From 0058ff04b6e060e5f2086e0d2c8697bbb2156695 Mon Sep 17 00:00:00 2001 From: bulatova_karina Date: Sat, 12 Oct 2024 09:33:30 +0400 Subject: [PATCH 1/3] lab2 --- lab_2/lab_2.ipynb | 1323 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1323 insertions(+) create mode 100644 lab_2/lab_2.ipynb diff --git a/lab_2/lab_2.ipynb b/lab_2/lab_2.ipynb new file mode 100644 index 0000000..8b4f6b6 --- /dev/null +++ b/lab_2/lab_2.ipynb @@ -0,0 +1,1323 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Начало лабораторной работы" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Цены на автомобили" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['ID', 'Price', 'Levy', 'Manufacturer', 'Model', 'Prod. year',\n", + " 'Category', 'Leather interior', 'Fuel type', 'Engine volume', 'Mileage',\n", + " 'Cylinders', 'Gear box type', 'Drive wheels', 'Doors', 'Wheel', 'Color',\n", + " 'Airbags'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проблемная область: Данные о ценах на автомобили, включая их характеристики\n", + "\n", + "Объект наблюдения: автомобиль\n", + "\n", + "Атрибуты: идентификатор, цена, налог, производитель, модель, год производства, категория, наличие кожаного салона, тип топлива, объем двигателя, пробег автомобиля, количество цилиндров в двигателе, тип коробки передач, тип привода, количество дверей, расположение руля, цвет, количество подушек безопасностей.\n", + "\n", + "Пример бизнес-цели: \n", + "1. Анализ данных: Изучение и очистка данных для выявления закономерностей и корреляций между характеристиками автомобилей и их ценами.\n", + "2. Разработка модели: Создание и обучение модели машинного обучения, которая будет прогнозировать цены на автомобили на основе их характеристик.\n", + "3. Внедрение: Интеграция модели в систему ценообразования компании для автоматического расчета цен на автомобили.\n", + "\n", + "\n", + "Актуальность: Данный датасет является актуальным и ценным ресурсом для компаний, занимающихся продажей автомобилей, а также для исследователей и инвесторов, поскольку он предоставляет обширную информацию о ценах и характеристиках автомобилей на вторичном рынке. Эти данные могут быть использованы для разработки моделей прогнозирования цен, анализа рыночных тенденций и принятия обоснованных бизнес-решений.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df['Prod. year'], df['Price'])\n", + "plt.xlabel('Prod. year')\n", + "plt.ylabel('Price')\n", + "plt.title('Scatter Plot of Prod. year vs Price')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "При проверке на шум можно заметить выброс в 2000 году. Цена там запредельная.\n", + "\n", + "Для удаления выбросов из датасета можно использовать метод межквартильного размаха. Зашумленность не очень высокая. Покрытие данных высокое и подошло бы для поставленной задачи по актуальности." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество строк до удаления выбросов: 19237\n", + "Количество строк после удаления выбросов: 17241\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n", + "\n", + "# Выбор столбцов для анализа\n", + "column1 = 'Prod. year'\n", + "column2 = 'Price'\n", + "\n", + "\n", + "# Функция для удаления выбросов\n", + "def remove_outliers(df, column):\n", + " Q1 = df[column].quantile(0.25)\n", + " Q3 = df[column].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n", + "\n", + "# Удаление выбросов для каждого столбца\n", + "df_cleaned = df.copy()\n", + "for column in [column1, column2]:\n", + " df_cleaned = remove_outliers(df_cleaned, column)\n", + "\n", + "# Построение точечной диаграммы после удаления выбросов\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df_cleaned[column1], df_cleaned[column2], alpha=0.5)\n", + "plt.xlabel(column1)\n", + "plt.ylabel(column2)\n", + "plt.title(f'Scatter Plot of {column1} vs {column2} (After Removing Outliers)')\n", + "plt.show()\n", + "\n", + "# Вывод количества строк до и после удаления выбросов\n", + "print(f\"Количество строк до удаления выбросов: {len(df)}\")\n", + "print(f\"Количество строк после удаления выбросов: {len(df_cleaned)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь очистим датасет от пустых строк" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Общая информация о датасете:\n", + "\n", + "RangeIndex: 19237 entries, 0 to 19236\n", + "Data columns (total 18 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ID 19237 non-null int64 \n", + " 1 Price 19237 non-null int64 \n", + " 2 Levy 19237 non-null object \n", + " 3 Manufacturer 19237 non-null object \n", + " 4 Model 19237 non-null object \n", + " 5 Prod. year 19237 non-null int64 \n", + " 6 Category 19237 non-null object \n", + " 7 Leather interior 19237 non-null object \n", + " 8 Fuel type 19237 non-null object \n", + " 9 Engine volume 19237 non-null object \n", + " 10 Mileage 19237 non-null object \n", + " 11 Cylinders 19237 non-null float64\n", + " 12 Gear box type 19237 non-null object \n", + " 13 Drive wheels 19237 non-null object \n", + " 14 Doors 19237 non-null object \n", + " 15 Wheel 19237 non-null object \n", + " 16 Color 19237 non-null object \n", + " 17 Airbags 19237 non-null int64 \n", + "dtypes: float64(1), int64(4), object(13)\n", + "memory usage: 2.6+ MB\n", + "None\n", + "\n", + "Таблица анализа пропущенных значений:\n", + " Количество пропущенных значений \\\n", + "ID 0 \n", + "Price 0 \n", + "Levy 0 \n", + "Manufacturer 0 \n", + "Model 0 \n", + "Prod. year 0 \n", + "Category 0 \n", + "Leather interior 0 \n", + "Fuel type 0 \n", + "Engine volume 0 \n", + "Mileage 0 \n", + "Cylinders 0 \n", + "Gear box type 0 \n", + "Drive wheels 0 \n", + "Doors 0 \n", + "Wheel 0 \n", + "Color 0 \n", + "Airbags 0 \n", + "\n", + " Процент пропущенных значений \n", + "ID 0.0 \n", + "Price 0.0 \n", + "Levy 0.0 \n", + "Manufacturer 0.0 \n", + "Model 0.0 \n", + "Prod. year 0.0 \n", + "Category 0.0 \n", + "Leather interior 0.0 \n", + "Fuel type 0.0 \n", + "Engine volume 0.0 \n", + "Mileage 0.0 \n", + "Cylinders 0.0 \n", + "Gear box type 0.0 \n", + "Drive wheels 0.0 \n", + "Doors 0.0 \n", + "Wheel 0.0 \n", + "Color 0.0 \n", + "Airbags 0.0 \n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n", + "\n", + "# Вывод общей информации о датасете\n", + "print(\"Общая информация о датасете:\")\n", + "print(df.info())\n", + "\n", + "# Вывод таблицы анализа пропущенных значений\n", + "missing_values = df.isnull().sum()\n", + "missing_values_percentage = (missing_values / len(df)) * 100\n", + "missing_data = pd.concat([missing_values, missing_values_percentage], axis=1, keys=['Количество пропущенных значений', 'Процент пропущенных значений'])\n", + "\n", + "print(\"\\nТаблица анализа пропущенных значений:\")\n", + "print(missing_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Пустых строк не было обнаружено.\n", + "\n", + "Теперь создадим выборки." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 11542\n", + "Размер контрольной выборки: 3847\n", + "Размер тестовой выборки: 3848\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n", + "\n", + "# Выбор признаков и целевой переменной\n", + "X = df.drop('Price', axis=1) # Признаки (все столбцы, кроме 'Price')\n", + "y = df['Price'] # Целевая переменная ('Price')\n", + "\n", + "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "\n", + "# Разбиение оставшейся части на контрольную и тестовую выборки\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Вывод размеров выборок\n", + "print(f\"Размер обучающей выборки: {X_train.shape[0]}\")\n", + "print(f\"Размер контрольной выборки: {X_val.shape[0]}\")\n", + "print(f\"Размер тестовой выборки: {X_test.shape[0]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проанализируем сбалансированность выборок" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение Price в обучающей выборке:\n", + "Price\n", + "1 1\n", + "3 8\n", + "6 4\n", + "19 1\n", + "20 4\n", + " ..\n", + "260296 1\n", + "297930 2\n", + "308906 1\n", + "872946 1\n", + "26307500 1\n", + "Name: count, Length: 1764, dtype: int64\n", + "Процент положительных значений: 100.00%\n", + "Процент отрицательных значений: 0.00%\n", + "\n", + "Необходима аугментация данных для балансировки классов.\n", + "\n", + "Распределение Price в контрольной выборке:\n", + "Price\n", + "1 1\n", + "3 4\n", + "6 1\n", + "20 1\n", + "25 3\n", + " ..\n", + "141124 1\n", + "144261 1\n", + "156805 1\n", + "172486 1\n", + "627220 1\n", + "Name: count, Length: 983, dtype: int64\n", + "Процент положительных значений: 100.00%\n", + "Процент отрицательных значений: 0.00%\n", + "\n", + "Необходима аугментация данных для балансировки классов.\n", + "\n", + "Распределение Price в тестовой выборке:\n", + "Price\n", + "3 3\n", + "6 1\n", + "9 1\n", + "20 2\n", + "25 7\n", + " ..\n", + "153669 1\n", + "156805 1\n", + "163077 1\n", + "216391 1\n", + "288521 1\n", + "Name: count, Length: 978, dtype: int64\n", + "Процент положительных значений: 100.00%\n", + "Процент отрицательных значений: 0.00%\n", + "\n", + "Необходима аугментация данных для балансировки классов.\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n", + "\n", + "# Выбор признаков и целевой переменной\n", + "X = df.drop('Price', axis=1) # Признаки (все столбцы, кроме 'Price')\n", + "y = df['Price'] # Целевая переменная ('Price')\n", + "\n", + "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "\n", + "# Разбиение оставшейся части на контрольную и тестовую выборки\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Функция для анализа распределения и вывода результатов\n", + "def analyze_distribution(data, title):\n", + " print(f\"Распределение Price в {title}:\")\n", + " distribution = data.value_counts().sort_index()\n", + " print(distribution)\n", + " total = len(data)\n", + " positive_count = (data > 0).sum()\n", + " negative_count = (data < 0).sum()\n", + " positive_percent = (positive_count / total) * 100\n", + " negative_percent = (negative_count / total) * 100\n", + " print(f\"Процент положительных значений: {positive_percent:.2f}%\")\n", + " print(f\"Процент отрицательных значений: {negative_percent:.2f}%\")\n", + " print(\"\\nНеобходима аугментация данных для балансировки классов.\\n\")\n", + "\n", + "# Анализ распределения для каждой выборки\n", + "analyze_distribution(y_train, \"обучающей выборке\")\n", + "analyze_distribution(y_val, \"контрольной выборке\")\n", + "analyze_distribution(y_test, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Выборки не сбалансированы, и для улучшения качества модели рекомендуется провести аугментацию данных." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение Price в обучающей выборке после oversampling:\n", + "Price\n", + "1 169\n", + "3 169\n", + "6 169\n", + "19 169\n", + "20 169\n", + " ... \n", + "260296 169\n", + "297930 169\n", + "308906 169\n", + "872946 169\n", + "26307500 169\n", + "Name: count, Length: 1764, dtype: int64\n", + "Процент положительных значений: 100.00%\n", + "Процент отрицательных значений: 0.00%\n", + "Распределение Price в контрольной выборке:\n", + "Price\n", + "1 1\n", + "3 4\n", + "6 1\n", + "20 1\n", + "25 3\n", + " ..\n", + "141124 1\n", + "144261 1\n", + "156805 1\n", + "172486 1\n", + "627220 1\n", + "Name: count, Length: 983, dtype: int64\n", + "Процент положительных значений: 100.00%\n", + "Процент отрицательных значений: 0.00%\n", + "Распределение Price в тестовой выборке:\n", + "Price\n", + "3 3\n", + "6 1\n", + "9 1\n", + "20 2\n", + "25 7\n", + " ..\n", + "153669 1\n", + "156805 1\n", + "163077 1\n", + "216391 1\n", + "288521 1\n", + "Name: count, Length: 978, dtype: int64\n", + "Процент положительных значений: 100.00%\n", + "Процент отрицательных значений: 0.00%\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from imblearn.over_sampling import RandomOverSampler\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//car_price_prediction.csv\")\n", + "\n", + "# Выбор признаков и целевой переменной\n", + "X = df.drop('Price', axis=1) # Признаки (все столбцы, кроме 'Price')\n", + "y = df['Price'] # Целевая переменная ('Price')\n", + "\n", + "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "\n", + "# Разбиение оставшейся части на контрольную и тестовую выборки\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Применение oversampling к обучающей выборке\n", + "oversampler = RandomOverSampler(random_state=42)\n", + "X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train, y_train)\n", + "\n", + "# Функция для анализа распределения и вывода результатов\n", + "def analyze_distribution(data, title):\n", + " print(f\"Распределение Price в {title}:\")\n", + " distribution = data.value_counts().sort_index()\n", + " print(distribution)\n", + " total = len(data)\n", + " positive_count = (data > 0).sum()\n", + " negative_count = (data < 0).sum()\n", + " positive_percent = (positive_count / total) * 100\n", + " negative_percent = (negative_count / total) * 100\n", + " print(f\"Процент положительных значений: {positive_percent:.2f}%\")\n", + " print(f\"Процент отрицательных значений: {negative_percent:.2f}%\")\n", + "\n", + "# Анализ распределения для каждой выборки\n", + "analyze_distribution(y_train_resampled, \"обучающей выборке после oversampling\")\n", + "analyze_distribution(y_val, \"контрольной выборке\")\n", + "analyze_distribution(y_test, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Цены на бриллианты" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Unnamed: 0', 'carat', 'cut', 'color', 'clarity', 'depth', 'table',\n", + " 'price', 'x', 'y', 'z'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\"..//static//csv//Diamonds Prices2022.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проблемная область: ценообразование бриллиантов\n", + "\n", + "Объект наблюдения: бриллиант\n", + "\n", + "Атрибуты: идентификатор, вес, качество огранки, цвет, чистота, общая глубина, ширина верхней грани, цена, длина, ширина, высота.\n", + "\n", + "Пример бизнес-цели: \n", + "1. Оптимизация ценообразования и повышение эффективности продаж:\n", + "Цель: Разработка модели прогнозирования цен на бриллианты, которая позволит компаниям устанавливать конкурентоспособные цены и повысить продажи.\n", + "\n", + "2. Повышение эффективности маркетинговых кампаний:\n", + "Цель: Использование данных о бриллиантах для разработки целевых маркетинговых кампаний, направленных на конкретные сегменты рынка.\n", + "\n", + "3. Повышение качества сервиса и удовлетворенности клиентов:\n", + "Цель: Использование данных для предоставления клиентам персонализированных рекомендаций и улучшения качества обслуживания.\n", + "\n", + "Актуальность: Данный датасет является актуальным и ценным ресурсом для компаний, работающих на рынке бриллиантов, а также для исследователей и инвесторов, поскольку он предоставляет обширную информацию о ценах и характеристиках бриллиантов. Эти данные могут быть использованы для разработки моделей прогнозирования цен, анализа рыночных тенденций и принятия обоснованных бизнес-решений." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Diamonds Prices2022.csv\")\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df['price'], df['carat'])\n", + "plt.xlabel('price')\n", + "plt.ylabel('carat')\n", + "plt.title('Scatter Plot of price vs carat')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "При проверке на шум можно заметить выброс при цене в 17500. Количество карат запредельно.\n", + "\n", + "Для удаления выбросов из датасета можно использовать метод межквартильного размаха. Зашумленность не очень высокая. Покрытие данных высокое и подошло бы для поставленной задачи по актуальности." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество строк до удаления выбросов: 53943\n", + "Количество строк после удаления выбросов: 49517\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Diamonds Prices2022.csv\")\n", + "\n", + "# Выбор столбцов для анализа\n", + "column1 = 'carat'\n", + "column2 = 'price'\n", + "\n", + "\n", + "# Функция для удаления выбросов\n", + "def remove_outliers(df, column):\n", + " Q1 = df[column].quantile(0.25)\n", + " Q3 = df[column].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n", + "\n", + "# Удаление выбросов для каждого столбца\n", + "df_cleaned = df.copy()\n", + "for column in [column1, column2]:\n", + " df_cleaned = remove_outliers(df_cleaned, column)\n", + "\n", + "# Построение точечной диаграммы после удаления выбросов\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df_cleaned[column1], df_cleaned[column2], alpha=0.5)\n", + "plt.xlabel(column1)\n", + "plt.ylabel(column2)\n", + "plt.title(f'Scatter Plot of {column1} vs {column2} (After Removing Outliers)')\n", + "plt.show()\n", + "\n", + "# Вывод количества строк до и после удаления выбросов\n", + "print(f\"Количество строк до удаления выбросов: {len(df)}\")\n", + "print(f\"Количество строк после удаления выбросов: {len(df_cleaned)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь очистим датасет от пустых строк" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Общая информация о датасете:\n", + "\n", + "RangeIndex: 53943 entries, 0 to 53942\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Unnamed: 0 53943 non-null int64 \n", + " 1 carat 53943 non-null float64\n", + " 2 cut 53943 non-null object \n", + " 3 color 53943 non-null object \n", + " 4 clarity 53943 non-null object \n", + " 5 depth 53943 non-null float64\n", + " 6 table 53943 non-null float64\n", + " 7 price 53943 non-null int64 \n", + " 8 x 53943 non-null float64\n", + " 9 y 53943 non-null float64\n", + " 10 z 53943 non-null float64\n", + "dtypes: float64(6), int64(2), object(3)\n", + "memory usage: 4.5+ MB\n", + "None\n", + "\n", + "Таблица анализа пропущенных значений:\n", + " Количество пропущенных значений Процент пропущенных значений\n", + "Unnamed: 0 0 0.0\n", + "carat 0 0.0\n", + "cut 0 0.0\n", + "color 0 0.0\n", + "clarity 0 0.0\n", + "depth 0 0.0\n", + "table 0 0.0\n", + "price 0 0.0\n", + "x 0 0.0\n", + "y 0 0.0\n", + "z 0 0.0\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Diamonds Prices2022.csv\")\n", + "\n", + "# Вывод общей информации о датасете\n", + "print(\"Общая информация о датасете:\")\n", + "print(df.info())\n", + "\n", + "# Вывод таблицы анализа пропущенных значений\n", + "missing_values = df.isnull().sum()\n", + "missing_values_percentage = (missing_values / len(df)) * 100\n", + "missing_data = pd.concat([missing_values, missing_values_percentage], axis=1, keys=['Количество пропущенных значений', 'Процент пропущенных значений'])\n", + "\n", + "print(\"\\nТаблица анализа пропущенных значений:\")\n", + "print(missing_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Пустых строк не было обнаружено.\n", + "\n", + "Теперь создадим выборки." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 32365\n", + "Размер контрольной выборки: 10789\n", + "Размер тестовой выборки: 10789\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Diamonds Prices2022.csv\")\n", + "\n", + "# Выбор признаков и целевой переменной\n", + "X = df.drop('carat', axis=1) # Признаки (все столбцы, кроме 'carat')\n", + "y = df['carat'] # Целевая переменная ('carat')\n", + "\n", + "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "\n", + "# Разбиение оставшейся части на контрольную и тестовую выборки\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Вывод размеров выборок\n", + "print(f\"Размер обучающей выборки: {X_train.shape[0]}\")\n", + "print(f\"Размер контрольной выборки: {X_val.shape[0]}\")\n", + "print(f\"Размер тестовой выборки: {X_test.shape[0]}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проанализируем сбалансированность выборок" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение carat в обучающей выборке:\n", + "carat\n", + "0.20 7\n", + "0.21 5\n", + "0.22 4\n", + "0.23 178\n", + "0.24 139\n", + " ... \n", + "3.40 1\n", + "3.65 1\n", + "3.67 1\n", + "4.13 1\n", + "4.50 1\n", + "Name: count, Length: 263, dtype: int64\n", + "Процент положительных значений: 100.00%\n", + "Процент отрицательных значений: 0.00%\n", + "Распределение carat в контрольной выборке:\n", + "carat\n", + "0.20 2\n", + "0.21 2\n", + "0.23 62\n", + "0.24 58\n", + "0.25 51\n", + " ..\n", + "3.11 1\n", + "3.51 1\n", + "4.00 1\n", + "4.01 1\n", + "5.01 1\n", + "Name: count, Length: 232, dtype: int64\n", + "Процент положительных значений: 100.00%\n", + "Процент отрицательных значений: 0.00%\n", + "Распределение carat в тестовой выборке:\n", + "carat\n", + "0.20 3\n", + "0.21 2\n", + "0.22 1\n", + "0.23 53\n", + "0.24 57\n", + " ..\n", + "3.00 1\n", + "3.01 1\n", + "3.04 1\n", + "3.50 1\n", + "4.01 1\n", + "Name: count, Length: 241, dtype: int64\n", + "Процент положительных значений: 100.00%\n", + "Процент отрицательных значений: 0.00%\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Diamonds Prices2022.csv\")\n", + "\n", + "# Выбор признаков и целевой переменной\n", + "X = df.drop('carat', axis=1) # Признаки (все столбцы, кроме 'carat')\n", + "y = df['carat'] # Целевая переменная ('carat')\n", + "\n", + "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n", + "\n", + "# Разбиение оставшейся части на контрольную и тестовую выборки\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Функция для анализа распределения и вывода результатов\n", + "def analyze_distribution(data, title):\n", + " print(f\"Распределение carat в {title}:\")\n", + " distribution = data.value_counts().sort_index()\n", + " print(distribution)\n", + " total = len(data)\n", + " positive_count = (data > 0).sum()\n", + " negative_count = (data < 0).sum()\n", + " positive_percent = (positive_count / total) * 100\n", + " negative_percent = (negative_count / total) * 100\n", + " print(f\"Процент положительных значений: {positive_percent:.2f}%\")\n", + " print(f\"Процент отрицательных значений: {negative_percent:.2f}%\")\n", + "\n", + "# Анализ распределения для каждой выборки\n", + "analyze_distribution(y_train, \"обучающей выборке\")\n", + "analyze_distribution(y_val, \"контрольной выборке\")\n", + "analyze_distribution(y_test, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Цены на кофе" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\"..//static//csv//Starbucks Dataset.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проблемная область: ценообразование кофе\n", + "\n", + "Объект наблюдения: кофе\n", + "\n", + "Атрибуты: дата, цена на момент открытия, максимальная цена, минимальная цена, цена на момент закрытия, скорректированная цена закрытия, объем\n", + "\n", + "Пример бизнес-цели: \n", + "1. Анализ рыночных тенденций:\n", + "Цель: Определить долгосрочные тенденции в ценах на кофе.\n", + "\n", + "2. Прогнозирование цен:\n", + "Цель: Разработать модель прогнозирования будущих цен на кофе.\n", + "\n", + "3. Оценка рисков:\n", + "Цель: Оценить риски, связанные с колебаниями цен на кофе.\n", + "\n", + "\n", + "Актуальность: Данные о ценах на кофе являются крайне актуальными для компаний, работающих в сфере кофейной индустрии, а также для инвесторов и трейдеров, заинтересованных в сырьевом рынке. Понимание динамики цен на кофе позволяет оптимизировать стратегии закупок, управления запасами и ценообразования, что в конечном итоге влияет на прибыльность бизнеса и эффективность инвестиций." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Starbucks Dataset.csv\")\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df['Open'], df['Volume'])\n", + "plt.xlabel('Open')\n", + "plt.ylabel('Volume')\n", + "plt.title('Scatter Plot of Open vs Volume')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Выброс присутствует. Сделаем очистку данных.\n", + "\n", + "Для удаления выбросов из датасета можно использовать метод межквартильного размаха. Зашумленность не очень высокая. Покрытие данных высокое и подошло бы для поставленной задачи по актуальности." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Starbucks Dataset.csv\")\n", + "\n", + "# Функция для удаления выбросов с использованием IQR\n", + "def remove_outliers_iqr(df, column):\n", + " Q1 = df[column].quantile(0.25)\n", + " Q3 = df[column].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n", + "\n", + "# Удаление выбросов для столбцов 'Open' и 'Volume'\n", + "df_cleaned = remove_outliers_iqr(df, 'Open')\n", + "df_cleaned = remove_outliers_iqr(df_cleaned, 'Volume')\n", + "\n", + "# Построение графика для очищенных данных\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df_cleaned['Open'], df_cleaned['Volume'])\n", + "plt.xlabel('Open')\n", + "plt.ylabel('Volume')\n", + "plt.title('Scatter Plot of Open vs Volume (Cleaned)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь очистим датасет от пустых строк" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Количество строк до очистки: 8036\n", + "Количество строк после удаления выбросов: 7585\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Starbucks Dataset.csv\")\n", + "\n", + "# Вывод количества строк до очистки\n", + "print(f\"Количество строк до очистки: {len(df)}\")\n", + "\n", + "# Удаление пустых строк\n", + "df_cleaned = df.dropna()\n", + "\n", + "\n", + "# Функция для удаления выбросов с использованием IQR\n", + "def remove_outliers_iqr(df, column):\n", + " Q1 = df[column].quantile(0.25)\n", + " Q3 = df[column].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n", + "\n", + "# Удаление выбросов для столбцов 'Open' и 'Volume'\n", + "df_cleaned = remove_outliers_iqr(df_cleaned, 'Open')\n", + "df_cleaned = remove_outliers_iqr(df_cleaned, 'Volume')\n", + "\n", + "# Вывод количества строк после удаления выбросов\n", + "print(f\"Количество строк после удаления выбросов: {len(df_cleaned)}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Теперь создадим выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 29709\n", + "Размер контрольной выборки: 9904\n", + "Размер тестовой выборки: 9904\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Diamonds Prices2022.csv\")\n", + "\n", + "# Выбор столбцов для анализа\n", + "column1 = 'carat'\n", + "column2 = 'price'\n", + "\n", + "# Функция для удаления выбросов\n", + "def remove_outliers(df, column):\n", + " Q1 = df[column].quantile(0.25)\n", + " Q3 = df[column].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n", + "\n", + "# Удаление выбросов для каждого столбца\n", + "df_cleaned = df.copy()\n", + "for column in [column1, column2]:\n", + " df_cleaned = remove_outliers(df_cleaned, column)\n", + "\n", + "# Разделение данных на обучающую и тестовую выборки\n", + "X = df_cleaned[[column1]]\n", + "y = df_cleaned[column2]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Разделение обучающей выборки на обучающую и контрольную выборки\n", + "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n", + "\n", + "# Вывод размеров выборок\n", + "print(f\"Размер обучающей выборки: {len(X_train)}\")\n", + "print(f\"Размер контрольной выборки: {len(X_val)}\")\n", + "print(f\"Размер тестовой выборки: {len(X_test)}\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проанализируем сбалансированность выборок" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 29709\n", + "Размер контрольной выборки: 9904\n", + "Размер тестовой выборки: 9904\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Статистика для Training Set:\n", + "Среднее: 3021.2620418055135\n", + "Медиана: 2090.0\n", + "Стандартное отклонение: 2574.5120319534017\n", + "\n", + "Статистика для Validation Set:\n", + "Среднее: 3012.331684168013\n", + "Медиана: 2059.5\n", + "Стандартное отклонение: 2587.9320915537055\n", + "\n", + "Статистика для Test Set:\n", + "Среднее: 3020.2212237479807\n", + "Медиана: 2097.5\n", + "Стандартное отклонение: 2568.915633156046\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Diamonds Prices2022.csv\")\n", + "\n", + "# Выбор столбцов для анализа\n", + "column1 = 'carat'\n", + "column2 = 'price'\n", + "\n", + "# Функция для удаления выбросов\n", + "def remove_outliers(df, column):\n", + " Q1 = df[column].quantile(0.25)\n", + " Q3 = df[column].