1811 lines
424 KiB
Plaintext
1811 lines
424 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Начало лабораторной работы"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 85,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"df = pd.read_csv(\"data/Medical_insurance.csv\")\n",
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"print(df.columns)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Бизнес-цели"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1. Прогнозирование стоимости страховых взносов:\n",
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"\n",
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"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",
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"\n",
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"Применение:\n",
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"Клиенты могут получить представление о примерной стоимости страховки до обращения в компанию.\n",
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"\n",
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"2. Оптимизация тарифной сетки:\n",
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"\n",
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"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",
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"\n",
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"Применение:\n",
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"Страховые компании могут использовать эти коэффициенты для корректировки тарифной сетки и повышения эффективности бизнеса."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1. Прогнозирование стоимости страховых взносов:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 86,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Среднее значение поля 'charges': 13261.369959046897\n",
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" age sex bmi children smoker region charges \\\n",
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"0 19 female 27.900 0 yes southwest 16884.92400 \n",
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"1 18 male 33.770 1 no southeast 1725.55230 \n",
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"2 28 male 33.000 3 no southeast 4449.46200 \n",
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"3 33 male 22.705 0 no northwest 21984.47061 \n",
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"4 32 male 28.880 0 no northwest 3866.85520 \n",
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"\n",
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" above_average_charges charges_volatility \n",
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"0 1 62648.55411 \n",
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"1 0 62648.55411 \n",
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"2 0 62648.55411 \n",
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"3 1 62648.55411 \n",
|
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"4 0 62648.55411 \n"
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]
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}
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],
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"source": [
|
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"import pandas as pd\n",
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"\n",
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"# Загружаем набор данных\n",
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"df = pd.read_csv(\"data/Medical_insurance.csv\")\n",
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"\n",
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"# Устанавливаем случайное состояние\n",
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"random_state = 42\n",
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"\n",
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"# Рассчитываем среднее значение стоимости страховых взносов\n",
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"average_charges = df['charges'].mean()\n",
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"print(f\"Среднее значение поля 'charges': {average_charges}\")\n",
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"\n",
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"# Создаем новую переменную, указывающую, превышает ли стоимость страховых взносов среднюю\n",
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"df['above_average_charges'] = (df['charges'] > average_charges).astype(int)\n",
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"\n",
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"# Рассчитываем волатильность (разницу между максимальной и минимальной стоимостью страховых взносов)\n",
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"df['charges_volatility'] = df['charges'].max() - df['charges'].min()\n",
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"\n",
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"# Выводим первые строки измененной таблицы для проверки\n",
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"print(df.head())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"2. Оптимизация тарифной сетки:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 87,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Средняя стоимость страховых взносов для 'age':\n",
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"age\n",
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"18 6714.267794\n",
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"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",
|
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"\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",
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||
"\n",
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||
"Средняя стоимость страховых взносов для 'smoker':\n",
|
||
"smoker\n",
|
||
"no 8417.874411\n",
|
||
"yes 32223.139764\n",
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||
"Name: charges, dtype: float64\n",
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"\n",
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||
"Средняя стоимость страховых взносов для 'region':\n",
|
||
"region\n",
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"northeast 13475.874737\n",
