZtW7Cfz4i4/fbbzxKF+vXr2w8//BCxz7efn2Fof8OGDe3ggw92z5SfMGjQIPv+++/zrE0HHXRQMG+mT59uQ4cOtY8//tiJft6DDZdcckmwaJTX9OnTx3799Vf3e4sWLaxdu3Y2ZMgQ27x5c3DO4MGD7YgjjoiZ+BdCCCGE2BuQ8BYiJAyjhTfO4Zw5c+zoo48O9pUuXdptu0vRokXtgAMOCDYoW7Zs8HlPrp3I4MiWKFEiQrwiWDt16mSlSpUKnFMvXE844QRLtPkRbvv27dtdmy+99NII4b1x40ZbvHixE96FCxd2z5SfscBfn61SpUpO/D7++OM2e/Zse++999w5JUuWjNsCBotGiO4HH3wwcOW/+OILGz58eMTilRBCCCFEsiHhLcT/Qxj0V199FRGm++GHHzrBhXjJKJR5w4YN1rdvX3cOQvGGG26I+P5//vMfF6qMy41AI9Q4O9LT0+2xxx6zpk2buuteccUVESHwhO7279/fjj/+eGvSpImNGjXKfSfs0J966qlWp04d991//vkn12MxadIkO+OMM+zYY4+1Ro0a2R133GE7d+50ghInOtwPHE/Cwv/880/3+aWXXgr6jMv5008/BREFtClaeHN9xm7WrFm7OMZA+3FNGzdu7FznAQMGBH3i+9zrtttuc8eefPLJiH7grp9++ul28803uzHKbmxXrlxpvXr1srp169pZZ51lv/32W3CMNjL2y5cvd59xrlkk6dKliwuZX7Zsmds/d+5ct58xCYeTM2eYX4Srh93fvHheYQ4//HDXNy+8w6HmCOFzzjnHOdWM1xtvvLHb841rMhaMrf/3UL58eefqv/DCC855v/vuu92/icqVK+9Rn4QQQggh9nYkvIX4f4466ig78MADnWDwIF4QRZnx8MMP2+rVq10eODmsCxcutNGjRwcCFJGF8Jg8ebIT7/369cu2HYiWN9980+6//357+eWXbf/993fhwjt27HDHEU3ck/NwFhFT48ePD77PvRBHtOfHH3+0p556KlfjgDgkT/i6666zadOmOdH9yiuv2Pvvv2/Vq1d3gtKLOnj33XedyCacmogB+oxQph2IuwsuuCAQkwg7H2qNUGdD0CGyvWuMmFuzZk3geF999dUulxgnl4UMxH84h5vzcZ8ZB0KdPVu2bLErr7zStZn+4LhmN7Y8H0K0WXi47LLL7Nlnnw2uR/9wlX37aS/t9iLbt59FA/qdkpKyixvMOEWHfu/p88oIQrsZp4wggoPjEydOdGJ7d+cbfSDUHaEdTo/o0KGDE+iMPVEOPXv23OP+CCGEEELs7eh1YkJEud6Ix7Zt2zox99lnn7mcVYRJRiD6ENSHHnqoC6N+6KGHgmOImIsuushdC7jO2LFjbevWrVa8ePEsi5Dh4OIEw5133ukE0ieffGKHHHKIE064pN5FvP322yNyanGEjzvuOPd7mzZt3GJAGMRpdNgvfW3fvr37fd9997W77rrLTjvtNPeZviF4f/nlF7ePImPkEyPOvPDGHfZt7927t7Vs2dJ9xillIQNnFYcU4T1hwgR3DKGKo879ELD33HOP6weO8ZFHHmnlypVzbWchgAWAatWque+NGDHCjemSJUuC9uNSV6lSJfiMO3/ttde6sH7Eog/1zmpsGU/G9oMPPnDjTBvmzZvn7u3xCweMK+33Qp/r8bl79+5OeOPOR4NAx/Wnv4R++8iI7J7X7kDo/qZNmzI8xrNHFPs5uLvzjT4wrhmlXhDyznNnUUUh5kIIIYQQEt5C7CK8r7nmGktNTXW5qbjgOICZgZtLISuqNrMR1uwF7NKlS53j56lQoYLddNNNWd4fsfTXX3850Rh2TBHrhHRTgAzBEw7d9Y68r4592GGHBccQROGiZUA4Ns5+mJEjRwa/I4YRZbj5ixYtcqHihFEjxgDR+8ADD7iwbFxRhCJh6YDLijDGwfVwf1+AixBucqAJVfdh5oDIpa2IWoSed7sR12XKlAlEN+BgkxPPMS/4WBwIM3XqVPcMaRfiOzdji9j01K5dexfhzSICCxUIbEQqsHDA74Rg04ecRDZ4snteuwN9RXxnBPPZi+49mW+ZwbxgsYMxwb1noYZnJoQQQgiRzEh4CxGCEGGgOBUuX+vWrbM8H7HNa5QIwyYfHFebCtMI2SJFcv/PC6cWcM7DYhMQm7xmKzuiQ5yjQVhGC9VwDjtOJ+HFFD1r1qyZ+51wcw/fRZAyPggzxKgvEkf7CT1mXMJ4EUhUAIsROMk42V644ooithGzCFciBcCL5ozGyY8VFCtWLOI4YeG0GSf8888/dw50dmPLQks4Vx5wqMPQV/KWWRxAkFatWjXYj+BkMYH7hIvx7enz2h1YLGExIyPCY5UX8y0aws65FtEd5JPfeuutLjQ9Fv0UQgghhNhb0F9CQoRALBMmS7g5IcfZuXvPPPOMy8sl1BrxgigjDBsIfQ6HDa9bt85OPPHELN/bjLuLI0lOLd9nQ0TiIuOg85l3W/tCZkBuMK57XkF+MwXDEMXdunVzbiVFxsKiFNebhQbEN46mB8GFg+rbzkZuNoI6vLjB4gRClVBkDw4pYzl//vygsBrXo4BdOKwcF54w7WihGIZ7ILYJ/eYVWzjz2Y0t0Q3kovsiaUBueXThMhYPSD3wbQSf583Yce9YVTHPCTjVCGYfhZAVeT3f6D9F8qhizqIJKQsspIRrEAghhBBCJCMS3kJkEG6OgECQZFeNGZGJQEVYInjId/ZuJznNFOdCnCJiyKPFLY52m6PB7SVUF/HPNXEMeW0Vog8XE/GOq4iricNK6DjFrPIKnFwcXa5PXjeFzBBmhFd7yEVG3OFc+1xwuPjii12fp0yZ4sQ6Ao6w73CoMc72O++841xzRKwHIetzrX14P99r3ry5C9FHwLHxO9dAKGcHOeZUNidHPbux5V449Tj2LJjw3CgoFg3u9ttvvx2EyXv4TORDVq9BI7+b+1IpPC/AsebZsFGJnDZTFI5++Dz77NiT+UZ/WBTx4pw8faqie7e9Vq1a7vqkHvhq8EIIIYQQyYhCzYWIglxm8oOzc7uBXF5ylilWRcEpRBdiEzp27OhcXUKecWgRluRNZwevHSP3lrB1vkfONWG7hP4C1+eaZ599tgvh5ieVo/NK2FBFnFdE+esTAdCjR48I95cccdpF2DJF0MJOOBXJ6Sc/qZ49ZsyYICQbcLnJIQ47xoBYQ4hHC9d7773XVSVHwOEkszBC+3K6iEDOPqH/5N5nN7bkrlORnRBpQvJZPKGKdxgcbRZYotvP53Hjxu2yPwwRBAh7QuDDlc13FxZ+fO49z4I200+un1N2d74B84KxRbBTRZ6q7xTXC8Pr9ogC8cUFhRBCCCGSkULp0UmNQgghRD7Ta/Q0m7N0VX43Q4gcUbNSeRvfv52tW7fJUlPTcvQdCv5XqFDa1qzZaPpLLH5o3OOPxjx/0LjHd5xzgkLNhRBCCCGEEEKIGCLhLYQQQgghhBBCxBAJbyGEEEIIIYQQIoaouJoQQoiEo0qFMrZle2p+N0OIHFGt4n+LEQohhBCZIeEthBAi4RjcvXF+N0GIXLFzZ5qlpamCkRBCiIyR8BZCCJFwUB1axJdy5Upq3PcARLeEtxBCiMyQ8BZCCJFwpKXhHuZ3K5LrdSjetdVrZ4QQQoi8R8XVhBBCCCGEEEKIGCLHWwghRMKRkpJiKVoajjuFC2vQPQodF0IIkZdIeAshhEjIfGMRfzTu/4Ow+/XrN0t8CyGEyBMkvIUQQiQcQyd+bgtXrM3vZogkfj3YsJ7NLCWlkIS3EEKIPEHCWwghRMKxbM0GW7hcwlsIIYQQBQMlcwkhhBBCCCGEEDFEwlsIIYQQQgghhIghEt6iQHL55ZfbzTffHLHvrbfesho1atgjjzwSsX/06NHWsWPHbK/J984///xMj69du9bOO+88q127tt10002W37Rq1cpee+21Xfazj2Ox5Pvvv3djvWLFioj9p59+uhufbdu2Bft27Nhhxx13nM2YMSPX98nLvvzxxx+uzfy844477IILLog4PmfOHHd84MCBEftff/11a9iwoXvvdHjM//33X5syZUq2zyM3bQtvjFmPHj3so48+Cs5j/5dffmn5QX7eWwghhBAi0ZHwFgWSBg0a2A8//BCxD1FQsWLFXcTBd99954TTnvLGG2/Yr7/+6sRWIgjv/OToo4+2fffd1wlwz8qVK2358uVWsmRJJ2I9CxYssO3bt7tnlijQlh9//NGJ6ZzMH87n9VevvPKKtW3b1u1/5pln7NVXX83Tdk2aNMk+/fRTt7399tt2zDHHWJ8+fey3336z/IY21atXL7+bIYQQQgiRkEh4iwJJ/fr1bfHixbZp06ZgH4Lp0ksvdUJp69atwf65c+fmifDG4axatapVr17dypcvb8lMkSJFrG7duhHCm/E/9thj3ViHxSvP46ijjrL99tvPEgWENM9zyZIlwT7afOGFF7oFhN9//z3DhRuee/Hixd3v6el5XwmZ6x9wwAFuq1y5slvgKVq0qM2cOdPyG9pEW4QQQgghxK5IeIsCCeHM++yzj3Mt4a+//nJhz926dbPSpUvbt99+6/YvXbrU/vnnn8Bt/fnnn104OWG8hEWPHz8+4rqERd9yyy1Wp04dO/XUU+2dd94JwtDZvv766yDklusMHTrUTjnlFDv55JOdkJs9e7YLD+b7CNPLLrvMVq1a5a5BGDLfGTNmjJ1wwgnWpEkT555PmzbNWrZs6do4YsSIoC24xMOGDbNGjRq57YYbbrD169fneqwyaxNub7NmzSJcW8Rk8+bNXXg1fPPNN9a5c2c3Xu3bt7d3330306gDxgSByjZr1qwMFz6459NPP+3GjGsyHj/99FNwLmP70EMPuf5eccUVEf3gu9dcc41LG9iwYYPb99577zkHmr517drVvvrqq4hnyfOhnfQpHLJ94IEHOmHr289YM2doV82aNYP2b9myxc0Znlc4nJzt0UcfdfejzZ5ffvnFzjnnHDc/O3Xq5Nz+PV3gAOa6h2fCs+AepD4QZeBhMYrFp+OPP949W9oYdvWzGi8/N/m+//fxySefRDwbnjH3YIHFh9kzdpw7fPjwPeqrEEIIIcTejIS3KJDgvCEevOOKUEIMEOaMSPKOK27lkUceaeXKlXMuOKITt5ywcdxE8r/Debo+RBphhVhF7C5btswuueQStxFqGw655TzEMgIH0dq7d28nqMk3Hzt2rAsRfvLJJyOuj5tKyPKZZ55pt99+uz333HNO8JBbjCidP3++O3fUqFE2b948e+qpp9w5CPt+/frlapw2btyYaZsInT7jjDOcGPMwXoh7BOjq1avddxHeb775pvXq1cu1EeGXUbh2WHgjaDdv3hz02Qvvxx57zMaNG2eDBg2yyZMnW6VKldx1/bnwwQcf2IsvvujGPgzCbuHCha4PZcqUcb/zDK+88kr3PDt06OCeL88LWCjhWowtYp4xDEP7/fzhJ9esVq2aE/1eeNOPEiVKWK1atSK+i3gNzwcPz5X+0J6yZcvabbfdZrsL0RwPPPCAW0BARIfD0W+99VZ3LxaVRo4cGdQg6NmzpwuX5xzu/cILLwT9zm684PHHH3fzkrnCAsTgwYMjhDsQ8UGNBe7LnOSZcs611167230VQgghhNjbkfAWBZawcEL0IZggHOocDhNGPO6///7Wv39/FzKOe4mrGhZkiBbEMOIC5w+RjohB0JPTjPMYDrnF6cZdRPQj7K+66iqXk4ubyndPO+0054J6EOeIpipVqtjZZ5/tHNW+ffs6kYMDSfsIf2Y/ookiYLiPuI333XefcyjDDjHiCvEX3sJiL7s2IbI+++wzJ6AAR7tFixZWqlQpFw3QuHFj56rSXpxm2vzss8+6c3HPcTtxQIk2IOqAsWChw0cdrFmzxh3jWdF3+sTiAcKeMcaRLly4sBOCHu5x+OGH2xFHHBHsY/GByABEd4UKFdw+fu/evbtzf2kfxdJwthHt3IvnhkPOQgzjgtjPbP4gtP084ad3gsP53WEINw/PBw+LNURKIOBxkBG7uaFdu3aurYwtY8mi0N13322HHXZYcA7CmbnOnGDO+HsgllkkYEwZW9rBWLOYk914eXj2LLRwP+7z559/ugWYaPh3wzMmOoTr3nXXXe7eQgghhBDJyn/jFIUogCCIvFuN0EZweOF0zz33OFGIcEJAAIIWkRIuELVz504n/Dw4m+GwXopbISwzA8fWgwAjvJiiW4QYL1q0yIlkBJQHYY1gg2LFirmfhx56aISgo9244jidhC2HwVmkwJsPb0ZYIqTDTJ8+PRBT2bUJgcc5hGEjwvnugAEDgvHCMQ6PF21CVPr2s+CAeEWY8rvvG2IX4Y3wR0CTu4wIx00nUsHDWPO98BiHxxQIi8f5PeiggyJELt+ZOnWqvfzyyxHta9q0qa1bt845wGGnmtDs6PnDIgvjzfxBkPr9f//9t3sGzB8fZp4TWNzwIEzD1d1zApEIhMEXKlTIjaVfZAgTFuHhezAezFcfng48O4QzoflZjZeHBSkPiy+Qmpq6SxtYeGJRiMWFLl265EkNBSGEEEKIvRkJb1FgQVQgyggH5qcXk95xJR8boelFAQLipJNOsiFDhmR6zWhnE6EbFuLRePEMFOVChCB+cIpxFz/88EOX4+wJiyIPIisaFgRgwoQJgZgNi/fw77iXmR3PSZsIm8bp5joIVlx8P16I0ehc63AfEKWEwyOwfcQBMOYUBEPYeeEaHqvovobDmaPPY3xwVXGsCRv3Ic18j1BpFhbC+OJn0QXQop8jIpOCb8wfxsMv3DB3iEBAdLOoEN3/rAgv4uwOhxxySMRCTE7mqCej8fXjyljlZLwymuuZFZFjEYv+kkrA4oUKrwkhhBAimVGouSiwIEhxNHHwcDN9qCtCDbFH/jXiylcgx6ml2BrCBpHJhrh6/vnng2uGw8IB4UXYc04gV5q83ieeeMJVx8Y5xTXdnerXOKeIGhxi31YcSMKOcWNzSk7a5MPNEd+E3/txZLzI//X3Z3v//fddyL6H6+GkI1yjhTdFycKh/ghaHFz2eRDm5Il7Fz0jcLlZMMGJJz/c5yTzHd5/HW4fc+Hjjz92Of3cK1z8zefOhyH0nlxzBHjY7aUvLFCQe86iRUZktGCSnzAejCVj6kEUM//pX1bjlVtIK3jwwQddZAn3IzdcCCGEECKZkfAWBRoENu87jg515TMiMRwmTDEpnFkcb8JuCa8mNzXsEJOPjPPJcYpGIdbI280JiBu+/8UXXzhxS9gwodu4gbkFkU2FdkKhCYPGub/xxhud6MzOEc1tm1i8ILed/Os2bdoE+ynUhZtNmDfh7QhuCr7hynqIMiB0nWuHQ9KJOsBhRfiGn8FFF11kDz/8sHPDGWOKdxEq7d+NnRWcQ2i8d6a5FlXnydGnYBzh9GwIaETxueee6+71+eefu3awaJHb+YMwz8zFZoGCSAvEbCJAdALP1c/vGTNmuAJzzF/GI6vxyi2EmfO8+TdFJALzijkqhBBCCJGsSHiLAg3CCFcy7LZ64USBsrCgQsxSpAsRSbgtRc4QZ1TuDheXwmU+66yzXLEqQpvJuc0JiFaECHnXhHcjmKkijQjaHfFNBXGcXq5HiDgh3gic3IQz57RNiFquS7GtcK41TiavlKLoFw4nbeJ6HlxsnFPytMPFtXzUwcEHHxyRp0wlcBYUENwU8cI5JeIgp+9Fp5gXQprFA0Q4BecIx6f9EydOtPvvvz8Q+oSI85wJTecZc99ocOwzmj/sZ3yyyu9u3bq1C+UmYiA3UQixgvlNITVENf1mgYIoh6uvvtodz268cgqREcwJ/v0AURJUzeeZxuLd5kIIIYQQewOF0vWXkBBCiASj1+hpNmfpf99xL0S8qVmpvI3v387WrdtkqamRr8zLK8hGqVChtK1Zs9H0l1j80LjHH415/qBxj+845wQ53kIIIYQQQgghRAyR8BZCCCGEEEIIIWKIXicmhBAi4ahSoYxt2b7rO8KFiAfVKpbN7yYIIYQoYEh4CyGESDgGd2+c300QSc7OnWmWlqbESCGEEHmDhLcQQoiEg6JWIr6UK1dS4x4C0S3hLYQQIq+Q8BZCCJFw8Cq2tNgUkxaZVGX1Lq+q3wohhBB5j4qrCSGEEEIIIYQQMUSOtxBCiIQjJSXFUrQ0HHcKF9agexRqLoQQIi+R8BZCCJGQ+cYi/mjc/wdh9+vXb5b4FkIIkSdIeAshhEg4hk783BauWJvfzRBJ/DqxYT2bWUpKIQlvIYQQeYKEtxBCiIRj2ZoNtnC5hLcQQgghCgZK5hJCCCGEEEIIIWKIhLcQQgghhBBCCBFDJLxFgaJVq1ZWo0YNt9WsWdPq1atn55xzjn3yySe5us4XX3xhixcvztG5AwcOdFu8+fLLL10/M+L888+3Rx55JKb3v+OOO+yCCy6I2DdnzhzXpujxeP31161hw4bu3cy5JS/7wnW43s6dO+3444+3KVOmRBwfMmSIaz9jG+aiiy6yu+66a5cxX7BggX377bfZPo+cwJj5uctWp04dN3e///77XdqfG9577z3r2bOn6+9JJ51kffv2tYULF+52O4UQQgghRO6R8BYFjkGDBtmnn35qH330kb388stOcPTu3ds+//zzHF8DobVmzZqYtnNvp0GDBvbjjz9GiGnEZ8WKFXcRrt999507n1dEJQKFCxd2izJhUZtZ++kf5zVq1Mh9h7nl6dOnj/3666951q42bdq467NNnjzZiW/m7qZNm9zxSy65JFeLEGPGjLGbbrrJXZdFhrFjx1q5cuWcoGdxSQghhBBCxIfE+CtYiDykdOnSdsABB9iBBx5oRx11lN1444125pln2t13353fTStQIKT//fdfW7JkSbAPwXrhhRfaypUr7ffff48Q3jjeidb+sPCmzcuXL3eOclh4//zzz7Z582arX7++FS1a1M2tWFG8eHF3fbbDDz/cBgwYYFu3brVZs2a54yVLlrT99tsvR9f66aefnEh/9NFHXZ8OO+wwO/roo+3OO++0rl272s0332zbtm2LWV+EEEIIIcT/kPAWScHZZ5/tBNSyZcvc50WLFtmll17qHMzatWu7UFwfWk64OhBG7d3FSZMm2RlnnGHHHnuscz4JsyZc2YMAveqqq9y12rdvHwglL+iuueYaO+GEE9z3zzrrLJs9e3Zw/LnnnrOWLVu673bu3Nm++eab4BhtRjQdd9xxdvrpp9v48eNz3ff09HR7/PHHXb+4f9OmTZ0Yg48//ti5qlu2bAnOx20lSgDBx3cfe+wx9x2E6hVXXGErVqxw57GwUblyZfvhhx/c5+3bt7uw61NOOcWF+fsx4Nr0g/7DX3/9Zf369XNCnLEcNmyY+y689tprzo3FSUbovvHGGxF9+e2336xx48b28MMPB/fk+1yH7YYbbrD169cH5/Oce/To4frI81y3bl1wjPYQcu3vTXsZn5NPPtnmzp0bjAm/H3nkkc4pDoeT81wQ6gjYcGj9iy++aM2aNXNzi2P++rtDkSJFnNjPLNScZ8V8Y3706tXLhg4dGrTl1Vdfdf1hvKJhrjIvfQpGdDj/H3/84frJT9iwYYNbBGBeMBe4D/MDGBPm1m233eaeGXOL5080hOfvv/92ot//+xNCCCGESDYkvEVSUL169UCIETqMgKxUqZLLPX7ppZeciB4xYoQ755VXXnE/ESKE9n711VdO3F133XU2bdo0J7o55/3334/Io8VdJ5y3SZMmdvXVV9vGjRvdMcQg1+c+HEew3n777e7Y/Pnz7b777nOiZerUqU7c9u/f37URYXPZZZcFApSQ4dGjR++Sl5wdnP/ss8+6HGXaj6ilbwgjRFmJEiWcAPdMnz7dCSnc1xdeeMHefPNNu//++13Y/v777+/GZMeOHbu4xvwsU6aMVatWzYlgL7wR5tyjVq1aToTiiCNqn3/+eXvwwQftww8/dGMQzhM/4ogjbOLEiU7kedauXesWSwibZiEDRo0aZfPmzbOnnnrKLWCwAIKoB+51+eWXu8UBBD0LF/TBg1gtVKhQkO+MgGQxAJFN1ITP3catpz/RMIYHHXSQS2245ZZbgv3vvvuuC+lGgDLeCODdITU11S207LPPPnbiiSfucpyIgiuvvDIII2fhJrwww/OgjxlRvnx595yiQ+0zg/4xn1lUYA7yTHHOPSxAMN6MM4tHzFnGwcPvPP8qVarkchSEEEIIIQoGEt4iKUBIAbmyCFpcVZxBwm+POeYY50Ijyr0ogbJly7rQ3n333deJ1tNOO80OPfRQ53zj3v3yyy/B9XEWEcwIfELbCQd+6623nGN86qmn2uDBg90xBOW5554b3AvBgvg75JBD3LW5BgsACG8EL0KXfVWrVnVimAUDBGYYnNXoLeyaH3zwwS7MnsJa3AMHmFBm2o+jSr8Q28ACwYwZM6xt27bu89NPP+36g/Ck/Yitf/75J3BKw8Iboe3DyfnJgkV0fjffw2mljziqtImCZgg6n8fMeCAouZ9/FoR6I6IRkrfeeqvbh3hnYYCFEPZzPQQ89yXMmpx+3G8WObgW486z8OAk8z3v2CO86Sf3xw334ea037v1YXjG5Iozt/z8AhZRWIRhAYaFjdwUMuOZ+2dI2xhvnGzmYTREYXAO7jVh6Sw44Ox7cPcz+p6HRZJwBEBmEGXAnPDPjHvieJOD7heXgHYirJnLpHaw6OBhUYl9QgghhBDJSpH8boAQ8QAnFEqVKuWENOITlxC3lBxlnOcKFSpk+F1ENe4v4c0IZkQdIbNhNzbsLCIwcfcIXUfEca933nnHOahLly519/QFybgGIo1wYcQ8YdrdunVzgph2IdoQYR6EMWIvTEYOOC67B7eUcGlca9pEJe7Vq1cHbUAQId5wLHGbcbNpF0KYsPBrr702oigaCxe+oBiCGmHLdxGq9MPvJ7wYVzYsXLk/iwgsangIX8bdReABiw2Mdxjccc7xwhi4Nm1lESUM/aJ9HOdePG8PrjBF9zx+4YDwchYEaItfOEA4ssjAtTIS3pnBYo4HQZ6bUHMWV/yzI/+alAQWTRDJOMlhmIf0J0zdunVdm/3CAH3KDEQz/x6yg2fGmDZv3jxiP/vCoeMs6nhYnGKxirnGIg9z30eUCCGEEEIkIxLeIilApABhxAhKikuRs4vQadeunRO548aNy/C7uLSEZ3fq1Mnl7vI7LmuYaDGMKCFEmJ+EZpMji4vM/RCLhKIDIdg4l7i0H3zwgQvVxf3lJ0LTO8JZkVH4bli4cv3hw4c7QY+7Tch6+DVgiErEKQ4xfcUVxg32ObwPPfSQC0sO44UzwhaBh2uMuMcJ9YKTPF9EN8IWpx6KFSu2S1t9rrz/mdE5RCVQaZ48YwQoDrY/f8KECRHi2ot3QvuJODWXI0cAACw5SURBVAjDMwmD8L7nnnvs66+/dgssPA8vvBGKLETgJnvnPSdEz4XoNmQFDnX4ebIog3jF2Y8W3twn+trhzywGhWsNhCESgEUgFlwyIly/gN95nhmFzJM2wXOPfm6MF3OXEHOqxOPEE5YvhBBCCJGsKNRcJAWIBsQb+b6I3FWrVrmQbcJjCQemYFhmAgnh2qVLFxf2i3hF9OHOhs/3wh4QzDjoCDYcckTdM88848Qnzir3Br6PsHviiSecK00hLlxW73QidhFHOImIMTaELO5vbkDIs1hALjKLByw44Eb79uNm41CSa03eug8JxmVFwOKO+/sTto4gpV0e8nkJO0aAI8Q9uNNckzBxxh7oEw5yuAAafcLhDzvF0eDAk8uMmPO5xTxLxCfX8u3DwcUhpn8ssnCvcDg0IjYM0QQ8Sx9m7uG7iHlCv/O7GjvPKaP3n9PGcAEzCH9mcQm32od8cw0WmZj3vGaMxRNfSJCFFh/qD+GK9DwzxpBIAz/OLMoQ1p+Vm8+9WEwiwkBh5kIIIYRIdiS8RYEDkYBYROAiiAl5JdTbV3tGICIGyVulajPCmqJUYRGB6CIHmmtxPgKZa7GP63D98PnkVCNmEDoUYsPVRnggXhG2b7/9tsvnRgT56tF8H2eaquG0gbZwHm0jl7ZDhw5O4OB4c10EDH1BDOcGhDbvbPZh7oSO075w+xFGFJpD9IcLeeEyUwBt5syZTsSSX03YMIsKYcecdkcLVD4j5BHm3gUm7xnBTN4444kji0vuxyo7WDxgUYL7IbJZCCHUHeHMIgfXJfyZxQoWVFgooDAY40cUAfMgDNdAwPJcwsLb53nT/qzCzJknREuEFxL2BJ43c4uNMHHai/hn0SGa7t27u0WLJ5980j1bKtczD30oPnOI+gAs6CC2mV/nnXeeE8wsBF1//fWBw4/bTx420Qlsvmo8sNBEpAch8BxD3HNN5mlWz4zICeYMC10s7AghhBBCJDMKNRcFDsKq2RAghLySO43QIKzYu5w+XByhiUBB3CLQEDuEz/J6JQQKbihh4QgNXkmGUGvRooXL2w67pzjJiB5ENOHBuNiIGjaEIfupwI17iHgl3BtXnLYgpqkUjZNLYSocZV+FnWrd9IXrswBAgbDevXvnajwQq2wdO3Z0oh0RR7vC7Sc3GIGOwMJ99lBFHCeU8SFPHoFGxe5wjjbjigiLrvzNfsR9WLgiwOkrYhvhSGg1eeFUjM8JjB/PhvBwngOLIPfee6+rcs5iAvdCiHIfNp4D403xPJ4z48fiQ3Q7ed1ZOJfeLxwQKp2V8GYejBw50gnM8Gu+dhfELxvwHAjP5nkTmRENVfkRyPSfnyxqUCMgHE5PQTqiEEijeOCBB5yzzXmEfzPPcNNZvLj44ovdGCDMmf/8WwjPM/4tsKDEQgztYp74IneZwb8V8sKZN7ldLBJCCCGEKGgUSs9NAqIQQoiEAKFMWgMLS2GhTcG1vn37Zvt93GuK55H3HysofIewJ1Ujt/QaPc3mLP1vWoYQ8aZmpfI2vn87W7duk6Wm7prqkRcQnFKhQmlbs2aj6S+x+KFxjz8a8/xB4x7fcc4JCjUXQoi9EKIxcKo/++wzl8ZAugIpBa1bt87R9ym+FivRTQoBkQ2E+CvMXAghhBBCoeZCCLFXQg41NQcIC6eYHGH4hJNTTT6/oV4A+fGkT2T1LnEhhBBCiGRBwlsIIfZSrrzySrclGlSWF0IIIYQQ/0PCWwghRMJRpUIZ27I9Nb+bIZKUahX/V0BSCCGEyAskvIUQQiQcg7s3zu8miCRn5840S0tTRSIhhBB5g4S3EEKIhINq0iK+lCtXUuMeAtEt4S2EECKvkPAWQgiRcKSl4TbmdyuS63Uo3uXVa2eEEEKIvEevExNCCCGEEEIIIWKIHG8hhBAJR0pKiqVoaTjuFC6cnIOusHIhhBCxRsJbCCFEQuYbi/iTrONOiP369ZslvoUQQsQMCW8hhBAJx9CJn9vCFWvzuxkiSV4dNqxnM0tJKSThLYQQImZIeAshhEg4lq3ZYAuXS3gLIYQQomCQnMlcQgghhBBCCCFEnJDwFiKX1KhRw7788ktLNjLr9yOPPGLnn39+TO89depUq127tu3YsSPYx+/16tWzVq1aRZz7119/ubYuWLAg1/fJy74wVrQDLr/8crv55psjjr/11lvuOPcMM3r0aOvYseMuY/7333+7ccjLeci9uc4XX3yxR9cRQgghhBBZI+EthEh4GjRoYNu3b7eFCxcG+3744QcrUaKEE9q///57sH/OnDlWtmzZQPQmSvtpbxhEc8WKFXcRz9999501bNjQ/f7pp5+6xQUYOXKkffTRR3naLsT/YYcdZlOmTMnT6wohhBBCiEgkvIUQCc8BBxxgVapUse+//z7Yh2A96aSTrFatWhHiFeFav3599zqqRIH2LF682DZt2hTso82XXnqpa+/WrVuD/XPnzg2EN/0uWrSo+z09PW+LPv3444/222+/2ZVXXmnTp0+PaJsQQgghhMhbEucvUyEKAIijxx9/3IU/H3vssda0aVN79NFHg+OEMY8ZM8YJruOOO85OP/10++STT4Lj69ats6uvvtq5nKeccoq9+OKLgXMbDl32DBw40G05uXdaWppzTRs1auQ2Qppbt24diNYNGzbYgAED7Pjjj3ffHTp0aIQgzCnvv/++derUyYWG4/Red911TtRt3LjR7Zs1a1Zw7r///uv2ffPNN+7ze++9Z23btrU6depY165d7auvvgrO5Vrz5s0LPtNuBCpb+Jph4bpt2zYbMWKEtWjRwurWrWtXXHGF/fnnn+7YH3/84cbzsccesxNOOMHuvPPOiH7w3R49etgll1zi3HZ46aWX3PjyfHiWP/30U0Rf6CvHeK5hh5s+7rPPPk7sAi79ihUrrFu3bla6dGn79ttv3f6lS5faP//84/oaDicnJHzy5MluC4fWM27t27d31z/vvPNs+fLluXK7a9as6dpK2D7iOwz3njRpkp166qmuT9dff32EOOfenTt3dvOYNrz77rvBuHHNcGj9TTfdZF26dLGdO3fmuH1CCCGEEAUJCW8h8hBCdp999lm76667bNq0adanTx8nmrzgAsTxmWeeGQifwYMHO1EMCLe1a9c6wT1kyBAnCvPq3k888YQ75/7777f//Oc/9uGHH0aEaN9yyy1OHHNvRDnCMVqMZgcOar9+/axnz54uH/nBBx