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n", + "\n", + "# Удаление выбросов для каждого столбца\n", + "df_cleaned = df.copy()\n", + "for column in [column1, column2]:\n", + " df_cleaned = remove_outliers(df_cleaned, column)\n", + "\n", + "# Разделение данных на обучающую и тестовую выборки\n", + "X = df_cleaned[[column1]]\n", + "y = df_cleaned[column2]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Разделение обучающей выборки на обучающую и контрольную выборки\n", + "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=42)\n", + "\n", + "# Вывод размеров выборок\n", + "print(f\"Размер обучающей выборки: {len(X_train)}\")\n", + "print(f\"Размер контрольной выборки: {len(X_val)}\")\n", + "print(f\"Размер тестовой выборки: {len(X_test)}\")\n", + "\n", + "# Построение гистограмм для каждой выборки\n", + "plt.figure(figsize=(15, 5))\n", + "\n", + "# Гистограмма для обучающей выборки\n", + "plt.subplot(1, 3, 1)\n", + "plt.hist(y_train, bins=30, alpha=0.5, label='Train')\n", + "plt.xlabel('Price')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Training Set')\n", + "\n", + "# Гистограмма для контрольной выборки\n", + "plt.subplot(1, 3, 2)\n", + "plt.hist(y_val, bins=30, alpha=0.5, label='Validation')\n", + "plt.xlabel('Price')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Validation Set')\n", + "\n", + "# Гистограмма для тестовой выборки\n", + "plt.subplot(1, 3, 3)\n", + "plt.hist(y_test, bins=30, alpha=0.5, label='Test')\n", + "plt.xlabel('Price')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Test Set')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Вычисление статистических показателей\n", + "def print_stats(data, name):\n", + " print(f\"\\nСтатистика для {name}:\")\n", + " print(f\"Среднее: {data.mean()}\")\n", + " print(f\"Медиана: {data.median()}\")\n", + " print(f\"Стандартное отклонение: {data.std()}\")\n", + "\n", + "print_stats(y_train, 'Training Set')\n", + "print_stats(y_val, 'Validation Set')\n", + "print_stats(y_test, 'Test Set')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Мы вычислили среднее, медиану и стандартное отклонение для каждой выборки. Если эти показатели для всех выборок близки, это также указывает на сбалансированность выборок." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aimenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From b7abedce5c88f5b33c0c4bc5e58916159e080e73 Mon Sep 17 00:00:00 2001 From: bulatova_karina Date: Sat, 26 Oct 2024 10:29:31 +0400 Subject: [PATCH 2/3] lab3 --- lab_3/lab_3.ipynb | 889 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 889 insertions(+) create mode 100644 lab_3/lab_3.ipynb diff --git a/lab_3/lab_3.ipynb b/lab_3/lab_3.ipynb new file mode 100644 index 0000000..0b2f7d0 --- /dev/null +++ b/lab_3/lab_3.ipynb @@ -0,0 +1,889 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Начало лабораторной работы" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Бизнес-цели\n", + "\n", + "1. Оптимизация тарифов:\n", + "\n", + "Цель: Разработка более точных и справедливых тарифов на страховку, основанных на индивидуальных рисках клиентов.\n", + "\n", + "Показатели успеха:\n", + "\n", + "Снижение оттока клиентов (уменьшение количества отказов от страховки).\n", + "\n", + "Увеличение прибыли за счет более точного ценообразования.\n", + "\n", + "Повышение удовлетворенности клиентов (опросы, отзывы).\n", + "\n", + "2. Оценка рисков:\n", + "\n", + "Цель: Оценка рисков для новых видов страхования или географических регионов.\n", + "\n", + "Показатели успеха:\n", + "\n", + "Снижение убытков от страховых случаев.\n", + "\n", + "Увеличение прибыли за счет выхода на новые рынки.\n", + "\n", + "Сокращение сроков разработки новых страховых продуктов." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Технические цели для каждой бизнес-цели:\n", + "\n", + "1. Оптимизация тарифов:\n", + "\n", + "* **Техническая цель:** Разработка и внедрение модели машинного обучения, которая будет прогнозировать стоимость страховки с высокой точностью на основе данных о клиентах (возраст, пол, ИМТ, количество детей, статус курения, регион проживания).\n", + "* **Ключевые задачи:**\n", + " * Сбор и подготовка данных (очистка, нормализация, обработка пропущенных значений).\n", + " * Исследование данных и выявление важных признаков.\n", + " * Выбор и обучение модели машинного обучения (линейная регрессия, случайный лес, градиентный бустинг и т.д.).\n", + " * Оценка качества модели (метрики: RMSE, MAE, R²).\n", + " * Разработка API для интеграции модели в существующие системы компании.\n", + " * Тестирование и развертывание модели в продакшн.\n", + " * Мониторинг и поддержка модели (обновление данных, переобучение модели).\n", + "\n", + "2. Оценка рисков:\n", + "\n", + "* **Техническая цель:** Разработка модели оценки рисков для новых видов страхования или географических регионов, которая позволит определить потенциальные убытки и скорректировать тарифы соответствующим образом.\n", + "* **Ключевые задачи:**\n", + " * Сбор и подготовка данных о новых видах страхования или регионах (исторические данные, демографическая информация, данные о рисках).\n", + " * Исследование данных и выявление закономерностей, связанных с рисками.\n", + " * Выбор и обучение модели машинного обучения для оценки рисков (классификация, регрессия).\n", + " * Оценка качества модели (метрики: точность, полнота, F1-мера, AUC-ROC).\n", + " * Разработка отчетов и дашбордов для визуализации результатов оценки рисков.\n", + " * Интеграция модели в процесс принятия решений о тарифах и страховых продуктах.\n", + " * Тестирование и развертывание модели в продакшн.\n", + " * Мониторинг и поддержка модели (обновление данных, переобучение модели).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: (1940, 6)\n", + "Размер контрольной выборки: (416, 6)\n", + "Размер тестовой выборки: (416, 6)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Перемешивание данных\n", + "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n", + "\n", + "# Разделение на признаки и целевую переменную\n", + "X = df.drop('charges', axis=1)\n", + "y = df['charges']\n", + "\n", + "# Разбиение на обучающую, контрольную и тестовую выборки\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Вывод размеров выборок\n", + "print(\"Размер обучающей выборки:\", X_train.shape)\n", + "print(\"Размер контрольной выборки:\", X_val.shape)\n", + "print(\"Размер тестовой выборки:\", X_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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X69nZ2Vq9erX69u3boLHWlDcOOp8Q7q+wAF8V2R3akeZdDQ0BAN7Na5qBDx06VEOHDq1yHqvVWumV/s2bN2vx4sX69ddfddZZpYO2vfjiixo2bJieeeYZJSQk1HnMNWUrsbtuZQ9poi3wnQX89FwbrQoAAE1GdmGJcm2l/d877zbzZq0CHdqSbdHaQ4XKs5UoyNo0f3cAAE7dvffeq3PPPVdPPvmkrr76av3yyy+aN2+e5s2bJ6n0gvDEiRP1+OOPq3379kpOTtaUKVOUkJCgK664wrPBV8EwDO3LKO32LrEBus+pLpPJpM7xIVq1M0ObDmWr07EW+QAAnIzXtMCvjm+//VYxMTHq2LGjbr/9dh05csT12sqVKxUeHu4q3kvSwIEDZTabtXr16gqXZ7PZlJ2d7faoTzmFpf3f+/uYZfXxjtv46lqIv6+CrT4yDCmVVvgAgCbiQGZpISAmxF++Fu//+RTma6j46EEV2aVv/krzdDgAAC/Uu3dvffzxx3rnnXfUrVs3PfbYY5o1a5ZGjx7tmueBBx7QXXfdpXHjxql3797Kzc3V4sWL5e/vvRezswqKlWsrkcVkUoKX3TXXOa60aL/vaIHr7nwAAE7G+/8DPWbIkCF68803tXz5cs2cOVPfffedhg4dKrvdLklKSUlRTEyM23t8fHwUERGhlJSUCpc5Y8YMhYWFuR6JiYn1ug1NvfscJ/rBBwA0NQePFfBbelFLvqqYTFL+Xz9Kkr5Yf8jD0QAAvNWIESO0YcMGFRYWavPmzbr11lvdXjeZTJo+fbpSUlJUWFioZcuWqUOHDh6KtnqcF91jQ61ed9E9NMDX1RXfNrrRAQBUU62yWdu2bd1awTtlZmaqbdu2tVl0OaNGjdJll12m7t2764orrtDnn3+uX3/9Vd9+++0pL3Py5MnKyspyPfbt21d3AVcg+1gL/FD/pl3Aj6eADwCNTkPm9MbowNHSYkBCuPe2ODxR3l8/SJJWbElTnq3Ew9EAAKqDfFx7zjFrvK31vVOH2BBJ0tbUHA9HAgBoLGpVwN+9e7erBXxZNptNBw4cqM2iT6pt27aKiorS9u3bJUlxcXFKS3O/RbykpEQZGRmV9ptvtVoVGhrq9qhPOYWlLfCbav/3TnGhpcWNNLrQAYBGw5M53dvl2UqUeewuuoQw7ywGVKQ4bZfigy2ylTi0nG50AKBRIB/XnvOuOW8t4LeLCZJJUlqOTZn5RZ4OBwDQCJxSJfmzzz5z/b1kyRKFhYW5ntvtdi1fvlxt2rSpdXBV2b9/v44cOaL4+HhJUt++fZWZmam1a9eqV69ekqRvvvlGDodDffr0qddYqsvZB35TL+BHh1hlkpRXZFdeUfkfnwAA7+ENOd3bOQsBUcF+8vdtXGPYdAjI06Fcf73zw2a1slfcpWBVoqKi1Lp163qIDABQFvm4brhfdPfOu+YC/XzUKiJA+zIKtC0tV73bRHg6JACAlzulSrJzxHmTyaSxY8e6vebr66s2bdro2WefrdEyc3NzXa3pJWnXrl1at26dIiIiFBERoWnTpmnkyJGKi4vTjh079MADD+i0007T4MGDJUmdO3fWkCFDdOutt+qVV15RcXGx7rzzTo0aNUoJCQmnspl17ngBv2l3oeNrMSsiyE9H8op0OJeBeQDAm9VHTm9qnF3CNabW99kZhyVJ7z77oOKvf04/7cjQuxOHSPaadaUTEBiovzZvpogPAPWMfFw3yl50t3rxRfcOMSEU8AEA1XZKBXyHwyFJSk5O1q+//qqoqKhaB7JmzRpdeOGFruf33XefJGns2LF6+eWXtX79es2fP1+ZmZlKSEjQoEGD9Nhjj8lqtbres2DBAt155526+OKLZTabNXLkSM2ePbvWsdWV5tKFjiTFhFqPFfC5JRAAvFl95PSmxlnAj/fSlnwVKcjNliQNvOwq7TAbKrQGavTMRYoNMKq9jNS9O7Rg5iSlp6dTwAeAekY+rhsHG8lF93YxwfpmS5oO59h0NL9ILQL9PB0SAMCL1aqSvGvXrrqKQwMGDJBhVP5P5ZIlS066jIiICC1cuLDOYqpLJQ6HqzuZ5lDAjw3x1+ZDOUqjBT4ANAp1mdObkhKHQ4dzbJKkuEZUwHeKSkiSKTxMGw9mK8capV7tYzwdEgCgCuTj2vH2/u+dAnwtat0iUHsy8rUtLVdn0wofAFCFWleSly9fruXLlystLc3VasDptddeq+3im4w8W2nx3mI2KcCLb+WrK7HHBrKlCx0AaDzI6eUdzrHJbhgK8LUoLKBxdoGXHBWkjQeztTM9T/07GDKZTJ4OCQBQBfLxqSkqOX7RPSHc+y+6t48NLi3gp+ZQwAcAVKlWBfxp06Zp+vTpOuussxQfH88/hFUo231Oc9hPUcF+MpukwhKHLCHRng4HAHAS5PSKle0+p7Huk8SIQPmYTcopLFF6bpGiQ6wnfxMAwCPIx6cuLadQhqRgq0+jGHeuXXSwvvkrTem5RcrIo+tZAEDlalXAf+WVV/TGG2/ouuuuq6t4mqzjA9g2/e5zJMnHYlZkkFWHc23yiz/N0+EAAE6CnF4xZwG/MXaf4+RrMat1RKB2pudpZ3ouBXwA8GLk41OX4szZoY0jZ/v7WtQ6IlC7j+RrW1qOWno6IACA1zLX5s1FRUU699xz6yqWJs1VwLd6f0uAuhITWlogsMa193AkAICTIadXLKURDmBbkeToIEnSrvQ8D0cCAKgK+fjUpWQ3vovu7WNDJEnbUnM9HAkAwJvVqoB/yy23eO2gsd6mbBc6zYWzH3y/OFrgA4C3I6eXl1NYrFxbiUym4zmtsUqOLC3gp2bblGsr8XA0AIDKkI9PjWEYja4FviS1iwqS2SQdyStSDsPHAQAqUatqcmFhoebNm6dly5apR48e8vV1b13+3HPP1Sq4pqS5daEjSbHHbtH3i2svwzA8HA0AoCrk9PKcLfmigqzytdSqzYPHBVl9FBfqr5TsQu1Kz1P3lmGeDgkAUAHy8anJtZUor8guk+n4neCNgdXXolYtArU3I18HCxr3bw0AQP2pVTV5/fr16tmzpyRp48aNbq8x2I674wX85tOFTmSwVWaTpIAQ7c8sVOeQEE+HBACoBDm9vLRsmyQpthEVAqqSHB2klOxC7TycSwEfALwU+fjUOFvfRwU3vovu7aKDtDcjX4fyG1fcAICGU6sC/ooVK+oqjibNMAzl2JpfFzoWs0mRQb46nFusPw/lqHNitKdDAgBUgpxeXlpOaQE/JqTx3IpflbZRQVq544j2HS1Qsd3R6AocANAckI9Pjav/+0bUfY5T26hgrdhyWEeKTDIHhmvz5s21XmZUVJRat25dB9EBALxB86kme5CtxKFie2kXMiHW5rXLo48V8H/fk6EhXWJOOr+vr6+s1qbR0hEA0HgZhqG0Y8WAxnQrflUig/wU6u+j7MIS7cvIV9voYE+HBABAnXD1f9+IBrB1Cvb3UUyIVWk5NgWedrbGjBlT62UGBAbqr82bKeIDQBNRq2ryhRdeWOVtfN98801tFt9kOLvPCfC1yKeZtXaLDCjd3v9+9LWevLr3SeePiY3T3j27KeIDQAMjp7vLLixRYYlDZpMUGezn6XDqhMlkUpuoIK3fn6XdRyjgA4A3Ih/XnMMwXHfNNcYW+JLULjpYaTk2BbQ/R/379lbHHr1OeVmpe3dowcxJSk9Pp4APAE1ErQr4zr75nIqLi7Vu3Tpt3LhRY8eOrc2im5ScwubXfY5ThH/pj8+Q5NN1zydrq/wxaivI16OjzlNxcTEFfABoYOR0d87W91HBVvmYm87F96TIQK3fn6U9R/JkGAb9KQOAlyEf11xmfrFKHIZ8LSa1CGycY861jQ7Syp1HFNCmp8JDjqhV+66eDgkA4EVqVVF+/vnnK5z+6KOPKjc3tzaLblKOD2Db/Ar44f5mOYptKva1qkB+ahHQNFoxAkBTQ053l+rq/75pXVBuFR4oi8mk7MISZRYUq0UgeRkAvAn5uObSco5fdG+sF6Yjg/zkryIV+vgpw960fnsAAGqvXpqUjRkzRq+99lp9LLpRyrUdK+BbG2drgNowm0wqStspSUo91poRANB4NNec7myBH9tIb8WvjJ+PWQnhpdu050i+h6MBAFRXc83H1ZHWBC66m0wmRShHknTE3rR+ewAAaq9eCvgrV66Uvz9Jx8lZwA/yt3g4Es8oStkuSTp87IcVAKDxaI453SjTl25TGcC2rKTIIEnS7iN5Ho4EAFBdzTEfV9fhbGcBv3Hvn8gyBXy7w/BwNAAAb1KrPl2uvPJKt+eGYejQoUNas2aNpkyZUqvAmpLcY13oBFubXxc6klR8rAX+4VwK+ADgrcjpx2UVFMtW4pDFZFJkUFMs4Afqx+3SgaMFKrE75GNpOn38A0BjRz6umbIX3aMbcQt8SQpVgez5WVJgmA5mFigxItDTIQEAvEStKsphYWFuz81mszp27Kjp06dr0KBBtQqsKclpxl3oSFJRamkBPz2niAHzAMBLkdOPcxYCokL8ZDE3vZwVGeSnYKuPcm0lOpBZ4GqRDwDwPPJxzWQVFKvI7pDFbFJEUOMe18UkqWDHLwrufol2Hs6jgA8AcKlVAf/111+vqziaLMMwXF3oBDfDQWwlqSh9b+mPkWK78mz2ZrsfAMCbkdOPS2sit+JXxmQyqXVEoDYdytaeI/kU8AHAi5CPa8bZTWtUcNO46J6/bXVpAT89Vxd0iKLxGwBAUi0L+E5r167V5s2bJUldu3bVGWecUReLbRJsJYar/7ogv+bZB77sxQrztyiz0K7DuTYK+ADgxcjpUmpO6QC2TbH/e6c2kccL+AAA70M+rp6m0n2OU+HudTLJUHZhiY7mFzf6uwoAAHWjVpXUtLQ0jRo1St9++63Cw8MlSZmZmbrwwgv17rvvKjo6ui5ibNRyi+ySpABfS7PuYzYy0Ke0gJ9jU3IULf0AwNuQ00uV7Us3tom2wJekxIhAmSRl5Bcpu6BYoQHNs5s/APA25OOacQ0630RytlFcqHCzTUcd/tqdnkcBHwAgSapVRfmuu+5STk6O/vzzT2VkZCgjI0MbN25Udna27r777rqKsVHLs5UW8Jt7q/OIwNLtT2cgWwDwSuT0UlkFxSoqaRp96VbF39eiuLDSYseeDFrhA4C3IB9Xn2EYri50YppIC3xJirCUbtOuI3kejgQA4C1qVVVevHixli1bps6dO7umdenSRXPnzmWAnWPyjrXAD7ZSwJeO91EIAPAu5PRSqdlNqy/dqiRFBupQVqH2HMlT95ZhJ38DAKDekY+rL9dWooJiu8ym0gHam4pIS6F2FIfpYGaBbCV2WX2aaVe8AACXWrXAdzgc8vUtf8u1r6+vHA5HbRbdZORSwJckRQSUbn/msZaNAADvQk4vlXas//um3H2Ok3Pw2n0ZBa7xegAAnkU+rj5n9zkRQX5NqrvaALNd4QG+chjSXu6SAwColgX8iy66SPfcc48OHjzomnbgwAHde++9uvjii2sdXFPgaoHfzLvQCfA1K8ha2nKAbnQAwPuQ00ulHWuB35QHsHWKDbHK39esIrtDqdmFng4HACDycU00tf7vy2pzbNy43ekU8AEAtSzgz5kzR9nZ2WrTpo3atWundu3aKTk5WdnZ2XrxxRfrKsZGLc9W2koipJm3wJek6ODSYshhCvgA4HXI6e4D2DbFYsCJTCaTElsESpL20cIPALwC+bj6nN2zRjeh/u+dkp0F/CN5MgzukgOA5q5WVeXExET99ttvWrZsmf766y9JUufOnTVw4MA6Ca4poA/846JDrNp9JF/p9IMPAF6HnC5lF5aoyO6QxdS0B7AtKzEiUNvScrX3aL76KNLT4QBAs0c+rr6mOICtU0K4v3wtJuUX2XU4x6aY0KbfsAAAULlTaoH/zTffqEuXLsrOzpbJZNIll1yiu+66S3fddZd69+6trl276ocffqjrWBulXLrQcYmiBT4AeB1y+nHOQkBEMxjA1ql1RGkL/JSsQsaoAQAPIh/XTJ6tRLm2EknH/89sSnzMZleO3nUkz8PRAAA87ZQK+LNmzdKtt96q0NDQcq+FhYVp/Pjxeu6552odXGNn8gtQsb30djda4B+/tTE9t0gOBssDAK9ATj/OOUZLVHDzaH0vSWEBvgr195HDkA5kFng6HABotsjHNeNsFNYi0Fd+Pk1nANuy2kTSDz4AoNQpZbo//vhDQ4YMqfT1QYMGae3ataccVFPhExIlSbL6mOVraZo/KmoiLMBXvhaT7A5DmQXFng4HACByelnOAn50E2zJVxVnCz/6wQcAzyEf10xzGLPGWcBPyS5UflGJh6MBAHjSKVWVU1NT5evrW+nrPj4+Onz48CkH1VRYjhXw6T6nlNlkOt6NDv3gA4BXIKcf58xNTfFW/KokHivg7z1KAR8APIV8XDNNeQBbp2B/H9ddgXuOkKMBoDk7pQJ+y5YttXHjxkpfX79+veLj4085qKbCVcCn+xwX+sEHAO9CTi9V7CgdxFZq2sWAiiS2KC3gH8ktUp6NFn4A4Ank45ppLt3eJUeVtsKngA8AzdspFfCHDRumKVOmqLCwsNxrBQUFmjp1qkaMGFHr4Bo7n5BISRTwy3J2S5BOC3wA8Ark9FJZRaWD1gZbfeTva/FwNA0rwM/iumixj1b4AOAR5OPqszukrPzSLlmb+l1zSRGlBfy9GfkyDMaRA4Dm6pQqyw8//LA++ugjdejQQXfeeac6duwoSfrrr780d+5c2e12PfTQQ3UaaGNEC/zynAUCWuADgHcgp5fKKi4t4Df1lnyVad0iUIdzbNqXUaBOceUHUAQA1C/ycfVll5hkSArwtSjQr2lfdI8L85efxayCYrvScmyKDW26ff4DACp3SpXl2NhY/fzzz7r99ts1efJk15Vgk8mkwYMHa+7cuYqNja3TQBsjH/rALycy2E8mSflFduXZShTExQ0A8ChyeilnAb+5dZ/jlBgRoLV7j7pa+JlMJk+HBADNCvm4+px3zUUG+zX5fGUxm9SqRYB2pudpT0Y+BXwAaKZOuXqalJSkL7/8UkePHtX27dtlGIbat2+vFi1a1GV8jZrlWBc6IRSpXXwtZoUH+upofrEO59oo4AOAFyCnHy8GNPVb8SuTEB4gi8mkXFuJMguK1SKwed6JAACeRD6unmznXXNBzSNnt44M1M70PO09kq+z20R4OhwAgAfUunraokUL9e7duy5iaXLoQqdiUcFWHc0vVnquTW0igzwdDgDgmGab003mMl3oNI9iwIl8LWbFh/lrf2aB9mXkU8AHAA9qtvm4mpw5O7KZdHuXFFE62PyhrALZSuyy+jTtboMAAOWd0iC2OLn8IrssASGS6ELnRM7iSHpukYcjAQBA8gmPk90wycdsUnigr6fD8ZjEYwWCvRkMZAsA8F7N7a658EA/hQX4ymFIB44WeDocAIAHUMCvJ2k5pYO0+ppN8rOwm8uKCiltKZGew0C2AADP84tJllTaks/cxPvSrUrrYwX8/UcLXH0vAwDgTcwBobI5mlcLfOl4K/w9R7jIDgDNEZXleuIs4AdZLU1+YJ2aij7WUuJofpFKHA4PRwMAaO6cBfzm0pKvMjEhVvlZzLKVOHQ4l4vsAADv4xfdRpIUFuAr32bUUC4p8lgBn7vkAKBZaj4Zr4GlZJd2DxPkxy4+UbDVR1YfsxyGdDSv2NPhAACaOd+YtpKOX2BursxmkxLC/SWVtsIHAMDb+B4r4Ec1o9b3ktSqRaDMJimroFiZ+XRFCwDNDdXlepJ6rAV+sB8DzJzIZDKV6QefFn4AAM/yi2kjiRb4UmmBQKKADwDwTs6cHRnUvHK2n49Z8WEBkmiFDwDNEQX8epKafbwLHZTnbDHBLfoAAE/KsTnkExoj6fgYLc1ZqxalxYEDRwvkoBt8AICX8Y1qI6n5tcCXpNbHutHZSz/4ANDsUMCvJ7TAr1pUyLEW+AxkCwDwoL1ZpV25BVoMWX3I2dEhVll9zCqyO5RZxBg+AADv4TAM+Ua1liRFNsO75pwD2e47mi87V9kBoFmhgF9PXC3wKeBX6HgXOkUyDH58AAA8Y3dmiSQpzI9cJElmk0ktw0tb4R+2UcAHAHiP1Fy7zH7+MpsMhQf4ejqcBhcTYlWAr0XFdkMpWYWeDgcA0IAo4NeTlBy60KlKZJCfTJIKiu3KL7J7OhwAQDO1+1gL/HBfCvhOzm50DhfyMxEA4D32ZJVedA/1MWQ2N7+LzCaTSa2PtcLfk5Hn4WgAAA2J/8zqga3Eroy80oIAXehUzNdiVnhgaasJBrIFAHjK8Rb4Dg9H4j2cA9mm20ySmd8xAADvsOfYRffmfNecsx/8PfSDDwDNCgX8epB2rPscR7FNVp/m1zKgupzd6DCQLQDAU27oGaKM5fMU0YyLASeKCvaTv69ZdsMka3x7T4cDAICkMi3wm/Fdc85+8NNybMovKvFwNACAhkIBvx60CPLTC1d1VcbXc2UyUcCvjGsg29wiD0cCAGiuukZblbPmMwX4eDoS72EymdQqvLRAYG3dw8PRAABQyjnwfFgzLuAHWX0UFewnSdqbQSt8AGguKODXg2Crjy7uGKW8jd94OhSv5vzhkZ5DC3wAALyJsx98fwr4AAAvUFhsV0pu6dhpoc38rrmkiCBJ0l660QGAZoMCPjwm+lgXOkfzi1TioO9hAAC8hbOAb23ZWcX25l0oAQB43rbUXDkMyZ6fJf9mXsVw9YOfkS/DIEcDQHPQzFMfPCnY6iOrj1kOQzp6bNBfAADgeRFBfrKaDZl9rdqaQVd3AADP2pySLUkqPrxbzb2X2oRwf/mYTcovstMdLQA0ExTw4TEmk8k1kG06A9kCQJP0r3/9SyaTSRMnTnRNKyws1IQJExQZGang4GCNHDlSqampngsS5ZhMJkX7l94dtzGN4gAAwLNC/X3UNdpPhfs3eToUj/Mxm9Xy2J1y9IMPAM0DBXx4lLMf/MMU8AGgyfn111/16quvqkcP937U7733Xv3vf//TokWL9N133+ngwYO68sorPRQlKhNtLb0tnwI+AMDThnSL12MXRirrxwWeDsUrJEWUdqNDAR8AmgcK+PCoqBBa4ANAU5Sbm6vRo0fr3//+t1q0aOGanpWVpf/+97967rnndNFFF6lXr156/fXX9fPPP2vVqlWVLs9msyk7O9vtgfrlbIG/5UiRCovtHo4GAAA4tT5WwD+QWaASO+PJAUBTRwEfHuXqQieniAF4AKAJmTBhgoYPH66BAwe6TV+7dq2Ki4vdpnfq1EmtW7fWypUrK13ejBkzFBYW5nokJibWW+woFewjleQcUYlD+m3PUU+HAwAAjokI8lOQ1SK7w9CBzAJPhwMAqGcU8OFRUUF+MkkqKLaroJiWAwDQFLz77rv67bffNGPGjHKvpaSkyM/PT+Hh4W7TY2NjlZKSUukyJ0+erKysLNdj3759dR02TmAySYV710uSft5xxMPRAAAAJ5PJ5GqFvy+DAj4ANHUU8OFRPhazwgN9JUlH8oo9HA0AoLb27dune+65RwsWLJC/v3+dLddqtSo0NNTtgfpXuKe0gL9yJwV8AAC8ibOAvycjz8ORAADqGwV8eJyrG518CvgA0NitXbtWaWlpOvPMM+Xj4yMfHx999913mj17tnx8fBQbG6uioiJlZma6vS81NVVxcXGeCRqVsh1rgf/Hvkzl2Uo8HA0AAHByFvDTc4vI0QDQxFHAh8c5B7LNyONHBwA0dhdffLE2bNigdevWuR5nnXWWRo8e7frb19dXy5cvd71ny5Yt2rt3r/r27evByFGRkqxURQdaVOIwtIZ+8AEA8BqBfj6KPva/9L6j+R6OBgBQn3w8HQAQFewnSTpCC3wAaPRCQkLUrVs3t2lBQUGKjIx0Tb/55pt13333KSIiQqGhobrrrrvUt29fnXPOOZ4IGSfRLcZPK3YXaOWOI+rfIdrT4QAAgGNaRwTqcI5Ne4/kq1Mc3QsCQFNFC3x4XPSxLnQyC0okC9eUAKCpe/755zVixAiNHDlSF1xwgeLi4vTRRx95OixUoltM6YX2VfSDDwCAV3F2o7M3I1+GYXg4GgBAfaFaCo8LtvrI6mOWrcQh38hET4cDAKhj3377rdtzf39/zZ07V3PnzvVMQKiRbtGlF9o3HMhSrq1EwVZ+PgIA4A0SwvxlMZuUV2TXkbwi1/hyAICmhRb48DiTyeT6oeEXnezhaAAAQFnRQRa1jgiU3WHo110Zng4HAAAc42Mxq1V4gKTSVvgAgKbp/7d35+FRllcfx7+zTyb7vpEEwhZ22UEFNxQVd+pWtGqtWotWS2uttVXbty0urdVal6pV64raonXFBRFll30LAQIkkH3fM5nJPO8fgZQoIEJgJjO/z3U9F2TmnmfOHULOzJn7ObcK+BIQ9vXBtyepgC8iIhJoJmbHA7BUbXREREQCSmb8/9roiIhIcFIBXwJCQmTHCnxbUm//BiIiIiLfMLFvRwFfffBFREQCy74++EU1LXjbfX6ORkREjgUV8CUgJO7XQkeb74iIiASWCXtX4G8sqqO+1ePnaERERGSf+HA7LrsFr8+gpK7V3+GIiMgxoAK+BIT4cDsmwBIeQ1WTCgMiIiKBJCXaSZ+EcHwGrNihPvgiIiKBwmQykbV3FX6B2uiIiAQlFfAlIFgtZqLDrADklTX6ORoRERH5ugnqgy8iIhKQ9rXRUR98EZHgpAK+BIx4194CfnmTnyMRERGRr5uQHQeoD76IiEigydhbwK9ocONu93MwIiLS7VTAl4ARH24DtAJfREQkEE3cuwJ/c0k9tc1tfo5GRES60/3334/JZOL222/vvK21tZWZM2cSHx9PREQE06dPp6yszH9BykGFO6wkRNgBKG9VmUdEJNjoN7sEjHhXRwF/q1bgi4iIBJykKCd9E8MxDFi+U33wRUSCxVdffcU//vEPhg8f3uX2n/3sZ7z77ru8+eabLFy4kOLiYi655BI/RSnfZl8bnbJWk58jERGR7qYCvgSMfSvwd1Y24/bquj8REZFAM7Fvxyp8tdEREQkOjY2NzJgxg2eeeYbY2NjO2+vq6vjnP//Jww8/zOmnn87o0aN5/vnnWbJkCcuWLfNjxHIw+wr4WoEvIhJ89JtdAka43Ux7ayNen0G+VuGLiIgEnM6NbPNVwBcRCQYzZ85k2rRpTJkypcvtq1atwuPxdLk9JyeHzMxMli5detDzud1u6uvruxxyfKTHhGExm2hpN2GLz/B3OCIi0o0CpoD/xRdfcP7555OWlobJZOLtt9/ucr9hGNxzzz2kpqYSFhbGlClT2LZtW5cx1dXVzJgxg6ioKGJiYrj++utpbFQ/9Z7CZDLhKd8JQG6JXuiJiIgEmn0F/C2lDVQ3qQ++iEhPNmfOHFavXs3s2bO/cV9paSl2u52YmJgutycnJ1NaWnrQc86ePZvo6OjOIyNDheTjxWoxkx4TBoCz90g/RyMiIt0pYAr4TU1NjBgxgscff/yA9z/44IP87W9/46mnnmL58uWEh4czdepUWltbO8fMmDGDTZs28cknn/Dee+/xxRdfcOONNx6vKUg3aFMBX0REJGAlRDgYkBwBwHK10RER6bF2797NbbfdxiuvvILT6ey28951113U1dV1Hrt37+62c8u329dGx9lHBXwRkWBi9XcA+5xzzjmcc845B7zPMAweeeQRfvOb33DhhRcC8OKLL5KcnMzbb7/NFVdcQW5uLvPmzeOrr75izJgxADz22GOce+65/PnPfyYtLe24zUWOnKeio4C/pbTBz5GIiIjIgUzMjmdrWSPLdlRxzrBUf4cjIiJHYNWqVZSXlzNq1KjO29rb2/niiy/4+9//zkcffURbWxu1tbVdVuGXlZWRkpJy0PM6HA4cDsexDF0OobOAnzEMT7vh52hERKS7BMwK/EPZuXMnpaWlXfrvRUdHM378+M7+e0uXLiUmJqazeA8wZcoUzGYzy5cvP+B51Z8v8Oy/At8w9IJDREQk0HT2wdcKfBGRHuuMM85gw4YNrF27tvMYM2YMM2bM6Py7zWZj/vz5nY/Jy8ujsLCQiRMn+jFyOZSECDsOs4HZ7iSvSq3uRESCRcCswD+UfT32kpOTu9y+f/+90tJSkpKSutxvtVqJi4s7aI++2bNn87vf/e4YRCxHylNZiNkEVU1tVDS6SYrsvss5RURE5OiN31vA31rWSGWjm4QIrbQUEelpIiMjGTp0aJfbwsPDiY+P77z9+uuvZ9asWcTFxREVFcWtt97KxIkTmTBhgj9ClsNgMplIdvoobLawttTND/wdkIiIdIsesQL/WFF/vsBjeNvI2nvZX26J2uiIiIgEmrhwOzkpkQAs0yp8EZGg9de//pXzzjuP6dOnM3nyZFJSUpg7d66/w5JvkRTWcSX7ujKtwBcRCRY9ooC/r8deWVlZl9v377+XkpJCeXl5l/u9Xi/V1dUH7dHncDiIiorqcoj/DUgOB2CLNrIVEREJSBP7dqzCVwFfRCR4fP755zzyyCOdXzudTh5//HGqq6tpampi7ty5h+x/L4EhyekDYEeNh+omFfFFRIJBjyjg9+nTh5SUlC799+rr61m+fHln/72JEydSW1vLqlWrOsd89tln+Hw+xo8ff9xjliM3MKmjgJ+rAr6IiEhA6uyDn68CvoiISCAJs3TsLWcAi7dX+jscERHpBgFTwG9sbOzcPAc6Nq5du3YthYWFmEwmbr/9dv7whz/wzjvvsGHDBn7wgx+QlpbGRRddBMCgQYM4++yzueGGG1ixYgWLFy/mlltu4YorriAtLc1/E5PvbEByBABbStVCR0REJBBN6BOPyQT5FU2U17f6OxwRERHZT8uuNQAs2qYCvohIMAiYAv7KlSsZOXIkI0eOBGDWrFmMHDmSe+65B4Bf/vKX3Hrrrdx4442MHTuWxsZG5s2bh9P5v01OX3nlFXJycjjjjDM499xzOfnkk3n66af9Mh85cvtW4G8vb8TtbfdzNCIiIvJ10S4bg1M7Wg8u21nt52hERERkf6271gLw5bYKDMPwbzAiInLUrP4OYJ9TTz31kInFZDLx+9//nt///vcHHRMXF8err756LMKT4yglykF0mI26Fg/55U0MTtPeBCIiIoFmQnY8m4rrWZpfxQUjdLWjiIhIoHDv3oTNDMV1reRXNNEvKcLfIYmIyFEImBX4IvuYTCZyUiIB9cEXEREJVBOztZGtiIhIIDK8bgYl2gFYtK3Cz9GIiMjRUgFfAtKgvZflb1YBX0REJCCNy47DbIKdlU2U1qkPvoiISCAZkewA4Ev1wRcR6fFUwJeANDQ9GoCNRXV+jkREREQOJMpp68zXWoUvIiISWE7YW8BfuqOKNq/Pz9GIiMjRCJge+CL7G7ZfAd/nMzCbTX6OSEREJDTl5uYe9L4+4V7WA++uyCPTKDvouISEBDIzM49BdCIiInIgWTFW4sPtVDW1saawhvF7W9+JiEjPowK+BKS+ieGE2Sw0tbWzo1Kb7oiIiBxv9dUdPXOvuuqqg45xZo8h+dL7mLd6B8/9+IaDjgtzudiSm6sivoiIyHFiNpk4uX8C/11bzJfbKlXAFxHpwVTAl4BktZgZkhbFyoIaNhTVqoAvIiJynLU0duxDM+2muxk4fPQBx3h88O4eA1tsKjc9OpfwA7yyLCvM55UH7qCyslIFfBERkeNoUv/EjgL+9kp+MXWgv8MREZEjpAK+BKxhvaJZWVDD+j11XDyyl7/DERERCUnxaVn06j/koPenNOympK4VT3QveqVFH8fIRERE5FBO7pcAwPo9tdQ2txHjsvs5IhERORLaxFYC1vBeHUWADXu0ka2IiEigyohzAbC7utnPkYiIiMj+UqKdDEiOwDBg0fZKf4cjIiJHSAV8CVjD0mMA2FRcj7fd599gRERE5IAyY/cV8FswDMPP0YiIiMj+JvdPBGBhXoWfIxERkSOlAr4ErOyEcMLtFlo87eRXNPk7HBERETmAlGgnVrOJFk87lY1t/g5HRERE9nNaThIAn2+twOfTB+0iIj2RCvgSsMxmE0PTO9rorN9T699gRERE5IAsZhPpsWEA7K5RGx0REZFAMqZ3LOF2CxUNbjaX1Ps7HBEROQIq4EtA6+yDX6Q++CIiIoHqf210VMAXEREJJA6rhRP3bmb7eV65n6MREZEjoQK+BLRhvWIAWK+NbEVERALWvo1si2pbaNfl+SIiIgHltIEdbXQWqA++iEiPpAK+BLThe1vobC6px6ONbEVERAJSQoSdMJsFT7tBaX2rv8MRERGR/Zw6sGMj2zWFNdQ0ab8aEZGeRgV8CWhZ8S4inVbavD62ljX4OxwRERE5AJPJRMa+PvhqoyMiIhJQ0mLCyEmJxGfAF9u0Cl9EpKdRAV8Cmslk+l8ffLXRERERCVj72ugUqoAvIiIScE7Zuwr/c7XRERHpcVTAl4A3LD0GgPXayFZERCRg7Svgl9W30uZV2zsREZFAsq8P/sKtFfi0X42ISI+iAr4EPK3AFxERCXzRYTainFZ8RsdmtiIiIhI4RmfFEumwUt3UpsVxIiI9jAr4EvCG7d3IdktpPW5vu5+jERERkYPJVBsdERGRgGSzmJk0IAGABVvK/RyNiIh8FyrgS8DrFRtGrMuGp91gS4k2shUREQlU6oMvIiISuE4d0NFG5/M8FfBFRHoSFfAl4HVsZBsDwNrdtX6NRURERA4uM86FCahuaqOh1ePvcERERGQ/+zayXbenjooGt5+jERGRw6UCvvQIozJjAVhdWOPnSERERORgnDYLyVFOAAqqtApfREQkkCRHORmaHgWojY6ISE+iAr70CKOyYgBYVaACvoiISCDLiu9oo1OgNjoiIiIB58xBKQB8vLnMz5GIiMjhUgFfeoQTMmIwmWBPTQvl9a3+DkdEREQOYl8Bv7C6GZ/P8HM0IiIisr8pgzv64C/aXkFLW7ufoxERkcOhAr70CJFOGwOTIwG10REREQlkyVFOHFYzbV4fpfrQXUREJKAMTo0iPSaMVo+PL7dV+DscERE5DCrgS48xKqujD77a6IiIiAQus8lEVtzeNjrqgy8iIhJQTCYTZw5OBuDTXLXRERHpCVTAlx5jdOdGtrX+DUREREQOKbOzD36TnyMRERGRr9tXwJ+fW0672t2JiAQ8FfClx9i3An/DnjrcXvXqExERCVRZ8eEAlNW7cStli4iIBJRxfeKIdFqpampjjVrUiogEPBXwpcfoHe8iLtxOW7uPDXvq/B2OiIiIHESEw0p8hB2A8la93BQREQkkNouZ0wZ2bGb7idroiIgEPL2jkh7DZDIxtnfHKvzlO6v9HI2IiIgcSu+4vavwW01+jkRERES+bl8bnU82q4AvIhLoVMCXHmV8n3gAVqiALyIiEtCy9vbBL2vRy00REZFAc+rARGwWEzsqmsivaPR3OCIicgh6RyU9yvjsOABW7qrG2+7zczQiIiJyMKkxTqxmE60+E7bEPv4OR0RERPYT6bQxIbtjgdynWoUvIhLQVMCXHiUnJYoop5WmtnY2Fdf7OxwRERE5CKvZTEZcxyr8sL5j/ByNiIiIfN2+Njofq4AvIhLQVMCXHsViNjG2d8cqfLXRERERCWx94jv64If1G+fnSEREROTr9hXwVxfWUFbf6udoRETkYFTAlx5nXxud5Tur/ByJiIiIHEqfhI4CviNtILWt7X6ORkRERPaXGh3GqMwYDAM+3FDi73BEROQgVMCXHmf/jWzbfYafoxEREZGDiXBaibH7MJnMrCpx+zscERER+Zppw9MAeF8FfBGRgKUCvvQ4Q9KiiHRYqW/1sll98EVERAJaaljHpvNfFevSfBERkUBz7rAUAFYW1FBap1wtIhKIVMCXHsdqMTOhb8cq/C+3V/g5GhERETmUtLCOq+XWlbbR6lEbHRERkUCSGh3G6KzYjjY6G7UKX0QkEKmALz3SpP4JAHy5tdLPkYiIiMihRNsMvPUVuNsNluZr/xoREZFAM21YKgDvr1cBX0QkEKmALz3Syf06CvirCmpoadNqPhERkUBlMkHL9hUAfJJb5udoRERE5OvO3VvAVxsdEZHApAK+9Eh9EsJJjwmjrd3H8p1azSciIhLImrcvB2B+bhmGoQ3oRUREAklKtJMxWbEAfKDNbEVEAo4K+NIjmUymzlX4i7apjY6IiEggay3cgNNqoqzezcYibUAvIiISaKYN39tGRwV8EZGAowK+9Fgn7+uDrwK+iIhIYGv3cEKyA1AbHRERkUB0ztBUTKaONrUldS3+DkdERPajAr70WCf3S8BsgryyBopq9QJDREQkkI1N7yjgz1cBX0REJODs30ZHm9mKiAQWFfClx4oNtzN67wsMFQNEREQC26gUByYTbCqu1wfvIiIiAWja3s1s31lX7OdIRERkfyrgS492xqBkAD7NLfdzJCIiInIo0U4LY3vHAfCh+uuKiIgEnPNHpGE1m1i/p45tZQ3+DkdERPZSAV96tCl7C/jL8qtodHv9HI2IiIgcynnaIE9ERCRgxUc4OHVgEgD/Xr3Hz9GIiMg+KuBLj9Y3MZze8S7a2n18ubXC3+GIiIjIIZw9NAWTCdYU1rKnptnf4YiIiMjXfG90OgBvrymi3Wf4ORoREQEV8KWHM5lMnavw1UZHREQksCVFOhnfZ18bnVI/RyMiIiJfd1pOEjEuG2X1bhZtr/R3OCIiggr4EgSmDO4o4M/fUkab1+fnaERERORQpg1PA+A9tdEREREJOA6rhQtGdOTq/6xSGx0RkUCgAr70eGN7x5EY6aC22cNirRAQEREJaGcPScFsgnW7a9ldrTY6IiIigWb6qF4AfLSplPpWj5+jERERFfClx7OYTUwb1rEp3rvri/0cjYiIiBxKYqSD8X3iAfhAq/BFREQCzvBe0fRLisDt9fHBeuVqERF/UwFfgsJ5wzsK+B9vKqPV0+7naERERORQpu3N2++rgC8iIhJwTCZT5yr8/6xWGx0REX9TAV+CwqjMWNKinTS6vXyeV+HvcEREROQQzh7a0UZn/Z46CqvURkdERCTQXDwyHbMJvtpVQ0FVk7/DEREJaSrgS1Awm02dq/neWVfk52hERETkUBIiHEzs29FGR6vwRUREAk9KtJOT+ycCMOer3X6ORkQktKmAL0HjopHpAHyyuYzqpjY/RyMiIiKHMm1YGgDvaf8aERGRgPT9cZkAvP7VbrWqFRHxIxXwJWgMSYtmWHo0nnaDt9ZoFb6IiEggO3toClaziU3F9Wwra/B3OCIiIvI1UwYlkRbtpLqpTRvPi4j4kQr4ElQuH5sBwOtfFWIYhp+jERERkYOJC7dz6sAkAP6zWh+8i4iIBBqrxcyMCVkAvLi0wM/RiIiELhXwJahccEIaTpuZrWWNrNld6+9wRERE5BCmj+pof/f2miLaffrgXUREJNBcPjYDu8XM2t21rN9T6+9wRERCkgr4ElSinDbOHdaxme3Ly7RCQEREJJCdPiiJKKeV0vpWluZX+TscERER+ZqECAfnDksBtApfRMRfVMCXgNPY2HhYh9vtPuDjfzCxNwDvriumrL4VALfbfdTnFRERke7lsFo4f0THZrb/Wb3Hz9GIiIjIgfzgxN4AvLOumJqmNv8GIyISglTAl4Dh9bSByUxqaiqRkZHfemRm9T5gsf2EjBjG9o7F027wwpJduN1uMrN6H9Y5D3VeERH5drNnz2bs2LFERkaSlJTERRddRF5eXpcxra2tzJw5k/j4eCIiIpg+fTplZWV+ilj8bfroXgB8uLGEuhaPn6MRERGRrxuZEcPQ9CjavD5eX7nb3+GIiIQcq78DENmn3esFw8fdLy0gIir6kGPdLc3cd8XJeDweHA7HN+7/0aRsvtq1ileWFXDduFTKy0q5b84iHGGuozqviIgc2sKFC5k5cyZjx47F6/Xy61//mrPOOovNmzcTHh4OwM9+9jPef/993nzzTaKjo7nlllu45JJLWLx4sZ+jF38YmRHDgOQItpY18s7aIq7eeyWdiIiIBAaTycQPJvTml/9Zz8vLCrhhUjYWs8nfYYmIhAwV8CXg2J0uHGHhR3WOKYOS6R3vYldVM/9eUwKAI+zozysiIoc2b968Ll+/8MILJCUlsWrVKiZPnkxdXR3//Oc/efXVVzn99NMBeP755xk0aBDLli1jwoQJ/ghb/MhkMnH52Ez+773NzPlqtwr4IiIiAeiCE9L404e57Klp4eNNpZyzd+85ERE59tRCR4KSxWzix6f0BeDZxYWYbE4/RyQiEprq6uoAiIuLA2DVqlV4PB6mTJnSOSYnJ4fMzEyWLl160PO43W7q6+u7HBI8LhmZjt1iZlNxPRv21Pk7HBEREfkap83C1ROyAHhqYT6GYfg5IhGR0KEV+BK0po/uxZML8ymoaiZy9Pn+DkdEJOT4fD5uv/12TjrpJIYOHQpAaWkpdrudmJiYLmOTk5MpLS096Llmz57N7373u2MZrvhRbLids4em8M66Yl5dUcjsXsP8HZKIiEiPlpub2y3nSUhIIDMzE4BrTuzN01/sYN2eOpbtqGZi3/hueQ4RETk0FfAlaNksZn42ZQC3v76WqPHTcXt9qKu9iMjxM3PmTDZu3MiiRYuO+lx33XUXs2bN6vy6vr6ejIyMoz6vBI4rx2Xyzrpi3l5TxK/OySE6zObvkERERHqc+uoKAK666qpuOV+Yy8WW3FwyMzNJiHBw6ZhevLyskKcW5quALyJynKiAL0Ht/BFpPP7ZNrZVwFe7GzhjcKS/QxIRCQm33HIL7733Hl988QW9evXqvD0lJYW2tjZqa2u7rMIvKysjJSXloOdzOBzaXDzITciO69zM9j+r9vDDk/v4OyQREZEep6Wxo83gtJvuZuDw0Ud1rrLCfF554A4qKys7V+HfMCmbV5cXsnBrBRuL6hiaHn3UMYuIyKGpgC9BzWI28csz+3LDq+vZVNLE8Aw3iZEqAImIHCuGYXDrrbfy1ltv8fnnn9OnT9ci7OjRo7HZbMyfP5/p06cDkJeXR2FhIRMnTvRHyBIgTCYTP5jYm9+8vZGXlhVw7Ym9MZtN/g5LRESkR4pPy6JX/yHdft6s+HAuGJHG22uLeeTTbTx7zZhufw4REelKm9hK0JuYHUtT7hcYwIK8cm22IyJyDM2cOZOXX36ZV199lcjISEpLSyktLaWlpQWA6Ohorr/+embNmsWCBQtYtWoV1113HRMnTmTChAl+jl787eKR6UQ6reysbOKLbRX+DkdEREQO4JbT+2Mywae5ZWws0ubzIiLHmgr4EhJqFvwTq9lESV0r6/boBYaIyLHy5JNPUldXx6mnnkpqamrn8frrr3eO+etf/8p5553H9OnTmTx5MikpKcydO9ePUUugCHdYuXR0x94G/1y008/RiIiIyIH0S4rgghFpADw6f5ufoxERCX4q4EtIaG+oYkJWFACLtldS3dTm54hERIKTYRgHPK699trOMU6nk8cff5zq6mqampqYO3fuIfvfS2i57qTemE3w5bZKckvq/R2OiEjQmj17NmPHjiUyMpKkpCQuuugi8vLyuoxpbW1l5syZxMfHExERwfTp0ykrK/NTxBJIbj29H2YTfLK5jLW7a/0djohIUFMBX0LGkBQXWXEu2n0GH20qxevz+TskERER+ZqMOBfnDEsF4Jkvdvg5GhGR4LVw4UJmzpzJsmXL+OSTT/B4PJx11lk0NTV1jvnZz37Gu+++y5tvvsnChQspLi7mkksu8WPUEij6JUVyyaheADzw4Ra1qhUROYZ6TAH/vvvuw2QydTlycnI679fKAPk2JpOJKYOTcVrNlDe4WZin3roiIiKB6KbJ2QC8s66Y4toWP0cjIhKc5s2bx7XXXsuQIUMYMWIEL7zwAoWFhaxatQqAuro6/vnPf/Lwww9z+umnM3r0aJ5//nmWLFnCsmXLDnhOt9tNfX19l0OC18/OHIDdYmbpjiq+3Fbp73BERIJWjyngAwwZMoSSkpLOY9GiRZ33aWWAHI4Ih5Wzh3a0adhYXM8G9cMXEREJOMN7xTAhOw6vz+BprcIXETku6uo63hvFxcUBsGrVKjweD1OmTOkck5OTQ2ZmJkuXLj3gOWbPnk10dHTnkZGRcewDF79Jjwnj6olZAMz+cAvtPq3CFxE5FnpUAd9qtZKSktJ5JCQkAEe2MgC0OiBUZcWHc2LfeAA+31rOrqqmb3mEiIiIHG8zT+sHwGsrCimvb/VzNCIiwc3n83H77bdz0kknMXToUABKS0ux