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||
"northwest 12463.129315\n",
|
||
"southeast 14748.777706\n",
|
||
"southwest 12164.196435\n",
|
||
"Name: charges, dtype: float64\n",
|
||
"\n",
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||
"Средняя стоимость страховых взносов для комбинации 'age' и 'smoker':\n",
|
||
"age smoker\n",
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||
"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(\"data/Medical_insurance.csv\")\n",
|
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"\n",
|
||
"# Рассчитываем среднюю стоимость страховых взносов для каждого значения каждого признака\n",
|
||
"for column in ['age', 'sex', 'bmi', 'children', 'smoker', 'region']:\n",
|
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" print(f\"Средняя стоимость страховых взносов для '{column}':\")\n",
|
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" print(df.groupby(column)['charges'].mean())\n",
|
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" print()\n",
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"\n",
|
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"# Рассчитываем среднюю стоимость страховых взносов для комбинаций признаков\n",
|
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"# для комбинации 'age' и 'smoker'\n",
|
||
"print(\"Средняя стоимость страховых взносов для комбинации 'age' и 'smoker':\")\n",
|
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"print(df.groupby(['age', 'smoker'])['charges'].mean())\n",
|
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"print()\n",
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"\n",
|
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"# Рассчитываем среднюю стоимость страховых взносов для комбинации 'bmi' и 'smoker'\n",
|
||
"print(\"Средняя стоимость страховых взносов для комбинации 'bmi' и 'smoker':\")\n",
|
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"print(df.groupby(['bmi', 'smoker'])['charges'].mean())\n",
|
||
"print()\n",
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"\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()"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 88,
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||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Пропуски не обнаружены.\n",
|
||
"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\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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",
|
||
"from imblearn.over_sampling import SMOTE\n",
|
||
"\n",
|
||
"# Загружаем набор данных\n",
|
||
"df = pd.read_csv(\"data/Medical_insurance.csv\")\n",
|
||
"\n",
|
||
"# Проверка наличия пропусков\n",
|
||
"if df.isnull().sum().any():\n",
|
||
" print(\"Пропуски обнаружены в следующих столбцах:\")\n",
|
||
" print(df.isnull().sum())\n",
|
||
"else:\n",
|
||
" print(\"Пропуски не обнаружены.\")\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": "code",
|
||
"execution_count": 89,
|
||
"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\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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: 1305.6051789457651\n",
|
||
"MSE: 7520094.230349512\n",
|
||
"RMSE: 2742.279021243008\n",
|
||
"R²: 0.9510030796737707\n",
|
||
"\n",
|
||
"Model: Gradient Boosting Regression\n",
|
||
"MAE: 2297.7789526178267\n",
|
||
"MSE: 19231434.895688985\n",
|
||
"RMSE: 4385.365993356653\n",
|
||
"R²: 0.8746982345593102\n",
|
||
"\n",
|
||
"Результаты для задачи классификации:\n",
|
||
"Model: Logistic Regression\n",
|
||
"Accuracy: 0.8864864864864865\n",
|
||
"MAE: 0.1723401794364032\n",
|
||
"MSE: 0.08904020819558058\n",
|
||
"RMSE: 0.29839605928292784\n",
|
||
"R²: 0.5752685271247916\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(\"data/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(max_iter=1000),\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",
|
||
"\n",
|
||
" # Вычисление вероятностей для логистической регрессии\n",
|
||
" if name == \"Logistic Regression\":\n",
|
||
" y_pred_probs = model.predict_proba(X_test_class)[:, 1]\n",
|
||
" mae = mean_absolute_error(y_test_class, y_pred_probs)\n",
|
||
" mse = mean_squared_error(y_test_class, y_pred_probs)\n",
|
||
" rmse = mean_squared_error(y_test_class, y_pred_probs, squared=False)\n",
|
||
" r2 = r2_score(y_test_class, y_pred_probs)\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": [
|
||
"1. Прогнозирование стоимости страховых взносов:\n",
|
||
"Конвейер для задачи регрессии:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 90,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Результаты для задачи регрессии:\n",
|
||
"Model: Linear Regression\n",
|
||
"MAE: 4165.371459891892\n",
|
||
"MSE: 39957124.62099743\n",
|
||
"RMSE: 6321.164815205931\n",
|
||
"R²: 0.7396607021732446\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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: 1301.4460596254232\n",
|
||
"MSE: 7345804.075966105\n",
|
||
"RMSE: 2710.3143869237947\n",
|
||
"R²: 0.9521386612963394\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\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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(\"data/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": 91,
|
||
"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: 0.9765765765765766\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(\"data/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": 92,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Результаты для задачи регрессии:\n",
|
||
"Model: Linear Regression\n",
|
||
"Best Parameters: {}\n",
|
||
"MAE: 4165.371459891892\n",
|
||
"MSE: 39957124.62099743\n",
|
||
"RMSE: 6321.164815205931\n",
|
||
"R²: 0.7396607021732446\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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: 1281.6915217498565\n",
|
||
"MSE: 7387848.666520319\n",
|
||
"RMSE: 2718.059724605094\n",
|
||
"R²: 0.9518647211846292\n",
|
||
"\n",
|
||
"Model: Gradient Boosting Regression\n",
|
||
"Best Parameters: {'model__learning_rate': 0.1, 'model__max_depth': 5, 'model__n_estimators': 200}\n",
|
||
"MAE: 1556.213395665512\n",
|
||
"MSE: 9345361.053511541\n",
|
||
"RMSE: 3057.018327310378\n",
|
||