+0zz//3CZOnOgEZrNmzZy49tCG8uXLO0eYMHIEGg7sG2+8YR06dLDLLrvMli1b5s5FjHrHGyFMSLkX3n7xgP301wvv2267zd3v3nvvdaI5NTXVrrrqqmC8AdH76quv2gUXXBDs4zjPgp8sXuA6z5w50/3O80IA02a+g1D291qyZIm98MILduutt7ox9vB9FhN8+1koYHGkZMmSTvT79uN+H3nkkVauXLmIcUX8t2nTxm2vvPJKsB9hzL3YRztYWMkJLNLwfFq2bOnaQOQAfYrmoYcectd/7rnn7Oeff3ZzElavXm29e/d2wvvNN9+0Xr16uQUgxHixYsXsjjvucHON/vL83377bbv77rutcOHCOWqfEEIIIURBQ8JbiDzk4IMPdgIDIXPooYc6x5Rw4V9++SU4B/cVwUJuLSITBxYhg9uJSEEkIsg5D/c7r+49YcIE69+/v3Ozjz76aLvnnnuC8GUE84wZM5w7jNOJi4njjRhDjHsQwrif4Q1B70GoItS6d+/u2sC9GjduHLSBBQeEsL8vLilislChQjZ27Fj3PdxTwsoRtc2bN3cLAYBARdjiLONqlylTxqpVq+YEOYXHGD8KqiH8CD9HiL7++utOLJ544oluTBGmnPfZZ58Fbb7wwgvds6hatWqwj74j+Onbvvvu6/Y9/fTTTmwiVjmXsaxUqZJbJGCMELL0/ZhjjnELDAj8MOGFA4Q2UQcQXjgI53eHQRwXL17cbSxUeJg/XIdnRoRAOAc+K2bPnu3mHW42nHbaaS66INox53mffPLJzlFnYYY+0tfx48e754rLzrOiGNzZZ5/tFn6A8SbqgUUgxp92HnXUUTlqmxBCCCFEQUTv8RYiD0FwIApxlcnpRQgiqsMOa1jglSpVyv3EiSVseb/99rPKlSsHxwmPzot746KvWrXKCSjP4Ycf7oqQAedzHkI3DPsQoLizMGzYMOfchnn++eeDkGv6hrtLOD1im23RokVBlW5EKwKOdiIWCbPHTfVtQNi9/PLLwbUJgUa8A+PCQgLh5jirXqDipCOqca4RhT6/+9dff3XtD7eX8UWscy9+AuI5DE76119/7b7nx8e3j4WJUaNGBfsIq+Y+iHnCqGmHJzzWXnj7ImYIbcQ90A8WQXDrEd6I1JzCgoGHcaA9OQEHmn6zAAOkNSCQWagILxiQduBhDtBH+soCyAcffBAUfvPPyo8pEL1AyDnuPVXdhRBCCCGSGQlvIfIQQn+HDx/ucndxEREf4RBmINc3GhzgIkWKZFlAC1c4GgQ738vu3v6c6Ov7zwgqhBsh19EceOCBEb/jcIYJi1McV5x28pARmhdddFHgggLuMeIbp3vlypVWoUIF5677NuCw4pSGweWNzvPGnW3Xrl2wH9cX0UoOMs444HxnBPcJL4REn4e7TEg57jYh3Iyn/96gQYNcREEYFk8yyq32RdE8iFQWPwjh56cXtYSWM/aIfRYpMnK8M2N3CsjRD1IRqCfghTcwJtHCOzxX/ZhxT+YdkQnkzIfx8wzIYed5bNmyxeXTh0W5EEIIIUSyoVBzIfIQwqLJrUagISBx+wiDzklF6urVq7vw6HDedbiYmBdBhFp7EDQ5uTdh2by6Kpxrzn0oqAaIItxixD3Cmo3Cavfdd19QWCwnINwQvrju5HkjqnHMw/0n3JzXYhHaTiE1D22gP/7+bLjfH3/8cXAObjZ9QLz6UG1ArJKDHA7VxiFHCLLPg9ikPVmJQEKi6QPOM/1Yv3590D6KooXbR74+1yd6gOcTLqg2f/78iOuy6EAIPH3CDedVaMCYc7/XXnvNRQyEQ8mzW3jZHXhnNxEQDz/8sHPg/UaONu69L/QG4XehMxfpI+PAxjiGx4KieuR7e3FP2D3h5xTww03P66rsQgghhBB7ExLeQuwG5OoiCMMbzh5iF2FDOC5C5dprr3UhuDkRr4gZwqoRzjjH5CEjjjw4o7i/iD1EMznHYXGX3b2pws31OIfr+6rTCDpEP3nJN9xwg+sb4pbjmzdvdqI9pxDKTdg516AdhFAjRsP9J5wdxzdaeOOOv/POOy70nJzzZ555xm3h0HwEKgXZaFPYeccJR3gjksmx9s41bjUh3YR202eqth900EHWpEmTbPtC7jduvg8tv/jii517j0ilfYSdExrP2OF6E07PvQij537hivLh9hPmHe1q8xnh6t36jECo46wTKbAncH/mElERLDL4jYUSnl/4nd7MF6IL6BNpBmeddZYbV85ljj3wwANOrCO4GadDDjnEfY9nyCIFhfZuvPFGd264KJwQQgghRLIh4S3EbkCRLsKiwxtiEtGMI40I69u3r8tjxvELO4dZQXE0nFGKjN1+++2uCJt3uhF3CDuEE2HWCMlzzz03+G5296YyNp85hqgk5BvR7a+Pu01BNAQwIpOFgHA+c05A3JOXzjUQZ4Qb48KHFwgIwaaoFwI4nBPN92gDReAQ5FRCx3EOi1EEI9+PFqiEatNewrfDlbMJt6cI2DXXXONC4AkrR8xHh4FnBOew+EAIP4sHtInFDMQo488CBrnsfmGAaueEkzN2uMcUHosGx57FjLBb74U3CzdZhZnzXFnMoNr77rrHLIBQ3I5CbNEwNsw3FhN8rjiRE/SF198x5vQRyA9nAYgcfcaC6vWcR9tYHGCMWMRhgYT0BIoEslCxZs2a3Wq3EEIIIcTeTqF0xf8JkRAgvKhqjiPsxTAiCMHCq6z2FFx5CmT5UGbCjclXxmlFcAsRhoUbnOvoRYJ40Wv0NJuzdFW+3FskFzUrlbfx/dvZunWbLDX1f/UfYg3ZIxUqlLY1azaa/hKLHxr3+KMxzx807vEd55wgx1uIBAHHEdead3cTSk51bX6nMnReQG4x16eAFxW6cdTJNZboFkIIIYQQIrZIeAuRIFAtGqGN6034LuG55F0T3pwXUOCKe5xzzjkulJ0q1dxPCCGEEEIIEVv0OjEhEgiKhJHbHAvItR09enRMri0KHv7d7EIIIYQQYs+R8BZCCJFwVKlQxrZsT83vZogkoFrFsvndBCGEEEmAhLcQQoiEY3D3xvndBJFE7NyZZmlpqj4khBAidkh4CyGESDioMC3iS7lyJZN23BHdEt5CCCFiiYS3EEKIhIPif2nxe7NT0sPrULzzq9fOCCGEEHmPqpoLIYQQQgghhBAxRI63EEKIhINX36VoaTjuFC6cXIOuEHMhhBDxQsJbCCFEQuYbi/iTbONOaP369ZslvoUQQsQcCW8hhBAJx9CJn9vCFWvzuxmigL9GbFjPZpaSUkjCWwghRMyR8BZCCJFwLFuzwRYul/AWQgghRMEguZK5hBBCCCGEEEKIOCPhLYQQQgghhBBCxBAJb5G0DBw40GrUqJHp9uWXX+7Rtdmy4o8//sjy/ueff/5u3z+79md2/JFHHtmj++aEqVOnWu3atW3Hjh3BPn6vV6+etWrVKuLcv/76y7V1wYIFub5PXvaFsaIdcPnll9vNN98ccfytt95yx7lnmNGjR1vHjh13GfO///7bjYNnT+abb1t4YywvvfRS++677ywRoK/0WQghhBAiWVGOt0habrnlFrv++uvd7++8846NGzfOXnnlleB42bJlY3r/gw8+2D799NPgc9euXe2SSy6xtm3bus/77LOPFUQaNGhg27dvt4ULFzoBDj/88IOVKFHCCe3ff//dKleu7PbPmTPHPQcvehOl/W+88cYu4rdixYruZ9++fYP9CN+GDRu633nWfk6NHDnS0tPTrU2bNnnWrvBc+vfff+2hhx5yiwTvv/++lS5d2vKL5cuXW//+/V07hBBCCCGSFTneImlBjBxwwAFu4/fChQsHn9mKFi0a0/tH34/P4Tbtt99+VhChb1WqVLHvv/8+2IdgPemkk6xWrVoRzi/CtX79+u6dzokC7Vm8eLFt2rQp2EebvcO8devWYP/cuXMD4R2eU4juvCY8l6pVq+YWlv755589itzIC2LRVyGEEEKIvY3E+WtWiAQD97Vfv35OODVq1MiGDRvmnFoPbmyPHj2sbt26LkT6xRdfzPA6GzZscC4oTukJJ5xgN9xwg3Mkc8JLL73krk3oMGHTP/30U3Bs8+bNNmTIENc2tsGDB9u2bduC49988421b9/eucrnnXeecx5zCy5lp06d3DVo/3XXXecE58aNG92+WbNmBefSJ/ZxX3jvvfece1+nTh3n5n/11VfBuVxr3rx5wWfEIePMFr5mWLjStxEjRliLFi3cmF9xxRX2559/RoTtP/bYY26M77zzzoh+8F2eFREF/hlmNbb0hb5y7PTTT3eOvIc+Eo3w448/BvNkxYoV1q1bN7dw8u2337r9S5cudcKXvobDyQlHnzx5stvCofV58bzCsJADtNWPDz8zCsV/7bXX3PjgxNPnk08+2SZNmhScy3mPPvqoO4fn2bNnT7f44OE58Dw4Rp84d+fOne7YKaecEvzkPkIIIYQQyYiEtxAZgDi78MILbcuWLfb888/bgw8+aB9++KHdd9997jiig+OIPMQEwvree+91YjOahx9+2FavXu2E+XPPPedCrMn9zY6ZM2c6AYOgRqThtF5wwQVOzMGtt95qs2fPdtciTJ7faacH4cQ5hM/zHURVbvjtt9/cwgMiixxdrv3555/bxIkTncBs1qxZRH8Zn/Lly7t20sebbrrJrrzySheW3aFDB7vsssts2bJl7lzEqHe8GWsWMbzw9g4t+xG3Xnjfdttt7n6MM6I5NTXVrrrqKktLSwvagOh99dVX3Th5OI6I5ifjieuc3dhyryVLltgLL7zgxvA///lPcD2+j8D07Weh4Nhjj7WSJUu6+eDbj/t95JFHWrly5SLGFfFPiDlbOLVhT59XmHXr1rm5yr0R0jmBxQVy6V9++WW7+uqr7Y477ogIX3/iiSfcIgTz/cADD3Rh7DwjHG3O33///d1Y3n333fbmm2/a448/HvTL//RpFEIIIYQQyYZyvIXIgE8++cRWrlzpRKbPy8VdRkhee+21bv/RRx/tBB0cfvjhTow//fTT1rp164hr4Vwiyg499FCXx0zubU7gWr1797aWLVu6z+TJfvzxx4GQnTZtmhOEiEbA5Q0XIaOtOOGA44xYDYMQ9q5odJEzQKgiBLt37+4+0/7GjRvbL7/84j6feeaZTgRzTqFChezdd991YpLfx44d676HgwuI2q+//totPlB0DoFKKDTOMm0uU6aMC4+uUKGCK8KFW0ykQLFixVz4OUL09ddft6eeespOPPFEd02EKc7sZ5995r4LLIYcdthhEX0aOnSoE/yI6H333TfbscXhZ6GBRZJjjjnGHUfgh1308MIBQtuPM4sEFFqLzu8Ow1woXry4+52Fipw+r+wIPzfC3Qnnf+CBB9zYMpbZwXNDrCOgjzrqKPe8mOdNmzZ1x5s3b24XXXRRMKYsvDD29AXHH2FNSgD/Flh0oQBdnz59gj7y0/dbCCGEECLZkPAWIgMQ0VWrVo0osHb88cc7lxUnmOPHHXfcLsInI7GE6ES4kcPMhmvoBWl2bSC0etSoUREh07/++qsTkoTyemHoxaAPa4awAMWhDoehA6HzOLdhcPd9yDX9x90dM2aME9tsixYtCqp0I1oRz4SDE8bMYgVi1bcd8Yp7Ghb1XsRRPI1cZMLNCbH2ApV21qxZ0znXhLP7/G76jKAMt5cceAQ39/LCu1KlShH9wUlHQPK98LPMamwR/Ywt7fD4InDhsZ4yZUogvBGiQD/uuece5wQjvBHTOSW755Udvj2MV6lSpXZx2rMDoY7o9uDih+cz89/D9f3YI6bXr18fLACFxT/OuxBCCCGEkPAWIkNwWqPxOav8zOg4YsOfEwax/dFHH7l8acKxcc4J4c0ulJhrDRo0yH0/DKJn1apV2fYhu4JkhAsjtsKExSnh4uT0krOL0MTtfPbZZ4PjuMeIb5xuogNwq/1iBG3HUcc9DhN2PH2eN7nf7dq1C/bj+iJaySXHGYeMxtvfJxxqHn0e7jIh5bjbhHCTh53d2GaUWx1daI9FFp4B4dn89KKU0HJEM2KfRYqMHO/M2NMCctHPMtrNjoZFpDBFikT+54AxCrcps+NcB5c7o/QJxiJchE4IIYQQIllRjrcQGYCbh/uJk+dBDCI+cCY5jtMb7a565zXMM88843KVzzrrLBdmTg7s9OnTc9QGCnchqPxG3iztwDEmTBxx7JkxY4a7R15BaDfC9/7773d53ohqnPZwlWrCzVlU4N7h/F3aTiGvcNtxvwnn9uCQMi6IVx9iDYjVn3/+OSJUm/4y9uH3UuOm0p6MxtxDyDR9wHmmH/55ZjW2iEgKkoULqs2fPz/iuiw6EAJPn3DDSSHwAtfn/RMxEA4lz04IxxL/arqwCA4XWgPGMnycRRHGzxOea0QjEPlBpANjSag5ffVjybWpbUA/491XIYQQQohERMJbiAxo0qSJE3s33nijC72mgBbhxDiz5MwiRMlNJlSZ0GSKSk2YMMHOPffcXa6FwCM/GFGHmMchJj88Oy6++GLnMBNCjMghNJrw7erVqztnFjf5rrvucrnGiETyeX3+c15AKDd95/r0kRBq7hOu7E7eL45vtPDGHefd6ISe03YWH9gQox4EKhEAjGfYrcUJR3gjkn0oPc41bjXPgNBuROCAAQPsoIMOcs8qO8j9xs33oeXZjS3h9NyLxRXuh2seDe1/++23d3G1+Ux0g3frMwKhjrNOpEA8IBqB98aTe8970lkYYOzDUCWfonKEj5PbTQ0B5rmHgmmMF8dJMTjkkEPcggnpA4T48zyYL6QOULSOPrI45BcleGZyv4UQQgiRrEh4C5EBCAYfOkuRMIqo8TokX2AL0UGVZ/KaydcmD5qiYV26dNnlWlQGJxQZ1xVBh8BB6GUHQpZCbjiHCP4vvvjC3ceLV0KlyUNGRBLWjQji/LyCV0jx2i5ENAIMV5NiWWH3lxDsU0891QngcE4036NQF4sR9AMhh+McFqOEZfP9aIFKeDIuKmMWLv5GwS6Ku11zzTUuBJ6wcsR8Tt63zjkU+6IAGIsH2Y0twpFwcsaW58rrvaLBsedZht16L7yphp9VmDnzgMUMiuTF4z3XhIT7RRr6jqjm9V9hEObk3VPYjeJzzNFw3jbznJzvzp07OwFNoTuiEHhGjB0h//xbocI/r3yj6B7ghNNPCtiFX1EmhBBCCJFMFEqPx199QgghEhYccFx9XrOW2SIMCwmI6njRa/Q0m7M0+1oGQuwuNSuVt/H929m6dZssNfV/tSLiBVkYFSqUtjVrNpr+EosfGvf4ozHPHzTu8R3nnCDHWwghhBBCCCGEiCES3kIIIYQQQgghRAzR68SEECLJIW+bLTN4v3u8qVKhjG3ZHvnKMyHykmoV//f6RCGEECLWSHgLIYRIOAZ3b5zfTRBJwM6daZaWpuRHIYQQsUfCWwghRMJBwSsRX8qVK5l0447olvAWQggRDyS8hRBCJBzp6TiR+d2K5KrKCrwWLtmq3/q+59d98+v+yYrGPf5ozPMHjXt8yM346nViQgghhBBCCCFEDFFVcyGEEEIIIYQQIoZIeAshhBBCCCGEEDFEwlsIIYQQQgghhIghEt5CCCGEEEIIIUQMkfAWQgghhBBCCCFiiIS3EEIIIYQQQggRQyS8hRBCCCGEEEKIGCLhLYQQQgghhBBCxBAJbyGEEHnKtm3bbNCgQdagQQNr2rSpjRs3LtNz58+fb926dbM6depYly5dbN68eRHH33rrLTv11FPd8T59+tjatWuDY+np6TZy5Eg78cQTrWHDhnbfffdZWlqaJSvxGne+W6NGjYitc+fOlozk5Zh7xowZYwMHDozYp7meP+OuuR6bcWc+P/nkk9aqVSs7/vjj7cILL7RFixZFHNd8j++Ya67HiXQhhBAiD7nzzjvT27dvnz5v3rz06dOnp9erVy996tSpu5y3adOm9CZNmqTfc8896YsWLUofOnRoeuPGjd1+mDt3bvpxxx2XPnny5PQFCxakn3feeemXX3558P2xY8emt2jRIv3rr79O/+KLL9KbNm2a/vTTT6cnK/Ea99dffz29Y8eO6atWrQq2tWvXpicjeTXmnjfffDO9Vq1a6TfddFPEfs31/Bl3zfXYjPuECRPSGzVqlD5z5sz0JUuWpA8aNCj95JNPTt+8ebM7rvke/zHXXI8PEt5CCCHyDP4jX7t27fRZs2YF+x577DEn3qKZNGlSeqtWrdLT0tLcZ362bt06/dVXX3WfBwwYEPGH8IoVK9Jr1KiR/ttvv7nP/GHmz4UpU6akt2zZMj0Ziee4jxo1Kv26665LT3bycsx37NiRPmTIEHe90047bRcBqLmeP+OuuR6bce/WrVv6E088EZy/ffv29Lp166Z/+umn7rPme/zHXHM9PijUXAghRJ6xcOFCS01NtXr16gX76tevb3Pnzt0lVJB9HCtUqJD7zE9C4L777rvgOOF1noMPPtgOOeQQt3/lypX2559/2gknnBBxn+XLl9uqVass2YjXuMPixYutatWqluzk5Zhv3rzZfvrpJ5s4cWLE9UBzPX/GHTTXYzPuN954o3Xo0CE4n+OYgRs3btR8z4cxB831+CDhLYQQIs9YvXq1lStXzooWLRrsq1ChgstTW79+/S7nVqxYMWLf/vvvb3/99Zf7nT+yMjvOdyF8nPuA/34yEa9x93+gLViwwNq3b28nn3yyDRkyxP79919LNvJyzMuUKWMvvfSS1axZM8P7gOZ6fMcdNNdjM+4s7B100EHBsUmTJjmBiXDUfI//mIPmenyQ8BZCCJFnbNmyJeKPBPCft2/fnqNz/Xlbt27N9DjHwtfO6j7JQLzGfceOHfb777+7n8OHD7e77rrLvv32WxswYIAlG3k55lmhuZ4/4665Hp9xx6m999577dJLL7UDDjhA8z0fxlxzPX4UieO9hBBCFHCKFSu2y3/o/efixYvn6Fx/XmbHS5QoEfHHB+eF78PxZCNe477PPvvYrFmz3Dn8Dvfcc4+roEuI6IEHHmjJQl6OeVZorufPuGuux37c58yZY5dddpk1b97c+vXr5/Zpvsd/zDXX44ccbyGEEHkG/4Fet26dC2ELh8DxH3/COqPPXbNmTcQ+PvtwucyOs0Lv/xDwYYnh3zmebMRr3KFUqVLBH2dQvXp195M/0JKJvBzz7O7jrx2+D2iux27cQXM9duP+5Zdf2iWXXOJeGXb//fdbSsp/JYnme/zHHDTX44OEtxBCiDyjVq1aVqRIkaCgC8yePdtq164d8R954F2jrL5T4AX4SXgb+/1xvuuh4A4b+/kjg4Jf4eP8zr6c/lFdkIjXuPPeVwr9EJboIS+Qe1epUsWSibwc86zQXM+fcddcj924//zzz3bllVdas2bN7MEHH4wQfJrv8R9zzfX4IeEthBAizyAUsFOnTnb77bfb999/bzNmzLBx48bZBRdcEKzW+xy+M844wzZs2ODyyfgPPz/JU2vTpo073qNHD3v99dddERiqu1KVlaIvlStXDo6PHDnSreKzsYLv75NsxGvcDz/8cPeH2ODBg90fct988437vVu3bla2bFlLJvJyzLNDcz3+4665Hrtxp3AXb0u4+eabnaPLd8Pf13yP75hrrseROL22TAghRJKwefPm9BtvvNG9I7Rp06bp//nPf4JjRx11VMT7WefOnZveqVMn967Srl27pv/4448R1+Jc3unKtfr06ZO+du3a4Fhqamr68OHD0xs0aJDeqFGj9BEjRgTvME