2+3ExMR0GZucnExpaekBz3PXXXdRV1fXeezevftYhy5+dstp/YhyWsktqeeNlfr3FhE5FnpUAX/btm2kpaWRnZ3NjBkzKCwsBI5sZQBodUAoG5MVy8CUSHwGfLChhFIVBkRERALKyf0SGJUZg9vr0yp8EZFjbObMmWzcuJE5c+Yc1XkcDgdRUVFdDgluseF2bp8yAIA/f5RHfavHzxGJiASfHlPAHz9+PC+88ALz5s3jySefZOfOnUyaNImGhoYjWhkAWh0QykwmE2cOSiYzzoWn3eDtNUWUqYgvIiISMEwmEz89oz8ALy8voKLB7eeIRESC0y233MJ7773HggUL6NWrV+ftKSkptLW1UVtb22V8WVkZKSkpxzlKCWRXT8wiOzGcqqY2Hv10m7/DEREJOj2mgH/OOedw6aWXMnz4cKZOncoHH3xAbW0tb7zxxhGfU6sDQpvFbGLasFRSo524vT7mri6itKHN32GJiIjIXqcMSOSEjBhaPT4eX7Dd3+GIiAQVwzC45ZZbeOutt/jss8/o06dPl/tHjx6NzWZj/vz5nbfl5eVRWFjIxIkTj3e4EsBsFjP3nj8EgOcX72T9nlr/BiQiEmR6TAH/62JiYhgwYADbt2/XygA5YnarmYtOSCc9Joy2dh/vbarCkTHU32GJiIgIHavwfzl1IACvLC+gsKrZzxGJiASPmTNn8vLLL/Pqq68SGRlJaWkppaWltLS0ABAdHc3111/PrFmzWLBgAatWreK6665j4sSJTJgwwc/RS6A5ZUAiF4xIw2fAnf/ZgKfd5++QRESCRo8t4Dc2NpKfn09qaqpWBshRsVvNXHhCGhlxYXh9BkmX3seX26v8HZaIiIgAJ/ZLYPKARDztBn/5JM/f4YiIBI0nn3ySuro6Tj31VFJTUzuP119/vXPMX//6V8477zymT5/O5MmTSUlJYe7cuX6MWgLZPecPJsZlI7eknme/3OnvcEREgkaPKeD/4he/YOHChezatYslS5Zw8cUXY7FYuPLKK7UyQI6azWLmguFpZMY4MNuc3PL6Rl5ZXuDvsERERAQ6V+H/d20x63bX+jcYEZEgYRjGAY9rr722c4zT6eTxxx+nurqapqYm5s6dq6vc5aASIhz8dtpgAB75dCu7Kpv8HJGISHCw+juAw7Vnzx6uvPJKqqqqSExM5OSTT2bZsmUkJiYCHSsDzGYz06dPx+12M3XqVJ544gk/Ry09idViZmpOHH995iUihk3h7rc2UlDVzK/OzsFsNvk7PBERkZA1ND2aS0amM3dNEfe+s4m5N5+o3CwiInKc5ObmHvbY3hiMSLazrqyNW15cwu9OicNk6sjZCQkJZGZmHqswRUSCVo8p4M+ZM+eQ9+9bGfD4448fp4gkGFnMJqo+eIS7bvkRjy3cxdNf7KCwqpm/Xn4CYXaLv8MTEREJWXeek8OHG0tYu7uWv769mNN6u474XCogiIiIfLv66goArrrqqu/0OGt0MqnXP87Gcjj1R7+lce2HAIS5XGzJzVUOFhH5jnpMAV/keLppUhb9UmO44831zNtUSskzy3j2B2NIjHT4OzQREZGQ5K4tp+LzF4k46Soe+WwXv3z2x/jcR3ZpvgoIIiIi366lsR6AaTfdzcDho7/TY7fVm1lfC4lTf8Ll19xIa+l2XnngDiorK5V/RUS+IxXwRQ6gsbGRM/pF88yMYfz0zU2s213LRX9fxBNXDKVvYnjnOJvNhsOhor6IiMixVllZSdXSf5N40qW0RMQy6e5XGR3f/p3PU1aYrwKCiIjIdxCflkWv/kO+02PSDYP6dcXsqmpmdX04k3r1PUbRiYgEPxXwRfbj9bSByUxqamrnbdbYNJK+dx9FpHH+3xZS8dafaC1YB0BScgqFBbtUxBcRETke2r2MSzKzsBx2NVkYNSCTjLgjb6UjIiIix4bJZOLMwcm8uryQ6uY21pnUklZE5EipgC+yn3avFwwfd7+0gIio6M7bWz0+PsqrpqQeUq/8I5Oyo8mOMnHfFSfj8XhUwBcRETlOEpwGw9Kj2VBUx/wt5Xx/XCZ2q9nfYYmIiMjXuOxWpg5JYe6aInY1WXANmuzvkEREeiS92xE5ALvThSMsvPOIjorkktEZ5KRE4jNgYX4da8q8gMnfoYqIiISck/rFE+GwUtfi4YttFf4OR0RERA4iI87FuN5xAMRPvYU99R4/RyQi0vOogC89WmNj42Ed3cFqNnPW4GTG9+l48bG2uJGEi35Fi+e7998VERGRI+ewWpg6JBmATcX1bC/vnlwvIiIi3W98nzgSHD7MDhezF9VQ29zm75BERHoUFfClR9q/V31kZOQhj3397NvbvUf9vCaTiQnZ8UwdkozZBOEDT+KHL62jvKH1qM8tIiIih69XrIvRWbEAfJpbRl2LVvSJiIgEIrPZxPgEL966Mkoa25n56mo87T5/hyUi0mOoB770SAfrVX8g9TWVzL72LNrbjW57/pyUKJwmL3NX7GBDMVz8+BKev24sA5Iju+05RERE5NAmZsdTVNNCaX0r728o4bLRvbBatD5FREQk0DgtUP7v35N90xMs3l7F79/dzP9dNNTfYYmI9Agq4EuPtq9X/aE4WpqPyXOnRjkofekXnHjXyxRUtzD9iSU8PmMUkwckHpPnExERka4sZhPnDkvhtRW7qWhw89mWcs4cnIzJdOz3qCksLKSysvKoz5OQkEBmZmY3RCQiIhLYPJUF3D4+hgeW1PDSsgIGJEdw9cTe/g5LRCTgqYAvchS8tSW8cu1IZr21hRU7q7nuha/4w0VDuXKc3oiLiIgcD5FOG2cPTeHttUXkljYQG25n7N7N8o6VwsJCcgYNoqX56BcJhLlcbMnNVRFfRERCwrh0J3dMHciD8/K4951NJEY6OHtoqr/DEhEJaCrgixylGJeNl64fx13/2cDcNUXcNXcDRTUt/PysAcdlBaCIiEioy4xzccqARD7Pq2BJfhVRThsDU45dW7vKykpampuZcedDJGf2PeLzlBXm88oDd1BZWakCvoiIhIybT+lLYVUzc77azU9fW8sLP7RxYt8Ef4clIhKwVMAX6QYOq4W/XDaCXnEu/jZ/G39fsJ2i2hYemD4cu7WjF6/b7cbjObwN9mw2Gw6H41iGLCIiElRG9IqhttnD2t21fLy5FIfNTO/4Q7fZO1rJmX3p1X/IMX0OERGRYGMymfjDRUOpaW7jo01l3PjiKubcOIGh6Yfe305EJFRply+RbmIymZh15gAenD4ci9nEW2uKuOa5FdS1eHC73WRm9SYyMvKwjsys3rjdbn9PSUREpEeZ1D+BAckR+Ax4f30Je2qOzT44IiIicnSsFjOPXjGSidnxNLq9XPPcCnZUNPo7LBGRgKQV+CLd7LKxGSRHO/nJy6tYuqOKS59awuOXDaG8rJT75izCEeY65OPdLc3cd8XJeDwercIXERH5DswmE2cNTqHNW8yuqmb+u7aY80ekkRl36NwrIiIix5/TZuHpH4zmymeWsbGonu8/s5w5N06gd8KxvYJORKSn0Qp8kWPglAGJvPHjiSRFOtha1siMF9ZgS+qDI8yFIyz8Ww4VGURERI6UxWxi2rBUese78PoM3llXzLbyBn+HJSIiIgcQ6bTxwnXj6J8UQWl9K1c+s4zCKl1BJyKyPxXwRY6RIWnRvDXzJAYkR1De0EbK9x9gd22rv8MSEREJelaLmWnDU8lOCKfdZ/DBhlJWFdRgGIa/QxMREZGvSYhw8OoNE+ibGE5JXUcRf3e1ivgiIvuogC9yDKXHhPHmj09kXFYMZoeLD3Or2VRc5++wgo7b7aaxsfGwDu0tICISGqxmM9OGpTKiV8eGeIu2V/LZlnJ8quGLiIgEnMRIB6/dMIHsxHCKalu44mkV8UVE9lEBX+QYiw6z8Y/vD6Nx42f4DPg0t5xlO6q0CrCbaINgERE5GLPZxKkDkzhlQCIAG4vrWVxhxeyK9nNkIiIi8nVJUU7m3DCB7ISOIv6lTy1le7k2thUR0Sa2IseBzWKm6v2HmXz2Baze08jyndXUt3o4IycZi9nk7/B6NI/How2CRUTkkE7IiCHKaeXDjaWUt5pJve4x1pW5GeXvwERERKSLpCgnc26cwIxnl7OtvJHL/7GUF68fx5A0ffguIqFLBXyR42hcZhQxES4W5JWTW9JAk7udc4el4LBa/B1aj7dvg2AREZEDyU6M4PKxGby7ehf1EXH8fmE15aYtzDpzADaLLkoVERE5HnJzcw9r3N0Tw/n9F63sqGnjsicX85tJcQxMsAOQkJBAZmbmsQxTRCSgqIAvcpwNS48mwmHlw40lFFY38+9Ve7hwRDoRTv13FBEROZYSIhycluzl5ffmE3nCOTz5eT6LtlVy//RhWtknIiJyDNVXVwBw1VVXHfZjTHYXSZfeC72GcOe8PVT85/9oLVxPmMvFltxcFfFFJGSoYijiB30Swpk+qhfvrCumsrGN11fu5sIT0kiIUFsXERGRY8lqhuqPHud3N1/J02sa2VBUxwV/X8yNk7O57Yz+OG26Kk5ERKS7tTTWAzDtprsZOHz0YT/O64OllT7KCSP1yj+Swx4+fOBmKisrVcAXkZChAr6InyRHObl8TAZvry2iptnDmyv3MG14Kslh/o5MREQk+J2YEcZlp43mvnc38cGGUp78PJ95G0u574IhnZveioiISPeKT8uiV/8h3+kxvfr7mLexlPyKJnLpRfiQ049RdCIigUkFfJGj1NjYeMRjosJsXDYmg/fWl1BU28J/1xZxSt+Ybo5QREREDiQpyskTM0bz0aZSfvv2RnZWNnHNcys4bWAid08bTL+kCH+HeNwVFhZSWVl5VOcItN7E3TEnCLx5iYiECqvZzDlDU/k0t4wtpQ0knDeLt7Y0MnKkgclk8nd4AU05UCQ4qIAvcoS8njYwmUlNTT3sx7S3e79xm9Nm4aKRaXyyuYytZY0s2F5L9MTLMQyjO8MVERGRg5g6JIUJ2fH8bf42/rVkFwvyKvhi2xdcPSGL26f0J8Zl93eIx0VhYSE5gwbR0tx8VOcJpN7E3TUnCKx5iYiEGovZxFmDk2lvqmVbg4WX1jdgi8zlN9MGYTariH8gyoEiwUMFfJEj1O71guHj7pcWEBF16I3v6msqmX3tWbS3H7gobzWbOXtICpHOKlYV1BAz+Wrue38r9186EpvFfCzCD3hutxuPx/Ot4w7nCggREZFvEx1m47fnDWbG+Ez+9MEWPs0t44Ulu3hrTRG3ndGfGRMycViDuz9+ZWUlLc3NzLjzIZIz+x7ROcoK83nlgTsCpjdxd8wJAm9eIiKhyGQyMTy2neX/fZ6403/Ec4t3Utno5s+XjsBuDc33zYeiHCgSPFTAFzlKdqcLR1j4Icc4Wr79E2+TycTJ/RIIM/v4Mr+a/6wtpbJ5JY/PGEWEI7T+q7rdbjKzelNeVnrYjznQ1Q0iIiLfVXZiBM9eM4bF2yv5v/c2s6W0gd+/t5l/LtrJ7VP6c/HIdH+HeMwlZ/b9zv2JA10wzklEJFQ1fPU2v7vrFzz+VR3vrCumprmNJ68aHXLvmw+XcqBIz6ffbiIBZmhqOHMf+jlZV97Hwq0VXP6PpTx/7ViSopz+Du248Xg8lJeVct+cRTjCXIcc+21XN4iIiByJk/ol8P5PJ/H6V7t5dP5WimpbuOPf63lqYT4X97MBulz/UHJzc4/6HOq3KyIiB3NKVhijhgzg5pdX8eW2Sq58ehnPXzeWhAiHv0MTEel2KuCLBKCW/BU8d/UIbn1jE5uK67n4iSW8cN1Y+idH+ju048oR1j1XN4iIiBwJi9nE98dncsmodF5cuosnPs8nv6KJP1dAyjV/pbTFRLqhDfT2V19dAcBVV1111OdSv10RETmUUwYk8toNE7juha/YUFTH9CeX8OIPx5EVf+j3kHJkuuPDeejeD+i1Sa+EChXwRQLUsLQo5t58Etc+v4IdlU1c8uQSnr56DBP7xvs7NBERkZDitFm4cXJfrhyXybNf7uQfC7dDSj8WV8DOVXs4sW8C6bFh/g4zILQ01gMw7aa7GTh89BGfR/12RUTkcIzIiOE/N5/ID55bTkFVMxc/sYRnfjCa0Vlx/g4taHR8OG/imp/MwhIRh8UVjSU8BrMjoss4w+umvbmO9uZafE21eOvLMTzub5yvuz6g1ya9EkpUwBcJUI2NjcRFRPDiD0Zw6xsbWbOnnh88t5w/nD+QaUOTO8fZbDYcjtC9TNDrM7BEJlLX6sVjbcNqNuG0WbSJkYiIdLtIp42fnTmAkRH1XHz3k8SOu5jiulb+vXoPWfEuJmbHkxxCLe8OJT4tS/12RUTkuOiTEM5/bj6R619YyYaiOq58Zjl/uXQE549I83doPVZdi4fC6mbK61spaIwl42dvYLZ/98UKLotBlK3jiHP48FXu5M0HZnXLB/TapFdCiQr4IgHG62kDk5nU1NT/3WixkXDeLMJzJnHn21u48fZfUb/83wAkJadQWLArJIr4rZ52dlc3s6emhcpGN9XNbbR6fPT6yfP8Z2MNUNM51m4xE+OyER9hJy06jKx4F3b/hS4iIkEkymGmdsFzXHHReew2JbCpuJ6CqmYKqprpmxjOhOx49eA9TIZh0ORup6nNS1Obl2Z3O23tPmrqzESfeAWvbmjgi+qtOG0WwmwWwuwW4lx24iPsJEQ4SIx04LRZ/D0NERHxs6RIJ6/fNIHb5qzlk81l3PraGgqrm/nJqX3V6u4wtPsMCqub2VnZRGF1M3Utnv3udWG2gwmDCKcNl92Cy27FaTV32RLI4zVo8bTT3Oalua0dt9dHc7uJ5nYTpa1AgwUYQNqPnuKJr2r5nr2USf0TCD/KzYe1Sa+EAhXwRQJMu9cLho+7X1pARFR05+2GYbB0Vz3rS5qIPfVaTrr8J4xNtfH7Kyfh8XiCtoBf1+Iht6SjMFJW38qBtqo12j047HZMJhNen0G7z6Ct3Ud5g5vyBje5JQ0AxIZZiT39R2wormdC/3C9kBMRCWFH08d132NdVjijfzKjM2NZvrOaLaUN5Fc0kV/RxICkCMZnxxMXro+P92n3GVQ0uCmu6/ggvqqxjeqmNry+A2V3KzGTruLfuY2Qu+2Q502KdJAV7yIzLpyseBdZ8S76JUXQPym09g4SEQklB8vjNw424fSG8+7WJh76KI/VWwu5aXQ0VvOB3/uFcu9zwzAob3CzpaSBvLIGWjztnfeZTZAaHUZqtJOGgo189uz/ceWt9zDyxFMO+/zNbV5qmjxUNbmpbGyjuK6FqkY3tvhefLqzhU93rsJmhmFJDsanO5mY4STCfvhX0ndXT36RnkAFfJEAZXd+cwPX0wZHEBtZy8KtFWwua6axzYHJFnyX6ru9PlyDTuGdjZUU17d1uS8u3E5mnIvkKAfx4Q5oqWX2VZP53ZvLiYyOAaDN66PR7aW6qY2KBjeF1R3F/5oWL1FjL+LK59aQk7KdS8dkcNEJacRrlaSISMjozk1WGxsbAYhx2Zk6JIUxWR2F/G3ljWwtb2RbeSM5KZGM6xNHjCs0C/k1zW3kVzSyp6aF4toWPO3fLNabTOCyWwi3W3HZLTisFloba9m46GMuu3Q6sfEJtHraafW00+Rup6a5jarGNioa3bR5//eB/Ve7arqc12YxkRZhIf7cn7Gt3oxR3UxylFNt9kREerDDzeMRI88lbspNzN/ZwnsLllLx9mwMd9M3xoVi7/N2n8HWsgbWFNZS0fi/HvVhNgv9kiLoHe+iV6yrM1+uKqjHW7WH77r+zWW34rJbu+wTtG7ZQt544WmcfUYS1nccxKayutTN6lI3TyyvpHn7cpo2fUbLjlXgaz/E2f9n3+sxkWCmAr5ID3NCRgwRDivzNpVSWOMmZcaD7KlpISci4tsfHODqmj28uHQXzy3eSeIFd3QW7zPjXPRPjiArzkWk09blMfWeb74Jt1vNxFntxIXb6ZcUwcS+8bR62tlRWsPbb71F3IjT2VLawP+9t5kHPtzCxSPTuWFyH/pppZ6ISNDrjk1Wc1cs5MN/PUpra2uX2+MjHJw7LJWKBjfLdlSxo7KJ3NIGtpQ1MDg1inF94oj6Wh4LRtb4XuzyRLBxeQFVjV0/iHdazaTFhJEU6SA+wkF8hJ1opw3z11ZG7tlWxRcfP84Ns3/IqFHDDvg8hmFQ2+yhoLqZgqomCquaO/+eV9pAfauXgjovEcPOYH0trF9ThMkEiREOUqOdpMWEkRYdRoRTb4lERHqK75LHS1p8rKg0E9b7BIb8Yg4nJXkI3+9Xfqj1Pnd72llXVMf63bU0tXUUxy1mE30TwslJjSIzzoXlIFcqdBdvUx0tO1Zy+plTGTAsngZvG8XNZnY3m6nHRnjOyYTnnIzTYtAnop0+ET7CDtIp72Cvx0SCkV6tivRA/ZIimO5I5911xZCczeXPrebx74/m5P4J/g7tiFQ0uHl20Q5eWVZIo9sLgLeunAlD+zIsI56osKMvdjhtFvomhFH53p9Z9/zdzN9exxtf7WZDUR2vr9zN6yt3c0ZOEjdOzmZcnzi11xERCXJHs8lqWWH+Ie9PjHRw/og0SutbWbajioKqZjYV17OlpIEh6VGM7R1HxFH2ew00bV4fW8saWEcW6T96igIP4GnDbIJesa7O1XwJEfZuy7Emk4nYcDux4XZOyIjpcp9hGBTVtvDeorX86v7HGHzW92kw7DS0ejtX7K/bUwdAdJiNrLiO1jv7rzgUEZHAdTh5vBfQu8HNO+uKaXB7WVgRxjlDU8iIcx2fIAOExwcrdlazurAGt9cHQLjdwvCMGIalRxPmh71k4tOyyBjQ8e83eO9tFQ1uckvrySttoLmtndw6K3n10C8xgpFZsaREde0+8G2vx0SCSXC9cxAJIanRYUwfnsg/3/2SurQB/OC55fzqnBxumJQdsMVnt9uNx/O/zXAa3V5eWLaHfy3bTYun44VE/6Rwrh6VxDWnX8CYt1fi6Ibi/ddFOa1cPSGLqydksaqgmqe/2MHHm8uYv6Wc+VvKGZMVy8zT+nHqwES/fy+//j07FJvNFrR7IYiI9EQpUU4uOiGd4toWlu6oYk9NC+v31LGpuJ7hvaJJO7wrwwNaVaObtbtryStr2Nsex4Xhayfe6mF0ThbZCeF+2WTWZDLRK9bFuHQndUvmMHHGZfTqP5CGVg8lda0U17ZQXNdKZYObuhYP64vqWF9Uh8VkIi3GSVZ8ONmJ4cSGaOsjEZFgkRjp4PIxGby7vpjyBjdvrS3i5H4JjPzaB7/ByO01iBo3nXnFNtp8VUBHS9oxWbEMSI485qvtv6vESAeJkYmc1DeB/IpG1u2upbiula17WxNmxrkY2zuW9Jgwv79PFzneVMAX6cEiHBZKX72T215cwtvry/jTB1tYuauGB783POB67brdbjKzelNeVgpmK5EnnE30iVdgCY/puL94K3VLXqMg/ys+3fuY9nbvMY9rdFYc/7g6jp2VTTzz5Q7+vWoPKwtquO6FrxiUGsXM0/pyztBUv7y46fI9OwxJySkUFuxSEV9EJMCkxYQxfVQvdlc3s3RHFSV1rawprGW9yUbM5GtocPv8HeJ3YhgGu6qaWbu7lsLq5s7bY1w2Ypr38OUTP+fUXz7I4NQDt77xp0injUinjQHJHW3z2rw+9tQ0s6uqo/VOfauX3TUt7K5pYdH2ShIjHPRLjiBy72fpR7thXihvligi4i8RTiuXju7F/C3lbClt4MttlZTVtzIoSLvaGYbBe+tL+P28CmJPu442X0eOntAnnv7JEZgDvPhtMZsYkBzJgORIKhrcrCmsYUtZA4XVzRRWN5Ma7WRidry/wxQ5rlTAF+np2j383/kDGdk7gT+8v5mPN5ex8dEv+duVIxnTO87f0XXyeDyUl5Xxgyc+Y3WpmwZ3x7LDaKeF8VlR9Jl4CqbvnQpAfU0ls689i/YDbHR3rPRJCOdPFw/j9jP68+yinby8rIDcknpueXUNfRK2cvMpfbloZPpxvay+43tWyn1zFuEIO/Rlnu6WZu674mQ8Ho8K+CIiASojzkWv2DAK9xbyy+rdRE+8lJs/KOeGxq1cP6lPQPfIb/P6yC2tZ+3uWmqbOyraJqBvYgQnZMSQFuNk9Wfr8TXV+jXO78JuNZOdGEF2YkRHT/0WD7sqm9hV1czummYqGt17N/izk3zVQ/z4oZdoyv0Co63liJ4vFDdLFBEJBFaLmbMGJ5Mc5eTLbRVsLWukzGbFGp3s79C61drdtfzfe5tZVdCxsbu3voIJvWOZOKLfN/ab6QkSIx2cNSSFCdnxrCyoYXNJPSV1rcxdU0QMGdiT+/o7RJHjQgV8kSBgMpm45sTejMqM5dbXVrOrqpnLn17G7Wf05+ZT+2K1+L+X65Id1aRc81cWFnSs1HPZLUzIjmdwatQ3Vrc7WpoPdIrjIinKya/PHcRPTu3LC0t28fziXeysbOKX/1nPXz/dyo2Ts7libCZh9uPXDsAR5sIRFn7cnk9ERI4dk8lEVnw4mXEuvlqfy8JNuyE5m0fnb+OFJbu4cXI2157Ym/AA6pFf1+Jh/Z5aNhXXd/bOtVvNDE2LYkSvmG7ZqyYQmEwmYl12YjPtjMyMpaWtnfyKRraVN1JY3YQzfRDO9EEknXMLvVw++kb4iHUc/mKDUNssUUQk0JhMJk7IiCExwsH7G0qo80DKNY+wsriVUaP8Hd3RKalr4aF5ecxdUwRAmM3ChQPCePCH08l69LUeWbzfX1SYjdNzkhjfJ46vdlWzoaiOWiOC1GsfZbO7hexWT0AvghA5WoHzzkBEjtqwXtG899NJ/OatDby9tpi/fLKVeZtKeWD6cIamR/slpo1FdTwwbwtfbqvEkdIPm8XEmKw4RmbGYAuADxYOJsZl5/YpA/jRpGxeW17I01/uoKSuld+9u5m/f7adH57ch6smZBEdJEULERE5vkwmE2kug5IXbuOxt7/k7Xwv28sbeeijPJ79cgczxmdx9cQskr+2YdvxYhgGBdXNrN9Tx87Kps7bY8JsnJARw6DUqKDf7DXMbmFoejRD06NZOv99PlqwiPTTr6bZsFHQZKGgyUJqtJMTMmLomxgRcL2ERUTkwNJjw/j+uEze+iqfaiL506Iaito3cte5g/yyb8vRaGlr5x9f5PPUwnxa9+4rd8modH45NYfi/M084HX7OcLuFe6wcurAJEZmxvL+knWUG5FUtIfx0tICxmTFMjorNiAWMIp0NxXwRYJAY2Njl6//b1o/xmVGcf/H29lUXM8Ff1/EtROzuOPsQcdt5fju6mb+8nEeb68tBsBqNlG94r/cevMNxERFHZcYukOEw8oNk7O5emIW/1m9h6cW5rO7uoWHPsrjyc/zuXhkOldNyKJ3rF2bzYqIyBEwODEjjJvPH8m764p55NOt7Kpq5u8LtvPUwnzOHZbKNSdmMSoz9rhs2GZ2hLOt3synSwuoa/lfXsuMczEiI5o+8eEhuXGcnXbqV8zlwnPPJmXIONbvqWNrWQMlda2U1JUS4bAyJiuWIWlRKhyIiPQAEU4rk5O9vDj3A6LGXsS/lhawbEc1f7tyJANTIv0aW2FhIZWVlYcc4zMMviho4eUNDVS3dBTucxJsXHdCFP3jfBTnbz7qfVsCWXSYjYEUs+6FJxh0/Z+p8zlYtrOaTSX1nDIgkb6JEf4OUaRbqYAv0oN5PW1gMpOamnrA+82uGOLOuIHwwafw3JICPtxUyi/PzuHCEenH7BK6/IpGnvw8n7fXFOH1dVxWfsGING4+uReDZ08j7Kc/PibPe6w5bRZmjM/i8jEZvLe+hMcXbGdbeSMvLSvgpWUFtJdtpXr5f2neuhi+ZfNdbTYrIqHoaN9EBvObUOjYsO2ikemcNzyVTzaX8fziXazYVc0764p5Z10xveNdXDQynYtHppMV371t1ZrbvHyaW85Li6rpdevLrK+1Ah7sVjODU6MY3iuaWJe9W5+zpzKZIDU6jNToME7ul8CGojrW76mj0e3l860VrNhVzeisWIalRwf0lYYiIgIWE9R89iwPzrqOp9Y0kVfWwAV/X7T3Suw+fvk9XlhYSM6gQbQ0H7ytrCN9ELGn34AjbQAA3royaj5/noIti/joAOO/vuAvmHjKdzLCUUV431F8ua2ShlYv760voW9iOKcOSCLCqbKnBAf9JIv0YO1eLxg+7n5pARFRB2+Rs720jnlrdlJCEj97fR1PfLadG07OZOqgxANe7v1dVoi73W7a2tpYV1TPS8uL+Di3gn3dYCf0ieFnp2czJDWyR75ocLvdB1xVP6V/NKf3G8XynbW8sbqYz/IqIXkAiRfcgdN6JwMSwxiQ5CLeZf3GKkVtNisioaa+ugKAq666qlvO1xPzyXdhtZg5Z1gq5wxLZWNRHc8v3sUHG0rYVdXMI59u45FPtzEoNYpJ/RM4uV8C4/rEfefL/Q3DYGtZI4u3V7Ikv4ol+ZU0t3VsLm+y2Ii2+RjdN4WclEgVoQ8h3GFlQnY8Y7Ji2VRcz8qCGhrdXr7cVsmqghom9IlncNo39/oREZHAMirVyYeTR3PHv9fxeV4FD8zbwrvrinlg+nCG9Tq+rWgrKytpaW5mxp0PkZzZdYPWJi9srLWwp7kj71tNBjlR7fTLiMUybBYwq8v43BUL+fBfj9La2nq8wvcLkwkGJEfSJyGcFTurWV1YQ35FE7urCzixXzzD06ND8upBCS4q4IsEAbvz0Juc9o5vo/ifPyFy1HlET7yUbRXwy7dymfWvhTSu/YimzZ/jrSvrHH+4K8R3lddx0oxZ0Hsctvhenbc3b11K3bI3eb1kK69/7THt37I6PVC43W4ys3pTXlb6rWMtEXFEDD+L9NN/QJPHx/qSJtaXNBEfbmdgSiQDUyK1oY6IhKyWxnoApt10NwOHjz7i84TKm9D9DU2P5i+XjeD3Fw7ho02lvLWmiMXbK8ktqSe3pJ6nv9iB3WImOzGcvokR9E2KICvORWy4DZfditlkwu1tp77FS1FtMzsrm9hR0cS28kaqm9q6PFdGXBjjks089otrmf67v9LLT3vn9ERWi5kRGTEMTY8mt7Ser3ZWU9/q5bO8clbvruHEvvH006X8IiIBLTHSwfPXjuXfq/bwxw9y2VxSz4WPL+LaE/tw2xn9iXYd3/dzyZl96dV/CABNbi8rC2rYUFpH+96r3IekRTExO/6Qm96XFeYfl1gDhc1i5qR+CQxMiWR+bjml9a18nlfBtrJGzhqcTJT2r5MeTAV8kRDQ7vVieFq59ZaZ2FyRbCxpYkNJE61RScRMvpqYyVeTEG4jPdpOvANeumsGza3uLgX8Vk87JXWtbCmpZ+2eWhZvr2RTUT220ZcAHT3us+OdjEiLIP7E6XDt9C4x1NdUMvvas2hvN/C3w1m92djYSHlZKffNWYQjzHXIsfvmdvvtt1PlsZFb2sDOyiaqmtr2rmysIj0mjJyUSDKjtJJRREJTfFpW5xvRIxFqb0L3F+6wcsmoXlwyqheVjW4Wb6/ky22VLNpWSWl9K1tKG9hS2vCdzum0mRnbO46T+iVwUt8EhqZHsWbNGh6uLDhGswh+FrOJoWnRDEqJYkNRHSt2VlPb7OGDDaWkx4QxyKnVfyIigWj/Nn19zfDwlFieX1vPl4WtPLd4J6+v2MUlgyI4t184DuuBf5cnJCSQmZnZrXG1tLWzqqCGdXtqO9vT9ooNY3L/RBIjdTX3wSREOLh0TC827KljcX4lRbUtvLK8kMkDEhicGqXV+NIjqYAvEkLsTheRkZGcGBnJuL4+tlc0sqm4nqKaFiqbPFQ2dbSLSb/xaUbO/pIIhxWbxYSn3aDRfeCV862FGzj79EkMSo/Hbj14cdrRcvAefsfLt+0ZcCBWu/2QVzfA/+ZmNpnITowgOzECt7ed7eWNbCltYE9NC0W1HYfZBAkX/orPt1YydYRLrQlEROQ7SYhwcOEJ6Vx4QjqGYbCnpoVt5Q3klzexvbyRPbXN1LV4aGlrxwDsFjORTivJUU6yEyPomxhOn4RwBqZE4rAen43tj0ZP3DvBYjZxQkYMg1IjWV1Yy+qCGopqWyjGSuzpP2LNxqOP6VgUikREQs23tflz9hlF7KnXQVIfXlrfwPOLd1K39A2aNn6G4el6RWCYy8WW3Nxu+d1si89gdbWF3Xt2dhbuU6KcTMiOIzPOpQL0YTCbTIzIiKF3QjgfbSqlpK6VT3PL2VHRxOk5SYe8ckEkEOknViREWS1mclKiyEmJornNS2FVM0W1LZTUNVNR24jZ5vxG0d5pM9M/KZIhaVGMz45jZKqLPqnnkXPF6kMW7wPF4e4ZAEd/xYDDamFIWjRD0qJpaPWQV9axOrKqsY3wnJO55Y1NxL+/jQtOSGP6qF4MSdNKABER+W5MJhMZcS4y4lycnuPvaLpXMOyd4LBamJgdz5DUKL7YVkF+RRNRYy/ivhU11Dzwc5o2f37E5+7OQpGISKg6nDZ/hgGFTV4211lojkwg/qyfkDz1ZnpH+MiOaCfS1nGV4CsP3EFlZeUR/15ubvOyYEsF/1hYRdqPnmRnI4BBUqSDCdnx9I5X4f5IRIfZ+N7oXqwurGFZfjU7KpsoWV7I6TlJ9EtSezvpOVTAFxFcdis5qVHkpEbhbmnirgvPobCsCo/JhtdnYLOYiXXZiA6zdXnR0FM3Evy2PQOge68YiHTaGJMVx5isOIoqa3numafpc9plVDW18fziXTy/eBc5KZH8YGJvLhqZhsuuX80iIhLagmnvhKgwG+cNT+PT+fNZW2XGFt+LhPN/Qc73ZjEqzkvkd2zJ2x2FIhER+Z9va/OXAYxv97GxuJ51u2upbfGwvcHC9gYLqdFOEmL6Y0vIwjC+2+KvfW3xPtxQyudby2n1+AAwfO2kh5uYOCiD9JgwFe6PktlkYkxWHFlx4Xy8uZTKxjbe31DCsPRoJvdP8Hd4IodFVSIROQCDWJeNiAh9It3dEsJt1Cz4Jzve/itrilv4z+o9fLy5jC2lDfz6rQ3c/2Eul47J4NoTexNr93e0IiIi/hVMeyfE0kTx83dx6l0vsrs9mkq3mfllDib0iWNUZixmswo0IiKBymoxc0JGDCN6RVNY3cy6PXXsrGyipK6VEqykXf84171Tzoi1yxmYHEl6bBgJEQ5cdgsmE7R5fVQ3eSitayG/ooncknp2VDZ1eY7MOBdjkkz87bbL+d7sp+gVe+i92OS7SYx0cPnYDJbtqGZVQQ0biuoormthVKS/IxP5dirgi4j4gdVs4rScJE7LSaKu2cObq3bz0rICCqqa+eeinbywZBfnDU3CGpfu71BFRESku7R7ybQ1curY4czfUk5hdTOL86vYVt7IlEHJ2pRQRCTAmUwmsuLDyYoPp7HVS35FI5sLyyhtaKMeJ19u69ho/nANTI5kyuAkzhmaypC0jg3l/1pffgxnENqsZjMn90sgIzaMjzaVUdXYxmdNNsKHTfnOV1CIHE8q4IuI+MH+7YcswBUjk7jshEQW51fz4vIilu6s4b/ry0j70ZN8klfNif1txIVrSb6IiEgwiAqzcdEJaeSWNPDFtgrKG9zM+aqQMVlxjO0Ti9Uc+HsLiYiEuginlREZMcS3FvHwH67gtQ+/xBeVxvbyRkrqWqhuaqPF045hgN3a0ZY2KcpJn/hw+idHcEJGDDEuvcfzh6z4cGaMz+SjzaXsrm4h4dzbeXR5LU8M9RKhDW4lAOmnUkTkOPJ62sBkJjU19ZDj7KkDiJ54Ga7+E8ivamVHdQFD06IZ3yeOcL2gEBER6fFMJhOD06LIinexIK+c/IomVuyqJr+ikTMHJ5Mc5fR3iCIicrjavQyItzNqlPYm6SnCHVYuPiGd+au2sLHGxBeFrZz/2CIeu3IkQ9Oj/R2eSBeqAomIHEftXi8YPu5+aQERUYd+UVBfU8mf77iRCbOeprC2jQ1FdWwprWdMVhyjMmOwWrQ6T0REpKcLd1g5b3ga28oaWJBXQVVTG6+v3M2YrFjG9YnTanwREZFjxGQykRPtY/7jdzP0xr+ws7KJS55Ywq/PzeGaE3trA2EJGHo1KCLiB3anC0dY+KEPpwtPxS6m9Itm+qh0kiIdeNoNlu6o4uXlhRRUNX37E4mIiEiP0D85kqsnZDEgOQLDgK921TBnxW7K6lv9HZqIiEhQcxfl8pczEzlzcDJt7T7ue3czN720itrmNn+HJgKogC8i0iP0inVxxdgMpg5JJtxhoa7Fw9tri/lgQwmNrV5/hyciIiLdIMxu4ZyhqUwblkqYzdK5Gn9JfiVen8/f4YmIiAStSIeZp68ezX3nD8ZuMfPx5jKm/W0Rqwqq/R2aiFroiIj0FCaTiZyUKPokhLNsRzXrdteyrbyRgqpmxmVGArq8T0SkJ8rNzT3qcyQkJJCZqb67waJfUgTpMWF8nlfO1vJGvtpVw46KJs4cnOzv0ERERIKWyWTi2pP6MKZ3HLe8uppdVc1c9o9lzDpzADef0hezWe+5xT9UwBcR6WEcVgunDEhkcGoUn20pp7S+lUU760i+6kG2lTcxMiLC3yGKiMhhqK+uAOCqq6466nOFuVxsyc1VET+IhNktnDMslX7lDSzY8r/e+AMjLWDR2zgREZFjZWh6NO/9dBJ3v7WB/64t5qGP8liaX8XDl48gKVKbzMvxp1d+IiI9VGKkg8vG9GL9njoW51dC+iAufXYVN5/al5mn9cNps/g7RBEROYSWxnoApt10NwOHjz7i85QV5vPKA3dQWVmpAn4Q6p8USa8YV+dq/C31FlKveYT8ag+j/B2ciIhIkIpwWHnk8hM4qV8C9/53E4u2V3Luo1/y18tPYFL/RH+HJyFGBXwRkR7MZDIxIiOGXpFmnp7zX1z9J/DYZ9t5f0MJsy8exvjseH+HKCIi3yI+LYte/Yf4OwwJYJ2r8csamL+5BBJ7c+f8Sgra87j1jH44rPrQXkREpLuZTCYuG5PBqMwYZr6yhryyBq7+5wquPbE3d56dQ5hd+VeOD21iKyISBCIcFirm/oGHpw8mMdLBjoomLn96GXfNXU9di8ff4YmIiEg36J8cyZmpHppyv8BnwN8XbOeCxxazfk+tv0MTEREJWv2SIvnvLSdx1YSOKx1fWLKLaX/7ktWFNX6OTEKFCvgickCNjY2HdUhgOWtQIp/OOoUrx3W8sHhtxW6mPLyQDzaUYBiGn6MTERGRo+WwQOU7D/KLiTHEh9vJK2vgwscXc89/N+pDexERkWPEabPwh4uG8cJ1Y0mOcrCjsonvPbmEhz7agtvb7u/wJMipgC8iXXg9bWAyk5qaSmRk5CGP1NRUANrbvX6OWvYXHWZj9iXDeP3GCWQnhFPR4OYnr6zmhhdXUVzb4u/wREREpBucmBHGxz+bzIUnpGEY8OLSAs74y+fMXb1HH9qLiIgcI6cOTOLj20/h4pHp+Ax4fEE+F/59MRuL6vwdmgQx9cAXkS7avV4wfNz90gIioqIPOba+ppLZ155Fe7veJAai8dnxfHDbJJ5YsJ0nF+bzaW4ZS/Mr+eXZOVw1IQuL2eTvEEVEROQoxEc4ePSKkVw+JoPf/HcjOyqamPXGOl5eVsDd0wYzOivW3yGKiIgEnWiXjb9efgJThyRz91sb2VLawAV/X8Q1J/Zm1pkDiHTa/B2iBBmtwBeRA7I7XTjCwg99OF3+DlO+hdNmYdZZA3n/p5MYlRlDU1s7976ziUueXMKKndX+Dk9ERES6wYn9Eph322TumDqQMJuF1YW1TH9yCTNfXc3u6mZ/hyciIhKUzh6aykc/m8x5w1PxGfD84l2c8ZeFvLuuWFfDSbdSAV9EJAQMSI7k3z8+kd9fOIQIh5V1u2u57B9L+eELX7GltN7f4YmIiMhRslvNzDytH5/fcSqXjemFyQTvry/hjL8s5Ddvb1AbPRERkWMgIcLB378/ihd/OI7e8S7KG9zc+toaLvvHUlYVaJNb6R4q4IuIhAiz2cQPJvZm/s9P4fvjM7GYTXy2pZxzHv2SW19bo559IiIiQSA5ysmD3xvB+7dO4uR+CbS1+3h5WSGnPLSAX7+1gT01WpEvIiLS3SYPSGTe7ZOZdeYAHFYzX+2qYfqTS7jppZXkVzT6Ozzp4VTAFxEJMclRTv508TA++dlkpg1LxTDg3XXFnPfYIq58ehkLtpTj8+lyPxERkZ5scFoUL/9oPHNunMDE7Hg87QavLi/klIc+Z+Yrq/lqV7Uu7xcREelGTpuFn57Rn4V3nMblYzIwm+CjTWWc+fBCbnl1tRbNyRHTJrYiIiHI7XaTFAYPXDiA68an8fyy3czbVM7SHVUs3VFFWrSDaUOTOW9YEjmpMTgcDr/H6/F4DmuszWbze7wiIiKBYkJ2PBNujGf5jir+9tk2Fm+v4v0NJby/oYSh6VHMGJ/FucNSiQ7ThnsiIiLdISXayQPfG86PJvXhgXl5fJpbxnvrS3hvfQmT+idw4+RsTuqbgNls8neo0kOogC8iEmLcbjeZWb0pLyvtcrslMpHIMecTOfwsioFnFhfyzOJCfJUF3Dr9VE4fnMqIXtFYLcf34q2DxXswSckpFBbsUhFfRERkP+Oz43klO57cknr+tWQXb60pYmNRPXfN3cC9/93EGYOSuGhkOqcMSMRps/g7XBERkR6vf3Ikz14zhs3F9fzji3zeXVfMl9sq+XJbJRlxYVw6OoNLx/QiNTrM36FKgFMBX0QkxHg8HsrLSrlvziIcYa5v3O9tN9hV08q2imYKa9yYE7J4fOFOHl+4kyinlQnZ8YzIiGF4r2iGpUcT47L7Nd79uVuaue+Kk/F4PCrgi0jIyc3N9evjpWcYlBrF/dOHc+fZObyxcjdzVxeRV9bAhxtL+XBjKU6bmZP6JnBaThKn5SSRHnP8igqFhYVUVlYe1TkSEhLIzMzspohERA5fd+RR5WL/6a7v/YHy0OC0KB69YiS/OGsgz365g7lrithd3cLDn2zlkU+3Mr5PPFOHJHPWkBTSjmPePRLdkav3Uc4+fCrgi4iEKEeYC0dY+DdvB4ZERDAkA2rr63n497/m+7P+wLJdtdS1ePh4cxkfby7rHJ8c5aB3fDi948PpFeMgIdxKQridhAg7ceE2wu0WHFYzJlPXywO/a6ubg8V7NNSaR0SCQX11BQBXXXVVt5yvsVEbrYWC2HA7N53SlxsnZ5Nb0sDba4t4d10xJXWtzN9Szvwt5QCkx4QxKiuWMVmxjMiIYUByBC5797+NLCwsJGfQIFqaj26T3TCXiy25uSoIiMhx0915GJSLj6fu/vc7VB7KiHPxuwuH8qtzBvHhxhJe/2o3y3dWd7ayve/dzQxLj+akfgmM7xPH6N6xRDkDp8Vdd+XqfZSzD19QFvAff/xxHnroIUpLSxkxYgSPPfYY48aN83dYIiI9TpjNQuP6T/jL9LmEucJZt6eWVbtqWF9Ux4Y9teyqaqas3k1ZvZvlO6sPeh7D147R1oKvrQXD04rhbcNqsTJixDCsFjNmkwmLyYTZDGaTiX21fhMm2tvbSbrs97y3uQqzuRZTxx1dmPZ7noQLf8Wv3s4lIsyBw2rGaev4AMGx70+rGZfditNq8KNrrqa6dA++thZ87maMtmYMb9sB56DWPN1P+Vqke7Q01gMw7aa7GTh89BGfJ3fFQj7816O0trZ2V2jSA5hMJganRTE4LYq7zslhS2kDn20pZ8GWclYX1lBU20JRbQvvriveOx4yYl0MSI6kd7yL9Ngw0mPCSIsJIz7CTnSYjTCb5Rsf3H+byspKWpqbmXHnQyRn9j2iuZQV5vPKA3dQWVmpYkA3Ur4WObTuysOgXOwP3fnvd7h5KMxu4ZJRvbhkVC8Kq5r5aFMpH28uZWVBDRuK6thQVMdTC/MxmyAnJYohaVEMSo0iJzWSAcmRxIfbv3Oe7Q7dkav3Uc7+boKugP/6668za9YsnnrqKcaPH88jjzzC1KlTycvLIykpyd/hiYgcU4ezUuNIV3NYzCZGZcYyKjO287a6Fg87KhopqGomr7iGh558nv4TzsLtg+Y2H61eHwAmswWTMwKzM6LLOTcU1R/Wc4f1GcWeWvdhjQ3POZn3NpYf1ljH2XeQ+rXbzCawWUzYLWbsFhM2ixmryUfesvm0uttUwO8mytci3S8+LYte/Ycc8ePLCvO7MRrpiUwmE4NSO4oEM0/rR5Pby7rdtawsqGFlQQ2bi+upbHRTWN1MYfXBV9/ZLWaiXTZiwmxEOK0dOdVq7vzTZjFjMoFhgAH4DIOa6hoSLvwVO8MGUtISCfzvfsMwMAD2G8/X7jMMcNsHkvKDh9lQ5mbUMf9uhQbla5HDd7R5GJSL/ak7/v2ORGa8ixsmZ3PD5GwqG918nlfBip1VLN9ZTUFVM5tL6tlc0vV9s8tuITPORa9YF0lRDuJcdmLDO66Aj3HZiQmzdVnIZreYcdjMWM0mfEZH7vQZgNGRU/fl1javD7fXh9vb3vn3tr1fuz0+8gpaCB92Jo2x/TE5Umj3GbT7DHyGgc8H7cb+Xxv7fQ2+vbcbe+fgdgwkelL3XbUS7IKugP/www9zww03cN111wHw1FNP8f777/Pcc8/xq1/9ys/RiYgcG15PG5jMpKZ+vRx9cO3t3qN+3ugwGyMzYxmZGUtj/2h+dd7D/OLGqzpb3fgMA2+7QVu7D097R/L3tPtobWnhud/fypv//g92u6MjwRt0JnvoeCMO0NLayg0/+hGX/ewPWO0dxfN9SX/fX/a9DPC0uZn75GxmP/hnMFv3vvjw0erpeMHh9rbT6vHR1OalvtnNV+s2EpvWB087tLX79sYMbq+B29veZa6uAROxmI//KodgpXwtIhL4wh1WTuyXwIn9Ejpvq25qY2tZA1vLGthd3dyxQr+mhaLaVmqb2/D6OvJ+RYObiobD+/C98/lyTqaoBWhpOsKIzThSB9DQ5jvCx8vXKV+LiBw/CREOvje6F98b3QuA0rpW1u6uYXNJA7kl9eSW1FNU20JzWztbShvYUtpw/GM89zbW1gA1R9sH34w9Ias7QgoJQVXAb2trY9WqVdx1112dt5nNZqZMmcLSpUu/Md7tduN2/+9FZV1dHQD19Ye3IvRQ9q1wrauqwBF26NWuDbVVHX/WVODzHvpFrsZq7JGM9ffza+xxGmv4+Onf/o0rIvKQY5vqanjsZ1dQW1mOx33oSzPdLS0AlJSUEB5+6P7zTU0db7YP9nvPDDj3HnZfC607VjEwoo3w8K/39OtaJG9q8tG0+XOSLU047Id+Q+72tdC45gOmZj1yWPEO+OkpXPPcRzjCwjAMA4/PwOM1Oj5saAePz4en3aClpZX3X3iC+lnj8fmOriiwL8cYhvEtI4PXd83XcOxy9r58vWfbJtwtR97Lcd9qqdJdW8kPP/Rmyz3lPIEUS6CdJ5BiCbTzBFIsgXae7oqlYs9OAFatWnXUPZLNZvMR57XeQO9IIBLMWWZ8PjuGYcPdbtDYZtDUZtDk8dHsNfDuXaHnaQevz8Djo+OTepMJM4AJKsrLePXVVxl9+nlExuz9wMDU8apg37H/1wAmk/G/vwN1VWUsevslMk7/y1HnB+XrwMrXEHg5O1DPFYgxdee5FNPxP1cgxtSd5+rOmLozR0PH71yXz8cYB4zpDfS24m2PoLKlnbKmdiqa2qlv89Hg9tHo9lHv6fizsc3Aa0Bbe8eCOs9hpnqbGawWEzazqePvZrBbTFjNJuwWaGttYdOG9fTK7k9YWDhmU8cV7Gbo8neTycB0wNv/l8Prq8tYtOxNGhvPOqocETL52ggiRUVFBmAsWbKky+133HGHMW7cuG+Mv/fee/deMKJDhw4dOnQc32P37t3HKz0GnO+arw1DOVuHDh06dPjnUL5WvtahQ4cOHYF/BHu+DqoV+N/VXXfdxaxZszq/9vl8VFdXEx8ff1ibQdTX15ORkcHu3buJioo6lqEGhFCabyjNFUJrvqE0Vwit+faUuRqGQUNDA2lpaf4OpUc5mpzdU342upPmrDkHo1CbL2jO/pyz8vWR0Xvs7yaU5htKc4XQmm8ozRVCa749Ya6hkq+DqoCfkJCAxWKhrKysy+1lZWWkpKR8Y7zD4fjGZoQxMTHf+XmjoqIC9gf5WAil+YbSXCG05htKc4XQmm9PmGt0dLS/Q/Cr75qvoXtydk/42ehumnNoCLU5h9p8QXP2F+Vr/+RrCIx//+MplOYbSnOF0JpvKM0VQmu+gT7XUMjXZn8H0J3sdjujR49m/vz5nbf5fD7mz5/PxIkT/RiZiIiI7KN8LSIiEviUr0VERAJDUK3AB5g1axbXXHMNY8aMYdy4cTzyyCM0NTVx3XXX+Ts0ERER2Uv5WkREJPApX4uIiPhf0BXwL7/8cioqKrjnnnsoLS3lhBNOYN68eSQnJ3f7czkcDu69995vXCIYrEJpvqE0Vwit+YbSXCG05htKcw0GytfHluYcGkJtzqE2X9Ccxf+OZ76G0Pv3D6X5htJcIbTmG0pzhdCabyjNNdCZDMMw/B2EiIiIiIiIiIiIiIh0FVQ98EVEREREREREREREgoUK+CIiIiIiIiIiIiIiAUgFfBERERERERERERGRAKQCvoiIiIiIiIiIiIhIAFIB/yg8/vjj9O7dG6fTyfjx41mxYoW/Q+riiy++4PzzzyctLQ2TycTbb7/d5X7DMLjnnntITU0lLCyMKVOmsG3bti5jqqurmTFjBlFRUcTExHD99dfT2NjYZcz69euZNGkSTqeTjIwMHnzwwW/E8uabb5KTk4PT6WTYsGF88MEH3TrX2bNnM3bsWCIjI0lKSuKiiy4iLy+vy5jW1lZmzpxJfHw8ERERTJ8+nbKysi5jCgsLmTZtGi6Xi6SkJO644w68Xm+XMZ9//jmjRo3C4XDQr18/XnjhhW/Ec6x/Np588kmGDx9OVFQUUVFRTJw4kQ8//DAo5/p1999/PyaTidtvv73ztmCa73333YfJZOpy5OTkBOVc9ykqKuKqq64iPj6esLAwhg0bxsqVKzvvD6bfVeIfgZ6v9wmlvA2hl7shtPM3BH8Oh9DM46BcLt2jJ+TrUMrVoZanQzlHB3t+DrXcrJwcpAw5InPmzDHsdrvx3HPPGZs2bTJuuOEGIyYmxigrK/N3aJ0++OAD4+677zbmzp1rAMZbb73V5f7777/fiI6ONt5++21j3bp1xgUXXGD06dPHaGlp6Rxz9tlnGyNGjDCWLVtmfPnll0a/fv2MK6+8svP+uro6Izk52ZgxY4axceNG47XXXjPCwsKMf/zjH51jFi9ebFgsFuPBBx80Nm/ebPzmN78xbDabsWHDhm6b69SpU43nn3/e2Lhxo7F27Vrj3HPPNTIzM43GxsbOMT/+8Y+NjIwMY/78+cbKlSuNCRMmGCeeeGLn/V6v1xg6dKgxZcoUY82aNcYHH3xgJCQkGHfddVfnmB07dhgul8uYNWuWsXnzZuOxxx4zLBaLMW/evM4xx+Nn45133jHef/99Y+vWrUZeXp7x61//2rDZbMbGjRuDbq77W7FihdG7d29j+PDhxm233dZ5ezDN99577zWGDBlilJSUdB4VFRVBOVfDMIzq6mojKyvLuPbaa43ly5cbO3bsMD766CNj+/btnWOC6XeVHH89IV/vE0p52zBCL3cbRujmb8MIjRxuGKGXxw1DuVy6R0/J16GUq0MtT4dqjg6F/BxKuVk5OXipgH+Exo0bZ8ycObPz6/b2diMtLc2YPXu2H6M6uK+/uPD5fEZKSorx0EMPdd5WW1trOBwO47XXXjMMwzA2b95sAMZXX33VOebDDz80TCaTUVRUZBiGYTzxxBNGbGys4Xa7O8fceeedxsCBAzu/vuyyy4xp06Z1iWf8+PHGTTfd1K1z3F95ebkBGAsXLuycm81mM958883OMbm5uQZgLF261DCMjhdjZrPZKC0t7Rzz5JNPGlFRUZ3z++Uvf2kMGTKky3NdfvnlxtSpUzu/9tfPRmxsrPHss88G7VwbGhqM/v37G5988olxyimndL64CLb53nvvvcaIESMOeF+wzdUwOn5fnHzyyQe9P9h/V8mx19Py9T6hlrcNIzRzt2EEf/42jNDJ4YYRenncMJTLpXv0xHwdark6FPN0sOfoUMnPoZSblZODl1roHIG2tjZWrVrFlClTOm8zm81MmTKFpUuX+jGyw7dz505KS0u7zCE6Oprx48d3zmHp0qXExMQwZsyYzjFTpkzBbDazfPnyzjGTJ0/Gbrd3jpk6dSp5eXnU1NR0jtn/efaNOZbfq7q6OgDi4uIAWLVqFR6Pp0scOTk5ZGZmdpnvsGHDSE5O7hJnfX09mzZtOqy5+ONno729nTlz5tDU1MTEiRODdq4zZ85k2rRp34gpGOe7bds20tLSyM7OZsaMGRQWFgbtXN955x3GjBnDpZdeSlJSEiNHjuSZZ57pvD/Yf1fJsRUM+XqfUPi/EEq5G0Inf0No5XAIrTwOyuVy9IIlXwf7z3oo5elQydGhlJ9DJTcrJwcvFfCPQGVlJe3t7V3+8wIkJydTWlrqp6i+m31xHmoOpaWlJCUldbnfarUSFxfXZcyBzrH/cxxszLH6Xvl8Pm6//XZOOukkhg4d2hmD3W4nJibmoHEczVzq6+tpaWk5rj8bGzZsICIiAofDwY9//GPeeustBg8eHJRznTNnDqtXr2b27NnfuC/Y5jt+/HheeOEF5s2bx5NPPsnOnTuZNGkSDQ0NQTdXgB07dvDkk0/Sv39/PvroI26++WZ++tOf8q9//atLzMH4u0qOvWDI1/sE+/+FUMndEFr5G0Irh0Po5XFQLpejFyz5Oph/1kMlT4dSjg6l/BxKuVk5OXhZ/R2ASHebOXMmGzduZNGiRf4O5ZgaOHAga9eupa6ujn//+99cc801LFy40N9hdbvdu3dz22238cknn+B0Ov0dzjF3zjnndP59+PDhjB8/nqysLN544w3CwsL8GNmx4fP5GDNmDH/6058AGDlyJBs3buSpp57immuu8XN0InK8hEruhtDJ3xB6ORxCL4+DcrlIKAiVPB0qOTrU8nMo5Wbl5OClFfhHICEhAYvF8o1dqcvKykhJSfFTVN/NvjgPNYeUlBTKy8u73O/1eqmuru4y5kDn2P85DjbmWHyvbrnlFt577z0WLFhAr169Om9PSUmhra2N2trag8ZxNHOJiooiLCzsuP5s2O12+vXrx+jRo5k9ezYjRozg0UcfDbq5rlq1ivLyckaNGoXVasVqtbJw4UL+9re/YbVaSU5ODqr5fl1MTAwDBgxg+/btQfdvC5CamsrgwYO73DZo0KDOSxqD9XeVHB/BkK/3Ceb/C6GUuyF08jcoh0Pw53FQLpejFyz5Olh/1kMpT4dKjg71/BzMuVk5OXipgH8E7HY7o0ePZv78+Z23+Xw+5s+fz8SJE/0Y2eHr06cPKSkpXeZQX1/P8uXLO+cwceJEamtrWbVqVeeYzz77DJ/Px/jx4zvHfPHFF3g8ns4xn3zyCQMHDiQ2NrZzzP7Ps29Md36vDMPglltu4a233uKzzz6jT58+Xe4fPXo0NputSxx5eXkUFhZ2me+GDRu6/KL65JNPiIqK6vwF+G1z8efPhs/nw+12B91czzjjDDZs2MDatWs7jzFjxjBjxozOvwfTfL+usbGR/Px8UlNTg+7fFuCkk04iLy+vy21bt24lKysLCL7fVXJ8BUO+3icY/y8od//vuYIxf4NyOAR/Hgflcjl6/v493F2C7WddeTp4c3So5+dgzs3KyUHMz5vo9lhz5swxHA6H8cILLxibN282brzxRiMmJqbLrtT+1tDQYKxZs8ZYs2aNARgPP/ywsWbNGqOgoMAwDMO4//77jZiYGOO///2vsX79euPCCy80+vTpY7S0tHSe4+yzzzZGjhxpLF++3Fi0aJHRv39/48orr+y8v7a21khOTjauvvpqY+PGjcacOXMMl8tl/OMf/+gcs3jxYsNqtRp//vOfjdzcXOPee+81bDabsWHDhm6b680332xER0cbn3/+uVFSUtJ5NDc3d4758Y9/bGRmZhqfffaZsXLlSmPixInGxIkTO+/3er3G0KFDjbPOOstYu3atMW/ePCMxMdG46667Osfs2LHDcLlcxh133GHk5uYajz/+uGGxWIx58+Z1jjkePxu/+tWvjIULFxo7d+401q9fb/zqV78yTCaT8fHHHwfdXA/klFNOMW677bbOr4Npvj//+c+Nzz//3Ni5c6exePFiY8qUKUZCQoJRXl4edHM1DMNYsWKFYbVajT/+8Y/Gtm3bjFdeecVwuVzGyy+/3DkmmH5XyfHXE/L1PqGUtw0j9HK3YSh/G0Zw53DDCL08bhjK5dI9ekq+DqVcHWp5OtRzdDDn51DKzcrJwUsF/KPw2GOPGZmZmYbdbjfGjRtnLFu2zN8hdbFgwQID+MZxzTXXGIZhGD6fz/jtb39rJCcnGw6HwzjjjDOMvLy8LueoqqoyrrzySiMiIsKIiooyrrvuOqOhoaHLmHXr1hknn3yy4XA4jPT0dOP+++//RixvvPGGMWDAAMNutxtDhgwx3n///W6d64HmCRjPP/9855iWlhbjJz/5iREbG2u4XC7j4osvNkpKSrqcZ9euXcY555xjhIWFGQkJCcbPf/5zw+PxdBmzYMEC44QTTjDsdruRnZ3d5Tn2OdY/Gz/84Q+NrKwsw263G4mJicYZZ5zR+cIi2OZ6IF9/cRFM87388suN1NRUw263G+np6cbll19ubN++PSjnus+7775rDB061HA4HEZOTo7x9NNPd7k/mH5XiX8Eer7eJ5TytmGEXu42DOVvwwjuHG4YoZnHDUO5XLpHT8jXoZSrQy1Ph3qODub8HGq5WTk5OJkMwzCO7Rp/ERERERERERERERH5rtQDX0REREREREREREQkAKmALyIiIiIiIiIiIiISgFTAFxEREREREREREREJQCrgi4iIiIiIiIiIiIgEIBXwRUREREREREREREQCkAr4IiIiIiIiIiIiIiIBSAV8EREREREREREREZEApAK+iIiIiIiIiIiIiEgAUgFfJATt2rULk8nE2rVr/R2KiIiIHITytYiISOBTvhaRY00FfBERERERERERERGRAKQCvoh0m7a2Nn+HICIiIt9C+VpERCTwKV+LyD4q4IsEMZ/Px4MPPki/fv1wOBxkZmbyxz/+sfP+HTt2cNppp+FyuRgxYgRLly7tvK+qqoorr7yS9PR0XC4Xw4YN47XXXuty/lNPPZVbbrmF22+/nYSEBKZOnQrAO++8Q//+/XE6nZx22mn861//wmQyUVtb2/nYRYsWMWnSJMLCwsjIyOCnP/0pTU1Nnfc/8cQTnedITk7me9/73jH6LomIiPiX8rWIiEjgU74WEX9RAV8kiN11113cf//9/Pa3v2Xz5s28+uqrJCcnd95/991384tf/IK1a9cyYMAArrzySrxeLwCtra2MHj2a999/n40bN3LjjTdy9dVXs2LFii7P8a9//Qu73c7ixYt56qmn2LlzJ9/73ve46KKLWLduHTfddBN33313l8fk5+dz9tlnM336dNavX8/rr7/OokWLuOWWWwBYuXIlP/3pT/n9739PXl4e8+bNY/Lkycf4uyUiIuIfytciIiKBT/laRPzGEJGgVF9fbzgcDuOZZ575xn07d+40AOPZZ5/tvG3Tpk0GYOTm5h70nNOmTTN+/vOfd359yimnGCNHjuwy5s477zSGDh3a5ba7777bAIyamhrDMAzj+uuvN2688cYuY7788kvDbDYbLS0txn/+8x8jKirKqK+vP+z5ioiI9ETK1yIiIoFP+VpE/Mnqt08OROSYys3Nxe12c8YZZxx0zPDhwzv/npqaCkB5eTk5OTm0t7fzpz/9iTfeeIOioiLa2tpwu924XK4u5xg9enSXr/Py8hg7dmyX28aNG9fl63Xr1rF+/XpeeeWVztsMw8Dn87Fz507OPPNMsrKyyM7O5uyzz+bss8/m4osv/sZzi4iI9HTK1yIiIoFP+VpE/EktdESCVFhY2LeOsdlsnX83mUxAR18/gIceeohHH32UO++8kwULFrB27VqmTp36jY10wsPDv3NsjY2N3HTTTaxdu7bzWLduHdu2baNv375ERkayevVqXnvtNVJTU7nnnnsYMWJElx5/IiIiwUD5WkREJPApX4uIP6mALxKk+vfvT1hYGPPnzz+ixy9evJgLL7yQq666ihEjRpCdnc3WrVu/9XEDBw5k5cqVXW776quvunw9atQoNm/eTL9+/b5x2O12AKxWK1OmTOHBBx9k/fr17Nq1i88+++yI5iIiIhKolK9FREQCn/K1iPiTCvgiQcrpdHLnnXfyy1/+khdffJH8/HyWLVvGP//5z8N6fP/+/fnkk09YsmQJubm53HTTTZSVlX3r42666Sa2bNnCnXfeydatW3njjTd44YUXgP+tQrjzzjtZsmQJt9xyC2vXrmXbtm3897//7dxk57333uNvf/sba9eupaCggBdffBGfz8fAgQOP7JshIiISoJSvRUREAp/ytYj4kwr4IkHst7/9LT//+c+55557GDRoEJdffjnl5eWH9djf/OY3jBo1iqlTp3LqqaeSkpLCRRdd9K2P69OnD//+97+ZO3cuw4cP58knn+Tuu+8GwOFwAB29ARcuXMjWrVuZNGkSI0eO5J577iEtLQ2AmJgY5s6dy+mnn86gQYN46qmneO211xgyZMiRfSNEREQCmPK1iIhI4FO+FhF/MRmGYfg7CBEJbn/84x956qmn2L17t79DERERkYNQvhYREQl8ytciocfq7wBEJPg88cQTjB07lvj4eBYvXsxDDz3UefmeiIiIBAblaxERkcCnfC0iKuCLSLfbtm0bf/jDH6iuriYzM5Of//zn3HXXXf4OS0RERPajfC0iIhL4lK9FRC10REREREREREREREQCkDaxFREREREREREREREJQCrgi4iIiIiIiIiIiIgEIBXwRUREREREREREREQCkAr4IiIiIiIiIiIiIiIBSAV8EREREREREREREZEApAK+iIiIiIiIiIiIiEgAUgFfRERERERERERERCQAqYAvIiIiIiIiIiIiIhKA/h/2skp1/tLeaAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Перемешивание данных\n", + "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n", + "\n", + "# Разделение на признаки и целевую переменную\n", + "X = df.drop('charges', axis=1)\n", + "y = df['charges']\n", + "\n", + "# Разбиение на обучающую, контрольную и тестовую выборки\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Функция для оценки сбалансированности выборок\n", + "def evaluate_balance(y_train, y_val, y_test):\n", + " plt.figure(figsize=(18, 6))\n", + " \n", + " plt.subplot(1, 3, 1)\n", + " sns.histplot(y_train, kde=True)\n", + " plt.title('Распределение целевой переменной в обучающей выборке')\n", + " \n", + " plt.subplot(1, 3, 2)\n", + " sns.histplot(y_val, kde=True)\n", + " plt.title('Распределение целевой переменной в контрольной выборке')\n", + " \n", + " plt.subplot(1, 3, 3)\n", + " sns.histplot(y_test, kde=True)\n", + " plt.title('Распределение целевой переменной в тестовой выборке')\n", + " \n", + " plt.show()\n", + "\n", + "# Оценка сбалансированности выборок\n", + "evaluate_balance(y_train, y_val, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Процесс конструирования признаков для обеих задач:**\n", + "1. **Анализ и очистка данных:**\n", + " - Проверить наличие пропущенных значений и дубликатов и обработать их (заполнение средним значением, медианой или удаление строк).