1G4jXuvNebfYx7w4YN04cOHZq+bdu29GQkL8fcw7uko98nrbmeP+OuuZ73475q1Sp3bkab/77me/zHXHM9PhTif+Ip9IUQQgghhBBCiGRCoeZCCCGEEEIIIUQMkfAWQgghhBBCCCFiiIS3EEIIIYQQQggRQyS8hRBCCCGEEEKIGCLhLYQQQgghhBBCxBAJbyGEEEIIIYQQIoZIeAshhBBCCCGEEDFEwlsIIYQQQgghhIghEt5CCCGEECKmnH/++fbII4/kdzPs77//tqlTp+Z3M4QQSYiEtxBCCCGESApGjhxpH330UX43QwiRhEh4CyGEEEKIpCA9PT2/myCESFIkvIUQQgghRFx47bXXXNj5mDFj7IQTTrAmTZrYlClTbNq0adayZUtr0KCBjRgxIji/VatW9swzz1j79u2tbt26dvnll9vq1auD44sXL7ZLL73Ujj/+eGvWrJk9+uijlpaW5o4R2n7VVVfZueeeaw0bNnT3nTx5stu4LixatMh9v169ela7dm3r2bOnuyZ8+eWX7rwJEya4a3P/AQMG2Pbt24P7v/7663bGGWdYnTp17JxzzrH58+cHx1566SX3fa7NvX/66ae4jLEQIjGR8BZCCCGEEHFjzpw59vvvv9srr7xiZ555pt1+++323HPPOTE+cOBAe/rppyMELAK6V69e9vLLL9uWLVusb9++bv/atWudUK5YsaJNmjTJbrvtNnvhhRfctTzvv/++tWvXzp599ll3/TZt2riNeyPQr7jiCqtUqZIT0AjlnTt3Rgj/VatW2bvvvuvaRDumT5/uFgrgk08+sVtuucUuvPBCe+ONN+zYY4+13r17O2E+c+ZMtwgwePBgJ/Tr169vF1xwgf3zzz9xHWshROIg4S2EEEIIIeIa7n3rrbdalSpV7Oyzzw7EdM2aNa1r1662//7725IlS4Lzu3TpYh07drQaNWrY8OHDnXD/+eef7a233rISJUrY0KFDrXr16nbqqadav379nEj2VKhQwXr06GG1atWyUqVKWfHixd1Wvnx527p1q3OpEfuHHXaYHXPMMXbWWWc5F9yzY8cO11bujevN9sMPP7hjLAQg6rk+fbnxxhvdZ8Q1bUCE4+JXrVrV+vfv7wQ+Al0IkZwUye8GCCGEEEKI5AFhve+++7rfixUr5n4eeuihwXGEcTicmzByT+XKlW2//fZz4eBsiOUiRf735yxh3YSib9iwwX1G7GYGbUA042DPmzfPiX2cdsR6GES1B/Gemprqfl+6dKkT7p6iRYvaTTfd5H6nbTjno0aNCo5v27bNfv3111yOlhCioCDhLYQQQggh4kZYKHsKFSqU4/MJB09JSQlEexif3805kNE5nk2bNjmHvVy5ci4XG7ca8T1u3LiI8xDUGRVoy6gf4TYOGjTITjrppIj9CHchRHKiUHMhhBBCCJGwLFy4MPh92bJltnHjRhf6Xa1aNfvxxx9dOLiHMHTCyHHFMyIs8L/66iuXw01OODnkjRs3thUrVuS48jlOeLhtiG0E/OzZs13b/vrrL3eO3x5//HH77rvvdnMUhBB7OxLeQgghhBAiYUEYUyQNkYuLTCV08qapdE5I+pAhQ1xo94wZM1wBNMLHM3PQyQlfvny5rVy50onzzZs3u+/98ccfrkDb+PHjI8Lcs4JK5eRsUzyNBYG7777biXbC3y+++GJX0I0w9t9++82FnU+dOtXlogshkhOFmgshhBBCiISFgmfkSuNGt2jRwu64444gbJsiZnfddZd16tTJOd1UGKeoWWZQpK1Pnz7WoUMHmzVrlvud65F/jYuOiKdSOcI8O3gdGpXUH3vsMZdXTlVzXG1y1Nu2bWtr1qyxhx9+2P084ogjXFV1FgyEEMlJofScxtMIIYQQQggRRwjdvvrqq61z58753RQhhNgjFGouhBBCCCGEEELEEAlvIYQQQgghhBAihijUXAghhBBCCCGEiCFyvIUQQgghhBBCiBgi4S2EEEIIIYQQQsQQCW8hhBBCCCGEECKGSHgLIYQQQgghhBAxRMJbCCGEEEIIIYSIIRLeQgghhBBCCCFEDJHwFkIIIYQQQgghYoiEtxBCCCGEEEIIEUMkvIUQQgghhBBCCIsd/wc/NmGWlaqWugAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " GradientBoosting | CV RMSE: 43977.94\n", " RandomForest | CV RMSE: 44815.55\n", "HistGradientBoosting | CV RMSE: 44118.37\n", " XGBoost | CV RMSE: 44148.17\n", " LightGBM | CV RMSE: 44856.83\n", "\n", "Best model: GradientBoosting\n", "\n", "--- Test Performance (GradientBoosting) ---\n", "RMSE: 41332.02\n", "MAE : 31771.10\n", "R² : 0.4177\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " GradientBoosting 43977.939388 1326.487811\n", "HistGradientBoosting 44118.368637 1486.742584\n", " XGBoost 44148.171496 1351.728062\n", " RandomForest 44815.552309 1201.191135\n", " LightGBM 44856.833039 1337.290858\n", "CAD\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances:\n", " Feature Importance\n", "6 YearsCodePro 0.305766\n", "8 OrgSize 0.054786\n", "465 OpSysProfessional use MacOS 0.048800\n", "5 YearsCode 0.043386\n", "398 OfficeStackSyncHaveWorkedWith Microsoft Teams 0.030796\n", "7 DevType 0.028363\n", "2 Employment 0.024607\n", "127 LanguageHaveWorkedWith Ruby 0.022418\n", "3 RemoteWork 0.018762\n", "403 OfficeStackSyncHaveWorkedWith Slack 0.017231\n", "121 LanguageHaveWorkedWith PHP 0.015557\n", "412 AISearchDevHaveWorkedWith Amazon Q 0.014661\n", "167 DatabaseHaveWorkedWith Redis 0.013285\n", "301 ToolsTechHaveWorkedWith Composer 0.010543\n", "35 JobSatPoints_1 0.010234\n", "19 Knowledge_4 0.009659\n", "66 LearnCodeOnline How-to videos 0.009635\n", "320 ToolsTechHaveWorkedWith Terraform 0.009266\n", "446 OpSysPersonal use MacOS 0.008345\n", "74 LearnCodeOnline Video-based Online Courses 0.007396\n", "\n", "--- Test Performance (GradientBoosting) ---\n", "RMSE: 41332.02\n", "MAE : 31771.10\n", "R² : 0.4177\n", "\n", "Model comparison (lower RMSE better):\n", " Model CV_RMSE Std\n", " GradientBoosting 43977.939388 1326.487811\n", "HistGradientBoosting 44118.368637 1486.742584\n", " XGBoost 44148.171496 1351.728062\n", " RandomForest 44815.552309 1201.191135\n", " LightGBM 44856.833039 1337.290858\n", "CAD\n", "****************************************\n", "****************************************\n", "\n", "Top 20 Feature Importances (all features):\n", " Feature Importance\n", "6 YearsCodePro 0.305766\n", "8 OrgSize 0.054786\n", "465 OpSysProfessional use MacOS 0.048800\n", "5 YearsCode 0.043386\n", "398 OfficeStackSyncHaveWorkedWith Microsoft Teams 0.030796\n", "7 DevType 0.028363\n", "2 Employment 0.024607\n", "127 LanguageHaveWorkedWith Ruby 0.022418\n", "3 RemoteWork 0.018762\n", "403 OfficeStackSyncHaveWorkedWith Slack 0.017231\n", "121 LanguageHaveWorkedWith PHP 0.015557\n", "412 AISearchDevHaveWorkedWith Amazon Q 0.014661\n", "167 DatabaseHaveWorkedWith Redis 0.013285\n", "301 ToolsTechHaveWorkedWith Composer 0.010543\n", "35 