\n", + "\n", + "2. **Кодирование категориальных признаков:**\n", + " - Применить One-Hot Encoding для категориальных признаков (`sex`, `smoker`, `region`).\n", + "\n", + "3. **Создание новых признаков:**\n", + " - **Возрастные группы:** Разделить возраст на группы (например, молодые, средний возраст, пожилые).\n", + " - **Индекс массы тела (ИМТ) группы:** Разделить ИМТ на группы (например, недостаточный вес, нормальный вес, избыточный вес, ожирение).\n", + " - **Количество детей:** Создать бинарный признак, указывающий, есть ли у клиента дети.\n", + " - **Комбинированные признаки:** Создать новые признаки, комбинируя существующие (например, возраст и ИМТ, возраст и статус курения).\n", + "\n", + "4. **Нормализация и стандартизация:**\n", + " - Применить нормализацию или стандартизацию к числовым признакам (`age`, `bmi`, `children`), чтобы привести их к одному масштабу.\n", + "\n", + "5. **Анализ важности признаков:**\n", + " - Удалить малозначимые признаки, чтобы упростить модель и улучшить ее производительность." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Оптимизация тарифов" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучающая выборка после конструирования признаков:\n", + " age bmi children sex_female sex_male smoker_no \\\n", + "1684 1.781292 0.374453 -0.907604 True False True \n", + "862 -0.083478 -0.570585 -0.085975 True False True \n", + "1992 -1.087585 -0.495147 -0.907604 True False True \n", + "889 0.705463 -0.586335 -0.907604 True False True \n", + "1362 0.777185 -0.680839 -0.907604 False True True \n", + "\n", + " smoker_yes region_northeast region_northwest region_southeast \\\n", + "1684 False False True False \n", + "862 False True False False \n", + "1992 False False False True \n", + "889 False False False True \n", + "1362 False False False False \n", + "\n", + " region_southwest age_bin age_bmi \n", + "1684 False 4.0 1.734561 \n", + "862 False 1.0 -0.339153 \n", + "1992 False 0.0 -1.055293 \n", + "889 False 2.0 0.231109 \n", + "1362 True 2.0 0.228540 \n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Перемешивание данных\n", + "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n", + "\n", + "# Разделение на признаки и целевую переменную\n", + "X = df.drop('charges', axis=1)\n", + "y = df['charges']\n", + "\n", + "# Разбиение на обучающую, контрольную и тестовую выборки\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Определение категориальных и числовых признаков\n", + "categorical_features = ['sex', 'smoker', 'region']\n", + "numerical_features = ['age', 'bmi', 'children']\n", + "\n", + "# Унитарное кодирование категориальных признаков (one-hot encoding)\n", + "train_data_encoded = pd.get_dummies(X_train, columns=categorical_features)\n", + "val_data_encoded = pd.get_dummies(X_val, columns=categorical_features)\n", + "test_data_encoded = pd.get_dummies(X_test, columns=categorical_features)\n", + "\n", + "# Дискретизация числовых признаков (пример для возраста)\n", + "age_bins = [18, 30, 40, 50, 60, 100]\n", + "train_data_encoded['age_bin'] = pd.cut(train_data_encoded['age'], bins=age_bins, labels=False)\n", + "val_data_encoded['age_bin'] = pd.cut(val_data_encoded['age'], bins=age_bins, labels=False)\n", + "test_data_encoded['age_bin'] = pd.cut(test_data_encoded['age'], bins=age_bins, labels=False)\n", + "\n", + "# «Ручной» синтез признаков (пример: комбинированный признак возраст и ИМТ)\n", + "train_data_encoded['age_bmi'] = train_data_encoded['age'] * train_data_encoded['bmi']\n", + "val_data_encoded['age_bmi'] = val_data_encoded['age'] * val_data_encoded['bmi']\n", + "test_data_encoded['age_bmi'] = test_data_encoded['age'] * test_data_encoded['bmi']\n", + "\n", + "# Масштабирование числовых признаков\n", + "numerical_features = ['age', 'bmi', 'children', 'age_bmi']\n", + "scaler = StandardScaler()\n", + "train_data_encoded[numerical_features] = scaler.fit_transform(train_data_encoded[numerical_features])\n", + "val_data_encoded[numerical_features] = scaler.transform(val_data_encoded[numerical_features])\n", + "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])\n", + "\n", + "# Вывод результатов\n", + "print(\"Обучающая выборка после конструирования признаков:\")\n", + "print(train_data_encoded.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Оценка рисков" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучающая выборка после конструирования признаков:\n", + " age bmi children sex_female sex_male smoker_no \\\n", + "1684 1.781292 0.374453 -0.907604 True False True \n", + "862 -0.083478 -0.570585 -0.085975 True False True \n", + "1992 -1.087585 -0.495147 -0.907604 True False True \n", + "889 0.705463 -0.586335 -0.907604 True False True \n", + "1362 0.777185 -0.680839 -0.907604 False True True \n", + "\n", + " smoker_yes region_northeast region_northwest region_southeast \\\n", + "1684 False False True False \n", + "862 False True False False \n", + "1992 False False False True \n", + "889 False False False True \n", + "1362 False False False False \n", + "\n", + " region_southwest age_bin age_bmi \n", + "1684 False 4.0 1.734561 \n", + "862 False 1.0 -0.339153 \n", + "1992 False 0.0 -1.055293 \n", + "889 False 2.0 0.231109 \n", + "1362 True 2.0 0.228540 \n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.pipeline import Pipeline\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Перемешивание данных\n", + "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n", + "\n", + "# Разделение на признаки и целевую переменную\n", + "X = df.drop('charges', axis=1)\n", + "y = df['charges']\n", + "\n", + "# Разбиение на обучающую, контрольную и тестовую выборки\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Определение категориальных и числовых признаков\n", + "categorical_features = ['sex', 'smoker', 'region']\n", + "numerical_features = ['age', 'bmi', 'children']\n", + "\n", + "# Унитарное кодирование категориальных признаков (one-hot encoding)\n", + "train_data_encoded = pd.get_dummies(X_train, columns=categorical_features)\n", + "val_data_encoded = pd.get_dummies(X_val, columns=categorical_features)\n", + "test_data_encoded = pd.get_dummies(X_test, columns=categorical_features)\n", + "\n", + "# Дискретизация числовых признаков (пример для возраста)\n", + "age_bins = [18, 30, 40, 50, 60, 100]\n", + "train_data_encoded['age_bin'] = pd.cut(train_data_encoded['age'], bins=age_bins, labels=False)\n", + "val_data_encoded['age_bin'] = pd.cut(val_data_encoded['age'], bins=age_bins, labels=False)\n", + "test_data_encoded['age_bin'] = pd.cut(test_data_encoded['age'], bins=age_bins, labels=False)\n", + "\n", + "# «Ручной» синтез признаков (пример: комбинированный признак возраст и ИМТ)\n", + "train_data_encoded['age_bmi'] = train_data_encoded['age'] * train_data_encoded['bmi']\n", + "val_data_encoded['age_bmi'] = val_data_encoded['age'] * val_data_encoded['bmi']\n", + "test_data_encoded['age_bmi'] = test_data_encoded['age'] * test_data_encoded['bmi']\n", + "\n", + "# Масштабирование числовых признаков\n", + "numerical_features = ['age', 'bmi', 'children', 'age_bmi']\n", + "scaler = StandardScaler()\n", + "train_data_encoded[numerical_features] = scaler.fit_transform(train_data_encoded[numerical_features])\n", + "val_data_encoded[numerical_features] = scaler.transform(val_data_encoded[numerical_features])\n", + "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])\n", + "\n", + "# Вывод результатов\n", + "print(\"Обучающая выборка после конструирования признаков:\")\n", + "print(train_data_encoded.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Процесс конструирования признаков с применением фреймворка Featuretools:\n", + "\n", + "### 1. Оптимизация тарифов" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучающая выборка после конструирования признаков:\n", + " age bmi children sex_female sex_male smoker_no \\\n", + "index \n", + "0 1.781292 0.374453 -0.907604 True False True \n", + "1 -0.083478 -0.570585 -0.085975 True False True \n", + "2 -1.087585 -0.495147 -0.907604 True False True \n", + "3 0.705463 -0.586335 -0.907604 True False True \n", + "4 0.777185 -0.680839 -0.907604 False True True \n", + "\n", + " smoker_yes region_northeast region_northwest region_southeast \\\n", + "index \n", + "0 False False True False \n", + "1 False True False False \n", + "2 False False False True \n", + "3 False False False True \n", + "4 False False False False \n", + "\n", + " region_southwest age_bin age_bmi \n", + "index \n", + "0 False 4 1.734561 \n", + "1 False 1 -0.339153 \n", + "2 False 0 -1.055293 \n", + "3 False 2 0.231109 \n", + "4 True 2 0.228540 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index index not found in dataframe, creating new integer column\n", + " warnings.warn(\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n", + " warnings.warn(\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df = pd.concat([df, default_df], sort=True)\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df = pd.concat([df, default_df], sort=True)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "import featuretools as ft\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Перемешивание данных\n", + "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n", + "\n", + "# Разделение на признаки и целевую переменную\n", + "X = df.drop('charges', axis=1)\n", + "y = df['charges']\n", + "\n", + "# Разбиение на обучающую, контрольную и тестовую выборки\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Определение категориальных и числовых признаков\n", + "categorical_features = ['sex', 'smoker', 'region']\n", + "numerical_features = ['age', 'bmi', 'children']\n", + "\n", + "# Унитарное кодирование категориальных признаков (one-hot encoding)\n", + "X_train_encoded = pd.get_dummies(X_train, columns=categorical_features)\n", + "X_val_encoded = pd.get_dummies(X_val, columns=categorical_features)\n", + "X_test_encoded = pd.get_dummies(X_test, columns=categorical_features)\n", + "\n", + "# Дискретизация числовых признаков (пример для возраста)\n", + "age_bins = [18, 30, 40, 50, 60, 100]\n", + "X_train_encoded['age_bin'] = pd.cut(X_train_encoded['age'], bins=age_bins, labels=False)\n", + "X_val_encoded['age_bin'] = pd.cut(X_val_encoded['age'], bins=age_bins, labels=False)\n", + "X_test_encoded['age_bin'] = pd.cut(X_test_encoded['age'], bins=age_bins, labels=False)\n", + "\n", + "# «Ручной» синтез признаков (пример: комбинированный признак возраст и ИМТ)\n", + "X_train_encoded['age_bmi'] = X_train_encoded['age'] * X_train_encoded['bmi']\n", + "X_val_encoded['age_bmi'] = X_val_encoded['age'] * X_val_encoded['bmi']\n", + "X_test_encoded['age_bmi'] = X_test_encoded['age'] * X_test_encoded['bmi']\n", + "\n", + "# Масштабирование числовых признаков\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "numerical_features = ['age', 'bmi', 'children', 'age_bmi']\n", + "scaler = StandardScaler()\n", + "X_train_encoded[numerical_features] = scaler.fit_transform(X_train_encoded[numerical_features])\n", + "X_val_encoded[numerical_features] = scaler.transform(X_val_encoded[numerical_features])\n", + "X_test_encoded[numerical_features] = scaler.transform(X_test_encoded[numerical_features])\n", + "\n", + "# Конструирование признаков с применением фреймворка Featuretools\n", + "es = ft.EntitySet(id='insurance_data')\n", + "es = es.add_dataframe(dataframe_name='train', dataframe=X_train_encoded, index='index')\n", + "\n", + "# Генерация признаков\n", + "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='train', max_depth=2)\n", + "\n", + "# Преобразование признаков для контрольной и тестовой выборок\n", + "X_val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_val_encoded.index)\n", + "X_test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_test_encoded.index)\n", + "\n", + "# Вывод результатов\n", + "print(\"Обучающая выборка после конструирования признаков:\")\n", + "print(feature_matrix.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Оценка рисков" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Обучающая выборка после конструирования признаков:\n", + " age bmi children sex_female sex_male smoker_no \\\n", + "index \n", + "0 1.781292 0.374453 -0.907604 True False True \n", + "1 -0.083478 -0.570585 -0.085975 True False True \n", + "2 -1.087585 -0.495147 -0.907604 True False True \n", + "3 0.705463 -0.586335 -0.907604 True False True \n", + "4 0.777185 -0.680839 -0.907604 False True True \n", + "\n", + " smoker_yes region_northeast region_northwest region_southeast \\\n", + "index \n", + "0 False False True False \n", + "1 False True False False \n", + "2 False False False True \n", + "3 False False False True \n", + "4 False False False False \n", + "\n", + " region_southwest age_bin age_bmi \n", + "index \n", + "0 False 4 1.734561 \n", + "1 False 1 -0.339153 \n", + "2 False 0 -1.055293 \n", + "3 False 2 0.231109 \n", + "4 True 2 0.228540 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\featuretools\\entityset\\entityset.py:1733: UserWarning: index index not found in dataframe, creating new integer column\n", + " warnings.warn(\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n", + " warnings.warn(\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df = pd.concat([df, default_df], sort=True)\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", + " df = pd.concat([df, default_df], sort=True)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "import featuretools as ft\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Перемешивание данных\n", + "df = df.sample(frac=1, random_state=42).reset_index(drop=True)\n", + "\n", + "# Разделение на признаки и целевую переменную\n", + "X = df.drop('charges', axis=1)\n", + "y = df['charges']\n", + "\n", + "# Разбиение на обучающую, контрольную и тестовую выборки\n", + "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n", + "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n", + "\n", + "# Определение категориальных и числовых признаков\n", + "categorical_features = ['sex', 'smoker', 'region']\n", + "numerical_features = ['age', 'bmi', 'children']\n", + "\n", + "# Унитарное кодирование категориальных признаков (one-hot encoding)\n", + "X_train_encoded = pd.get_dummies(X_train, columns=categorical_features)\n", + "X_val_encoded = pd.get_dummies(X_val, columns=categorical_features)\n", + "X_test_encoded = pd.get_dummies(X_test, columns=categorical_features)\n", + "\n", + "# Дискретизация числовых признаков (пример для возраста)\n", + "age_bins = [18, 30, 40, 50, 60, 100]\n", + "X_train_encoded['age_bin'] = pd.cut(X_train_encoded['age'], bins=age_bins, labels=False)\n", + "X_val_encoded['age_bin'] = pd.cut(X_val_encoded['age'], bins=age_bins, labels=False)\n", + "X_test_encoded['age_bin'] = pd.cut(X_test_encoded['age'], bins=age_bins, labels=False)\n", + "\n", + "# «Ручной» синтез признаков (пример: комбинированный признак возраст и ИМТ)\n", + "X_train_encoded['age_bmi'] = X_train_encoded['age'] * X_train_encoded['bmi']\n", + "X_val_encoded['age_bmi'] = X_val_encoded['age'] * X_val_encoded['bmi']\n", + "X_test_encoded['age_bmi'] = X_test_encoded['age'] * X_test_encoded['bmi']\n", + "\n", + "# Масштабирование числовых признаков\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "numerical_features = ['age', 'bmi', 'children', 'age_bmi']\n", + "scaler = StandardScaler()\n", + "X_train_encoded[numerical_features] = scaler.fit_transform(X_train_encoded[numerical_features])\n", + "X_val_encoded[numerical_features] = scaler.transform(X_val_encoded[numerical_features])\n", + "X_test_encoded[numerical_features] = scaler.transform(X_test_encoded[numerical_features])\n", + "\n", + "# Конструирование признаков с применением фреймворка Featuretools\n", + "es = ft.EntitySet(id='insurance_data')\n", + "es = es.add_dataframe(dataframe_name='train', dataframe=X_train_encoded, index='index')\n", + "\n", + "# Генерация признаков\n", + "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='train', max_depth=2)\n", + "\n", + "# Преобразование признаков для контрольной и тестовой выборок\n", + "X_val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_val_encoded.index)\n", + "X_test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_test_encoded.index)\n", + "\n", + "# Вывод результатов\n", + "print(\"Обучающая выборка после конструирования признаков:\")\n", + "print(feature_matrix.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Оценка качества наборов признаков по критериям:\n", + "\n", + "### 1. Предсказательная способность\n", + "\n", + "**Определение:** \n", + "Предсказательная способность набора признаков определяет, насколько хорошо эти признаки позволяют модели предсказывать целевую переменную.\n", + "\n", + "**Оценка:**\n", + "- **Обучающая выборка:** \n", + " - Оценивается с помощью метрик качества модели (например, RMSE, MAE, R²) на обучающей выборке.\n", + " - Высокие значения метрик указывают на высокую предсказательную способность.\n", + "- **Контрольная и тестовая выборки:**\n", + " - Оценивается с помощью метрик качества модели на контрольной и тестовой выборках.\n", + " - Близкие значения метрик на всех выборках указывают на хорошую обобщающую способность модели.\n", + "\n", + "### 2. Скорость вычисления\n", + "\n", + "**Определение:**\n", + "Скорость вычисления набора признаков определяет, насколько быстро можно вычислить эти признаки и обучить модель на них.\n", + "\n", + "**Оценка:**\n", + "- **Время вычисления признаков:**\n", + " - Измеряется время, затрачиваемое на вычисление признаков для всех выборок.\n", + " - Меньшее время указывает на более быстрое вычисление.\n", + "- **Время обучения модели:**\n", + " - Измеряется время, затрачиваемое на обучение модели на вычисленных признаках.\n", + " - Меньшее время указывает на более быстрое обучение.\n", + "\n", + "### 3. Надежность\n", + "\n", + "**Определение:**\n", + "Надежность набора признаков определяет, насколько стабильно модель, обученная на этих признаках, показывает хорошие результаты на разных выборках данных.\n", + "\n", + "**Оценка:**\n", + "- **Стабильность метрик:**\n", + " - Оценивается стабильность метрик качества модели (например, RMSE, MAE, R²) на разных выборках данных.\n", + " - Близкие значения метрик на разных выборках указывают на высокую надежность.\n", + "- **Устойчивость к изменениям данных:**\n", + " - Оценивается, как меняются метрики качества модели при добавлении или удалении данных.\n", + " - Небольшие изменения метрик указывают на высокую устойчивость.\n", + "\n", + "### 4. Корреляция\n", + "\n", + "**Определение:**\n", + "Корреляция набора признаков определяет, насколько сильно признаки коррелируют друг с другом и с целевой переменной.\n", + "\n", + "**Оценка:**\n", + "- **Корреляция между признаками:**\n", + " - Оценивается с помощью матрицы корреляции признаков.\n", + " - Высокая корреляция между признаками может привести к мультиколлинеарности, что снижает качество модели.\n", + "- **Корреляция с целевой переменной:**\n", + " - Оценивается с помощью коэффициента корреляции Пирсона или Спирмена.\n", + " - Высокая корреляция с целевой переменной указывает на высокую предсказательную способность признаков.\n", + "\n", + "### 5. Цельность\n", + "\n", + "**Определение:**\n", + "Цельность набора признаков определяет, насколько хорошо признаки соответствуют бизнес-целям и задачам модели.\n", + "\n", + "**Оценка:**\n", + "- **Соответствие бизнес-целям:**\n", + " - Оценивается, насколько признаки помогают решать поставленные бизнес-задачи (например, оптимизация тарифов, оценка рисков).\n", + " - Признаки, которые помогают решать бизнес-задачи, считаются целесообразными.\n", + "- **Интерпретируемость:**\n", + " - Оценивается, насколько легко интерпретировать значения признаков и их влияние на целевую переменную.\n", + " - Интерпретируемые признаки считаются более целесообразными." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 2750.642231395856\n", + "R²: 0.9507037692209687\n", + "MAE: 1279.1669853384874\n", + "Cross-validated RMSE: 3242.964333689781\n" + ] + }, + { + "data": { + "image/png": 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Istfw0UcfKTIyUp6enipXrpzi4+N18uRJ8xHg8ePHq0KFCvLz89Po0aN1/vx5DRkyRP7+/rrjjjs0d+5ch/E2b96spk2bmuP16dNHOTk5Vzx+Wlqaypcvr0mTJkmSjh8/rscee0zly5eX3W5X06ZNtWnTJrP/qFGjVLduXc2ZM0dVqlSRh4fHVc9v3rx5KleunM6cOePQ3r59e3Xt2tVc/+yzz1SvXj15eHioatWqSkpK0vnz5yVJhmFo1KhRqly5stzd3RUcHKz+/fsX7AIDAAAAQCERZK8iMzNTnTt3Vs+ePZWenq6UlBR16NBBhmFIkr7//nv98ccfWrlypV566SWNHDlSbdq0UdmyZbVmzRo9/vjj+s9//qPffvtNknTy5Em1bNlSZcuWVVpamv773//qu+++U79+/S57/O+//17NmzfXuHHj9Nxzz0mSOnbsqEOHDunrr7/WunXrVK9ePTVr1kxHjx4199u9e7c+/vhjLVq0SBs3brzqOXbs2FG5ubn6/PPPzbZDhw7pyy+/VM+ePSVJq1atUrdu3TRgwABt27ZNb7zxhpKTkzVu3DhJ0scff6xp06bpjTfe0K5du/Tpp58qMjLyssc7c+aMsrOzHRYAAAAAKAybcTGVIZ/169erfv36ysjIUGhoqMO27t27KyUlRXv37lWpUhf+PaBmzZoKDAzUypUrJUm5ubny9fXVnDlz1KlTJ82ePVvPPfecDhw4IC8vL0nSV199pbZt2+qPP/5QhQoV1L17dx0/flyJiYnq1q2b5syZo4cffliS9MMPP+jBBx/UoUOH5O7ubtZSrVo1Pfvss+rTp49GjRql8ePH6/fff1f58uULdJ5PPvmkMjIy9NVXX0mSXnrpJb366qvavXu3bDab4uPj1axZMw0dOtTc57333tOzzz6rP/74Qy+99JLeeOMNbdmyRaVLl77qsUaNGqWkpKR87XWeel0u7p4FqhcAAADA9Vs3pZuzS3CQnZ0tX19fZWVlyW63X7UvM7JXUadOHTVr1kyRkZHq2LGjZs+erWPHjpnba9eubYZYSapQoYLDTKSLi4vKlSunQ4cOSZLS09NVp04dM8RKUkxMjPLy8rRjxw6zbc2aNerYsaPeffddM8RK0qZNm5STk6Ny5crJ29vbXPbt26c9e/aY/UJDQwscYiWpd+/e+vbbb/X7779LkpKTk9W9e3fZbDbzuKNHj3Y4Zu/evZWZmalTp06pY8eO+t///qeqVauqd+/e+uSTT8zHji81dOhQZWVlmcuBAwcKXCcAAAAASJKrswu4mbm4uGjp0qVavXq1vv32W73yyisaNmyY1qxZI0n5Zh9tNttl2/Ly8gp13DvvvFPlypXT22+/rQcffNAcMycnR0FBQUpJScm3j5+fn/n570G5IKKjo1WnTh3NmzdPLVq00NatW/Xll1+a23NycpSUlKQOHTrk29fDw0MhISHasWOHvvvuOy1dulRPPvmkpkyZohUrVuS7Hu7u7g6zyQAAAABQWATZa7DZbIqJiVFMTIxGjBih0NBQffLJJ0UaKyIiQsnJyTp58qQZNlNTU1WqVCnVqFHD7BcQEKBFixYpLi5OCQkJ+vDDD1W6dGnVq1dPf/75p1xdXRUWFlYcp2d67LHHNH36dP3++++Kj49XSEiIua1evXrasWOHqlWrdsX9PT091bZtW7Vt21Z9+/ZVzZo1tXnzZtWrV69Y6wQAAAAAHi2+ijVr1mj8+PFau3at9u/fr0WLFumvv/5SREREkcbr0qWLPDw8lJiYqC1btmj58uV66qmn1LVrV1WoUMGhb2BgoL7//ntt375dnTt31vnz5xUfH69GjRqpffv2+vbbb5WRkaHVq1dr2LBhWrt27XWd6yOPPKLffvtNs2fPNl/ydNGIESM0b948JSUlaevWrUpPT9eCBQv0wgsvSLrwKPJbb72lLVu2aO/evXrvvffk6emZ73vFAAAAAFAcCLJXYbfbtXLlSrVu3VrVq1fXCy+8oKlTp6pVq1ZFGq9MmTL65ptvdPToUd19993697//rWbNmmnmzJmX7V+xYkV9//332rx5s7p06aK8vDx99dVXatKkiXr06KHq1aurU6dO+vXXX/MF4cLy9fXVv/71L3l7e6t9+/YO21q2bKnFixfr22+/1d13361//OMfmjZtmhlU/fz8NHv2bMXExCgqKkrfffedvvjiC5UrV+66agIAAACAy+GtxTA1a9ZMtWvX1ssvv3zDjnnxzWS8tRgAAAC4saz81mK+IwsdO3ZMKSkpSklJ0WuvvebscgAAAADgqgiyt7j9+/erVq1aV9y+bds2NWnSRMeOHdOkSZMcXjoFAAAAADcjguwtLjg4WBs3brzq9oyMjBtWDwAAAABcL4LsLc7V1fWqP5sDAAAAAFbDW4sBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAICluDq7AECSVo7tLLvd7uwyAAAAAFgAM7IAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALMXV2QUAktTkhQ/k4u7p7DIAFIN1U7o5uwQAAHCLY0YWAAAAAGApBFkAAAAAgKUQZAEAAAAAlkKQBQAAAABYCkEWAAAAAGApBFkAAAAAgKUQZAEAAAAAlkKQBQAAAABYCkEWAAAAAGApBFkAAAAAgKUQZAEAAAAAlkKQBQAAAABYCkEWAAAAAGApBFkAAAAAgKUQZAEAAAAAlkKQBQAAAABYCkEWAAAAAGApBNnbWFxcnAYOHFisYyYnJ8vPz69YxwQAAACAvyPIolg9/PDD2rlzp7PLAAAAAHALc3V2Abi1eHp6ytPT09llAAAAALiFMSN7mzt//rz69esnX19fBQQEaPjw4TIMQ5IUFhamsWPHqlu3bvL29lZoaKg+//xz/fXXX2rXrp28vb0VFRWltWvXmuPxaDEAAACAkkaQvc298847cnV11c8//6wZM2bopZde0pw5c8zt06ZNU0xMjDZs2KAHH3xQXbt2Vbdu3fToo49q/fr1uvPOO9WtWzcz/F7LmTNnlJ2d7bAAAAAAQGEQZG9zISEhmjZtmmrUqKEuXbroqaee0rRp08ztrVu31n/+8x+Fh4drxIgRys7O1t13362OHTuqevXqeu6555Senq6DBw8W6HgTJkyQr6+vuYSEhJTUqQEAAAC4RRFkb3P/+Mc/ZLPZzPVGjRpp165dys3NlSRFRUWZ2ypUqCBJioyMzNd26NChAh1v6NChysrKMpcDBw5c9zkAAAAAuL3wsidcVenSpc3PFwPv5dry8vIKNJ67u7vc3d2LsUIAAAAAtxtmZG9za9ascVj/6aefFB4eLhcXFydVBAAAAABXR5C9ze3fv1+DBg3Sjh079MEHH+iVV17RgAEDnF0WAAAAAFwRjxbf5rp166b//e9/atiwoVxcXDRgwAD16dPH2WUBAAAAwBXZjIL+bgpQArKzs+Xr66s6T70uF3dPZ5cDoBism9LN2SUAAAALupgNsrKyZLfbr9qXR4sBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAICluDq7AECSVo7tLLvd7uwyAAAAAFgAM7IAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFFdnFwBIUpMXPpCLu6ezy4DFrZvSzdklAAAA4AZgRhYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQfY2t2TJEt17773y8/NTuXLl1KZNG+3Zs8fcvnr1atWtW1ceHh5q0KCBPv30U9lsNm3cuNHss2XLFrVq1Ure3t6qUKGCunbtqsOHDzvhbAAAAADcDgiyt7mTJ09q0KBBWrt2rZYtW6ZSpUrpn//8p/Ly8pSdna22bdsqMjJS69ev15gxY/Tcc8857H/8+HE1bdpU0dHRWrt2rZYsWaKDBw8qISHhssc7c+aMsrOzHRYAAAAAKAxXZxcA5/rXv/7lsP7222+rfPny2rZtm3744QfZbDbNnj1bHh4eqlWrln7//Xf17t3b7D9z5kxFR0dr/PjxDmOEhIRo586dql69usP4EyZMUFJSUsmeFAAAAIBbGjOyt7ldu3apc+fOqlq1qux2u8LCwiRJ+/fv144dOxQVFSUPDw+zf8OGDR3237Rpk5YvXy5vb29zqVmzpiQ5PKJ80dChQ5WVlWUuBw4cKLmTAwAAAHBLYkb2Nte2bVuFhoZq9uzZCg4OVl5enu666y6dPXu2QPvn5OSobdu2mjRpUr5tQUFB+drc3d3l7u5+3XUDAAAAuH0RZG9jR44c0Y4dOzR79mzdd999kqQffvjB3F6jRg299957OnPmjBk+09LSHMaoV6+ePv74Y4WFhcnVlT9OAAAAAEoejxbfxsqWLaty5crpzTff1O7du/X9999r0KBB5vZHHnlEeXl56tOnj9LT0/XNN9/oxRdflCTZbDZJUt++fXX06FF17txZaWlp2rNnj7755hv16NFDubm5TjkvAAAAALc2guxtrFSpUlqwYIHWrVunu+66S08//bSmTJlibrfb7friiy+0ceNG1a1bV8OGDdOIESMkyfzebHBwsFJTU5Wbm6sWLVooMjJSAwcOlJ+fn0qV4o8XAAAAgOLHs6C3ufj4eG3bts2hzTAM83Pjxo21adMmc33+/PkqXbq0KleubLaFh4dr0aJFJV8sAAAAAIggi2uYN2+eqlatqkqVKmnTpk167rnnlJCQIE9PT2eXBgAAAOA2RZDFVf35558aMWKE/vzzTwUFBaljx44aN26cs8sCAAAAcBsjyOKqnn32WT377LPOLgMAAAAATLyNBwAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKa7OLgCQpJVjO8tutzu7DAAAAAAWwIwsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALMXV2QUAktTkhQ/k4u5ZLGOtm9KtWMYBAAAAcHNiRhYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkE2ZtcRkaGbDabNm7ceMU+ycnJ8vPzM9dHjRqlunXrXnXc7t27q3379sVSIwAAAADcSATZW8DDDz+snTt3OrsMAAAAALghXJ1dAK6fp6enPD09i3XMs2fPys3NrVjHBAAAAIDiwIzsTSIvL0+TJ09WtWrV5O7ursqVK2vcuHHm9r179+r+++9XmTJlVKdOHf3444/mtksfLb5Ubm6uBg0aJD8/P5UrV07PPvusDMNw6BMXF6d+/fpp4MCBCggIUMuWLSVJW7ZsUatWreTt7a0KFSqoa9euOnz4sMN+/fv317PPPit/f39VrFhRo0aNKp6LAgAAAACXQZC9SQwdOlQTJ07U8OHDtW3bNr3//vuqUKGCuX3YsGEaPHiwNm7cqOrVq6tz5846f/58gcaeOnWqkpOT9fbbb+uHH37Q0aNH9cknn+Tr984778jNzU2pqal6/fXXdfz4cTVt2lTR0dFau3atlixZooMHDyohISHffl5eXlqzZo0mT56s0aNHa+nSpZet5cyZM8rOznZYAAAAAKAweLT4JnDixAnNmDFDM2fOVGJioiTpzjvv1L333quMjAxJ0uDBg/Xggw9KkpKSklS7dm3t3r1bNWvWvOb406dP19ChQ9WhQwdJ0uuvv65vvvkmX7/w8HBNnjzZXB87dqyio6M1fvx4s+3tt99WSEiIdu7cqerVq0uSoqKiNHLkSHOMmTNnatmyZWrevHm+Y0yYMEFJSUkFuSwAAAAAcFnMyN4E0tPTdebMGTVr1uyKfaKioszPQUFBkqRDhw5dc+ysrCxlZmbqnnvuMdtcXV3VoEGDfH3r16/vsL5p0yYtX75c3t7e5nIxOO/Zs+eytV2s70q1DR06VFlZWeZy4MCBa54DAAAAAPwdM7I3gYK8qKl06dLmZ5vNJunC92qLk5eXl8N6Tk6O2rZtq0mTJuXrezFMX1rbxfquVJu7u7vc3d2LoVoAAAAAtytmZG8C4eHh8vT01LJly4p9bF9fXwUFBWnNmjVm2/nz57Vu3bpr7luvXj1t3bpVYWFhqlatmsNyaegFAAAAgBuFIHsT8PDw0HPPPadnn31W8+bN0549e/TTTz/prbfeKpbxBwwYoIkTJ+rTTz/V9u3b9eSTT+r48ePX3K9v3746evSoOnfurLS0NO3Zs0fffPONevToodzc3GKpDQAAAAAKi0eLbxLDhw+Xq6urRowYoT/++ENBQUF6/PHHi2XsZ555RpmZmUpMTFSpUqXUs2dP/fOf/1RWVtZV9wsODlZqaqqee+45tWjRQmfOnFFoaKgeeOABlSrFv4EAAAAAcA6bcekPigI3UHZ2tnx9fVXnqdfl4n7t7woXxLop3YplHAAAAAA3zsVskJWVJbvdftW+RZ5We/fddxUTE6Pg4GD9+uuvki78zMtnn31W1CEBAAAAALimIgXZWbNmadCgQWrdurWOHz9ufl/Sz89P06dPL876AAAAAABwUKQg+8orr2j27NkaNmyYXFxczPYGDRpo8+bNxVYcAAAAAACXKlKQ3bdvn6Kjo/O1u7u76+TJk9ddFAAAAAAAV1KkIFulShVt3LgxX/uSJUsUERFxvTUBAAAAAHBFRfr5nUGDBqlv3746ffq0DMPQzz//rA8++EATJkzQnDlzirtGAAAAAABMRQqyjz32mDw9PfXCCy/o1KlTeuSRRxQcHKwZM2aoU6dOxV0jAAAAAACmQgfZ8+fP6/3331fLli3VpUsXnTp1Sjk5OQoMDCyJ+gAAAAAAcFDo78i6urrq8ccf1+nTpyVJZcqUIcQCAAAAAG6YIr3sqWHDhtqwYUNx1wIAAAAAwDUV6TuyTz75pJ555hn99ttvql+/vry8vBy2R0VFFUtxAAAAAABcqkhB9uILnfr372+22Ww2GYYhm82m3Nzc4qkOAAAAAIBLFCnI7tu3r7jrAAAAAACgQIoUZENDQ4u7DgAAAAAACqRIQXbevHlX3d6tW7ciFQMAAAAAwLUUKcgOGDDAYf3cuXM6deqU3NzcVKZMGYIsAAAAAKDEFCnIHjt2LF/brl279MQTT2jIkCHXXRRuPyvHdpbdbnd2GQAAAAAsoEi/I3s54eHhmjhxYr7ZWgAAAAAAilOxBVlJcnV11R9//FGcQwIAAAAA4KBIjxZ//vnnDuuGYSgzM1MzZ85UTExMsRQGAAAAAMDlFCnItm/f3mHdZrOpfPnyatq0qaZOnVocdQEAAAAAcFlFCrJ5eXnFXQcAAAAAAAVSpO/Ijh49WqdOncrX/r///U+jR4++7qIAAAAAALgSm2EYRmF3cnFxUWZmpgIDAx3ajxw5osDAQOXm5hZbgbi1ZWdny9fXV1lZWfz8DgAAAHAbK0w2KNKMrGEYstls+do3bdokf3//ogwJAAAAAECBFOo7smXLlpXNZpPNZlP16tUdwmxubq5ycnL0+OOPF3uRAAAAAABcVKggO336dBmGoZ49eyopKUm+vr7mNjc3N4WFhalRo0bFXiQAAAAAABcVKsgmJiZKkqpUqaLGjRurdOnSJVIUbj9NXvhALu6e+drXTenmhGoAAAAA3MyK9PM7sbGx5ufTp0/r7NmzDtt5aQ8AAAAAoKQU6WVPp06dUr9+/RQYGCgvLy+VLVvWYQEAAAAAoKQUKcgOGTJE33//vWbNmiV3d3fNmTNHSUlJCg4O1rx584q7RgAAAAAATEV6tPiLL77QvHnzFBcXpx49eui+++5TtWrVFBoaqvnz56tLly7FXScAAAAAAJKKOCN79OhRVa1aVdKF78MePXpUknTvvfdq5cqVxVcdAAAAAACXKFKQrVq1qvbt2ydJqlmzpj788ENJF2Zq/fz8iq04AAAAAAAuVaQg26NHD23atEmS9Pzzz+vVV1+Vh4eHnn76aQ0ZMqRYCwQAAAAA4O+K9B3Zp59+2vwcHx+v7du3a926dapWrZqioqKKrTgAAAAAAC5VpCD7d6dPn1ZoaKhCQ0OLox4AAAAAAK6qSI8W5+bmasyYMapUqZK8vb21d+9eSdLw4cP11ltvFWuBAAAAAAD8XZGC7Lhx45ScnKzJkyfLzc3NbL/rrrs0Z86cYisOAAAAAIBLFSnIzps3T2+++aa6dOkiFxcXs71OnTravn17sRUHAAAAAMClihRkf//9d1WrVi1fe15ens6dO3fdRQEAAAAAcCVFCrK1atXSqlWr8rV/9NFHio6Ovu6iAAAAAAC4kiK9tXjEiBFKTEzU77//rry8PC1atEg7duzQvHnztHjx4uKuEQAAAAAAU6FmZPfu3SvDMNSuXTt98cUX+u677+Tl5aURI0YoPT1dX3zxhZo3b15StQIAAAAAULgZ2fDwcGVmZiowMFD33Xef/P39tXnzZlWoUKGk6gMAAAAAwEGhZmQNw3BY//rrr3Xy5MliLQgAAAAAgKsp0sueLro02OLWZ7PZ9Omnnzq7DAAAAAC3sUIFWZvNJpvNlq8NAAAAAIAbpVDfkTUMQ927d5e7u7sk6fTp03r88cfl5eXl0G/RokXFVyEAAAAAAH9TqBnZxMREBQYGytfXV76+vnr00UcVHBxsrl9c8P989NFHioyMlKenp8qVK6f4+Hjze8Vz5sxRRESEPDw8VLNmTb322mvmfj179lRUVJTOnDkjSTp79qyio6PVrVu3ax4zIyNDNptNH374oe677z55enrq7rvv1s6dO5WWlqYGDRrI29tbrVq10l9//WXul5aWpubNmysgIEC+vr6KjY3V+vXrr3qsAwcOKCEhQX5+fvL391e7du2UkZFRhCsFAAAAAAVTqBnZuXPnllQdt6TMzEx17txZkydP1j//+U+dOHFCq1atkmEYmj9/vkaMGKGZM2cqOjpaGzZsUO/eveXl5aXExES9/PLLqlOnjp5//nlNmzZNw4YN0/HjxzVz5swCH3/kyJGaPn26KleurJ49e+qRRx6Rj4+PZsyYoTJlyighIUEjRozQrFmzJEknTpxQYmKiXnnlFRmGoalTp6p169batWuXfHx88o1/7tw5tWzZUo0aNdKqVavk6uqqsWPH6oEHHtAvv/wiNze3fPucOXPGDOeSlJ2dXYQrCwAAAOB2Vqggi8LJzMzU+fPn1aFDB4WGhkqSIiMjJV0ImVOnTlWHDh0kSVWqVNG2bdv0xhtvKDExUd7e3nrvvfcUGxsrHx8fTZ8+XcuXL5fdbi/w8QcPHqyWLVtKkgYMGKDOnTtr2bJliomJkST16tVLycnJZv+mTZs67P/mm2/Kz89PK1asUJs2bfKNv3DhQuXl5WnOnDnmd6Xnzp0rPz8/paSkqEWLFvn2mTBhgpKSkgp8DgAAAABwqet6azGurk6dOmrWrJkiIyPVsWNHzZ49W8eOHdPJkye1Z88e9erVS97e3uYyduxY7dmzx9y/UaNGGjx4sMaMGaNnnnlG9957b6GOHxUVZX6++Fu/F4P0xbZDhw6Z6wcPHlTv3r0VHh4uX19f2e125eTkaP/+/Zcdf9OmTdq9e7d8fHzMc/D399fp06cdzuPvhg4dqqysLHM5cOBAoc4JAAAAAJiRLUEuLi5aunSpVq9erW+//VavvPKKhg0bpi+++EKSNHv2bN1zzz359rkoLy9PqampcnFx0e7duwt9/NKlS5ufL86YXtqWl5dnricmJurIkSOaMWOGQkND5e7urkaNGuns2bOXHT8nJ0f169fX/Pnz820rX778Zfdxd3c3XxYGAAAAAEVBkC1hNptNMTExiomJ0YgRIxQaGqrU1FQFBwdr79696tKlyxX3nTJlirZv364VK1aoZcuWmjt3rnr06FFitaampuq1115T69atJV14kdPhw4ev2L9evXpauHChAgMDC/XIMwAAAABcDx4tLkFr1qzR+PHjtXbtWu3fv1+LFi3SX3/9pYiICCUlJWnChAl6+eWXtXPnTm3evFlz587VSy+9JEnasGGDRowYoTlz5igmJkYvvfSSBgwYoL1795ZYveHh4Xr33XeVnp6uNWvWqEuXLvL09Lxi/y5duiggIEDt2rXTqlWrtG/fPqWkpKh///767bffSqxOAAAAALc3gmwJstvtWrlypVq3bq3q1avrhRde0NSpU9WqVSs99thjmjNnjubOnavIyEjFxsYqOTlZVapU0enTp/Xoo4+qe/fuatu2rSSpT58+uv/++9W1a1fl5uaWSL1vvfWWjh07pnr16qlr167q37+/AgMDr9i/TJkyWrlypSpXrqwOHTooIiJCvXr10unTp5mhBQAAAFBibIZhGM4uArev7Oxs+fr6qs5Tr8vFPf/s77op1/7dXAAAAADWdzEbZGVlXXNijBlZAAAAAIClEGQtaPz48Q4/2/P3pVWrVs4uDwAAAABKFG8ttqDHH39cCQkJl912tZczAQAAAMCtgCBrQf7+/vL393d2GQAAAADgFDxaDAAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALMXV2QUAkrRybGfZ7XZnlwEAAADAApiRBQAAAABYCkEWAAAAAGApBFkAAAAAgKUQZAEAAAAAlkKQBQAAAABYCkEWAAAAAGApBFkAAAAAgKUQZAEAAAAAlkKQBQAAAABYCkEWAAAAAGAprs4uAJCkJi98IBd3z3zt66Z0c0I1AAAAAG5mzMgCAAAAACyFIAsAAAAAsBSCLAAAAADAUgiyAAAAAABLIcgCAAAAACyFIAsAAAAAsBSCLAAAAADAUgiyAAAAAABLIcgCAAAAACyFIAsAAAAAsBSCLAAAAADAUgiyAAAAAABLIcgCAAAAACyFIAsAAAAAsBSCLAAAAADAUgiyAAAAAABLIcgCAAAAACyFIAsAAAAAsJRbNsiGhYVp+vTpzi6jxKSkpMhms+n48ePOLgUAAAAAbqhbNsimpaWpT58+zi6jWMTFxWngwIHOLuOqRo0apbp16zq7DAAAAAC3gZsuyJ49e7ZYxilfvrzKlClTLGM5S3FdCwAAAAC4lTg9yMbFxalfv34aOHCgAgIC1LJlS23ZskWtWrWSt7e3KlSooK5du+rw4cPmPidOnFCXLl3k5eWloKAgTZs2Ld+s5aWPFu/fv1/t2rWTt7e37Ha7EhISdPDgQXP7xRnFd999V2FhYfL19VWnTp104sSJAp9H//799eyzz8rf318VK1bUqFGjHPoUtIY5c+aoSpUq8vDwUPfu3bVixQrNmDFDNptNNptNGRkZ5j7r1q1TgwYNVKZMGTVu3Fg7duyQJGVlZcnFxUVr166VJOXl5cnf31//+Mc/zH3fe+89hYSEmOsHDhxQQkKC/Pz85O/vr3bt2jkcKyUlRQ0bNpSXl5f8/PwUExOjX3/9VcnJyUpKStKmTZvMGpOTky97nc6cOaPs7GyHBQAAAAAKw+lBVpLeeecdubm5KTU1VRMnTlTTpk0VHR2ttWvXasmSJTp48KASEhLM/oMGDVJqaqo+//xzLV26VKtWrdL69euvOH5eXp7atWuno0ePasWKFVq6dKn27t2rhx9+2KHfnj179Omnn2rx4sVavHixVqxYoYkTJxbqPLy8vLRmzRpNnjxZo0eP1tKlSwtVw+7du/Xxxx9r0aJF2rhxo2bMmKFGjRqpd+/eyszMVGZmpkP4HDZsmKZOnaq1a9fK1dVVPXv2lCT5+vqqbt26SklJkSRt3rxZNptNGzZsUE5OjiRpxYoVio2NlSSdO3dOLVu2lI+Pj1atWqXU1FR5e3vrgQce0NmzZ3X+/Hm1b99esbGx+uWXX/Tjjz+qT58+stlsevjhh/XMM8+odu3aZo2XntdFEyZMkK+vr7n8/VwAAAAAoCBcnV2AJIWHh2vy5MmSpLFjxyo6Olrjx483t7/99tsKCQnRzp07FRQUpHfeeUfvv/++mjVrJkmaO3eugoODrzj+smXLtHnzZu3bt88MTvPmzVPt2rWVlpamu+++W9KFsJmcnCwfHx9JUteuXbVs2TKNGzeuQOcRFRWlkSNHmuc0c+ZMLVu2TM2bNy9wDWfPntW8efNUvnx5c1w3NzeVKVNGFStWzHfMcePGmWH0+eef14MPPqjTp0/Lw8NDcXFxSklJ0eDBg5WSkqLmzZtr+/bt+uGHH/TAAw8oJSVFzz77rCRp4cKFysvL05w5c2Sz2czr6ufnp5SUFDVo0EBZWVlq06aN7rzzTklSRESEWYe3t7dcXV0vW+PfDR06VIMGDTLXs7OzCbMAAAAACuWmmJGtX7+++XnTpk1avny5vL29zaVmzZqSLsyY7t27V+fOnVPDhg3NfXx9fVWjRo0rjp+enq6QkBCHwFSrVi35+fkpPT3dbAsLCzNDrCQFBQXp0KFDBT6PqKgoh/W/71/QGkJDQx1CbGGOGRQUJEnmMWNjY/XDDz8oNzdXK1asUFxcnBlu//jjD+3evVtxcXGSLlz33bt3y8fHx7zu/v7+On36tPbs2SN/f391795dLVu2VNu2bTVjxgxlZmYWuM6L3N3dZbfbHRYAAAAAKIybYkbWy8vL/JyTk6O2bdtq0qRJ+foFBQVp9+7dJVZH6dKlHdZtNpvy8vJu2P6S47Uo7DEvzqRePGaTJk104sQJrV+/XitXrtT48eNVsWJFTZw4UXXq1FFwcLDCw8MlXbju9evX1/z58/Md42Kwnjt3rvr3768lS5Zo4cKFeuGFF7R06VKH790CAAAAQEm7KWZk/65evXraunWrwsLCVK1aNYfFy8tLVatWVenSpZWWlmbuk5WVpZ07d15xzIiICB04cEAHDhww27Zt26bjx4+rVq1aJXo+xVGDm5ubcnNzC31MPz8/RUVFaebMmSpdurRq1qypJk2aaMOGDVq8eLH5SLJ04brv2rVLgYGB+a67r6+v2S86OlpDhw7V6tWrddddd+n999+/rhoBAAAAoLBuuiDbt29fHT16VJ07d1ZaWpr27Nmjb775Rj169FBubq58fHyUmJioIUOGaPny5dq6dat69eqlUqVKmTOSl4qPj1dkZKS6dOmi9evX6+eff1a3bt0UGxurBg0a3JDzup4awsLCtGbNGmVkZOjw4cOFmuWNi4vT/PnzzdDq7++viIgILVy40CHIdunSRQEBAWrXrp1WrVqlffv2KSUlRf3799dvv/2mffv2aejQofrxxx/166+/6ttvv9WuXbvM78mGhYVp37592rhxow4fPqwzZ84U4SoBAAAAwLXddEE2ODhYqampys3NVYsWLRQZGamBAwfKz89PpUpdKPell15So0aN1KZNG8XHxysmJkYRERHy8PC47Jg2m02fffaZypYtqyZNmig+Pl5Vq1bVwoULb9h5XU8NgwcPlouLi2rVqqXy5ctr//79BT5ubGyscnNzze/CShfC7aVtZcqU0cqVK1W5cmV16NBBERER6tWrl06fPi273a4yZcpo+/bt+te//qXq1aurT58+6tu3r/7zn/9Ikv71r3/pgQce0P3336/y5cvrgw8+KHCNAAAAAFAYNsMwDGcXcb1OnjypSpUqaerUqerVq5ezy0EhZGdny9fXV3Weel0u7p75tq+b0s0JVQEAAAC40S5mg6ysrGu+FPameNlTYW3YsEHbt29Xw4YNlZWVpdGjR0uS2rVr5+TKAAAAAAAlzZJBVpJefPFF7dixQ25ubqpfv75WrVqlgICAEjnW/v37r/pCpm3btqly5colcmwAAAAAgCNLBtno6GitW7fuhh0vODhYGzduvOp2AAAAAMCNYckge6O5urqqWrVqzi4DAAAAAKCb8K3FAAAAAABcDUEWAAAAAGApBFkAAAAAgKUQZAEAAAAAlkKQBQAAAABYCkEWAAAAAGApBFkAAAAAgKUQZAEAAAAAlkKQBQAAAABYCkEWAAAAAGApBFkAAAAAgKW4OrsAQJJWju0su93u7DIAAAAAWAAzsgAAAAAASyHIAgAAAAAshSALAAAAALAUgiwAAAAAwFIIsgAAAAAASyHIAgAAAAAshSALAAAAALAUgiwAAAAAwFIIsgAAAAAASyHIAgAAAAAshSALAAAAALAUV2cXAEhSkxc+kIu7p0PbuindnFQNAAAAgJsZM7IAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEshyAIAAAAALIUgCwAAAACwFIIsAAAAAMBSCLIAAAAAAEu55YNsWFiYpk+f7uwyStztcp4AAAAAcMsH2bS0NPXp08fZZRSb5ORk+fn5ObuMyyJMAwAAALgRXJ1dwJWcPXtWbm5u1z1O+fLli6EaAAAAAMDN4qaZkY2Li1O/fv00cOBABQQEqGXLltqyZYtatWolb29vVahQQV27dtXhw4fNfU6cOKEuXbrIy8tLQUFBmjZtmuLi4jRw4ECzz6WzhPv371e7du3k7e0tu92uhIQEHTx40Nw+atQo1a1bV++++67CwsLk6+urTp066cSJEwU6j48++kiRkZHy9PRUuXLlFB8fr5MnT0qS8vLyNHr0aN1xxx1yd3dX3bp1tWTJEnPflJQU2Ww2HT9+3GzbuHGjbDabMjIylJKSoh49eigrK0s2m002m02jRo0y+546dUo9e/aUj4+PKleurDfffNOhtgMHDighIUF+fn7y9/dXu3btlJGRYW5PS0tT8+bNFRAQIF9fX8XGxmr9+vXmdsMwNGrUKFWuXFnu7u4KDg5W//79zfv366+/6umnnzZrAwAAAICScNMEWUl655135ObmptTUVE2cOFFNmzZVdHS01q5dqyVLlujgwYNKSEgw+w8aNEipqan6/PPPtXTpUq1atcoheF0qLy9P7dq109GjR7VixQotXbpUe/fu1cMPP+zQb8+ePfr000+1ePFiLV68WCtWrNDEiROvWX9mZqY6d+6snj17Kj09XSkpKerQoYMMw5AkzZgxQ1OnTtWLL76oX375RS1bttRDDz2kXbt2Fej6NG7cWNOnT5fdbldmZqYyMzM1ePBgc/vUqVPVoEEDbdiwQU8++aSeeOIJ7dixQ5J07tw5tWzZUj4+Plq1apVSU1Pl7e2tBx54QGfPnpV04R8GEhMT9cMPP+inn35SeHi4WrdubYb4jz/+WNOmTdMbb7yhXbt26dNPP1VkZKQkadGiRbrjjjs0evRos7bLOXPmjLKzsx0WAAAAACiMm+rR4vDwcE2ePFmSNHbsWEVHR2v8+PHm9rffflshISHauXOngoKC9M477+j9999Xs2bNJElz585VcHDwFcdftmyZNm/erH379ikkJESSNG/ePNWuXVtpaWm6++67JV0IvMnJyfLx8ZEkde3aVcuWLdO4ceOuWn9mZqbOnz+vDh06KDQ0VJLMoCdJL774op577jl16tRJkjRp0iQtX75c06dP16uvvnrN6+Pm5iZfX1/ZbDZVrFgx3/bWrVvrySeflCQ999xzmjZtmpYvX64aNWpo4cKFysvL05w5c8zZ0rlz58rPz08pKSlq0aKFmjZt6jDem2++KT8/P61YsUJt2rTR/v37VbFiRcXHx6t06dKqXLmyGjZsKEny9/eXi4uLfHx8LlvbRRMmTFBSUtI1zxUAAAAAruSmmpGtX7+++XnTpk1avny5vL29zaVmzZqSLsyY7t27V+fOnTODlCT5+vqqRo0aVxw/PT1dISEhZoiVpFq1asnPz0/p6elmW1hYmBliJSkoKEiHDh26Zv116tRRs2bNFBkZqY4dO2r27Nk6duyYJCk7O1t//PGHYmJiHPaJiYlxOPb1iIqKMj9fDLsX6960aZN2794tHx8f83r6+/vr9OnT2rNnjyTp4MGD6t27t8LDw+Xr6yu73a6cnBzt379fktSxY0f973//U9WqVdW7d2998sknOn/+fKFqHDp0qLKysszlwIEDxXLuAAAAAG4fN9WMrJeXl/k5JydHbdu21aRJk/L1CwoK0u7du0usjtKlSzus22w25eXlXXM/FxcXLV26VKtXr9a3336rV155RcOGDdOaNWtUrly5a+5fqtSFf1e4+CiydOGR4OKoOycnR/Xr19f8+fPz7XfxhViJiYk6cuSIZsyYodDQULm7u6tRo0bmo8chISHasWOHvvvuOy1dulRPPvmkpkyZohUrVuQ79pW4u7vL3d29wOcEAAAAAJe6qWZk/65evXraunWrwsLCVK1aNYfFy8tLVatWVenSpZWWlmbuk5WVpZ07d15xzIiICB04cMBhFnDbtm06fvy4atWqVSx122w2xcTEKCkpSRs2bJCbm5s++eQT2e12BQcHKzU11aF/amqqeeyLgfLv3y/duHGjQ383Nzfl5uYWuq569epp165dCgwMzHc9fX19zVr69++v1q1bq3bt2nJ3d3d4uZYkeXp6qm3btnr55ZeVkpKiH3/8UZs3b76u2gAAAACgMG7aINu3b18dPXpUnTt3Vlpamvbs2aNvvvlGPXr0UG5urnx8fJSYmKghQ4Zo+fLl2rp1q3r16qVSpUpd8Y258fHxioyMVJcuXbR+/Xr9/PPP6tatm2JjY9WgQYPrrnnNmjUaP3681q5dq/3792vRokX666+/FBERIUkaMmSIJk2apIULF2rHjh16/vnntXHjRg0YMECSVK1aNYWEhGjUqFHatWuXvvzyS02dOtXhGGFhYcrJydGyZct0+PBhnTp1qkC1denSRQEBAWrXrp1WrVqlffv2KSUlRf3799dvv/0m6cJ3lN99912lp6drzZo16tKlizw9Pc0xkpOT9dZbb2nLli3au3ev3nvvPXl6eprfBw4LC9PKlSv1+++/5wvAAAAAAFBcbtoge3H2Mjc3Vy1atFBkZKQGDhwoPz8/8xHcl156SY0aNVKbNm0UHx+vmJgYRUREyMPD47Jj2mw2ffbZZypbtqyaNGmi+Ph4Va1aVQsXLiyWmu12u1auXKnWrVurevXqeuGFFzR16lS1atVKktS/f38NGjRIzzzzjCIjI7VkyRJ9/vnnCg8Pl3Th0eAPPvhA27dvV1RUlCZNmqSxY8c6HKNx48Z6/PHH9fDDD6t8+fLmy7GupUyZMlq5cqUqV66sDh06KCIiQr169dLp06dlt9slSW+99ZaOHTumevXqqWvXrurfv78CAwPNMfz8/DR79mzFxMQoKipK3333nb744gvzsenRo0crIyNDd955J7/fCwAAAKDE2Iy/fyHT4k6ePKlKlSpp6tSp6tWrl7PLQQFkZ2fL19dXdZ56XS7ung7b1k3p5qSqAAAAANxoF7NBVlaWOdl2JTfVy54Ka8OGDdq+fbsaNmyorKwsjR49WpLUrl07J1cGAAAAACgplg6y0oXfZt2xY4fc3NxUv359rVq1SgEBASVyrP3791/1pVDbtm1T5cqVS+TYAAAAAIALLB1ko6OjtW7duht2vODg4HxvEb50OwAAAACgZFk6yN5orq6uqlatmrPLAAAAAIDb2k371mIAAAAAAC6HIAsAAAAAsBSCLAAAAADAUgiyAAAAAABLIcgCAAAAACyFIAsAAAAAsBSCLAAAAADAUgiyAAAAAABLIcgCAAAAACyFIAsAAAAAsBSCLAAAAADAUgiyAAAAAABLcXV2AYAkrRzbWXa73dllAAAAALAAZmQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKa7OLgCQpCYvfCAXd09zfd2Ubk6sBgAAAMDNjBlZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAIClEGQBAAAAAJZCkAUAAAAAWApBFgAAAABgKQRZAAAAAICl3JJBNiwsTNOnT3d2GSXudjlPAAAAAPi7WzLIpqWlqU+fPs4uo9gkJyfLz8/P2WVcVUZGhmw2mzZu3OjsUgAAAADc4lydXcDfnT17Vm5ubtc9Tvny5YuhGgAAAADAzcipM7JxcXHq16+fBg4cqICAALVs2VJbtmxRq1at5O3trQoVKqhr1646fPiwuc+JEyfUpUsXeXl5KSgoSNOmTVNcXJwGDhxo9rn0kdv9+/erXbt28vb2lt1uV0JCgg4ePGhuHzVqlOrWrat3331XYWFh8vX1VadOnXTixIkCncdHH32kyMhIeXp6qly5coqPj9fJkyclSXl5eRo9erTuuOMOubu7q27dulqyZIm5b0pKimw2m44fP262bdy4UTabTRkZGUpJSVGPHj2UlZUlm80mm82mUaNGmX1PnTqlnj17ysfHR5UrV9abb75pbvv3v/+tfv36mesDBw6UzWbT9u3bJV34hwMvLy999913Zq0TJkxQlSpV5OnpqTp16uijjz4y9z927Ji6dOmi8uXLy9PTU+Hh4Zo7d64kqUqVKpKk6Oho2Ww2xcXFFejaAQAAAEBhOf3R4nfeeUdubm5KTU3VxIkT1bRpU0VHR2vt2rVasmSJDh48qISEBLP/oEGDlJqaqs8//1xLly7VqlWrtH79+iuOn5eXp3bt2uno0aNasWKFli5dqr179+rhhx926Ldnzx59+umnWrx4sRYvXqwVK1Zo4sSJ16w/MzNTnTt3Vs+ePZWenq6UlBR16NBBhmFIkmbMmKGpU6fqxRdf1C+//KKWLVvqoYce0q5duwp0fRo3bqzp06fLbrcrMzNTmZmZGjx4sLl96tSpatCggTZs2KAnn3xSTzzxhHbs2CFJio2NVUpKitl3xYoVCggIMNvS0tJ07tw5NW7cWJI0YcIEzZs3T6+//rq2bt2qp59+Wo8++qhWrFghSRo+fLi2bdumr7/+Wunp6Zo1a5YCAgIkST///LMk6bvvvlNmZqYWLVp02fM5c+aMsrOzHRYAAAAAKBTDiWJjY43o6GhzfcyYMUaLFi0c+hw4cMCQZOzYscPIzs42Spcubfz3v/81tx8/ftwoU6aMMWDAALMtNDTUmDZtmmEYhvHtt98aLi4uxv79+83tW7duNSQZP//8s2EYhjFy5EijTJkyRnZ2ttlnyJAhxj333HPNc1i3bp0hycjIyLjs9uDgYGPcuHEObXfffbfx5JNPGoZhGMuXLzckGceOHTO3b9iwwZBk7Nu3zzAMw5g7d67h6+ubb+zQ0FDj0UcfNdfz8vKMwMBAY9asWYZhGMYvv/xi2Gw249ChQ8bRo0cNNzc3Y8yYMcbDDz9sGIZhjB071mjcuLFhGIZx+vRpo0yZMsbq1asdjtGrVy+jc+fOhmEYRtu2bY0ePXpc9jz37dtnSDI2bNhw2e0XjRw50pCUb6nz1OtGvcHvmAsAAACA20tWVpYhycjKyrpmX6fPyNavX9/8vGnTJi1fvlze3t7mUrNmTUkXZkz37t2rc+fOqWHDhuY+vr6+qlGjxhXHT09PV0hIiEJCQsy2WrVqyc/PT+np6WZbWFiYfHx8zPWgoCAdOnTomvXXqVNHzZo1U2RkpDp27KjZs2fr2LFjkqTs7Gz98ccfiomJcdgnJibG4djXIyoqyvxss9lUsWJFs+677rpL/v7+WrFihVatWqXo6Gi1adPGnGFdsWKF+Qjw7t27derUKTVv3tzh+s+bN0979uyRJD3xxBNasGCB6tatq2effVarV68udL1Dhw5VVlaWuRw4cOA6rwAAAACA243TX/bk5eVlfs7JyVHbtm01adKkfP2CgoK0e/fuEqujdOnSDus2m015eXnX3M/FxUVLly7V6tWr9e233+qVV17RsGHDtGbNGpUrV+6a+5cqdeHfEoz//1FkSTp37lyx1G2z2dSkSROlpKTI3d1dcXFxioqK0pkzZ7RlyxatXr3afEw5JydHkvTll1+qUqVKDmO6u7tLklq1aqVff/1VX331lZYuXapmzZqpb9++evHFFwtcr7u7uzkeAAAAABSF02dk/65evXraunWrwsLCVK1aNYfFy8tLVatWVenSpZWWlmbuk5WVpZ07d15xzIiICB04cMBh5m/btm06fvy4atWqVSx122w2xcTEKCkpSRs2bJCbm5s++eQT2e12BQcHKzU11aF/amqqeeyLb1jOzMw0t1/6EzZubm7Kzc0tUm0XvyebkpKiuLg4lSpVSk2aNNGUKVN05swZc7a4Vq1acnd31/79+/Nd+7/PZpcvX16JiYl67733NH36dPPlUhffNl3UOgEAAACgoJw+I/t3ffv21ezZs9W5c2c9++yz8vf31+7du7VgwQLNmTNHPj4+SkxM1JAhQ+Tv76/AwECNHDlSpUqVks1mu+yY8fHxioyMVJcuXTR9+nSdP39eTz75pGJjY9WgQYPrrnnNmjVatmyZWrRoocDAQK1Zs0Z//fWXIiIiJElDhgzRyJEjdeedd6pu3bqaO3euNm7cqPnz50uSGRRHjRqlcePGaefOnZo6darDMcLCwpSTk6Nly5apTp06KlOmjMqUKVOg+uLi4vT000/Lzc1N9957r9k2ePBg3X333eaMuI+PjwYPHqynn35aeXl5uvfee5WVlaXU1FTZ7XYlJiZqxIgRql+/vmrXrq0zZ85o8eLF5nkGBgbK09NTS5Ys0R133CEPDw/5+vpe9/UFAAAAgEvdVDOyF2cvc3Nz1aJFC0VGRmrgwIHy8/MzH8F96aWX1KhRI7Vp00bx8fGKiYlRRESEPDw8LjumzWbTZ599prJly6pJkyaKj49X1apVtXDhwmKp2W63a+XKlWrdurWqV6+uF154QVOnTlWrVq0kSf3799egQYP0zDPPKDIyUkuWLNHnn3+u8PBwSRceDf7ggw+0fft2RUVFadKkSRo7dqzDMRo3bqzHH39cDz/8sMqXL6/JkycXuL7IyEj5+fmpbt268vb2lnQhyObm5ub7iZwxY8Zo+PDhmjBhgiIiIvTAAw/oyy+/NH9ax83NTUOHDlVUVJSaNGkiFxcXLViwQJLk6uqql19+WW+88YaCg4PVrl27Il1PAAAAALgWm/H3L2da0MmTJ1WpUiVNnTpVvXr1cnY5KKTs7Gz5+vqqzlOvy8Xd02xfN6WbE6sCAAAAcKNdzAZZWVmy2+1X7XtTPVpcEBs2bND27dvVsGFDZWVlafTo0ZLEDCAAAAAA3CYsF2Ql6cUXX9SOHTvk5uam+vXra9WqVQoICCiRY+3fv/+qL4Xatm2bKleuXCLHBgAAAADkZ7kgGx0drXXr1t2w4wUHB+d7i/Cl2wEAAAAAN47lguyN5urqqmrVqjm7DAAAAADA/++memsxAAAAAADXQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApbg6uwBAklaO7Sy73e7sMgAAAABYADOyAAAAAABLIcgCAAAAACyFIAsAAAAAsBSCLAAAAADAUnjZE5zKMAxJUnZ2tpMrAQAAAOBMFzPBxYxwNQRZONWRI0ckSSEhIU6uBAAAAMDN4MSJE/L19b1qH4IsnMrf31+StH///mv+YYV1ZGdnKyQkRAcOHOBnlW4R3NNbD/f01sM9vTVxX2893NMrMwxDJ06cUHBw8DX7EmThVKVKXfiatq+vL3+Rb0F2u537eovhnt56uKe3Hu7prYn7euvhnl5eQSe3eNkTAAAAAMBSCLIAAAAAAEshyMKp3N3dNXLkSLm7uzu7FBQj7uuth3t66+Ge3nq4p7cm7uuth3taPGxGQd5tDAAAAADATYIZWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWZS4V199VWFhYfLw8NA999yjn3/++ar9//vf/6pmzZry8PBQZGSkvvrqqxtUKQqjMPd169at+te//qWwsDDZbDZNnz79xhWKAivMPZ09e7buu+8+lS1bVmXLllV8fPw1/27jxivMPV20aJEaNGggPz8/eXl5qW7dunr33XdvYLUoiML+N/WiBQsWyGazqX379iVbIIqkMPc1OTlZNpvNYfHw8LiB1aIgCvt39fjx4+rbt6+CgoLk7u6u6tWr8//A10CQRYlauHChBg0apJEjR2r9+vWqU6eOWrZsqUOHDl22/+rVq9W5c2f16tVLGzZsUPv27dW+fXtt2bLlBleOqynsfT116pSqVq2qiRMnqmLFije4WhREYe9pSkqKOnfurOXLl+vHH39USEiIWrRood9///0GV44rKew99ff317Bhw/Tjjz/ql19+UY8ePdSjRw998803N7hyXElh7+lFGRkZGjx4sO67774bVCkKoyj31W63KzMz01x+/fXXG1gxrqWw9/Ts2bNq3ry5MjIy9NFHH2nHjh2aPXu2KlWqdIMrtxgDKEENGzY0+vbta67n5uYawcHBxoQJEy7bPyEhwXjwwQcd2u655x7jP//5T4nWicIp7H39u9DQUGPatGklWB2K4nruqWEYxvnz5w0fHx/jnXfeKakSUUjXe08NwzCio6ONF154oSTKQxEU5Z6eP3/eaNy4sTFnzhwjMTHRaNeu3Q2oFIVR2Ps6d+5cw9fX9wZVh6Io7D2dNWuWUbVqVePs2bM3qsRbAjOyKDFnz57VunXrFB8fb7aVKlVK8fHx+vHHHy+7z48//ujQX5Jatmx5xf648YpyX3FzK457eurUKZ07d07+/v4lVSYK4XrvqWEYWrZsmXbs2KEmTZqUZKkooKLe09GjRyswMFC9evW6EWWikIp6X3NychQaGqqQkBC1a9dOW7duvRHlogCKck8///xzNWrUSH379lWFChV01113afz48crNzb1RZVsSQRYl5vDhw8rNzVWFChUc2itUqKA///zzsvv8+eefheqPG68o9xU3t+K4p88995yCg4Pz/UMUnKOo9zQrK0ve3t5yc3PTgw8+qFdeeUXNmzcv6XJRAEW5pz/88IPeeustzZ49+0aUiCIoyn2tUaOG3n77bX322Wd67733lJeXp8aNG+u33367ESXjGopyT/fu3auPPvpIubm5+uqrrzR8+HBNnTpVY8eOvRElW5arswsAAFjbxIkTtWDBAqWkpPDCEYvz8fHRxo0blZOTo2XLlmnQoEGqWrWq4uLinF0aCunEiRPq2rWrZs+erYCAAGeXg2LUqFEjNWrUyFxv3LixIiIi9MYbb2jMmDFOrAxFlZeXp8DAQL355ptycXFR/fr19fvvv2vKlCkaOXKks8u7aRFkUWICAgLk4uKigwcPOrQfPHjwii/8qVixYqH648Yryn3Fze167umLL76oiRMn6rvvvlNUVFRJlolCKOo9LVWqlKpVqyZJqlu3rtLT0zVhwgSC7E2gsPd0z549ysjIUNu2bc22vLw8SZKrq6t27NihO++8s2SLxjUVx39TS5curejoaO3evbskSkQhFeWeBgUFqXTp0nJxcTHbIiIi9Oeff+rs2bNyc3Mr0ZqtikeLUWLc3NxUv359LVu2zGzLy8vTsmXLHP4l8e8aNWrk0F+Sli5desX+uPGKcl9xcyvqPZ08ebLGjBmjJUuWqEGDBjeiVBRQcf09zcvL05kzZ0qiRBRSYe9pzZo1tXnzZm3cuNFcHnroId1///3auHGjQkJCbmT5uILi+Luam5urzZs3KygoqKTKRCEU5Z7GxMRo9+7d5j82SdLOnTsVFBREiL0aZ79tCre2BQsWGO7u7kZycrKxbds2o0+fPoafn5/x559/GoZhGF27djWef/55s39qaqrh6upqvPjii0Z6eroxcuRIo3Tp0sbmzZuddQq4jMLe1zNnzhgbNmwwNmzYYAQFBRmDBw82NmzYYOzatctZp4BLFPaeTpw40XBzczM++ugjIzMz01xOnDjhrFPAJQp7T8ePH298++23xp49e4xt27YZL774ouHq6mrMnj3bWaeASxT2nl6KtxbfnAp7X5OSkoxvvvnG2LNnj7Fu3TqjU6dOhoeHh7F161ZnnQIuUdh7un//fsPHx8fo16+fsWPHDmPx4sVGYGCgMXbsWGedgiUQZFHiXnnlFaNy5cqGm5ub0bBhQ+Onn34yt8XGxhqJiYkO/T/88EOjevXqhpubm1G7dm3jyy+/vMEVoyAKc1/37dtnSMq3xMbG3vjCcUWFuaehoaGXvacjR4688YXjigpzT4cNG2ZUq1bN8PDwMMqWLWs0atTIWLBggROqxtUU9r+pf0eQvXkV5r4OHDjQ7FuhQgWjdevWxvr1651QNa6msH9XV69ebdxzzz2Gu7u7UbVqVWPcuHHG+fPnb3DV1mIzDMNw1mwwAAAAAACFxXdkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAOA20r17d7Vv397ZZVxWRkaGbDabNm7c6OxSAAA3OYIsAABwurNnzzq7BACAhRBkAQC4TcXFxempp57SwIEDVbZsWVWoUEGzZ8/WyZMn1aNHD/n4+KhatWr6+uuvzX1SUlJks9n05ZdfKioqSh4eHvrHP/6hLVu2OIz98ccfq3bt2nJ3d1dYWJimTp3qsD0sLExjxoxRt27dZLfb1adPH1WpUkWSFB0dLZvNpri4OElSWlqamjdvroCAAPn6+io2Nlbr1693GM9ms2nOnDn65z//qTJlyig8PFyff/65Q5+tW7eqTZs2stvt8vHx0X333ac9e/aY2+fMmaOIiAh5eHioZs2aeu211677GgMASgZBFgCA29g777yjgIAA/fzzz3rqqaf0xBNPqGPHjmrcuLHWr1+vFi1aqGvXrjp16pTDfkOGDNHUqVOVlpam8uXLq23btjp37pwkad26dUpISFCnTp20efNmjRo1SsOHD1dycrLDGC+++KLq1KmjDRs2aPjw4fr5558lSd99950yMzO1aNEiSdKJEyeUmJioH374QT/99JPCw8PVunVrnThxwmG8pKQkJSQk6JdfflHr1q3VpUsXHT16VJL0+++/q0mTJnJ3d9f333+vdevWqWfPnjp//rwkaf78+RoxYoTGjRun9PR0jR8/XsOHD9c777xT7NccAFAMDAAAcNtITEw02rVrZxiGYcTGxhr33nuvue38+fOGl5eX0bVrV7MtMzPTkGT8+OOPhmEYxvLlyw1JxoIFC8w+R44cMTw9PY2FCxcahmEYjzzyiNG8eXOH4w4ZMsSoVauWuR4aGmq0b9/eoc++ffsMScaGDRuueg65ubmGj4+P8cUXX5htkowXXnjBXM/JyTEkGV9//bVhGIYxdOhQo0qVKsbZs2cvO+add95pvP/++w5tY8aMMRo1anTVWgAAzsGMLAAAt7GoqCjzs4uLi8qVK6fIyEizrUKFCpKkQ4cOOezXqFEj87O/v79q1Kih9PR0SVJ6erpiYmIc+sfExGjXrl3Kzc012xo0aFCgGg8ePKjevXsrPDxcvr6+stvtysnJ0f79+694Ll5eXrLb7WbdGzdu1H333afSpUvnG//kyZPas2ePevXqJW9vb3MZO3asw6PHAICbh6uzCwAAAM5zabCz2WwObTabTZKUl5dX7Mf28vIqUL/ExEQdOXJEM2bMUGhoqNzd3dWoUaN8L4i63LlcrNvT0/OK4+fk5EiSZs+erXvuucdhm4uLS4FqBADcWARZAABQaD/99JMqV64sSTp27Jh27typiIgISVJERIRSU1Md+qempqp69epXDYZubm6S5DBre3Hf1157Ta1bt5YkHThwQIcPHy5UvVFRUXrnnXd07ty5fIG3QoUKCg4O1t69e9WlS5dCjQsAcA6CLAAAKLTRo0erXLlyqlChgoYNG6aAgADz92mfeeYZ3X333RozZowefvhh/fjjj5o5c+Y13wIcGBgoT09PLVmyRHfccYc8PDzk6+ur8PBwvfvuu2rQoIGys7M1ZMiQq86wXk6/fv30yiuvqFOnTho6dKh8fX31008/qWHDhqpRo4aSkpLUv39/+fr66oEHHtCZM2e0du1aHTt2TIMGDSrqZQIAlBC+IwsAAApt4sSJGjBggOrXr68///xTX3zxhTmjWq9ePX344YdasGCB7rrrLo0YMUKjR49W9+7drzqmq6urXn75Zb3xxhsKDg5Wu3btJElvvfWWjh07pnr16qlr167q37+/AgMDC1VvuXLl9P333ysnJ0exsbGqX7++Zs+ebc7OPvbYY5ozZ47mzp2ryMhIxcbGKjk52fxJIADAzcVmGIbh7CIAAIA1pKSk6P7779exY8fk5+fn7HIAALcpZmQBAAAAAJZCkAUAAAAAWAqPFgMAAAAALIUZWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCkEWQAAAACApRBkAQAAAACWQpAFAAAAAFgKQRYAAAAAYCn/H8RxqgjFCacTAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train RMSE: 1072.3827255198853\n", + "Train R²: 0.9921277274068127\n", + "Train MAE: 480.25126389741285\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n", + "from sklearn.model_selection import cross_val_score\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# Загрузка данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Предобработка данных\n", + "# Преобразуем категориальные переменные в числовые\n", + "df = pd.get_dummies(df, drop_first=True)\n", + "\n", + "# Разделение данных на признаки и целевую переменную\n", + "X = df.drop('charges', axis=1)\n", + "y = df['charges']\n", + "\n", + "# Разделение данных на обучающую и тестовую выборки\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Выбор модели\n", + "model = RandomForestRegressor(random_state=42)\n", + "\n", + "# Обучение модели\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Предсказание и оценка\n", + "y_pred = model.predict(X_test)\n", + "\n", + "rmse = mean_squared_error(y_test, y_pred, squared=False)\n", + "r2 = r2_score(y_test, y_pred)\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "\n", + "print(f\"RMSE: {rmse}\")\n", + "print(f\"R²: {r2}\")\n", + "print(f\"MAE: {mae}\")\n", + "\n", + "# Кросс-валидация\n", + "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n", + "rmse_cv = (-scores.mean())**0.5\n", + "print(f\"Cross-validated RMSE: {rmse_cv}\")\n", + "\n", + "# Анализ важности признаков\n", + "feature_importances = model.feature_importances_\n", + "feature_names = X_train.columns\n", + "\n", + "importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})\n", + "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "sns.barplot(x='Importance', y='Feature', data=importance_df)\n", + "plt.title('Feature Importance')\n", + "plt.show()\n", + "\n", + "# Проверка на переобучение\n", + "y_train_pred = model.predict(X_train)\n", + "\n", + "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n", + "r2_train = r2_score(y_train, y_train_pred)\n", + "mae_train = mean_absolute_error(y_train, y_train_pred)\n", + "\n", + "print(f\"Train RMSE: {rmse_train}\")\n", + "print(f\"Train R²: {r2_train}\")\n", + "print(f\"Train MAE: {mae_train}\")\n", + "\n", + "# Визуализация результатов\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(y_test, y_pred, alpha=0.5)\n", + "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n", + "plt.xlabel('Actual Charges')\n", + "plt.ylabel('Predicted Charges')\n", + "plt.title('Actual vs Predicted Charges')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Вывод по данным:\n", + "\n", + "1. Train RMSE: Значение RMSE на обучающей выборке составляет 1072.38, что указывает на среднеквадратичную ошибку в предсказании стоимости медицинского страхования.\n", + "\n", + "2. Train R²: Значение R² на обучающей выборке составляет 0.9921, что говорит о том, что модель объясняет 99.21% вариации в данных. Это очень высокий показатель, что может указывать на потенциальное переобучение.\n", + "\n", + "3. Train MAE: Значение MAE на обучающей выборке составляет 480.25, что указывает на среднюю абсолютную ошибку в предсказании стоимости медицинского страхования." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aimenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 947ff81783946999d79f13bb1ebaefeeed42f910 Mon Sep 17 00:00:00 2001 From: bulatova_karina Date: Sat, 9 Nov 2024 11:45:34 +0400 Subject: [PATCH 3/3] lab_4 --- lab_4/lab_4.ipynb | 1935 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1935 insertions(+) create mode 100644 lab_4/lab_4.ipynb diff --git a/lab_4/lab_4.ipynb b/lab_4/lab_4.ipynb new file mode 100644 index 0000000..a88831b --- /dev/null +++ b/lab_4/lab_4.ipynb @@ -0,0 +1,1935 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Начало лабораторной работы" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "print(df.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Бизнес-цели" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Прогнозирование стоимости страховых взносов:\n", + "\n", + "Цель: Разработать модель, которая будет предсказывать стоимость страховых взносов для новых клиентов на основе их характеристик (возраст, пол, ИМТ, количество детей, статус курения, регион проживания).\n", + "\n", + "Применение:\n", + "Клиенты могут получить представление о примерной стоимости страховки до обращения в компанию.\n", + "\n", + "2. Оптимизация тарифной сетки:\n", + "\n", + "Цель: Определить оптимальные коэффициенты для различных факторов, влияющих на стоимость страховки (например, возраст, ИМТ, статус курения), чтобы максимизировать прибыль компании при сохранении конкурентоспособных тарифов.\n", + "\n", + "Применение:\n", + "Страховые компании могут использовать эти коэффициенты для корректировки тарифной сетки и повышения эффективности бизнеса." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Прогнозирование стоимости страховых взносов:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Среднее значение поля 'charges': 13261.369959046897\n", + " age sex bmi children smoker region charges \\\n", + "0 19 female 27.900 0 yes southwest 16884.92400 \n", + "1 18 male 33.770 1 no southeast 1725.55230 \n", + "2 28 male 33.000 3 no southeast 4449.46200 \n", + "3 33 male 22.705 0 no northwest 21984.47061 \n", + "4 32 male 28.880 0 no northwest 3866.85520 \n", + "\n", + " above_average_charges charges_volatility \n", + "0 1 62648.55411 \n", + "1 0 62648.55411 \n", + "2 0 62648.55411 \n", + "3 1 62648.55411 \n", + "4 0 62648.55411 \n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Устанавливаем случайное состояние\n", + "random_state = 42\n", + "\n", + "# Рассчитываем среднее значение стоимости страховых взносов\n", + "average_charges = df['charges'].mean()\n", + "print(f\"Среднее значение поля 'charges': {average_charges}\")\n", + "\n", + "# Создаем новую переменную, указывающую, превышает ли стоимость страховых взносов среднюю\n", + "df['above_average_charges'] = (df['charges'] > average_charges).astype(int)\n", + "\n", + "# Рассчитываем волатильность (разницу между максимальной и минимальной стоимостью страховых взносов)\n", + "df['charges_volatility'] = df['charges'].max() - df['charges'].min()\n", + "\n", + "# Выводим первые строки измененной таблицы для проверки\n", + "print(df.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Оптимизация тарифной сетки:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Средняя стоимость страховых взносов для 'age':\n", + "age\n", + "18 6714.267794\n", + "19 9634.641344\n", + "20 10159.697736\n", + "21 5349.737625\n", + "22 10675.132648\n", + "23 12050.721224\n", + "24 10648.015962\n", + "25 9610.781531\n", + "26 5955.403311\n", + "27 13130.462272\n", + "28 8757.474523\n", + "29 10430.158727\n", + "30 13580.480238\n", + "31 10196.980573\n", + "32 10071.740266\n", + "33 12118.482617\n", + "34 11613.528121\n", + "35 11307.182031\n", + "36 12204.476138\n", + "37 17595.511688\n", + "38 8102.733674\n", + "39 11468.895088\n", + "40 11772.251310\n", + "41 9533.603123\n", + "42 13061.038669\n", + "43 19267.278653\n", + "44 16439.727524\n", + "45 14404.055995\n", + "46 14201.069951\n", + "47 18153.128652\n", + "48 14632.500445\n", + "49 12696.006264\n", + "50 15663.003301\n", + "51 15452.800438\n", + "52 18951.581034\n", + "53 15795.645012\n", + "54 18252.834139\n", + "55 16164.545488\n", + "56 14727.018377\n", + "57 16283.671944\n", + "58 13815.290525\n", + "59 18639.637165\n", + "60 21979.418507\n", + "61 22024.457609\n", + "62 18926.646066\n", + "63 19884.998461\n", + "64 24419.101775\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'sex':\n", + "sex\n", + "female 12486.831977\n", + "male 14013.872721\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'bmi':\n", + "bmi\n", + "15.960 1694.796400\n", + "16.815 4904.000350\n", + "17.195 14455.644050\n", + "17.290 7813.353433\n", + "17.385 2775.192150\n", + " ... \n", + "48.070 9432.925300\n", + "49.060 11381.325400\n", + "50.380 2438.055200\n", + "52.580 44501.398200\n", + "53.130 1163.462700\n", + "Name: charges, Length: 548, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'children':\n", + "children\n", + "0 12317.920881\n", + "1 12722.650521\n", + "2 15268.182723\n", + "3 15304.070620\n", + "4 13550.983876\n", + "5 8706.036629\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'smoker':\n", + "smoker\n", + "no 8417.874411\n", + "yes 32223.139764\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'region':\n", + "region\n", + "northeast 13475.874737\n", + "northwest 12463.129315\n", + "southeast 14748.777706\n", + "southwest 12164.196435\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для комбинации 'age' и 'smoker':\n", + "age smoker\n", + "18 no 3083.404099\n", + " yes 25473.730221\n", + "19 no 3492.047133\n", + " yes 26445.951817\n", + "20 no 3673.112925\n", + " ... \n", + "62 yes 37084.607312\n", + "63 no 14205.335706\n", + " yes 40331.784380\n", + "64 no 15805.350545\n", + " yes 40569.885331\n", + "Name: charges, Length: 94, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для комбинации 'bmi' и 'smoker':\n", + "bmi smoker\n", + "15.960 no 1694.79640\n", + "16.815 no 4904.00035\n", + "17.195 yes 14455.64405\n", + "17.290 no 5305.30260\n", + " yes 12829.45510\n", + " ... \n", + "48.070 no 9432.92530\n", + "49.060 no 11381.32540\n", + "50.380 no 2438.05520\n", + "52.580 yes 44501.39820\n", + "53.130 no 1163.46270\n", + "Name: charges, Length: 701, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для комбинации 'region' и 'smoker':\n", + "region smoker\n", + "northeast no 9225.395851\n", + " yes 29790.212814\n", + "northwest no 8681.948181\n", + " yes 29959.103039\n", + "southeast no 7887.181702\n", + " yes 35262.090761\n", + "southwest no 7956.579615\n", + " yes 32346.494062\n", + "Name: charges, dtype: float64\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Устанавливаем случайное состояние\n", + "random_state = 42\n", + "\n", + "# Рассчитываем среднюю стоимость страховых взносов для каждого значения каждого признака\n", + "for column in ['age', 'sex', 'bmi', 'children', 'smoker', 'region']:\n", + " print(f\"Средняя стоимость страховых взносов для '{column}':\")\n", + " print(df.groupby(column)['charges'].mean())\n", + " print()\n", + "\n", + "# Рассчитываем среднюю стоимость страховых взносов для комбинаций признаков\n", + "# для комбинации 'age' и 'smoker'\n", + "print(\"Средняя стоимость страховых взносов для комбинации 'age' и 'smoker':\")\n", + "print(df.groupby(['age', 'smoker'])['charges'].mean())\n", + "print()\n", + "\n", + "# Рассчитываем среднюю стоимость страховых взносов для комбинации 'bmi' и 'smoker'\n", + "print(\"Средняя стоимость страховых взносов для комбинации 'bmi' и 'smoker':\")\n", + "print(df.groupby(['bmi', 'smoker'])['charges'].mean())\n", + "print()\n", + "\n", + "# Рассчитываем среднюю стоимость страховых взносов для комбинации 'region' и 'smoker'\n", + "print(\"Средняя стоимость страховых взносов для комбинации 'region' и 'smoker':\")\n", + "print(df.groupby(['region', 'smoker'])['charges'].mean())\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Выбор ориентира для каждой задачи:\n", + "1. Прогнозирование стоимости страховых взносов:\n", + "Ориентир:\n", + "\n", + "R² (коэффициент детерминации): 0.75 - 0.85\n", + "\n", + "MAE (средняя абсолютная ошибка): 2000 - 3000 долларов\n", + "\n", + "RMSE (среднеквадратичная ошибка): 3000 - 5000 долларов\n", + "\n", + "Объяснение:\n", + "\n", + "R²: Значение 0.75 - 0.85 будет означать, что модель объясняет 75-85% вариации стоимости страховых взносов, что является хорошим результатом для задачи регрессии.\n", + "\n", + "MAE: Значение 2000 - 3000 долларов будет означать, что в среднем модель ошибается на 2000 - 3000 долларов при прогнозировании стоимости страховых взносов.\n", + "\n", + "RMSE: Значение 3000 - 5000 долларов будет означать, что среднеквадратичная ошибка модели составляет 3000 - 4000 долларов.\n", + "\n", + "2. Оптимизация тарифной сетки:\n", + "Ориентир:\n", + "\n", + "Увеличение прибыли компании: 5% - 10%\n", + "\n", + "Сохранение конкурентоспособных тарифов:\n", + "\n", + "Средняя стоимость страховых взносов не должна увеличиваться более чем на 5% по сравнению с текущими тарифами.\n", + "\n", + "Доля клиентов, считающих тарифы дорогими, не должна увеличиваться более чем на 2%.\n", + "\n", + "Объяснение:\n", + "\n", + "Увеличение прибыли компании: Цель оптимизации тарифной сетки - максимизировать прибыль компании. Ориентир в 5% - 10% увеличения прибыли является реалистичным и достижимым.\n", + "\n", + "Сохранение конкурентоспособных тарифов: Важно, чтобы оптимизация тарифной сетки не привела к значительному увеличению стоимости страховых взносов для клиентов. Ориентир в 5% увеличения средней стоимости страховых взносов и 2% увеличения доли клиентов, считающих тарифы дорогими, позволяет сохранить конкурентоспособность компании." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAE: 4160.247974762991\n", + "MSE: 39933194.54805147\n", + "RMSE: 6319.271678607549\n", + "R²: 0.73981661775643\n", + "Ориентиры для прогнозирования стоимости страховых взносов не достигнуты.\n", + "Средняя стоимость страховых взносов для 'age':\n", + "age\n", + "18 6714.267794\n", + "19 9634.641344\n", + "20 10159.697736\n", + "21 5349.737625\n", + "22 10675.132648\n", + "23 12050.721224\n", + "24 10648.015962\n", + "25 9610.781531\n", + "26 5955.403311\n", + "27 13130.462272\n", + "28 8757.474523\n", + "29 10430.158727\n", + "30 13580.480238\n", + "31 10196.980573\n", + "32 10071.740266\n", + "33 12118.482617\n", + "34 11613.528121\n", + "35 11307.182031\n", + "36 12204.476138\n", + "37 17595.511688\n", + "38 8102.733674\n", + "39 11468.895088\n", + "40 11772.251310\n", + "41 9533.603123\n", + "42 13061.038669\n", + "43 19267.278653\n", + "44 16439.727524\n", + "45 14404.055995\n", + "46 14201.069951\n", + "47 18153.128652\n", + "48 14632.500445\n", + "49 12696.006264\n", + "50 15663.003301\n", + "51 15452.800438\n", + "52 18951.581034\n", + "53 15795.645012\n", + "54 18252.834139\n", + "55 16164.545488\n", + "56 14727.018377\n", + "57 16283.671944\n", + "58 13815.290525\n", + "59 18639.637165\n", + "60 21979.418507\n", + "61 22024.457609\n", + "62 18926.646066\n", + "63 19884.998461\n", + "64 24419.101775\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'bmi':\n", + "bmi\n", + "15.960 1694.796400\n", + "16.815 4904.000350\n", + "17.195 14455.644050\n", + "17.290 7813.353433\n", + "17.385 2775.192150\n", + " ... \n", + "48.070 9432.925300\n", + "49.060 11381.325400\n", + "50.380 2438.055200\n", + "52.580 44501.398200\n", + "53.130 1163.462700\n", + "Name: charges, Length: 548, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'children':\n", + "children\n", + "0 12317.920881\n", + "1 12722.650521\n", + "2 15268.182723\n", + "3 15304.070620\n", + "4 13550.983876\n", + "5 8706.036629\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'sex_male':\n", + "sex_male\n", + "False 12486.831977\n", + "True 14013.872721\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'smoker_yes':\n", + "smoker_yes\n", + "False 8417.874411\n", + "True 32223.139764\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'region_northwest':\n", + "region_northwest\n", + "False 13512.808188\n", + "True 12463.129315\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'region_southeast':\n", + "region_southeast\n", + "False 12693.396712\n", + "True 14748.777706\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для 'region_southwest':\n", + "region_southwest\n", + "False 13620.788872\n", + "True 12164.196435\n", + "Name: charges, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для комбинации 'age' и 'smoker_yes':\n", + "age smoker_yes\n", + "18 False 3083.404099\n", + " True 25473.730221\n", + "19 False 3492.047133\n", + " True 26445.951817\n", + "20 False 3673.112925\n", + " ... \n", + "62 True 37084.607312\n", + "63 False 14205.335706\n", + " True 40331.784380\n", + "64 False 15805.350545\n", + " True 40569.885331\n", + "Name: charges, Length: 94, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для комбинации 'bmi' и 'smoker_yes':\n", + "bmi smoker_yes\n", + "15.960 False 1694.79640\n", + "16.815 False 4904.00035\n", + "17.195 True 14455.64405\n", + "17.290 False 5305.30260\n", + " True 12829.45510\n", + " ... \n", + "48.070 False 9432.92530\n", + "49.060 False 11381.32540\n", + "50.380 False 2438.05520\n", + "52.580 True 44501.39820\n", + "53.130 False 1163.46270\n", + "Name: charges, Length: 701, dtype: float64\n", + "\n", + "Средняя стоимость страховых взносов для комбинации 'region_northwest' и 'smoker_yes':\n", + "region_northwest smoker_yes\n", + "False False 8331.120934\n", + " True 32822.144996\n", + "True False 8681.948181\n", + " True 29959.103039\n", + "Name: charges, dtype: float64\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Преобразуем категориальные переменные в числовые\n", + "df = pd.get_dummies(df, columns=['sex', 'smoker', 'region'], drop_first=True)\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y)\n", + "X = df.drop('charges', axis=1)\n", + "y = df['charges']\n", + "\n", + "# Разделяем данные на обучающую и тестовую выборки\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Стандартизируем признаки\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "# Обучаем модель линейной регрессии\n", + "model = LinearRegression()\n", + "model.fit(X_train, y_train)\n", + "\n", + "# Делаем предсказания на тестовой выборке\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Оцениваем качество модели\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "rmse = mean_squared_error(y_test, y_pred, squared=False)\n", + "r2 = r2_score(y_test, y_pred)\n", + "\n", + "print(f\"MAE: {mae}\")\n", + "print(f\"MSE: {mse}\")\n", + "print(f\"RMSE: {rmse}\")\n", + "print(f\"R²: {r2}\")\n", + "\n", + "# Проверяем, достигнуты ли ориентиры\n", + "if r2 >= 0.75 and mae <= 3000 and rmse <= 5000:\n", + " print(\"Ориентиры для прогнозирования стоимости страховых взносов достигнуты!\")\n", + "else:\n", + " print(\"Ориентиры для прогнозирования стоимости страховых взносов не достигнуты.\")\n", + "\n", + "# Оптимизация тарифной сетки\n", + "# Убедитесь, что столбцы существуют\n", + "columns_to_group = ['age', 'bmi', 'children', 'sex_male', 'smoker_yes', 'region_northwest', 'region_southeast', 'region_southwest']\n", + "\n", + "# Рассчитываем среднюю стоимость страховых взносов для каждого значения каждого признака\n", + "for column in columns_to_group:\n", + " print(f\"Средняя стоимость страховых взносов для '{column}':\")\n", + " print(df.groupby(column)['charges'].mean())\n", + " print()\n", + "\n", + "# Рассчитываем среднюю стоимость страховых взносов для комбинаций признаков\n", + "# Например, для комбинации 'age' и 'smoker_yes'\n", + "print(\"Средняя стоимость страховых взносов для комбинации 'age' и 'smoker_yes':\")\n", + "print(df.groupby(['age', 'smoker_yes'])['charges'].mean())\n", + "print()\n", + "\n", + "# Рассчитываем среднюю стоимость страховых взносов для комбинации 'bmi' и 'smoker_yes'\n", + "print(\"Средняя стоимость страховых взносов для комбинации 'bmi' и 'smoker_yes':\")\n", + "print(df.groupby(['bmi', 'smoker_yes'])['charges'].mean())\n", + "print()\n", + "\n", + "# Рассчитываем среднюю стоимость страховых взносов для комбинации 'region_northwest' и 'smoker_yes'\n", + "print(\"Средняя стоимость страховых взносов для комбинации 'region_northwest' и 'smoker_yes':\")\n", + "print(df.groupby(['region_northwest', 'smoker_yes'])['charges'].mean())\n", + "print()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Анализ применимости алгоритмов обучения с учителем для решения поставленных задач:\n", + "1. Прогнозирование стоимости страховых взносов:\n", + "Задача: Регрессия\n", + "\n", + "Свойства алгоритмов:\n", + "\n", + "Линейная регрессия:\n", + "Применимость: Хорошо подходит для задач, где зависимость между признаками и целевой переменной линейна.\n", + "Преимущества: Проста в реализации, интерпретируема.\n", + "Недостатки: Может плохо работать, если зависимость нелинейна.\n", + "\n", + "Деревья решений (регрессия):\n", + "Применимость: Подходит для задач с нелинейными зависимостями.\n", + "Преимущества: Может обрабатывать категориальные признаки, не требует масштабирования данных.\n", + "Недостатки: Подвержены переобучению, могут давать нестабильные результаты.\n", + "\n", + "Случайный лес (регрессия):\n", + "Применимость: Хорошо подходит для задач с нелинейными зависимостями и большим количеством признаков.\n", + "Преимущества: Устойчив к переобучению, может обрабатывать категориальные признаки.\n", + "Недостатки: Менее интерпретируем, чем линейная регрессия.\n", + "\n", + "Градиентный бустинг (регрессия):\n", + "Применимость: Подходит для задач с нелинейными зависимостями и сложными взаимосвязями между признаками.\n", + "Преимущества: Может достигать высокой точности, устойчив к переобучению.\n", + "Недостатки: Сложнее в настройке, чем случайный лес, менее интерпретируем.\n", + "\n", + "Нейронные сети (регрессия):\n", + "Применимость: Подходит для задач с очень сложными зависимостями и большим количеством данных.\n", + "Преимущества: Может моделировать очень сложные зависимости.\n", + "Недостатки: Требует большого количества данных, сложнее в настройке и интерпретации.\n", + "\n", + "Вывод:\n", + "\n", + "Линейная регрессия: Может быть хорошим выбором для начала, особенно если зависимость между признаками и целевой переменной линейна.\n", + "\n", + "Деревья решений и случайный лес: Подходят для задач с нелинейными зависимостями.\n", + "\n", + "Градиентный бустинг: Может давать более высокую точность, чем случайный лес, но требует больше времени на настройку.\n", + "\n", + "Нейронные сети: Могут быть излишними для этой задачи, если данных недостаточно много.\n", + "\n", + "2. Оптимизация тарифной сетки:\n", + "Задача: Классификация (группировка клиентов по группам риска)\n", + "\n", + "Свойства алгоритмов:\n", + "\n", + "Логистическая регрессия:\n", + "Применимость: Хорошо подходит для задач бинарной классификации, где зависимость между признаками и целевой переменной линейна.\n", + "Преимущества: Проста в реализации, интерпретируема.\n", + "Недостатки: Может плохо работать, если зависимость нелинейна.\n", + "\n", + "Деревья решений (классификация):\n", + "Применимость: Подходит для задач с нелинейными зависимостями.\n", + "Преимущества: Может обрабатывать категориальные признаки, не требует масштабирования данных.\n", + "Недостатки: Подвержены переобучению, могут давать нестабильные результаты.\n", + "\n", + "Случайный лес (классификация):\n", + "Применимость: Хорошо подходит для задач с нелинейными зависимостями и большим количеством признаков.\n", + "Преимущества: Устойчив к переобучению, может обрабатывать категориальные признаки.\n", + "Недостатки: Менее интерпретируем, чем линейная регрессия.\n", + "\n", + "Градиентный бустинг (классификация):\n", + "Применимость: Подходит для задач с нелинейными зависимостями и сложными взаимосвязями между признаками.\n", + "Преимущества: Может достигать высокой точности, устойчив к переобучению.\n", + "Недостатки: Сложнее в настройке, чем случайный лес, менее интерпретируем.\n", + "\n", + "Нейронные сети (классификация):\n", + "Применимость: Подходит для задач с очень сложными зависимостями и большим количеством данных.\n", + "Преимущества: Может моделировать очень сложные зависимости.\n", + "Недостатки: Требует большого количества данных, сложнее в настройке и интерпретации.\n", + "\n", + "Вывод:\n", + "\n", + "Логистическая регрессия: Может быть хорошим выбором для начала, особенно если зависимость между признаками и целевой переменной линейна.\n", + "\n", + "Деревья решений и случайный лес: Подходят для задач с нелинейными зависимостями.\n", + "\n", + "Градиентный бустинг: Может давать более высокую точность, чем случайный лес, но требует больше времени на настройку.\n", + "\n", + "Нейронные сети: Могут быть излишними для этой задачи, если данных недостаточно много.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Прогнозирование стоимости страховых взносов:\n", + "Выбранные модели:\n", + "\n", + "Линейная регрессия\n", + "\n", + "Случайный лес (регрессия)\n", + "\n", + "Градиентный бустинг (регрессия)\n", + "\n", + "2. Оптимизация тарифной сетки:\n", + "Выбранные модели:\n", + "\n", + "Логистическая регрессия\n", + "\n", + "Случайный лес (классификация)\n", + "\n", + "Градиентный бустинг (классификация)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Результаты для задачи регрессии:\n", + "Model: Linear Regression\n", + "MAE: 4160.247974762991\n", + "MSE: 39933194.54805147\n", + "RMSE: 6319.271678607549\n", + "R²: 0.73981661775643\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: Random Forest Regression\n", + "MAE: 1303.0047135437117\n", + "MSE: 7810682.767902057\n", + "RMSE: 2794.759876608732\n", + "R²: 0.9491097598580805\n", + "\n", + "Model: Gradient Boosting Regression\n", + "MAE: 2297.7789526178262\n", + "MSE: 19231434.89568898\n", + "RMSE: 4385.365993356652\n", + "R²: 0.8746982345593103\n", + "\n", + "Результаты для задачи классификации:\n", + "Model: Logistic Regression\n", + "Accuracy: 0.8864864864864865\n", + "\n", + "Model: Random Forest Classification\n", + "Accuracy: 0.9765765765765766\n", + "\n", + "Model: Gradient Boosting Classification\n", + "Accuracy: 0.9225225225225225\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, accuracy_score\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Преобразуем категориальные переменные в числовые\n", + "df = pd.get_dummies(df, columns=['sex', 'smoker', 'region'], drop_first=True)\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n", + "X_reg = df.drop('charges', axis=1)\n", + "y_reg = df['charges']\n", + "\n", + "# Разделяем данные на обучающую и тестовую выборки для задачи регрессии\n", + "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n", + "\n", + "# Стандартизируем признаки для задачи регрессии\n", + "scaler_reg = StandardScaler()\n", + "X_train_reg = scaler_reg.fit_transform(X_train_reg)\n", + "X_test_reg = scaler_reg.transform(X_test_reg)\n", + "\n", + "# Список моделей для задачи регрессии\n", + "models_reg = {\n", + " \"Linear Regression\": LinearRegression(),\n", + " \"Random Forest Regression\": RandomForestRegressor(),\n", + " \"Gradient Boosting Regression\": GradientBoostingRegressor()\n", + "}\n", + "\n", + "# Обучаем и оцениваем модели для задачи регрессии\n", + "print(\"Результаты для задачи регрессии:\")\n", + "for name, model in models_reg.items():\n", + " model.fit(X_train_reg, y_train_reg)\n", + " y_pred_reg = model.predict(X_test_reg)\n", + " mae = mean_absolute_error(y_test_reg, y_pred_reg)\n", + " mse = mean_squared_error(y_test_reg, y_pred_reg)\n", + " rmse = mean_squared_error(y_test_reg, y_pred_reg, squared=False)\n", + " r2 = r2_score(y_test_reg, y_pred_reg)\n", + " print(f\"Model: {name}\")\n", + " print(f\"MAE: {mae}\")\n", + " print(f\"MSE: {mse}\")\n", + " print(f\"RMSE: {rmse}\")\n", + " print(f\"R²: {r2}\")\n", + " print()\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n", + "X_class = df.drop('charges', axis=1)\n", + "y_class = (df['charges'] > df['charges'].mean()).astype(int)\n", + "\n", + "# Разделяем данные на обучающую и тестовую выборки для задачи классификации\n", + "X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n", + "\n", + "# Стандартизируем признаки для задачи классификации\n", + "scaler_class = StandardScaler()\n", + "X_train_class = scaler_class.fit_transform(X_train_class)\n", + "X_test_class = scaler_class.transform(X_test_class)\n", + "\n", + "# Список моделей для задачи классификации\n", + "models_class = {\n", + " \"Logistic Regression\": LogisticRegression(),\n", + " \"Random Forest Classification\": RandomForestClassifier(),\n", + " \"Gradient Boosting Classification\": GradientBoostingClassifier()\n", + "}\n", + "\n", + "# Обучаем и оцениваем модели для задачи классификации\n", + "print(\"Результаты для задачи классификации:\")\n", + "for name, model in models_class.items():\n", + " model.fit(X_train_class, y_train_class)\n", + " y_pred_class = model.predict(X_test_class)\n", + " accuracy = accuracy_score(y_test_class, y_pred_class)\n", + " print(f\"Model: {name}\")\n", + " print(f\"Accuracy: {accuracy}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Прогнозирование стоимости страховых взносов:\n", + "Конвейер для задачи регрессии:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Результаты для задачи регрессии:\n", + "Model: Linear Regression\n", + "MAE: 4158.694987099099\n", + "MSE: 39908584.112821\n", + "RMSE: 6317.32412599045\n", + "R²: 0.7399769662171334\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: Random Forest Regression\n", + "MAE: 1302.1428312961073\n", + "MSE: 7363635.134840652\n", + "RMSE: 2713.6018747857343\n", + "R²: 0.9520224836336337\n", + "\n", + "Model: Gradient Boosting Regression\n", + "MAE: 2304.718628546955\n", + "MSE: 19256343.733882822\n", + "RMSE: 4388.205069716184\n", + "R²: 0.8745359418641633\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Определяем категориальные и числовые столбцы\n", + "categorical_cols = ['sex', 'smoker', 'region']\n", + "numerical_cols = ['age', 'bmi', 'children']\n", + "\n", + "# Создаем преобразователь для категориальных и числовых столбцов\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('cat', OneHotEncoder(), categorical_cols),\n", + " ('num', StandardScaler(), numerical_cols)\n", + " ])\n", + "\n", + "# Список моделей для задачи регрессии\n", + "models_reg = {\n", + " \"Linear Regression\": LinearRegression(),\n", + " \"Random Forest Regression\": RandomForestRegressor(),\n", + " \"Gradient Boosting Regression\": GradientBoostingRegressor()\n", + "}\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n", + "X_reg = df[categorical_cols + numerical_cols]\n", + "y_reg = df['charges']\n", + "\n", + "# Разделяем данные на обучающую и тестовую выборки для задачи регрессии\n", + "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n", + "\n", + "# Обучаем и оцениваем модели для задачи регрессии\n", + "print(\"Результаты для задачи регрессии:\")\n", + "for name, model in models_reg.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " pipeline.fit(X_train_reg, y_train_reg)\n", + " y_pred_reg = pipeline.predict(X_test_reg)\n", + " mae = mean_absolute_error(y_test_reg, y_pred_reg)\n", + " mse = mean_squared_error(y_test_reg, y_pred_reg)\n", + " rmse = mean_squared_error(y_test_reg, y_pred_reg, squared=False)\n", + " r2 = r2_score(y_test_reg, y_pred_reg)\n", + " print(f\"Model: {name}\")\n", + " print(f\"MAE: {mae}\")\n", + " print(f\"MSE: {mse}\")\n", + " print(f\"RMSE: {rmse}\")\n", + " print(f\"R²: {r2}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Оптимизация тарифной сетки:\n", + "Конвейер для задачи классификации:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Результаты для задачи классификации:\n", + "Model: Logistic Regression\n", + "Accuracy: 0.8846846846846846\n", + "\n", + "Model: Random Forest Classification\n", + "Accuracy: 0.9801801801801802\n", + "\n", + "Model: Gradient Boosting Classification\n", + "Accuracy: 0.9243243243243243\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Определяем категориальные и числовые столбцы\n", + "categorical_cols = ['sex', 'smoker', 'region']\n", + "numerical_cols = ['age', 'bmi', 'children']\n", + "\n", + "# Создаем преобразователь для категориальных и числовых столбцов\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('cat', OneHotEncoder(), categorical_cols),\n", + " ('num', StandardScaler(), numerical_cols)\n", + " ])\n", + "\n", + "# Список моделей для задачи классификации\n", + "models_class = {\n", + " \"Logistic Regression\": LogisticRegression(),\n", + " \"Random Forest Classification\": RandomForestClassifier(),\n", + " \"Gradient Boosting Classification\": GradientBoostingClassifier()\n", + "}\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n", + "X_class = df[categorical_cols + numerical_cols]\n", + "y_class = (df['charges'] > df['charges'].mean()).astype(int)\n", + "\n", + "# Разделяем данные на обучающую и тестовую выборки для задачи классификации\n", + "X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n", + "\n", + "# Обучаем и оцениваем модели для задачи классификации\n", + "print(\"Результаты для задачи классификации:\")\n", + "for name, model in models_class.