JobSatPoints_1 0.010234\n", "19 Knowledge_4 0.009659\n", "66 LearnCodeOnline How-to videos 0.009635\n", "320 ToolsTechHaveWorkedWith Terraform 0.009266\n", "446 OpSysPersonal use MacOS 0.008345\n", "74 LearnCodeOnline Video-based Online Courses 0.007396\n", "\n", "=== Top-50 Feature Refit (GradientBoosting) in Canada [CAD] ===\n", "BEFORE -> RMSE: 41332.02 | MAE: 31771.10 | R²: 0.4177 (all 475 numeric features)\n", "AFTER -> RMSE: 41388.71 | MAE: 31943.70 | R²: 0.4161 (top 50 features)\n", "ΔRMSE (top-50 - all): +56.70\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " from sklearn.model_selection import train_test_split, cross_val_score, KFold\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", "from sklearn.base import clone\n", "from sklearn.inspection import permutation_importance\n", "import numpy as np\n", "# --- baseline models ---\n", "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, HistGradientBoostingRegressor\n", "# kaggle top models\n", "from xgboost import XGBRegressor\n", "from lightgbm import LGBMRegressor\n", "\n", "# ========== 1. Data preparation ==========\n", "\n", "df_cleaned = df.loc[df[\"CompTotal\"] >= 1000].copy()\n", "df_cleaned = df_cleaned.loc[df[\"CompTotal\"] <= 300000].copy()\n", "\n", "df_cleaned.drop(columns=[\"JobSat\"], errors=\"ignore\", inplace=True)\n", "\n", "for group in df_cleaned.groupby(['Country', 'Currency']):\n", " train_df = group[1]\n", " if train_df.shape[0]<1000:\n", " continue\n", " # Split features and target\n", " X = train_df.drop(columns=[\"CompTotal\"])\n", " y = train_df[\"CompTotal\"]\n", "\n", " # Split into training / testing\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", " # ========== 2. Preprocessing pipeline ==========\n", " # Identify numeric and categorical columns\n", " num_cols = X_train.select_dtypes(include=[\"number\"]).columns\n", " cat_cols = X_train.select_dtypes(exclude=[\"number\"]).columns\n", "\n", " # Numeric pipeline\n", " numeric_pipeline = Pipeline(steps=[\n", "\n", " (\"scaler\", StandardScaler())\n", " ])\n", "\n", "\n", "\n", " # Combine into column transformer\n", " preprocessor = ColumnTransformer(\n", " transformers=[\n", " (\"num\", numeric_pipeline, num_cols),\n", "\n", " ]\n", " )\n", "\n", " # ========== 3. Define models ==========\n", " models = {\n", " \"GradientBoosting\": GradientBoostingRegressor(\n", " n_estimators=200, learning_rate=0.05, max_depth=3, random_state=42\n", " ),\n", " \"RandomForest\": RandomForestRegressor(\n", " n_estimators=400, random_state=42, n_jobs=-1\n", " ),\n", " \"HistGradientBoosting\": HistGradientBoostingRegressor(\n", " learning_rate=0.05, random_state=42\n", " )\n", " }\n", "\n", "\n", " models[\"XGBoost\"] = XGBRegressor(\n", " n_estimators=600, learning_rate=0.05, max_depth=6,\n", " subsample=0.8, colsample_bytree=0.8, n_jobs=-1, random_state=42\n", " )\n", "\n", "\n", " models[\"LightGBM\"] = LGBMRegressor(\n", " n_estimators=600, learning_rate=0.05, num_leaves=31,\n", " subsample=0.8, colsample_bytree=0.8, n_jobs=-1, random_state=42\n", " )\n", "\n", " # ========== 4. Evaluate each model with cross-validation ==========\n", " cv = KFold(n_splits=5, shuffle=True, random_state=42)\n", " results = []\n", "\n", " for name, model in models.items():\n", " pipe = Pipeline(steps=[(\"preprocessor\", preprocessor),\n", " (\"model\", model)])\n", " scores = cross_val_score(\n", " pipe, X_train, y_train,\n", " scoring=\"neg_root_mean_squared_error\",\n", " cv=cv, n_jobs=-1,error_score='raise'\n", " )\n", " rmse = -scores.mean()\n", " print(f\"{name:>20s} | CV RMSE: {rmse:.2f}\")\n", " results.append((name, rmse, scores.std()))\n", "\n", " # Sort and select best\n", " results_df = pd.DataFrame(results, columns=[\"Model\", \"CV_RMSE\", \"Std\"]).sort_values(\"CV_RMSE\")\n", " best_model_name = results_df.iloc[0][\"Model\"]\n", " print(\"\\nBest model:\", best_model_name)\n", "\n", " # ========== 5. Fit best model pipeline and evaluate on test set ==========\n", " best_model = models[best_model_name]\n", " best_pipeline = Pipeline(steps=[\n", " (\"preprocessor\", preprocessor),\n", " (\"model\", best_model)\n", " ])\n", "\n", " best_pipeline.fit(X_train, y_train)\n", " y_pred = best_pipeline.predict(X_test)\n", "\n", " rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", " mae = mean_absolute_error(y_test, y_pred)\n", " r2 = r2_score(y_test, y_pred)\n", "\n", " print(f\"\\n--- Test Performance ({best_model_name}) ---\")\n", " print(f\"RMSE: {rmse:.2f}\")\n", " print(f\"MAE : {mae:.2f}\")\n", " print(f\"R² : {r2:.4f}\")\n", "\n", " # Optional: show model comparison table\n", " print(\"\\nModel comparison (lower RMSE better):\")\n", " print(results_df.to_string(index=False))\n", " _country = ''\n", " for x in value_maps['Country'].keys() :\n", " if value_maps['Country'][x]==group[0][0]:\n", " _country = x\n", "\n", " for x in