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " pipeline.fit(X_train_class, y_train_class)\n", + " y_pred_class = pipeline.predict(X_test_class)\n", + " accuracy = accuracy_score(y_test_class, y_pred_class)\n", + " print(f\"Model: {name}\")\n", + " print(f\"Accuracy: {accuracy}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Прогнозирование стоимости страховых взносов:\n", + "\n", + "Настройка гиперпараметров для задачи регрессии:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Результаты для задачи регрессии:\n", + "Model: Linear Regression\n", + "Best Parameters: {}\n", + "MAE: 4158.694987099099\n", + "MSE: 39908584.112821\n", + "RMSE: 6317.32412599045\n", + "R²: 0.7399769662171334\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n", + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: Random Forest Regression\n", + "Best Parameters: {'model__max_depth': None, 'model__n_estimators': 200}\n", + "MAE: 1292.4041719905688\n", + "MSE: 7345926.566814439\n", + "RMSE: 2710.3369839956135\n", + "R²: 0.9521378632113484\n", + "\n", + "Model: Gradient Boosting Regression\n", + "Best Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 5, 'model__n_estimators': 200}\n", + "MAE: 1556.4693865098072\n", + "MSE: 9320749.44024657\n", + "RMSE: 3052.990245684806\n", + "R²: 0.9392709713847187\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Определяем категориальные и числовые столбцы\n", + "categorical_cols = ['sex', 'smoker', 'region']\n", + "numerical_cols = ['age', 'bmi', 'children']\n", + "\n", + "# Создаем преобразователь для категориальных и числовых столбцов\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('cat', OneHotEncoder(), categorical_cols),\n", + " ('num', StandardScaler(), numerical_cols)\n", + " ])\n", + "\n", + "# Список моделей и их гиперпараметров для задачи регрессии\n", + "models_reg = {\n", + " \"Linear Regression\": (LinearRegression(), {}),\n", + " \"Random Forest Regression\": (RandomForestRegressor(), {\n", + " 'model__n_estimators': [100, 200],\n", + " 'model__max_depth': [None, 10, 20]\n", + " }),\n", + " \"Gradient Boosting Regression\": (GradientBoostingRegressor(), {\n", + " 'model__n_estimators': [100, 200],\n", + " 'model__learning_rate': [0.01, 0.1],\n", + " 'model__max_depth': [3, 5]\n", + " })\n", + "}\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n", + "X_reg = df[categorical_cols + numerical_cols]\n", + "y_reg = df['charges']\n", + "\n", + "# Разделяем данные на обучающую и тестовую выборки для задачи регрессии\n", + "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n", + "\n", + "# Обучаем и оцениваем модели для задачи регрессии\n", + "print(\"Результаты для задачи регрессии:\")\n", + "for name, (model, params) in models_reg.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " grid_search = GridSearchCV(pipeline, params, cv=5, scoring='neg_mean_absolute_error')\n", + " grid_search.fit(X_train_reg, y_train_reg)\n", + " best_model = grid_search.best_estimator_\n", + " y_pred_reg = best_model.predict(X_test_reg)\n", + " mae = mean_absolute_error(y_test_reg, y_pred_reg)\n", + " mse = mean_squared_error(y_test_reg, y_pred_reg)\n", + " rmse = mean_squared_error(y_test_reg, y_pred_reg, squared=False)\n", + " r2 = r2_score(y_test_reg, y_pred_reg)\n", + " print(f\"Model: {name}\")\n", + " print(f\"Best Parameters: {grid_search.best_params_}\")\n", + " print(f\"MAE: {mae}\")\n", + " print(f\"MSE: {mse}\")\n", + " print(f\"RMSE: {rmse}\")\n", + " print(f\"R²: {r2}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Оптимизация тарифной сетки:\n", + "\n", + "Настройка гиперпараметров для задачи классификации:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Результаты для задачи классификации:\n", + "Model: Logistic Regression\n", + "Best Parameters: {'model__C': 10, 'model__solver': 'liblinear'}\n", + "Accuracy: 0.8864864864864865\n", + "\n", + "Model: Random Forest Classification\n", + "Best Parameters: {'model__max_depth': None, 'model__n_estimators': 100}\n", + "Accuracy: 0.9783783783783784\n", + "\n", + "Model: Gradient Boosting Classification\n", + "Best Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 5, 'model__n_estimators': 200}\n", + "Accuracy: 0.9621621621621622\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Определяем категориальные и числовые столбцы\n", + "categorical_cols = ['sex', 'smoker', 'region']\n", + "numerical_cols = ['age', 'bmi', 'children']\n", + "\n", + "# Создаем преобразователь для категориальных и числовых столбцов\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('cat', OneHotEncoder(), categorical_cols),\n", + " ('num', StandardScaler(), numerical_cols)\n", + " ])\n", + "\n", + "# Список моделей и их гиперпараметров для задачи классификации\n", + "models_class = {\n", + " \"Logistic Regression\": (LogisticRegression(), {\n", + " 'model__C': [0.1, 1, 10],\n", + " 'model__solver': ['liblinear', 'lbfgs']\n", + " }),\n", + " \"Random Forest Classification\": (RandomForestClassifier(), {\n", + " 'model__n_estimators': [100, 200],\n", + " 'model__max_depth': [None, 10, 20]\n", + " }),\n", + " \"Gradient Boosting Classification\": (GradientBoostingClassifier(), {\n", + " 'model__n_estimators': [100, 200],\n", + " 'model__learning_rate': [0.01, 0.1],\n", + " 'model__max_depth': [3, 5]\n", + " })\n", + "}\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n", + "X_class = df[categorical_cols + numerical_cols]\n", + "y_class = (df['charges'] > df['charges'].mean()).astype(int)\n", + "\n", + "# Разделяем данные на обучающую и тестовую выборки для задачи классификации\n", + "X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n", + "\n", + "# Обучаем и оцениваем модели для задачи классификации\n", + "print(\"Результаты для задачи классификации:\")\n", + "for name, (model, params) in models_class.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " grid_search = GridSearchCV(pipeline, params, cv=5, scoring='accuracy')\n", + " grid_search.fit(X_train_class, y_train_class)\n", + " best_model = grid_search.best_estimator_\n", + " y_pred_class = best_model.predict(X_test_class)\n", + " accuracy = accuracy_score(y_test_class, y_pred_class)\n", + " print(f\"Model: {name}\")\n", + " print(f\"Best Parameters: {grid_search.best_params_}\")\n", + " print(f\"Accuracy: {accuracy}\")\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Прогнозирование стоимости страховых взносов:\n", + "Задача: Регрессия\n", + "\n", + "Выбор метрик:\n", + "\n", + "MAE (Mean Absolute Error): Средняя абсолютная ошибка. Показывает среднее отклонение предсказанных значений от фактических. Эта метрика легко интерпретируется, так как она измеряется в тех же единицах, что и целевая переменная (доллары).\n", + "\n", + "MSE (Mean Squared Error): Среднеквадратичная ошибка. Показывает среднее квадратичное отклонение предсказанных значений от фактических. Эта метрика чувствительна к выбросам, так как ошибки возводятся в квадрат.\n", + "\n", + "RMSE (Root Mean Squared Error): Квадратный корень из среднеквадратичной ошибки. Показывает среднее отклонение предсказанных значений от фактических в тех же единицах, что и целевая переменная. Эта метрика также чувствительна к выбросам, но легче интерпретируется, чем MSE.\n", + "\n", + "R² (R-squared): Коэффициент детерминации. Показывает, какую долю дисперсии целевой переменной объясняет модель. Значение R² близкое к 1 указывает на хорошее качество модели.\n", + "\n", + "Обоснование:\n", + "\n", + "MAE: Хорошо подходит для задач, где важно понимать среднее отклонение предсказаний от фактических значений.\n", + "\n", + "MSE и RMSE: Полезны для задач, где важно минимизировать влияние выбросов, так как они возводят ошибки в квадрат.\n", + "\n", + "R²: Позволяет оценить, насколько хорошо модель объясняет вариацию целевой переменной.\n", + "\n", + "2. Оптимизация тарифной сетки:\n", + "Задача: Классификация\n", + "\n", + "Выбор метрик:\n", + "\n", + "Accuracy: Доля правильных предсказаний среди всех предсказаний. Эта метрика показывает общую точность модели.\n", + "\n", + "Precision: Доля правильных положительных предсказаний среди всех положительных предсказаний. Эта метрика важна, если важно минимизировать количество ложноположительных результатов.\n", + "\n", + "Recall (Sensitivity): Доля правильных положительных предсказаний среди всех фактических положительных случаев. Эта метрика важна, если важно минимизировать количество ложноотрицательных результатов.\n", + "\n", + "F1-score: Гармоническое среднее между precision и recall. Эта метрика показывает баланс между precision и recall.\n", + "\n", + "Обоснование:\n", + "\n", + "Accuracy: Хорошо подходит для задач, где классы сбалансированы.\n", + "\n", + "Precision и Recall: Важны для задач, где важно минимизировать ошибки определенного типа (ложноположительные или ложноотрицательные).\n", + "\n", + "F1-score: Позволяет оценить баланс между precision и recall." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Результаты для задачи регрессии:\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: Linear Regression\n", + "Best Parameters: {}\n", + "MAE: 4158.694987099099\n", + "MSE: 39908584.112821\n", + "RMSE: 6317.32412599045\n", + "R²: 0.7399769662171334\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: Random Forest Regression\n", + "Best Parameters: {'model__max_depth': None, 'model__n_estimators': 100}\n", + "MAE: 1309.2968994795137\n", + "MSE: 7399293.51911523\n", + "RMSE: 2720.164244878465\n", + "R²: 0.9517901526335497\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\midni\\AIM\\AIM-PIbd-32-Bulatova-K-R\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: Gradient Boosting Regression\n", + "Best Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 5, 'model__n_estimators': 200}\n", + "MAE: 1556.235766949439\n", + "MSE: 9320073.834850596\n", + "RMSE: 3052.879597175525\n", + "R²: 0.9392753732688899\n", + "\n", + "Результаты для задачи классификации:\n", + "Model: Logistic Regression\n", + "Best Parameters: {'model__C': 10, 'model__solver': 'liblinear'}\n", + "Accuracy: 0.8864864864864865\n", + "Precision: 1.0\n", + "Recall: 0.6204819277108434\n", + "F1-score: 0.7657992565055762\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: Random Forest Classification\n", + "Best Parameters: {'model__max_depth': 20, 'model__n_estimators': 200}\n", + "Accuracy: 0.9801801801801802\n", + "Precision: 0.9874213836477987\n", + "Recall: 0.9457831325301205\n", + "F1-score: 0.9661538461538461\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: Gradient Boosting Classification\n", + "Best Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 5, 'model__n_estimators': 200}\n", + "Accuracy: 0.9621621621621622\n", + "Precision: 0.9738562091503268\n", + "Recall: 0.8975903614457831\n", + "F1-score: 0.9341692789968652\n", + "\n" + ] + }, + { + "data": { + "image/png": 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48GHEx8fj4MGDiI+Pl7L+F6lUKpiYPPvxycvLw6NHj2BtbY1atWrh3LlzOu9TpVKhf//+OrX19fXFp59+iunTpyMgIADm5uZYunSpzvt60a5du+Ds7IxevXpJ68zMzDBy5EikpaXhyJEjWu27deum07fG/Nf8xW9au3btgoODg7S4u7sXeG5h+zA1NZXmUWg0Gjx+/Bi5ublo2rSp1mu9a9culCtXDsOGDdN67ogRI14Z85YtW6DRaNCjRw/8/fff0uLs7IwaNWrg0KFDWu2tra21KgZKpRLNmjWT5Xdn4cKFiIiIQEREBH7++We0adMGgwYN0jpbYdeuXTA1NcXIkSO1njt69GgIIaSzQnT9TMj/Pd25c6es8w18fHxQrVo16XH9+vWhVqul1ykvLw/79+9Hly5dtKps1atXh7+//yv71/czsjAWFhbS/zMzM/H333/j3XffBQCtny87OzucOnUK9+/fL7Sf/ArE3r17kZGR8drxFCUiIgKpqamYMGFCgTkOz5+y+/zxpKen4++//8Z7770HIQTOnz+v937z8vKwb98+dOnSBVWrVpXWu7i44JNPPsHx48cLfM4OGTJEK6b3338feXl5uH37tt77NyZvdEKhVqsBAKmpqTq1v337NkxMTFC9enWt9c7OzrCzsyvwZlauXLlAH+XLl8eTJ09eM+KCPv74Y3h7e2PQoEFwcnJCz5498csvv7w0uciPs1atWgW21alTB3///TfS09O11r94LOXLlwcAvY6lQ4cOsLGxwcaNG7Fu3Tq88847BV7LfBqNBnPnzkWNGjWgUqlQsWJFODg44NKlS0hOTtZ5n2+99ZZekw6/++472Nvb48KFC5g/fz4cHR11fu6Lbt++jRo1akiJUb46depI259XpUoVnfrN/3BPS0vTWu/t7S39ofT19S30uUXtY/Xq1ahfvz7Mzc1RoUIFODg44LffftN6rW/fvg0XF5cCiUxhP0cvunHjBoQQqFGjhlbS4+DggGvXriExMVGrfaVKlQpcd0Gu351mzZrBx8cHPj4+6N27N3777Td4eHhg+PDhyM7OBvDsWF1dXQv8IX3xvdP1M6FVq1bo1q0bpk2bhooVK6Jz585YtWqVwaf2vuozJjExEU+fPi3096yo373n6fsZWZjHjx/j888/h5OTEywsLODg4CD9HD7/8zV79mz8+eefcHNzQ7NmzTB16lStBLJKlSoYNWoUli9fjooVK8LPzw8LFy7U6/PgZWJiYgAA9erVe2m7uLg46cuYtbU1HBwc0KpVqwLHo6uHDx8iIyOjyM9jjUaDO3fuaK2X4/PYGL3xCYWrqyv+/PNPvZ6n6wVmippBLIR47X3k5eVpPbawsMDRo0exf/9+9O3bF5cuXcLHH3+Mdu3aFWhrCEOOJZ9KpUJAQABWr16NrVu3FlmdAJ5d12HUqFFo2bIlfv75Z+zduxcRERGoW7euzpUYQPvbhC7Onz8v/XG7fPmyXs81lK6x1q5dGwAK/Nw6ODhIfyifP1vpVfv4+eefERQUhGrVqmHFihXYs2cPIiIi8MEHH+j1Wr+MRqOBQqGQ+n5xebESJMfPm65MTEzQpk0bPHjwADdu3HitPl71maBQKLBp0yZERkZi+PDhuHfvHgYMGIAmTZoUSAz1UdyvU/7PmiG/Cz169MCyZcukOVT79u3Dnj17AEDr56tHjx64efMmFixYAFdXV8yZMwd169bVukbI999/j0uXLuHLL7/E06dPMXLkSNStWxd379597fj0kZeXh3bt2uG3337D+PHjsW3bNkREREgX8pLr9+VVSvL3403yRicUAPDhhx8iJiYGkZGRr2zr7u4OjUZT4EMnISEBSUlJhZaYX1f58uWRlJRUYH1hJS0TExO0bdsW//nPf3D16lXMnDkTBw8eLFBGzpcfZ1RUVIFt169fR8WKFWFlZWXYARThk08+wfnz55GamoqePXsW2W7Tpk1o06YNVqxYgZ49e8LX1xc+Pj4FXhM5rx6Ynp6O/v37w8PDA0OGDMHs2bNx+vTp1+7P3d0dN27cKPAhc/36dWn76/D394epqSnWrVv32rE9b9OmTahatSq2bNmCvn37ws/PDz4+PsjMzNRq5+7ujgcPHhT4A1jYz9GLqlWrBiEEqlSpIiU9zy/5JXB9yPne5+bmAvhf1cfd3R33798v8M38xfdO38+Ed999FzNnzsSZM2ewbt06XLlyBRs2bJD9ePI5OjrC3Nwc0dHRBbYVtu5FNWvWRK1atbB9+/bXSnyePHmCAwcOYMKECZg2bRq6du2Kdu3aaZX2n+fi4oLPPvsM27ZtQ2xsLCpUqICZM2dqtfH09MTXX3+No0eP4tixY7h37x6WLFmid2wvyh86etkXzMuXL+Ovv/7C999/j/Hjx6Nz587w8fEpdNK2ru+ng4MDLC0ti/w8NjExgZubm45H8c/2xicU48aNg5WVFQYNGoSEhIQC22NiYvDDDz8AeFayB4B58+ZptfnPf/4DAOjYsaNscVWrVg3Jycm4dOmStO7BgwcFziR5/Phxgefmz8ovqpzq4uKChg0bYvXq1Vp/oP/880/s27dPOs7i0KZNG8yYMQM//vgjnJ2di2xnampaINv+9ddfce/ePa11+YlPYcmXvsaPH4+4uDisXr0a//nPf/D2228jMDDwtcvSHTp0QHx8vNbM69zcXCxYsADW1tZSmVRflStXxoABA7B79278+OOPhbbR55tK/red559z6tSpAkl2hw4dkJubi8WLF0vr8vLysGDBglfuIyAgAKamppg2bVqB2IQQePTokc7x5pPrvc/JycG+ffugVCqlIY0OHTogLy+vwOs7d+5cKBQKaf6Brp8JT548KXDcL/6eWlpaynI8zzM1NYWPjw+2bdumNTchOjpa56uDTps2DY8ePcKgQYOkxOt5+/btw86dO4vcP1Dw5/HF1ysvL6/AcIGjoyNcXV2l1yclJaXA/j09PWFiYiLLVWF9fX1hY2OD0NDQAsl0fvyFHY8QQvob8Txdfz5NTU3h6+uL7du3a10GPCEhAeHh4WjRooU09FTWvfGnjVarVg3h4eH4+OOPUadOHfTr1w/16tVDdnY2Tp48KZ3mBwANGjRAYGAgfvrpJyQlJaFVq1b4448/sHr1anTp0gVt2rSRLa6ePXti/Pjx6Nq1K0aOHImMjAwsXrwYNWvW1JrINH36dBw9ehQdO3aEu7s7EhMTsWjRIlSqVAktWrQosv85c+bA398fXl5eGDhwoHTaqK2tbbFeLtbExARff/31K9t9+OGHmD59Ovr374/33nsPly9fxrp16wp8s6lWrRrs7OywZMkS2NjYwMrKCs2bN9d5PkK+gwcPYtGiRZgyZYp0GuuqVavQunVrTJo0CbNnz9arP+DZxKmlS5ciKCgIZ8+exdtvv41NmzbhxIkTmDdvnkET3ebNm4fY2FiMGDECGzZsQKdOneDo6Ii///4bJ06cwI4dO3Sa2wA8e623bNmCrl27omPHjoiNjcWSJUvg4eGh9a20U6dO8Pb2xoQJE3Dr1i14eHhgy5YtOo0bV6tWDd988w0mTpyIW7duoUuXLrCxsUFsbCy2bt2KIUOGYMyYMXq9Bq/73u/evVuqNCQmJiI8PBw3btzAhAkTpA/uTp06oU2bNvjqq69w69YtNGjQAPv27cP27dvxxRdfSN9mdf1MWL16NRYtWoSuXbuiWrVqSE1NxbJly6BWq6WkxMLCAh4eHti4cSNq1qwJe3t71KtX75Vj+q8ydepU7Nu3D97e3hg2bJiUKNWrV0+ny5Z//PHH0mWrz58/j169esHd3R2PHj3Cnj17cODAAYSHhxf6XLVajZYtW2L27NnIycnBW2+9hX379iE2NlarXWpqKipVqoTu3bujQYMGsLa2xv79+3H69Gl8//33AJ79jg4fPhz/+te/ULNmTeTm5mLt2rUwNTVFt27dDHqN8mOdO3cuBg0ahHfeeQeffPIJypcvj4sXLyIjIwOrV69G7dq1Ua1aNYwZMwb37t2DWq3G5s2bC5270KRJEwDAyJEj4efnB1NT0yKrst988w0iIiLQokULfPbZZyhXrhyWLl2KrKys1/rs+ccq4bNKXttff/0lBg8eLN5++22hVCqFjY2N8Pb2FgsWLNC6QE1OTo6YNm2aqFKlijAzMxNubm4vvbDVi148XbGoU5qEeHbBqnr16gmlUilq1aolfv755wKnjx04cEB07txZuLq6CqVSKVxdXUWvXr3EX3/9VWAfL55et3//fuHt7S0sLCyEWq0WnTp1KvLCVi+ellrURWFe9Pxpo0Up6rTR0aNHCxcXF2FhYSG8vb1FZGRkoad7bt++XXh4eEinwb14YavCPN9PSkqKcHd3F40bNy5wOdyQkBBhYmJS4AIzLyrq/U5ISBD9+/cXFStWFEqlUnh6ehZ4H172M/Ayubm5YtWqVeKDDz4Q9vb2oly5cqJixYqibdu2YsmSJVqnvr1sHxqNRsyaNUu4u7sLlUolGjVqJHbu3FnoqcuPHj0Sffv2lS5s1bdvX70ubLV582bRokULYWVlJaysrETt2rVFcHCwiIqKktoU9b4VFk9R731hCjtt1NzcXDRs2FAsXry4wAWrUlNTRUhIiHB1dRVmZmaiRo0aRV7Y6lWfCefOnRO9evUSlStXFiqVSjg6OooPP/xQnDlzRquvkydPiiZNmgilUqnzha1e9OJpk0I8+5xo1KiRUCqVolq1amL58uVi9OjRwtzcvMjX60X5nzWOjo6iXLlywsHBQXTq1Els375dalPYZ83du3dF165dhZ2dnbC1tRX/+te/xP3797WOLysrS4wdO1Y0aNBAurBTgwYNxKJFi6R+bt68KQYMGCCqVasmzM3Nhb29vWjTpo3Yv3//S49f19NG8/33v/8V7733nvS52KxZM7F+/Xpp+9WrV4WPj4+wtrYWFStWFIMHD5ZO133+uHNzc8WIESOEg4ODUCgUOl3Yys/PT1hbWwtLS0vRpk0bcfLkyUJjfvH04/wLvR06dEj8kymE+IfPEiEiMkJdunTBlStXXnsiKlFJe+PnUBAR/dO9eKffGzduYNeuXWjdunXpBET0GlihICIqZS4uLggKCkLVqlVx+/ZtLF68GFlZWTh//jxq1KhR2uER6eSNn5RJRPRP1759e6xfvx7x8fFQqVTw8vLCrFmzmEyQUWGFgoiIiAzGORRERERkMCYUREREZDDOodCBRqPB/fv3YWNjUyyX3yUiouIjhEBqaipcXV0L3AxQTpmZmdIN7AylVCoL3FX1TceEQgf379/ntdqJiIzcnTt3UKlSpWLpOzMzExY2FYBceW7d7uzsjNjYWKNKKphQ6CD/EsxKj0AoTHW/1TaRMYk7/F1ph0BULFJTUlC9iptBl9N/lezsbCA3AyqPQMDQvxN52Yi/uhrZ2dlMKP5p8oc5FKZKJhT0j8UbHNE/XYkMWZczN/jvhFAY5/RGJhRERERyUQAwNHEx0ql6TCiIiIjkojB5thjahxEyzqiJiIjojcIKBRERkVwUChmGPIxzzIMJBRERkVw45EFERET0+lihICIikguHPIiIiMhwMgx5GOnggXFGTURERG8UViiIiIjkwiEPIiIiMhjP8iAiIiJ6faxQEBERyYVDHkRERGSwMjzkwYSCiIhILmW4QmGcaRARERG9UVihICIikguHPIiIiMhgCoUMCQWHPIiIiKiMYoWCiIhILiaKZ4uhfRghJhRERERyKcNzKIwzaiIiInqjsEJBREQklzJ8HQomFERERHLhkAcRERHR62OFgoiISC4c8iAiIiKDcciDiIiIDJZfoTB00dHixYtRv359qNVqqNVqeHl5Yffu3dL21q1bQ6FQaC1Dhw7V6iMuLg4dO3aEpaUlHB0dMXbsWOTm5up96KxQEBERGalKlSrh22+/RY0aNSCEwOrVq9G5c2ecP38edevWBQAMHjwY06dPl55jaWkp/T8vLw8dO3aEs7MzTp48iQcPHqBfv34wMzPDrFmz9IqFCQUREZFcSnjIo1OnTlqPZ86cicWLF+P333+XEgpLS0s4OzsX+vx9+/bh6tWr2L9/P5ycnNCwYUPMmDED48ePx9SpU6FUKnWOhUMeREREcpFxyCMlJUVrycrKeumu8/LysGHDBqSnp8PLy0tav27dOlSsWBH16tXDxIkTkZGRIW2LjIyEp6cnnJycpHV+fn5ISUnBlStX9Dp0ViiIiIjeQG5ublqPp0yZgqlTpxZod/nyZXh5eSEzMxPW1tbYunUrPDw8AACffPIJ3N3d4erqikuXLmH8+PGIiorCli1bAADx8fFayQQA6XF8fLxe8TKhICIiko0MQx7/P3hw584dqNVqaa1KpSq0da1atXDhwgUkJydj06ZNCAwMxJEjR+Dh4YEhQ4ZI7Tw9PeHi4oK2bdsiJiYG1apVMzDOwqImIiIiw8k45JF/5kb+UlRCoVQqUb16dTRp0gShoaFo0KABfvjhh0LbNm/eHAAQHR0NAHB2dkZCQoJWm/zHRc27KAoTCiIion8QjUZT5HyLCxcuAABcXFwAAF5eXrh8+TISExOlNhEREVCr1dKwia445EFERCQXhUKGszx0vw7FxIkT4e/vj8qVKyM1NRXh4eE4fPgw9u7di5iYGISHh6NDhw6oUKECLl26hJCQELRs2RL169cHAPj6+sLDwwN9+/bF7NmzER8fj6+//hrBwcFFVkSKwoSCiIhILiV82mhiYiL69euHBw8ewNbWFvXr18fevXvRrl073LlzB/v378e8efOQnp4ONzc3dOvWDV9//bX0fFNTU+zcuRPDhg2Dl5cXrKysEBgYqHXdCl0xoSAiIjJSK1asKHKbm5sbjhw58so+3N3dsWvXLoNjYUJBREQkF94cjIiIiAxWhm8OxoSCiIhILmW4QmGcaRARERG9UVihICIikguHPIiIiMhgHPIgIiIien2sUBAREclEoVBAUUYrFEwoiIiIZFKWEwoOeRAREZHBWKEgIiKSi+L/F0P7MEJMKIiIiGTCIQ8iIiIiA7BCQUREJJOyXKFgQkFERCQTJhRERERksLKcUHAOBRERERmMFQoiIiK58LRRIiIiMhSHPIiIiIgMwAoFERGRTJ7dvdzQCoU8sZQ0JhREREQyUUCGIQ8jzSg45EFEREQGY4WCiIhIJmV5UiYTCiIiIrmU4dNGOeRBREREBmOFgoiISC4yDHkIDnkQERGVbXLMoTD8LJHSwYSCiIhIJmU5oeAcCiIiIjIYKxRERERyKcNneTChICIikgmHPIiIiIgMwAoFERGRTMpyhYIJBRERkUzKckLBIQ8iIiIyGCsUREREMmGFgoiIiAynkGnR0eLFi1G/fn2o1Wqo1Wp4eXlh9+7d0vbMzEwEBwejQoUKsLa2Rrdu3ZCQkKDVR1xcHDp27AhLS0s4Ojpi7NixyM3N1fvQmVAQEREZqUqVKuHbb7/F2bNncebMGXzwwQfo3Lkzrly5AgAICQnBjh078Ouvv+LIkSO4f/8+AgICpOfn5eWhY8eOyM7OxsmTJ7F69WqEhYVh8uTJeseiEEII2Y7sHyolJQW2trZQeQ6GwlRZ2uEQFYsnp38s7RCIikVKSgqcKtgiOTkZarW62PZha2sL5wE/w0RpaVBfmuwMxK/s89rx2tvbY86cOejevTscHBwQHh6O7t27AwCuX7+OOnXqIDIyEu+++y52796NDz/8EPfv34eTkxMAYMmSJRg/fjwePnwIpVL3v3msUBAREckkfw6FocvryMvLw4YNG5Ceng4vLy+cPXsWOTk58PHxkdrUrl0blStXRmRkJAAgMjISnp6eUjIBAH5+fkhJSZGqHLripEwiIiKZyDkpMyUlRWu9SqWCSqUq0P7y5cvw8vJCZmYmrK2tsXXrVnh4eODChQtQKpWws7PTau/k5IT4+HgAQHx8vFYykb89f5s+WKEgIiJ6A7m5ucHW1lZaQkNDC21Xq1YtXLhwAadOncKwYcMQGBiIq1evlnC0rFAQERHJR8abg925c0drDkVh1QkAUCqVqF69OgCgSZMmOH36NH744Qd8/PHHyM7ORlJSklaVIiEhAc7OzgAAZ2dn/PHHH1r95Z8Fkt9GV6xQEBERyUTOORT5p4LmL0UlFC/SaDTIyspCkyZNYGZmhgMHDkjboqKiEBcXBy8vLwCAl5cXLl++jMTERKlNREQE1Go1PDw89Dp2ViiIiIiM1MSJE+Hv74/KlSsjNTUV4eHhOHz4MPbu3QtbW1sMHDgQo0aNgr29PdRqNUaMGAEvLy+8++67AABfX194eHigb9++mD17NuLj4/H1118jODhY5wQmHxMKKhEDurXAgG7vw83FHgBw/WY85qzYjf0nn43zOVawwfSRXdG6eW1YW6oQfTsR36/cix2HLkh9XNw+DZVdK2j1O+3H7Zi3OqLEjoNILnPD9mH6wv9iaM/WCB3dvbTDIZmU9JUyExMT0a9fPzx48AC2traoX78+9u7di3bt2gEA5s6dCxMTE3Tr1g1ZWVnw8/PDokWLpOebmppi586dGDZsGLy8vGBlZYXAwEBMnz5d77jfqIQiKCgISUlJ2LZtW2mHQjK7n5iEaT9uR8ydh1AoFOjVsTnWfTcErfp8i+s347F4aj/Y2ljgk1FL8Sg5Dd39mmJV6AC06Tcbl/+6K/Uzc8lOrNl2Qnqclp5VGodDZJBzV24jbOsJ1K3xVmmHQjJTQIaEQo9JGCtWrHjpdnNzcyxcuBALFy4sso27uzt27dql8z6LwjkUVCL2HPsTESev4uadh4iJS8Q3i3cgPSMLTetVAQA0q18VyzYewbmrt3H73iN8v3IvklOfomEdN61+0jIykfgoVVoyMrNL43CIXltaRhaGTA7DD1/2gp2NRWmHQyQbo0ko/vzzT/j7+8Pa2hpOTk7o27cv/v77b2n7pk2b4OnpCQsLC1SoUAE+Pj5IT08HABw+fBjNmjWDlZUV7Ozs4O3tjdu3b5fWoZR5JiYKBLRrAksLJU5fjgUA/HHpJrq2awI7tSUUimfbVapyOH72htZzvwj0RUzEv3Hk5/EY0actTE2N5keYCAAwdvZG+HrXQ+vmtUs7FCoGpXlhq9L2Rg15FCUpKQkffPABBg0ahLlz5+Lp06cYP348evTogYMHD+LBgwfo1asXZs+eja5duyI1NRXHjh2DEAK5ubno0qULBg8ejPXr1yM7Oxt//PGH0b5hxsyjmiv2rhwNc2U5pD/NQt+xyxAV++zCKf0nrsTKWQMQe2A2cnLz8DQzG33HLkPs3f8ljUs3HsHF63eQlJKOZvWrYnLwR3CqaIuv520prUMi0svmfWdw8fodHFw9rrRDoeIi42mjxsYoEooff/wRjRo1wqxZs6R1K1euhJubG/766y+kpaUhNzcXAQEBcHd3BwB4enoCAB4/fozk5GR8+OGHqFatGgCgTp06L91fVlYWsrL+Nzb/4tXK6PXcuJ2Alr1Doba2QOe2jbBoal98+OkPiIqNx1dDP4StjQU6fzYfj5PS0aFVfawKHYAOg+fhasx9AMCi8INSX1ei7yM7Jxdzv+yF6Qv/i+wc/e+MR1SS7sY/wcTvN2PLj8NhrjIr7XCIZGcUCcXFixdx6NAhWFtbF9gWExMDX19ftG3bFp6envDz84Ovry+6d++O8uXLw97eHkFBQfDz80O7du3g4+ODHj16wMXFpcj9hYaGYtq0acV5SGVSTm6eVHG4eP0OGnlUxtCerfHDmv0Y8nEreH38Da7ffFax+PPGPXg1qoZB/2qJUd9uKLS/s1duwaycKSq72iP6dmKhbYjeFBevx+Hh41S07vtvaV1engYnz8dg2a9HkXBiHofw/gFK+iyPN4lR/PSmpaWhU6dOuHDhgtZy48YNtGzZEqampoiIiMDu3bvh4eGBBQsWoFatWoiNfTY+v2rVKkRGRuK9997Dxo0bUbNmTfz+++9F7m/ixIlITk6Wljt37pTUoZYpJgoFlMpysDR/djc7jUb7xrd5eQIKk6J/sTxrVkJengYPH6cWa5xEcmj5Ti2cWP8ljv48QVoa1amMf7VviqM/T2Ay8Q/BORRvuMaNG2Pz5s14++23Ua5c4SErFAp4e3vD29sbkydPhru7O7Zu3YpRo0YBABo1aoRGjRph4sSJ8PLyQnh4uHRhjxcVdQMWen2Tgz/C/pNXcCf+CWwszdG9fVO0aFID3UYswl+34hETl4i5E3th0g9b8Tg5HR1b10eb5rXQM2QJAOAdzypoUs8dx8/cQGpGJpp5VsHMkG74ZfdpJKc+LeWjI3o1GytzeFR31VpnaaGEva1VgfVkvBSKZ4uhfRijNy6hSE5OxoULF7TWDRkyBMuWLUOvXr0wbtw42NvbIzo6Ghs2bMDy5ctx5swZHDhwAL6+vnB0dMSpU6fw8OFD1KlTB7Gxsfjpp5/w0UcfwdXVFVFRUbhx4wb69etXOgdYRlUsb43FU/vBqaIaKWmZuBJ9D91GLMLhP64DAHp8sRhThnfG+v98CitLFWLvPMRnU9ci4v8vfJWVnYOAdk0wYXAHKM3K4fb9R1i8/hAWrjv4st0SEVEJeeMSisOHD6NRo0Za6wYOHIgTJ05g/Pjx8PX1RVZWFtzd3dG+fXuYmJhArVbj6NGjmDdvHlJSUuDu7o7vv/8e/v7+SEhIwPXr17F69Wo8evQILi4uCA4OxqefflpKR1g2jfwm/KXbb955iMDxy4vcfinqLnwHfC93WESlaufSL0o7BJLZswqFoXMoZAqmhCmEEOLVzcq2lJQU2NraQuU5GApTZWmHQ1Qsnpz+sbRDICoWKSkpcKpgi+TkZK27d8q9D1tbW1QduQmmKiuD+srLSsfN+d2LNd7iwFlAREREZLA3bsiDiIjIWJXl00aZUBAREcmkLJ/lwSEPIiIiMhgrFERERDIxMVHA5CUX5NOFMPD5pYUJBRERkUw45EFERERkAFYoiIiIZMKzPIiIiMhgZXnIgwkFERGRTMpyhYJzKIiIiMhgrFAQERHJpCxXKJhQEBERyaQsz6HgkAcREREZjBUKIiIimSggw5AHjLNEwYSCiIhIJhzyICIiIjIAKxREREQy4VkeREREZDAOeRAREREZgBUKIiIimXDIg4iIiAxWloc8mFAQERHJpCxXKDiHgoiIiAzGCgUREZFcZBjyMNILZTKhICIikguHPIiIiIgMwAoFERGRTMryWR6sUBAREckkf8jD0EVXoaGheOedd2BjYwNHR0d06dIFUVFRWm1at25doP+hQ4dqtYmLi0PHjh1haWkJR0dHjB07Frm5uXodOysURERERurIkSMIDg7GO++8g9zcXHz55Zfw9fXF1atXYWVlJbUbPHgwpk+fLj22tLSU/p+Xl4eOHTvC2dkZJ0+exIMHD9CvXz+YmZlh1qxZOsfChIKIiEgmJT3ksWfPHq3HYWFhcHR0xNmzZ9GyZUtpvaWlJZydnQvtY9++fbh69Sr2798PJycnNGzYEDNmzMD48eMxdepUKJVKnWLhkAcREZFM5BzySElJ0VqysrJeuf/k5GQAgL29vdb6devWoWLFiqhXrx4mTpyIjIwMaVtkZCQ8PT3h5OQkrfPz80NKSgquXLmi87GzQkFERPQGcnNz03o8ZcoUTJ06tcj2Go0GX3zxBby9vVGvXj1p/SeffAJ3d3e4urri0qVLGD9+PKKiorBlyxYAQHx8vFYyAUB6HB8fr3O8TCiIiIhkIud1KO7cuQO1Wi2tV6lUL31ecHAw/vzzTxw/flxr/ZAhQ6T/e3p6wsXFBW3btkVMTAyqVatmUKzP45AHERGRTPLnUBi6AIBardZaXpZQDB8+HDt37sShQ4dQqVKll8bYvHlzAEB0dDQAwNnZGQkJCVpt8h8XNe+iMEwoiIiIZFLSp40KITB8+HBs3boVBw8eRJUqVV75nAsXLgAAXFxcAABeXl64fPkyEhMTpTYRERFQq9Xw8PDQORYOeRARERmp4OBghIeHY/v27bCxsZHmPNja2sLCwgIxMTEIDw9Hhw4dUKFCBVy6dAkhISFo2bIl6tevDwDw9fWFh4cH+vbti9mzZyM+Ph5ff/01goODXznM8jxWKIiIiGQi55CHLhYvXozk5GS0bt0aLi4u0rJx40YAgFKpxP79++Hr64vatWtj9OjR6NatG3bs2CH1YWpqip07d8LU1BReXl7o06cP+vXrp3XdCl2wQkFERCSTkr45mBDipdvd3Nxw5MiRV/bj7u6OXbt26bzfwrBCQURERAZjhYKIiEgmCshwpUxZIil5TCiIiIhkYqJQwMTAjMLQ55cWDnkQERGRwVihICIikklJ3xzsTcKEgoiISCYlfZbHm4QJBRERkUxMFM8WQ/swRpxDQURERAZjhYKIiEguChmGLIy0QsGEgoiISCZleVImhzyIiIjIYKxQEBERyUTx//8M7cMYMaEgIiKSCc/yICIiIjIAKxREREQy4YWtXuG///2vzh1+9NFHrx0MERGRMSvLZ3nolFB06dJFp84UCgXy8vIMiYeIiIiMkE4JhUajKe44iIiIjF5Zvn25QXMoMjMzYW5uLlcsRERERq0sD3nofZZHXl4eZsyYgbfeegvW1ta4efMmAGDSpElYsWKF7AESEREZi/xJmYYuxkjvhGLmzJkICwvD7NmzoVQqpfX16tXD8uXLZQ2OiIiIjIPeCcWaNWvw008/oXfv3jA1NZXWN2jQANevX5c1OCIiImOSP+Rh6GKM9J5Dce/ePVSvXr3Aeo1Gg5ycHFmCIiIiMkZleVKm3hUKDw8PHDt2rMD6TZs2oVGjRrIERURERMZF7wrF5MmTERgYiHv37kGj0WDLli2IiorCmjVrsHPnzuKIkYiIyCgo/n8xtA9jpHeFonPnztixYwf2798PKysrTJ48GdeuXcOOHTvQrl274oiRiIjIKJTlszxe6zoU77//PiIiIuSOhYiIiIzUa1/Y6syZM7h27RqAZ/MqmjRpIltQRERExqgs375c74Ti7t276NWrF06cOAE7OzsAQFJSEt577z1s2LABlSpVkjtGIiIio1CW7zaq9xyKQYMGIScnB9euXcPjx4/x+PFjXLt2DRqNBoMGDSqOGImIiOgNp3eF4siRIzh58iRq1aolratVqxYWLFiA999/X9bgiIiIjI2RFhgMpndC4ebmVugFrPLy8uDq6ipLUERERMaIQx56mDNnDkaMGIEzZ85I686cOYPPP/8c3333nazBERERGZP8SZmGLsZIpwpF+fLltTKm9PR0NG/eHOXKPXt6bm4uypUrhwEDBqBLly7FEigRERG9uXRKKObNm1fMYRARERm/sjzkoVNCERgYWNxxEBERGb2yfOnt176wFQBkZmYiOztba51arTYoICIiIjI+ek/KTE9Px/Dhw+Ho6AgrKyuUL19eayEiIiqr8m9fbuiiq9DQULzzzjuwsbGBo6MjunTpgqioKK02mZmZCA4ORoUKFWBtbY1u3bohISFBq01cXBw6duwIS0tLODo6YuzYscjNzdXv2PVqDWDcuHE4ePAgFi9eDJVKheXLl2PatGlwdXXFmjVr9O2OiIjoH0OhkGfR1ZEjRxAcHIzff/8dERERyMnJga+vL9LT06U2ISEh2LFjB3799VccOXIE9+/fR0BAgLQ9Ly8PHTt2RHZ2Nk6ePInVq1cjLCwMkydP1u/YhRBCnydUrlwZa9asQevWraFWq3Hu3DlUr14da9euxfr167Fr1y69AjAGKSkpsLW1hcpzMBSmytIOh6hYPDn9Y2mHQFQsUlJS4FTBFsnJycU2LJ//d6LfqkgoLa0N6is7Iw1r+nu9VrwPHz6Eo6Mjjhw5gpYtWyI5ORkODg4IDw9H9+7dAQDXr19HnTp1EBkZiXfffRe7d+/Ghx9+iPv378PJyQkAsGTJEowfPx4PHz6EUqnb3z29KxSPHz9G1apVATybL/H48WMAQIsWLXD06FF9uyMiIvrHKO3blycnJwMA7O3tAQBnz55FTk4OfHx8pDa1a9dG5cqVERkZCQCIjIyEp6enlEwAgJ+fH1JSUnDlyhWd9613QlG1alXExsZKQf3yyy8AgB07dkg3CyMiIiqL5BzySElJ0VqysrJeum+NRoMvvvgC3t7eqFevHgAgPj4eSqWywN9nJycnxMfHS22eTybyt+dv05XeCUX//v1x8eJFAMCECROwcOFCmJubIyQkBGPHjtW3OyIiIiqEm5sbbG1tpSU0NPSl7YODg/Hnn39iw4YNJRShNr1PGw0JCZH+7+Pjg+vXr+Ps2bOoXr066tevL2twRERExkTfszSK6gMA7ty5ozWHQqVSFfmc4cOHY+fOnTh69CgqVaokrXd2dkZ2djaSkpK0qhQJCQlwdnaW2vzxxx9a/eWfBZLfRqe4dW5ZBHd3dwQEBDCZICKiMk/OIQ+1Wq21FJZQCCEwfPhwbN26FQcPHkSVKlW0tjdp0gRmZmY4cOCAtC4qKgpxcXHw8vICAHh5eeHy5ctITEyU2kRERECtVsPDw0PnY9epQjF//nydOxw5cqTObYmIiP5JSvrS28HBwQgPD8f27dthY2MjzXmwtbWFhYUFbG1tMXDgQIwaNQr29vZQq9UYMWIEvLy88O677wIAfH194eHhgb59+2L27NmIj4/H119/jeDg4JdWRV6kU0Ixd+5cnTpTKBRMKIiIiErI4sWLAQCtW7fWWr9q1SoEBQUBePY33MTEBN26dUNWVhb8/PywaNEiqa2pqSl27tyJYcOGwcvLC1ZWVggMDMT06dP1ikXv61CURfnnF8fFP+alxekf69dLd0s7BKJi8TQ9FSPbepbIdSiG/PyHLNeh+KlPs2KNtzgYdC8PIiIi+p+yfLdRgydlEhEREbFCQUREJBOFAjAxsMBgpAUKJhRERERyMZEhoTD0+aWFQx5ERERksNdKKI4dO4Y+ffrAy8sL9+7dAwCsXbsWx48flzU4IiIiY1LaNwcrTXonFJs3b4afnx8sLCxw/vx56WYlycnJmDVrluwBEhERGYv8IQ9DF2Okd0LxzTffYMmSJVi2bBnMzMyk9d7e3jh37pyswREREZFx0HtSZlRUFFq2bFlgva2tLZKSkuSIiYiIyCg9fy8OQ/owRnpXKJydnREdHV1g/fHjx1G1alVZgiIiIjJG+XcbNXQxRnonFIMHD8bnn3+OU6dOQaFQ4P79+1i3bh3GjBmDYcOGFUeMRERERsFEpsUY6T3kMWHCBGg0GrRt2xYZGRlo2bIlVCoVxowZgxEjRhRHjERERPSG0zuhUCgU+OqrrzB27FhER0cjLS0NHh4esLY27GYoRERExq4sz6F47StlKpVKeHh4yBkLERGRUTOB4XMgTGCcGYXeCUWbNm1eetGNgwcPGhQQERERGR+9E4qGDRtqPc7JycGFCxfw559/IjAwUK64iIiIjA6HPPQwd+7cQtdPnToVaWlpBgdERERkrHhzMBn06dMHK1eulKs7IiIiMiKy3b48MjIS5ubmcnVHRERkdBQKGDwps8wMeQQEBGg9FkLgwYMHOHPmDCZNmiRbYERERMaGcyj0YGtrq/XYxMQEtWrVwvTp0+Hr6ytbYERERGQ89Eoo8vLy0L9/f3h6eqJ8+fLFFRMREZFR4qRMHZmamsLX15d3FSUiIiqEQqZ/xkjvszzq1auHmzdvFkcsRERERi2/QmHoYoz0Tii++eYbjBkzBjt37sSDBw+QkpKitRAREVHZo/MciunTp2P06NHo0KEDAOCjjz7SugS3EAIKhQJ5eXnyR0lERGQEyvIcCp0TimnTpmHo0KE4dOhQccZDRERktBQKxUvvd6VrH8ZI54RCCAEAaNWqVbEFQ0RERMZJr9NGjTVrIiIiKgkc8tBRzZo1X5lUPH782KCAiIiIjBWvlKmjadOmFbhSJhEREZFeCUXPnj3h6OhYXLEQEREZNROFwuCbgxn6/NKic0LB+RNEREQvV5bnUOh8Yav8szyIiIiIXqRzhUKj0RRnHERERMZPhkmZRnorD/1vX05ERESFM4ECJgZmBIY+v7QwoSAiIpJJWT5tVO+bgxERERG9iAkFERGRTErj9uVHjx5Fp06d4OrqCoVCgW3btmltDwoKku4xkr+0b99eq83jx4/Ru3dvqNVq2NnZYeDAgUhLS9Pv2PULm4iIiIqSfx0KQxd9pKeno0GDBli4cGGRbdq3b48HDx5Iy/r167W29+7dG1euXEFERAR27tyJo0ePYsiQIXrFwTkURERERszf3x/+/v4vbaNSqeDs7FzotmvXrmHPnj04ffo0mjZtCgBYsGABOnTogO+++w6urq46xcEKBRERkUzyJ2UaugBASkqK1pKVlfXacR0+fBiOjo6oVasWhg0bhkePHknbIiMjYWdnJyUTAODj4wMTExOcOnVK530woSAiIpKJCWQY8vj/00bd3Nxga2srLaGhoa8VU/v27bFmzRocOHAA//73v3HkyBH4+/sjLy8PABAfH1/gthrlypWDvb094uPjdd4PhzyIiIjeQHfu3IFarZYeq1Sq1+qnZ8+e0v89PT1Rv359VKtWDYcPH0bbtm0NjjMfKxREREQykXPIQ61Way2vm1C8qGrVqqhYsSKio6MBAM7OzkhMTNRqk5ubi8ePHxc576IwTCiIiIhkYiLTUpzu3r2LR48ewcXFBQDg5eWFpKQknD17Vmpz8OBBaDQaNG/eXOd+OeRBRERkxNLS0qRqAwDExsbiwoULsLe3h729PaZNm4Zu3brB2dkZMTExGDduHKpXrw4/Pz8AQJ06ddC+fXsMHjwYS5YsQU5ODoYPH46ePXvqfIYHwAoFERGRbF68gNTrLvo4c+YMGjVqhEaNGgEARo0ahUaNGmHy5MkwNTXFpUuX8NFHH6FmzZoYOHAgmjRpgmPHjmkNoaxbtw61a9dG27Zt0aFDB7Ro0QI//fSTXnGwQkFERCQTBQy/Wai+z2/dujWEEEVu37t37yv7sLe3R3h4uJ571saEgoiISCavc6XLwvowRhzyICIiIoOxQkFERCQj46wvGI4JBRERkUyev46EIX0YIw55EBERkcFYoSAiIpLJ65z2WVgfxogJBRERkUzkuNKlsQ4dGGvcRERE9AZhhYKIiEgmHPIgIiIig5XGlTLfFBzyICIiIoOxQkFERCQTDnkQERGRwcryWR5MKIiIiGRSlisUxpoIERER0RuEFQoiIiKZlOWzPJhQEBERyYQ3ByMiIiIyACsUREREMjGBAiYGDloY+vzSwoSCiIhIJhzyICIiIjIAKxREREQyUfz/P0P7MEZMKIiIiGTCIQ8iIiIiA7BCQUREJBOFDGd5cMiDiIiojCvLQx5MKIiIiGRSlhMKzqEgIiIig7FCQUREJBOeNkpEREQGM1E8WwztwxhxyIOIiIgMxgoFERGRTDjkQURERAbjWR5EREREBmCFgoiISCYKGD5kYaQFCiYUREREcuFZHkREREQGYIWCSk3k+WgsCj+IS1F3kPB3ClaFDoR/q/rSduf3Pi/0eZOCP0Jw77YlFSaRTqJv3MGBfacRFxePlOR0DBraBQ0a1ii07YZ1+3Di2EUE/KsN2rRtKq2/E5eA7VuOIO52PBQmCjRsVBMB3dtAZa4sqcMgA5XGWR5Hjx7FnDlzcPbsWTx48ABbt25Fly5dpO1CCEyZMgXLli1DUlISvL29sXjxYtSo8b+fz8ePH2PEiBHYsWMHTExM0K1bN/zwww+wtrbWOY5SrVAEBQVBoVBg6NChBbYFBwdDoVAgKCio5AOjEpGRmY261d9C6OjuhW6/tGOG1jL3y15QKBT4sHWDEo6U6NWysnLwViUH9Ojp89J2F8//hVux92Frq/1BnZyUhh/n/QIHRzuMHt8Hn43ojgf3/8bPq3cXZ9gks/yzPAxd9JGeno4GDRpg4cKFhW6fPXs25s+fjyVLluDUqVOwsrKCn58fMjMzpTa9e/fGlStXEBERgZ07d+Lo0aMYMmSIXnGUeoXCzc0NGzZswNy5c2FhYQEAyMzMRHh4OCpXrvza/QohkJeXh3LlSv0QqQhtvTzQ1sujyO2OFdRaj/ce+xPejavD/a2KxR0akd7q1quKuvWqvrRN0pNUbNp4AJ+N/BeW/LhZa9ufl2NgamqCf/VsB5P/H0Tv2dsXoTPC8DDxCRwcyxdb7CQfBQyfVKnv8/39/eHv71/oNiEE5s2bh6+//hqdO3cGAKxZswZOTk7Ytm0bevbsiWvXrmHPnj04ffo0mjZ9VjFbsGABOnTogO+++w6urq46xVHqcygaN24MNzc3bNmyRVq3ZcsWVK5cGY0aNZLWZWVlYeTIkXB0dIS5uTlatGiB06dPS9sPHz4MhUKB3bt3o0mTJlCpVDh+/Dg0Gg1CQ0NRpUoVWFhYoEGDBti0aVOJHiMZ7uHjFOw/eQWfdHq3tEMhei0ajcCasF1o264ZXFwLJsW5uXkwLWcqJRMAYGb27AtRTPS9EouT/lliY2MRHx8PH5//Vc5sbW3RvHlzREZGAgAiIyNhZ2cnJRMA4OPjAxMTE5w6dUrnfZV6QgEAAwYMwKpVq6THK1euRP/+/bXajBs3Dps3b8bq1atx7tw5VK9eHX5+fnj8+LFWuwkTJuDbb7/FtWvXUL9+fYSGhmLNmjVYsmQJrly5gpCQEPTp0wdHjhwpMp6srCykpKRoLVS6Nu46DWtLc3RoxeEOMk77952CqYkCrT5oXOj2mrUqIyU5Hfv3/YHc3DxkpGfiv1uPAgBSUtJKMlQygAkUMFEYuPx/jeLFv0NZWVl6xxMfHw8AcHJy0lrv5OQkbYuPj4ejo6PW9nLlysHe3l5qo9uxvwH69OmD48eP4/bt27h9+zZOnDiBPn36SNvT09OxePFizJkzB/7+/vDw8MCyZctgYWGBFStWaPU1ffp0tGvXDtWqVYOVlRVmzZqFlStXws/PD1WrVkVQUBD69OmDpUuXFhlPaGgobG1tpcXNza3Yjp10s2Hn7wjwawJzlVlph0Kkt7jb8Th88Cz6BHaAoogBchfXiugb5I+D+09j9Mi5+Gr8IlSoaAsbtWWRz6E3j0KmBXg2JeD5v0WhoaEleSh6eyMmGDg4OKBjx44ICwuDEAIdO3ZExYr/KwnGxMQgJycH3t7e0jozMzM0a9YM165d0+rr+ZJNdHQ0MjIy0K5dO6022dnZWsMpL5o4cSJGjRolPU5JSWFSUYp+vxCD6LhELJ0RVNqhEL2WmOi7SEvNwOQvl0jrNBqBrZsO4/CBs5g261MAQNNmHmjazAMpKelQKc0ABXBw/xlUrGhXSpFTabpz5w7U6v/NJVOpVHr34ezsDABISEiAi4uLtD4hIQENGzaU2iQmJmo9Lzc3F48fP5aer4s3IqEAng17DB8+HACKnKmqCysrK+n/aWnPyoS//fYb3nrrLa12L3tjVCrVa71xVDzCd/6O+rXdULfGW69uTPQGata8LmrVdtdat2j+Jrzzrgfe9fIs0F6tfvY5FnniMszMyqFWHfcCbegNJeOsTLVarZVQvI4qVarA2dkZBw4ckBKIlJQUnDp1CsOGDQMAeHl5ISkpCWfPnkWTJk0AAAcPHoRGo0Hz5s113tcbk1C0b98e2dnZUCgU8PPz09pWrVo1KJVKnDhxAu7uz36xcnJycPr0aXzxxRdF9unh4QGVSoW4uDi0atWqOMOn15CekYXYuw+lx3EPHuHPv+7CTm2JSs72AIDU9EzsOHgBU0d0Lq0wiXSSlZmNhw+fSI8f/Z2Mu3cSYGllAXt7NaysLbTam5qaQK22gtP//6wDwJFD51C12ltQqcxw/dotbNt8BB91bQlLS/MSOw4yTGlchyItLQ3R0dHS49jYWFy4cAH29vaoXLkyvvjiC3zzzTeoUaMGqlSpgkmTJsHV1VW6VkWdOnXQvn17DB48GEuWLEFOTg6GDx+Onj176nyGB/AGJRSmpqbS8IWpqanWNisrKwwbNgxjx46VXqDZs2cjIyMDAwcOLLJPGxsbjBkzBiEhIdBoNGjRogWSk5Nx4sQJqNVqBAYGFusx0ctduB6HbsN/lB5Pmb8NANCjQzPM/7o3AGBbxDlACHRt16Q0QiTSWdzteMyfu1F6vHXTIQBAs3from9QB536uH3rAXbtPIHsrBw4OtmjZ29fNHu3brHES/8cZ86cQZs2baTH+UP2gYGBCAsLw7hx45Ceno4hQ4YgKSkJLVq0wJ49e2Bu/r9Edd26dRg+fDjatm0rXdhq/vz5esWhEEIIeQ5Jf0FBQUhKSsK2bdsK3d6lSxfY2dkhLCwMmZmZGDduHNavX4/U1FQ0bdoUc+fOxTvvvAPg2Wmjbdq0wZMnT2BnZyf1IYTA/PnzsXjxYty8eRN2dnZo3LgxvvzyS7Rs2VKnOFNSUmBra4u4+McGl5+I3lS/Xrpb2iEQFYun6akY2dYTycnJxfYZnv934sCFOFjbGLaPtNQUtG1YuVjjLQ6lmlAYCyYUVBYwoaB/qpJMKA7KlFB8YIQJxRtx2igREREZtzdmDgUREZHRK41rb78hmFAQERHJpDTO8nhTMKEgIiKSyevcLbSwPowR51AQERGRwVihICIikkkZnkLBhIKIiEg2ZTij4JAHERERGYwVCiIiIpnwLA8iIiIyGM/yICIiIjIAKxREREQyKcNzMplQEBERyaYMZxQc8iAiIiKDsUJBREQkE57lQURERAYry2d5MKEgIiKSSRmeQsE5FERERGQ4ViiIiIjkUoZLFEwoiIiIZFKWJ2VyyIOIiIgMxgoFERGRTHiWBxERERmsDE+h4JAHERERGY4VCiIiIrmU4RIFEwoiIiKZ8CwPIiIiIgOwQkFERCQTnuVBREREBivDUyiYUBAREcmmDGcUnENBREREBmOFgoiISCZl+SwPJhRERERykWFSppHmExzyICIiIsOxQkFERCSTMjwnkwkFERGRbMpwRsEhDyIiIjIYEwoiIiKZKGT6p6upU6dCoVBoLbVr15a2Z2ZmIjg4GBUqVIC1tTW6deuGhISE4jh0JhRERERyyb/0tqGLPurWrYsHDx5Iy/Hjx6VtISEh2LFjB3799VccOXIE9+/fR0BAgMxH/QznUBARERmxcuXKwdnZucD65ORkrFixAuHh4fjggw8AAKtWrUKdOnXw+++/491335U1DlYoiIiIZKKQaQGAlJQUrSUrK6vQfd64cQOurq6oWrUqevfujbi4OADA2bNnkZOTAx8fH6lt7dq1UblyZURGRsp85EwoiIiI5CNjRuHm5gZbW1tpCQ0NLbC75s2bIywsDHv27MHixYsRGxuL999/H6mpqYiPj4dSqYSdnZ3Wc5ycnBAfHy/7oXPIg4iISCZyXnr7zp07UKvV0nqVSlWgrb+/v/T/+vXro3nz5nB3d8cvv/wCCwsLg+LQFysUREREbyC1Wq21FJZQvMjOzg41a9ZEdHQ0nJ2dkZ2djaSkJK02CQkJhc65MBQTCiIiIpkoIMNZHgbsPy0tDTExMXBxcUGTJk1gZmaGAwcOSNujoqIQFxcHLy8vg4/1RRzyICIikklJXyhzzJgx6NSpE9zd3XH//n1MmTIFpqam6NWrF2xtbTFw4ECMGjUK9vb2UKvVGDFiBLy8vGQ/wwNgQkFERGS07t69i169euHRo0dwcHBAixYt8Pvvv8PBwQEAMHfuXJiYmKBbt27IysqCn58fFi1aVCyxMKEgIiKSyetcmKqwPnS1YcOGl243NzfHwoULsXDhQsOC0gETCiIiItmU3buDcVImERERGYwVCiIiIpmU9JDHm4QJBRERkUzK7oAHhzyIiIhIBqxQEBERyYRDHkRERGQwOe/lYWyYUBAREcmlDE+i4BwKIiIiMhgrFERERDIpwwUKJhRERERyKcuTMjnkQURERAZjhYKIiEgmPMuDiIiIDFeGJ1FwyIOIiIgMxgoFERGRTMpwgYIJBRERkVx4lgcRERGRAVihICIiko3hZ3kY66AHEwoiIiKZcMiDiIiIyABMKIiIiMhgHPIgIiKSSVke8mBCQUREJJOyfOltDnkQERGRwVihICIikgmHPIiIiMhgZfnS2xzyICIiIoOxQkFERCSXMlyiYEJBREQkE57lQURERGQAViiIiIhkwrM8iIiIyGBleAoFEwoiIiLZlOGMgnMoiIiIyGCsUBAREcmkLJ/lwYSCiIhIJpyUSS8lhAAApKamlHIkRMXnaXpqaYdAVCyepqcB+N9neXFKSTH874QcfZQGJhQ6SE199kFbt8bbpRsIERG9ttTUVNja2hZL30qlEs7OzqhRxU2W/pydnaFUKmXpq6QoREmkbEZOo9Hg/v37sLGxgcJYa1FGJCUlBW5ubrhz5w7UanVph0MkO/6MlywhBFJTU+Hq6goTk+I7FyEzMxPZ2dmy9KVUKmFubi5LXyWFFQodmJiYoFKlSqUdRpmjVqv5YUv/aPwZLznFVZl4nrm5udElAXLiaaNERERkMCYUREREZDAmFPTGUalUmDJlClQqVWmHQlQs+DNO/0SclElEREQGY4WCiIiIDMaEgoiIiAzGhIKIiIgMxoSCiIiIDMaEgopVUFAQunTpUtphEMkuKCgICoUCQ4cOLbAtODgYCoUCQUFBJR8YUSlhQkFE9Jrc3NywYcMGPH36VFqXmZmJ8PBwVK5c+bX7FUIgNzdXjhCJSgwTCio1f/75J/z9/WFtbQ0nJyf07dsXf//9t7R906ZN8PT0hIWFBSpUqAAfHx+kp6cDAA4fPoxmzZrBysoKdnZ28Pb2xu3bt0vrUKiMaty4Mdzc3LBlyxZp3ZYtW1C5cmU0atRIWpeVlYWRI0fC0dER5ubmaNGiBU6fPi1tP3z4MBQKBXbv3o0mTZpApVLh+PHj0Gg0CA0NRZUqVWBhYYEGDRpg06ZNJXqMRLpiQkGlIikpCR988AEaNWqEM2fOYM+ePUhISECPHj0AAA8ePECvXr0wYMAAXLt2DYcPH0ZAQID0za1Lly5o1aoVLl26hMjISAwZMoQ3bqNSMWDAAKxatUp6vHLlSvTv31+rzbhx47B582asXr0a586dQ/Xq1eHn54fHjx9rtZswYQK+/fZbXLt2DfXr10doaCjWrFmDJUuW4MqVKwgJCUGfPn1w5MiREjk2Ir0IomIUGBgoOnfuXGD9jBkzhK+vr9a6O3fuCAAiKipKnD17VgAQt27dKvDcR48eCQDi8OHDxRU20Svl/2wnJiYKlUolbt26JW7duiXMzc3Fw4cPRefOnUVgYKBIS0sTZmZmYt26ddJzs7Ozhaurq5g9e7YQQohDhw4JAGLbtm1Sm8zMTGFpaSlOnjyptd+BAweKXr16lcxBEumBdxulUnHx4kUcOnQI1tbWBbbFxMTA19cXbdu2haenJ/z8/ODr64vu3bujfPnysLe3R1BQEPz8/NCuXTv4+PigR48ecHFxKYUjobLOwcEBHTt2RFhYGIQQ6NixIypWrChtj4mJQU5ODry9vaV1ZmZmaNasGa5du6bVV9OmTaX/R0dHIyMjA+3atdNqk52drTWcQvSmYEJBpSItLQ2dOnXCv//97wLbXFxcYGpqioiICJw8eRL79u3DggUL8NVXX+HUqVOoUqUKVq1ahZEjR2LPnj3YuHEjvv76a0RERODdd98thaOhsm7AgAEYPnw4AGDhwoWv3Y+VlZX0/7S0NADAb7/9hrfeekurHe8BQm8izqGgUtG4cWNcuXIFb7/9NqpXr6615H+oKhQKeHt7Y9q0aTh//jyUSiW2bt0q9dGoUSNMnDgRJ0+eRL169RAeHl5ah0NlXPv27ZGdnY2cnBz4+flpbatWrRqUSiVOnDghrcvJycHp06fh4eFRZJ8eHh5QqVSIi4sr8Dvi5uZWbMdC9LpYoaBil5ycjAsXLmitGzJkCJYtW4ZevXph3LhxsLe3R3R0NDZs2IDly5fjzJkzOHDgAHx9feHo6IhTp07h4cOHqFOnDmJjY/HTTz/ho48+gqurK6KionDjxg3069evdA6QyjxTU1Np+MLU1FRrm5WVFYYNG4axY8fC3t4elStXxuzZs5GRkYGBAwcW2aeNjQ3GjBmDkJAQaDQatGjRAsnJyThx4gTUajUCAwOL9ZiI9MWEgord4cOHC4z5Dhw4ECdOnMD48ePh6+uLrKwsuLu7o3379jAxMYFarcbRo0cxb948pKSkwN3dHd9//z38/f2RkJCA69evY/Xq1Xj06BFcXFwQHByMTz/9tJSOkAhQq9VFbvv222+h0WjQt29fpKamomnTpti7dy/Kly//0j5nzJgBBwcHhIaG4ubNm7Czs0Pjxo3x5Zdfyh0+kcF4+3IiIiIyGOdQEBERkcGYUBAREZHBmFAQERGRwZhQEBERkcGYUBAREZHBmFAQERGRwZhQEBERkcGYUBAZiaCgIHTp0kV63Lp1a3zxxRclHsfhw4ehUCiQlJRUZBuFQoFt27bp3OfUqVPRsGFDg+K6desWFApFgauyElHJYEJBZICgoCAoFAooFAoolUpUr14d06dPR25ubrHve8uWLZgxY4ZObXVJAoiIDMFLbxMZqH379li1ahWysrKwa9cuBAcHw8zMDBMnTizQNjs7G0qlUpb92tvby9IPEZEcWKEgMpBKpYKzszPc3d0xbNgw+Pj44L///S+A/w1TzJw5E66urqhVqxYA4M6dO+jRowfs7Oxgb2+Pzp0749atW1KfeXl5GDVqFOzs7FChQgWMGzcOL14l/8Uhj6ysLIwfPx5ubm5QqVSoXr06VqxYgVu3bqFNmzYAgPLly0OhUCAoKAgAoNFoEBoaiipVqsDCwgINGjTApk2btPaza9cu1KxZExYWFmjTpo1WnLoaP348atasCUtLS1StWhWTJk1CTk5OgXZLly6Fm5sbLC0t0aNHDyQnJ2ttX758OerUqQNzc3PUrl0bixYt0jsWIioeTCiIZGZhYYHs7Gzp8YEDBxAVFYWIiAjs3LlTusW1jY0Njh07hhMnTsDa2lq6BTYAfP/99wgLC8PKlStx/PhxPH78WOvW7YXp168f1q9fj/nz5+PatWtYunQprK2t4ebmhs2bNwMAoqKi8ODBA/zwww8AgNDQUKxZswZLlizBlStXEBISgj59+uDIkSMAniU+AQEB6NSpEy5cuIBBgwZhwoQJer8mNjY2CAsLw9WrV/HDDz9g2bJlmDt3rlab6Oho/PLLL9ixYwf27NmD8+fP47PPPpO2r1u3DpMnT8bMmTNx7do1zJo1C5MmTcLq1av1joeIioEgotcWGBgoOnfuLIQQQqPRiIiICKFSqcSYMWOk7U5OTiIrK0t6ztq1a0WtWrWERqOR1mVlZQkLCwuxd+9eIYQQLi4uYvbs2dL2nJwcUalSJWlfQgjRqlUr8fnnnwshhIiKihIARERERKFxHjp0SAAQT548kdZlZmYKS0tLcfLkSa22AwcOFL169RJCCDFx4kTh4eGhtX38+PEF+noRALF169Yit8+ZM0c0adJEejxlyhRhamoq7t69K63bvXu3MDExEQ8ePBBCCFGtWjURHh6u1c+MGTOEl5eXEEKI2NhYAUCcP3++yP0SUfHhHAoiA+3cuRPW1tbIycmBRqPBJ598gqlTp0rbPT09teZNXLx4EdHR0bCxsdHqJzMzEzExMUhOTsaDBw/QvHlzaVu5cuXQtGnTAsMe+S5cuABTU1O0atVK57ijo6ORkZGBdu3aaa3Pzs6Wbjd/7do1rTgAwMvLS+d95Nu4cSPmz5+PmJgYpKWlITc3t8DtvitXroy33npLaz8ajQZRUVGwsbFBTEwMBg4ciMGDB0ttcnNzYWtrq3c8RCQ/JhREBmrTpg0WL14MpVIJV1dXlCun/WtlZWWl9TgtLQ1NmjTBunXrCvTl4ODwWjFYWFjo/Zy0tDQAwG+//ab1hxx4Ni9ELpGRkejduzemTZsGPz8/2NraYsOGDfj+++/1jnXZsmUFEhxTU1PZYiWi18eEgshAVlZWqF69us7tGzdujI0bN8LR0bHAt/R8Li4uOHXqFFq2bAng2Tfxs2fPonHjxoW29/T0hEajwZEjR+Dj41Nge36FJC8vT1rn4eEBlUqFuLi4IisbderUkSaY5vv9999ffZDPOXnyJNzd3fHVV19J627fvl2gXVxcHO7fvw9XV1dpPyYmJqhVqxacnJzg6uqKmzdvonfv3nrtn4hKBidlEpWw3r17o2LFiujcuTOOHTuG2NhYHD58GCNHjsTdu3cBAJ9//jm+/fZbbNu2DdevX8dnn3320mtIvP322wgMDMSAAQOwbds2qc9ffvkFAODu7g6FQoGdO3fi4cOHSEtLg42NDcaMGYOQkBCsXr0aMTExOHfuHBYsWCBNdBw6dChu3LiBsWPHIioqCuHh4QgLC9PreGvUqIG4uDhs2LABMTExmD9/fqETTM3NzREYGIiLFy/i2LFjGDlyJHr06AFnZ2cAwLRp0xAaGor58+fjr7/+wuXLl7Fq1Sr85z//0SseIioeTCiISpilpSWOHj2KypUrIyAgAHXq1MHAgQORmZkpVSxGjx6Nvn37IjAwEF5eXrCxsUHXrl1f2u/ixYvRvXt3fPbZZ6hduzYGDx6M9PR0AMBbb72FadOmYcKECXBycsLw4cMBADNmzMCkSZMQGhqKOnXqoH379vjtt99QpUoVAM/mNWzevBnbtm1DgwYNsGTJEsyaNUuv4/3oo48QEhKC4cOHo2HDhjh58iQmTZpUoF316tUREBCADh06wNfXF/Xr19c6LXTQoEFYvnw5Vq1aBU9PT7Rq1QphYWFSrERUuhSiqFleRERERDpihYKIiIgMxoSCiIiIDMaEgoiIiAzGhIKIiIgMxoSCiIiIDMaEgoiIiAzGhIKIiIgMxoSCiIiIDMaEgoiIiAzGhIKIiIgMxoSCiIiIDMaEgoiIiAz2fyeITLmaUR3uAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.model_selection import train_test_split, GridSearchCV\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Определяем категориальные и числовые столбцы\n", + "categorical_cols = ['sex', 'smoker', 'region']\n", + "numerical_cols = ['age', 'bmi', 'children']\n", + "\n", + "# Создаем преобразователь для категориальных и числовых столбцов\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('cat', OneHotEncoder(), categorical_cols),\n", + " ('num', StandardScaler(), numerical_cols)\n", + " ])\n", + "\n", + "# Список моделей и их гиперпараметров для задачи регрессии\n", + "models_reg = {\n", + " \"Linear Regression\": (LinearRegression(), {}),\n", + " \"Random Forest Regression\": (RandomForestRegressor(), {\n", + " 'model__n_estimators': [100, 200],\n", + " 'model__max_depth': [None, 10, 20]\n", + " }),\n", + " \"Gradient Boosting Regression\": (GradientBoostingRegressor(), {\n", + " 'model__n_estimators': [100, 200],\n", + " 'model__learning_rate': [0.01, 0.1],\n", + " 'model__max_depth': [3, 5]\n", + " })\n", + "}\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n", + "X_reg = df[categorical_cols + numerical_cols]\n", + "y_reg = df['charges']\n", + "\n", + "# Разделяем данные на обучающую и тестовую выборки для задачи регрессии\n", + "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(X_reg, y_reg, test_size=0.2, random_state=42)\n", + "\n", + "# Обучаем и оцениваем модели для задачи регрессии\n", + "print(\"Результаты для задачи регрессии:\")\n", + "for name, (model, params) in models_reg.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " grid_search = GridSearchCV(pipeline, params, cv=5, scoring='neg_mean_absolute_error')\n", + " grid_search.fit(X_train_reg, y_train_reg)\n", + " best_model = grid_search.best_estimator_\n", + " y_pred_reg = best_model.predict(X_test_reg)\n", + " mae = mean_absolute_error(y_test_reg, y_pred_reg)\n", + " mse = mean_squared_error(y_test_reg, y_pred_reg)\n", + " rmse = mean_squared_error(y_test_reg, y_pred_reg, squared=False)\n", + " r2 = r2_score(y_test_reg, y_pred_reg)\n", + " print(f\"Model: {name}\")\n", + " print(f\"Best Parameters: {grid_search.best_params_}\")\n", + " print(f\"MAE: {mae}\")\n", + " print(f\"MSE: {mse}\")\n", + " print(f\"RMSE: {rmse}\")\n", + " print(f\"R²: {r2}\")\n", + " print()\n", + "\n", + "# Список моделей и их гиперпараметров для задачи классификации\n", + "models_class = {\n", + " \"Logistic Regression\": (LogisticRegression(), {\n", + " 'model__C': [0.1, 1, 10],\n", + " 'model__solver': ['liblinear', 'lbfgs']\n", + " }),\n", + " \"Random Forest Classification\": (RandomForestClassifier(), {\n", + " 'model__n_estimators': [100, 200],\n", + " 'model__max_depth': [None, 10, 20]\n", + " }),\n", + " \"Gradient Boosting Classification\": (GradientBoostingClassifier(), {\n", + " 'model__n_estimators': [100, 200],\n", + " 'model__learning_rate': [0.01, 0.1],\n", + " 'model__max_depth': [3, 5]\n", + " })\n", + "}\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n", + "X_class = df[categorical_cols + numerical_cols]\n", + "y_class = (df['charges'] > df['charges'].mean()).astype(int)\n", + "\n", + "# Разделяем данные на обучающую и тестовую выборки для задачи классификации\n", + "X_train_class, X_test_class, y_train_class, y_test_class = train_test_split(X_class, y_class, test_size=0.2, random_state=42)\n", + "\n", + "# Обучаем и оцениваем модели для задачи классификации\n", + "print(\"Результаты для задачи классификации:\")\n", + "for name, (model, params) in models_class.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " grid_search = GridSearchCV(pipeline, params, cv=5, scoring='accuracy')\n", + " grid_search.fit(X_train_class, y_train_class)\n", + " best_model = grid_search.best_estimator_\n", + " y_pred_class = best_model.predict(X_test_class)\n", + " accuracy = accuracy_score(y_test_class, y_pred_class)\n", + " precision = precision_score(y_test_class, y_pred_class)\n", + " recall = recall_score(y_test_class, y_pred_class)\n", + " f1 = f1_score(y_test_class, y_pred_class)\n", + " print(f\"Model: {name}\")\n", + " print(f\"Best Parameters: {grid_search.best_params_}\")\n", + " print(f\"Accuracy: {accuracy}\")\n", + " print(f\"Precision: {precision}\")\n", + " print(f\"Recall: {recall}\")\n", + " print(f\"F1-score: {f1}\")\n", + " print()\n", + "\n", + " # Визуализация матрицы ошибок\n", + " cm = confusion_matrix(y_test_class, y_pred_class)\n", + " disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Less', 'More'])\n", + " disp.plot(cmap=plt.cm.Blues)\n", + " plt.title(f'Confusion Matrix for {name}')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Давайте проанализируем полученные значения метрик и определим, являются ли они нормальными или их можно улучшить.\n", + "\n", + "### Оценка смещения и дисперсии для задачи регрессии:\n", + "\n", + "### Вывод для задачи регрессии:\n", + "\n", + "- **Random Forest Regression** демонстрирует наилучшие результаты по метрикам MAE и R², что указывает на высокую точность и стабильность модели.\n", + "- **Linear Regression** и **Gradient Boosting Regression** также показывают хорошие результаты, но уступают случайному лесу.\n", + "\n", + "### Вывод для задачи классификации:\n", + "\n", + "- **Random Forest Classification** демонстрирует наилучшие результаты по всем метрикам (Accuracy, Precision, Recall, F1-score), что указывает на высокую точность и стабильность модели.\n", + "- **Logistic Regression** и **Gradient Boosting Classification** также показывают хорошие результаты, но уступают случайному лесу.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Для оценки смещения (bias) и дисперсии (variance) моделей можно использовать метод перекрестной проверки (cross-validation). Этот метод позволяет оценить, насколько хорошо модель обобщается на новых данных.\n", + "\n", + "Оценка смещения и дисперсии для задачи регрессии:\n", + "Для задачи регрессии мы будем использовать метрики MAE (Mean Absolute Error) и R² (R-squared) для оценки смещения и дисперсии.\n", + "\n", + "Оценка смещения и дисперсии для задачи классификации:\n", + "Для задачи классификации мы будем использовать метрики Accuracy, Precision, Recall и F1-score для оценки смещения и дисперсии.\n", + "\n", + "Пример кода для оценки смещения и дисперсии:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Оценка смещения и дисперсии для задачи регрессии:\n", + "Model: Linear Regression\n", + "MAE (Cross-Validation): Mean = 4185.5421072063355, Std = 92.43821200753904\n", + "R² (Cross-Validation): Mean = 0.7497293059031158, Std = 0.008805026203658282\n", + "\n", + "Model: Random Forest Regression\n", + "MAE (Cross-Validation): Mean = 956.4848341519103, Std = 88.92646158556248\n", + "R² (Cross-Validation): Mean = 0.9768968000358168, Std = 0.003942950295159459\n", + "\n", + "Model: Gradient Boosting Regression\n", + "MAE (Cross-Validation): Mean = 2189.5694438069927, Std = 96.31677771605824\n", + "R² (Cross-Validation): Mean = 0.8873175260899913, Std = 0.011188839103376261\n", + "\n", + "Оценка смещения и дисперсии для задачи классификации:\n", + "Model: Logistic Regression\n", + "Accuracy (Cross-Validation): Mean = 0.8906956776270857, Std = 0.011756347754179863\n", + "Precision (Cross-Validation): Mean = 0.9965558019216555, Std = 0.0042291925480556145\n", + "Recall (Cross-Validation): Mean = 0.6516534480440919, Std = 0.038303791687037965\n", + "F1-score (Cross-Validation): Mean = 0.7873345301431043, Std = 0.027701698921697982\n", + "\n", + "Model: Random Forest Classification\n", + "Accuracy (Cross-Validation): Mean = 0.9978352359579796, Std = 0.001767524034697101\n", + "Precision (Cross-Validation): Mean = 0.9976878612716764, Std = 0.002831780049460347\n", + "Recall (Cross-Validation): Mean = 0.9953757225433526, Std = 0.004325615476039242\n", + "F1-score (Cross-Validation): Mean = 0.9965250705740019, Std = 0.002837294532624193\n", + "\n", + "Model: Gradient Boosting Classification\n", + "Accuracy (Cross-Validation): Mean = 0.9354219923895014, Std = 0.005560116809131367\n", + "Precision (Cross-Validation): Mean = 0.9873539623899337, Std = 0.011266123317032629\n", + "Recall (Cross-Validation): Mean = 0.803226240086033, Std = 0.017698107137353723\n", + "F1-score (Cross-Validation): Mean = 0.8856692810850337, Std = 0.01067664691021022\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Определяем категориальные и числовые столбцы\n", + "categorical_cols = ['sex', 'smoker', 'region']\n", + "numerical_cols = ['age', 'bmi', 'children']\n", + "\n", + "# Создаем преобразователь для категориальных и числовых столбцов\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('cat', OneHotEncoder(), categorical_cols),\n", + " ('num', StandardScaler(), numerical_cols)\n", + " ])\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n", + "X_reg = df[categorical_cols + numerical_cols]\n", + "y_reg = df['charges']\n", + "\n", + "# Список моделей для задачи регрессии\n", + "models_reg = {\n", + " \"Linear Regression\": LinearRegression(),\n", + " \"Random Forest Regression\": RandomForestRegressor(),\n", + " \"Gradient Boosting Regression\": GradientBoostingRegressor()\n", + "}\n", + "\n", + "# Оценка смещения и дисперсии для задачи регрессии\n", + "print(\"Оценка смещения и дисперсии для задачи регрессии:\")\n", + "for name, model in models_reg.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " mae_scores = -cross_val_score(pipeline, X_reg, y_reg, cv=5, scoring='neg_mean_absolute_error')\n", + " r2_scores = cross_val_score(pipeline, X_reg, y_reg, cv=5, scoring='r2')\n", + " print(f\"Model: {name}\")\n", + " print(f\"MAE (Cross-Validation): Mean = {mae_scores.mean()}, Std = {mae_scores.std()}\")\n", + " print(f\"R² (Cross-Validation): Mean = {r2_scores.mean()}, Std = {r2_scores.std()}\")\n", + " print()\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n", + "X_class = df[categorical_cols + numerical_cols]\n", + "y_class = (df['charges'] > df['charges'].mean()).astype(int)\n", + "\n", + "# Список моделей для задачи классификации\n", + "models_class = {\n", + " \"Logistic Regression\": LogisticRegression(),\n", + " \"Random Forest Classification\": RandomForestClassifier(),\n", + " \"Gradient Boosting Classification\": GradientBoostingClassifier()\n", + "}\n", + "\n", + "# Оценка смещения и дисперсии для задачи классификации\n", + "print(\"Оценка смещения и дисперсии для задачи классификации:\")\n", + "for name, model in models_class.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " accuracy_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='accuracy')\n", + " precision_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='precision')\n", + " recall_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='recall')\n", + " f1_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='f1')\n", + " print(f\"Model: {name}\")\n", + " print(f\"Accuracy (Cross-Validation): Mean = {accuracy_scores.mean()}, Std = {accuracy_scores.std()}\")\n", + " print(f\"Precision (Cross-Validation): Mean = {precision_scores.mean()}, Std = {precision_scores.std()}\")\n", + " print(f\"Recall (Cross-Validation): Mean = {recall_scores.mean()}, Std = {recall_scores.std()}\")\n", + " print(f\"F1-score (Cross-Validation): Mean = {f1_scores.mean()}, Std = {f1_scores.std()}\")\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import cross_val_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.linear_model import LinearRegression, LogisticRegression\n", + "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n", + "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "\n", + "# Загружаем набор данных\n", + "df = pd.read_csv(\"..//static//csv//Medical_insurance.csv\")\n", + "\n", + "# Определяем категориальные и числовые столбцы\n", + "categorical_cols = ['sex', 'smoker', 'region']\n", + "numerical_cols = ['age', 'bmi', 'children']\n", + "\n", + "# Создаем преобразователь для категориальных и числовых столбцов\n", + "preprocessor = ColumnTransformer(\n", + " transformers=[\n", + " ('cat', OneHotEncoder(), categorical_cols),\n", + " ('num', StandardScaler(), numerical_cols)\n", + " ])\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи регрессии\n", + "X_reg = df[categorical_cols + numerical_cols]\n", + "y_reg = df['charges']\n", + "\n", + "# Список моделей для задачи регрессии\n", + "models_reg = {\n", + " \"Linear Regression\": LinearRegression(),\n", + " \"Random Forest Regression\": RandomForestRegressor(),\n", + " \"Gradient Boosting Regression\": GradientBoostingRegressor()\n", + "}\n", + "\n", + "# Оценка смещения и дисперсии для задачи регрессии\n", + "mae_means = []\n", + "mae_stds = []\n", + "r2_means = []\n", + "r2_stds = []\n", + "\n", + "for name, model in models_reg.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " mae_scores = -cross_val_score(pipeline, X_reg, y_reg, cv=5, scoring='neg_mean_absolute_error')\n", + " r2_scores = cross_val_score(pipeline, X_reg, y_reg, cv=5, scoring='r2')\n", + " mae_means.append(mae_scores.mean())\n", + " mae_stds.append(mae_scores.std())\n", + " r2_means.append(r2_scores.mean())\n", + " r2_stds.append(r2_scores.std())\n", + "\n", + "# Визуализация результатов для задачи регрессии\n", + "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n", + "\n", + "ax[0].bar(models_reg.keys(), mae_means, yerr=mae_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n", + "ax[0].set_ylabel('MAE')\n", + "ax[0].set_title('Mean Absolute Error (MAE) for Regression Models')\n", + "ax[0].yaxis.grid(True)\n", + "\n", + "ax[1].bar(models_reg.keys(), r2_means, yerr=r2_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n", + "ax[1].set_ylabel('R²')\n", + "ax[1].set_title('R-squared (R²) for Regression Models')\n", + "ax[1].yaxis.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# Разделяем данные на признаки (X) и целевую переменную (y) для задачи классификации\n", + "X_class = df[categorical_cols + numerical_cols]\n", + "y_class = (df['charges'] > df['charges'].mean()).astype(int)\n", + "\n", + "# Список моделей для задачи классификации\n", + "models_class = {\n", + " \"Logistic Regression\": LogisticRegression(),\n", + " \"Random Forest Classification\": RandomForestClassifier(),\n", + " \"Gradient Boosting Classification\": GradientBoostingClassifier()\n", + "}\n", + "\n", + "# Оценка смещения и дисперсии для задачи классификации\n", + "accuracy_means = []\n", + "accuracy_stds = []\n", + "precision_means = []\n", + "precision_stds = []\n", + "recall_means = []\n", + "recall_stds = []\n", + "f1_means = []\n", + "f1_stds = []\n", + "\n", + "for name, model in models_class.items():\n", + " pipeline = Pipeline(steps=[\n", + " ('preprocessor', preprocessor),\n", + " ('model', model)\n", + " ])\n", + " accuracy_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='accuracy')\n", + " precision_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='precision')\n", + " recall_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='recall')\n", + " f1_scores = cross_val_score(pipeline, X_class, y_class, cv=5, scoring='f1')\n", + " accuracy_means.append(accuracy_scores.mean())\n", + " accuracy_stds.append(accuracy_scores.std())\n", + " precision_means.append(precision_scores.mean())\n", + " precision_stds.append(precision_scores.std())\n", + " recall_means.append(recall_scores.mean())\n", + " recall_stds.append(recall_scores.std())\n", + " f1_means.append(f1_scores.mean())\n", + " f1_stds.append(f1_scores.std())\n", + "\n", + "# Визуализация результатов для задачи классификации\n", + "fig, ax = plt.subplots(2, 2, figsize=(12, 12))\n", + "\n", + "ax[0, 0].bar(models_class.keys(), accuracy_means, yerr=accuracy_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n", + "ax[0, 0].set_ylabel('Accuracy')\n", + "ax[0, 0].set_title('Accuracy for Classification Models')\n", + "ax[0, 0].yaxis.grid(True)\n", + "\n", + "ax[0, 1].bar(models_class.keys(), precision_means, yerr=precision_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n", + "ax[0, 1].set_ylabel('Precision')\n", + "ax[0, 1].set_title('Precision for Classification Models')\n", + "ax[0, 1].yaxis.grid(True)\n", + "\n", + "ax[1, 0].bar(models_class.keys(), recall_means, yerr=recall_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n", + "ax[1, 0].set_ylabel('Recall')\n", + "ax[1, 0].set_title('Recall for Classification Models')\n", + "ax[1, 0].yaxis.grid(True)\n", + "\n", + "ax[1, 1].bar(models_class.keys(), f1_means, yerr=f1_stds, align='center', alpha=0.5, ecolor='black', capsize=10)\n", + "ax[1, 1].set_ylabel('F1-score')\n", + "ax[1, 1].set_title('F1-score for Classification Models')\n", + "ax[1, 1].yaxis.grid(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "aimenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}