value_maps['Currency'].keys() :\n", " if value_maps['Currency'][x]==group[0][1]:\n", " print(x)\n", " print('*'*40)\n", " print('*'*40)\n", " feature_names_num = num_cols.tolist()\n", "\n", " feature_names = feature_names_num\n", "\n", " importances = best_model.feature_importances_\n", " feature_importance_df = pd.DataFrame({\n", " 'Feature': feature_names,\n", " 'Importance': importances\n", " }).sort_values(by='Importance', ascending=False)\n", "\n", " print(\"\\nTop 20 Feature Importances:\")\n", " print(feature_importance_df.head(20))\n", " # ---------- existing code up to the first evaluation ----------\n", " best_pipeline.fit(X_train, y_train)\n", " y_pred = best_pipeline.predict(X_test)\n", "\n", " rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", " mae = mean_absolute_error(y_test, y_pred)\n", " r2 = r2_score(y_test, y_pred)\n", "\n", " print(f\"\\n--- Test Performance ({best_model_name}) ---\")\n", " print(f\"RMSE: {rmse:.2f}\")\n", " print(f\"MAE : {mae:.2f}\")\n", " print(f\"R² : {r2:.4f}\")\n", "\n", " print(\"\\nModel comparison (lower RMSE better):\")\n", " print(results_df.to_string(index=False))\n", "\n", " # --------- Nice labels for Country/Currency (unchanged) ---------\n", " _country = ''\n", " for x in value_maps['Country'].keys():\n", " if value_maps['Country'][x] == group[0][0]:\n", " _country = x\n", "\n", " _currency = ''\n", " for x in value_maps['Currency'].keys():\n", " if value_maps['Currency'][x] == group[0][1]:\n", " _currency = x\n", " print(_currency)\n", " print('*'*40)\n", " print('*'*40)\n", "\n", " # ===================== TOP-50 FEATURE REFIT =====================\n", " # 1) Get feature importances (fallback to permutation importance if needed)\n", " feature_names = num_cols.tolist() # only numeric used in your preprocessor\n", " if hasattr(best_model, \"feature_importances_\"):\n", " importances = best_model.feature_importances_\n", " else:\n", " # Fallback: permutation importance on training set\n", " # (uses the full pipeline so it handles scaling correctly)\n", " perm = permutation_importance(\n", " best_pipeline, X_train, y_train,\n", " n_repeats=5,\n", " scoring=\"neg_root_mean_squared_error\",\n", " random_state=42,\n", " n_jobs=-1\n", " )\n", " # With your current preprocessor (only numeric), importances align to feature_names\n", " importances = perm.importances_mean\n", "\n", " feature_importance_df = pd.DataFrame({\n", " 'Feature': feature_names,\n", " 'Importance': importances\n", " }).sort_values(by='Importance', ascending=False)\n", "\n", " print(\"\\nTop 20 Feature Importances (all features):\")\n", " print(feature_importance_df.head(20))\n", "\n", " # 2) Pick top-K features\n", " TOP_K = min(50, len(feature_names))\n", " top_features = feature_importance_df.head(TOP_K)['Feature'].tolist()\n", "\n", " # 3) Build a new preprocessor that only uses the top-K features\n", " preprocessor_top = ColumnTransformer(\n", " transformers=[\n", " (\"num\", numeric_pipeline, top_features),\n", " ]\n", " )\n", "\n", " # 4) Clone the best model (clean, unfitted) and retrain on top-K\n", " best_model_top = clone(best_model)\n", " best_pipeline_top = Pipeline(steps=[\n", " (\"preprocessor\", preprocessor_top),\n", " (\"model\", best_model_top)\n", " ])\n", " best_pipeline_top.fit(X_train, y_train)\n", " y_pred_top = best_pipeline_top.predict(X_test)\n", "\n", " rmse_top = np.sqrt(mean_squared_error(y_test, y_pred_top))\n", " mae_top = mean_absolute_error(y_test, y_pred_top)\n", " r2_top = r2_score(y_test, y_pred_top)\n", "\n", " # 5) Report before/after\n", " print(f\"\\n=== Top-{TOP_K} Feature Refit ({best_model_name}) in {_country} [{_currency}] ===\")\n", " print(f\"BEFORE -> RMSE: {rmse:.2f} | MAE: {mae:.2f} | R²: {r2:.4f} (all {len(feature_names)} numeric features)\")\n", " print(f\"AFTER -> RMSE: {rmse_top:.2f} | MAE: {mae_top:.2f} | R²: {r2_top:.4f} (top {TOP_K} features)\")\n", " delta = rmse_top - rmse\n", " print(f\"ΔRMSE (top-{TOP_K} - all): {delta:+.2f}\")\n", "\n", " # Optional: plot top 20 features among the selected set (from original ranking)\n", " plt.figure(figsize=(10, 6))\n", " sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(20))\n", " plt.title(f'Top 20 Feature Importances in {_country}')\n", " plt.tight_layout()\n", " plt.show()\n", " # # Plot top 20 features\n", " # plt.figure(figsize=(10, 6))\n", " # sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(20))\n", " # plt.title(f'Top 20 Feature Importances in {_country}')\n", " # plt.tight_layout()\n", " # plt.show()\n", " #\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "6398003c45a17b3c", "metadata": { "ExecuteTime": { "end_time": "2025-10-06T07:02:54.769365Z", "start_time": "2025-10-06T07:02:54.766939Z" } }, "outputs": [], "source": [ "# using top 30 feature the train again\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.7" } }, "nbformat": 4, "nbformat_minor": 5 }