"R²: 0.9391106152485712\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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(\"data/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": 93,
|
||
"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: 0.9765765765765766\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(\"data/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": "code",
|
||
"execution_count": 94,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Пропуски не обнаружены.\n",
|
||
"Результаты для задачи регрессии:\n",
|
||
"Model: Linear Regression\n",
|
||
"Best Parameters: {}\n",
|
||
"MAE: 4165.371459891892\n",
|
||
"MSE: 39957124.62099743\n",
|
||
"RMSE: 6321.164815205931\n",
|
||
"R²: 0.7396607021732446\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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': 20, 'model__n_estimators': 200}\n",
|
||
"MAE: 1268.148178236854\n",
|
||
"MSE: 7192020.470646844\n",
|
||
"RMSE: 2681.7942632959084\n",
|
||
"R²: 0.9531406331901089\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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: 1554.914386244714\n",
|
||
"MSE: 9310054.341873825\n",
|
||
"RMSE: 3051.2381653803795\n",
|
||
"R²: 0.9393406549374507\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",
|
||
"AUC: 0.8852866478768545\n",
|
||
"MAE: 0.17141106227002562\n",
|
||
"MSE: 0.0889310663866395\n",
|
||
"RMSE: 0.29821312242528747\n",
|
||
"R²: 0.5757891454185178\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\Users\\Админ\\AppData\\Local\\Programs\\Python\\Python311\\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": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"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': 100}\n",
|
||
"Accuracy: 0.9765765765765766\n",
|
||
"Precision: 0.9872611464968153\n",
|
||
"Recall: 0.9337349397590361\n",
|
||
"F1-score: 0.9597523219814241\n",
|
||
"AUC: 0.9846300368569394\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"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",
|
||
"AUC: 0.9856211478303961\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": [
|
||
"<Figure size 640x480 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"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, \n",
|
||
" accuracy_score, precision_score, recall_score, \n",
|
||
" f1_score, confusion_matrix, ConfusionMatrixDisplay, \n",
|
||
" roc_curve, auc)\n",
|
||
"\n",
|
||
"# Загружаем набор данных\n",
|
||
"df = pd.read_csv(\"data/Medical_insurance.csv\")\n",
|
||
"\n",
|
||
"# Проверка наличия пропусков\n",
|
||
"if df.isnull().sum().any():\n",
|
||
" print(\"Пропуски обнаружены в следующих столбцах:\")\n",
|
||
" print(df.isnull().sum())\n",
|
||
"else:\n",
|
||
" print(\"Пропуски не обнаружены.\")\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",
|
||
"# Распределяем данные на признаки и целевую переменную 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=[('preprocessor', preprocessor), ('model', model)])\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\")\n",
|
||
"\n",
|
||
"# Список моделей и их гиперпараметров для задачи классификации\n",
|
||
"models_class = {\n",
|
||
" \"Logistic Regression\": (LogisticRegression(max_iter=1000), {\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 и целевую переменную 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=[('preprocessor', preprocessor), ('model', model)])\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",
|
||
" y_pred_prob = best_model.predict_proba(X_test_class)[:, 1] # Предсказанные вероятности для позитива\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",
|
||
" roc_auc = auc(*roc_curve(y_test_class, y_pred_prob)[:2]) # Добавляем вычисление AUC\n",
|
||
"\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(f\"AUC: {roc_auc}\")\n",
|
||
"\n",
|
||
" # Вычисляем метрики MAE, MSE, RMSE и R² для логистической регрессии\n",
|
||
" if name == \"Logistic Regression\":\n",
|
||
" mae = mean_absolute_error(y_test_class, y_pred_prob)\n",
|
||
" mse = mean_squared_error(y_test_class, y_pred_prob)\n",
|
||
" rmse = mean_squared_error(y_test_class, y_pred_prob, squared=False)\n",
|
||
" r2 = r2_score(y_test_class, y_pred_prob)\n",
|
||
" print(f\"MAE: {mae}\")\n",
|
||
" print(f\"MSE: {mse}\")\n",
|
||
" print(f\"RMSE: {rmse}\")\n",
|
||
" print(f\"R²: {r2}\\n\")\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()\n",
|
||
"\n",
|
||
" # ROC-кривая\n",
|
||
" fpr, tpr, thresholds = roc_curve(y_test_class, y_pred_prob)\n",
|
||
" plt.figure()\n",
|
||
" plt.plot(fpr, tpr, color='blue', label=f'ROC curve (area = {roc_auc:.2f})')\n",
|
||
" plt.plot([0, 1], [0, 1], color='red', linestyle='--')\n",
|
||
" plt.xlim([0.0, 1.0])\n",
|
||
" plt.ylim([0.0, 1.05])\n",
|
||
" plt.xlabel('False Positive Rate')\n",
|
||
" plt.ylabel('True Positive Rate')\n",
|
||
" plt.title(f'ROC Curve for {name}')\n",
|
||
" plt.legend(loc=\"lower right\")\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": "code",
|
||
"execution_count": 95,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Оценка смещения и дисперсии для задачи регрессии:\n",
|
||
"Model: Linear Regression\n",
|
||
"MAE (Cross-Validation): Mean = 4187.079314814689, Std = 91.59869329622924\n",
|
||
"R² (Cross-Validation): Mean = 0.7497258834991787, Std = 0.008634448950998193\n",
|
||
"\n",
|
||
"Model: Random Forest Regression\n",
|
||
"MAE (Cross-Validation): Mean = 940.1339838727054, Std = 81.49494082597198\n",
|
||
"R² (Cross-Validation): Mean = 0.9767515503658635, Std = 0.004497963918334725\n",
|
||
"\n",
|
||
"Model: Gradient Boosting Regression\n",
|
||
"MAE (Cross-Validation): Mean = 2189.5694438069922, Std = 96.31677771605804\n",
|
||
"R² (Cross-Validation): Mean = 0.8873175260899913, Std = 0.011188839103376287\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(\"data/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": 96,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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E27dv15AhQ1S7dm19/PHH6tu3b67bfDv7kvT0dEVERCgtLU0jRoyQn5+ffv/9d61evVrnzp1T+fLl8/UcALCP7EDnxn1+bm61z5WkCRMmaMqUKerYsaM6duyoPXv2KDw8/G+NdC7IsWRuxypZWVnq3Lmztm/frkGDBql27dr64YcfNHPmTB06dEiffPKJdfmnn35a7733nnr16qX7779fmzZtyvfI0t9//10nTpy46fHJjbJ/JLpxxNE333yj7t27a8mSJfrHP/6RY7nGjRvnGshcb9asWfrf//6njz/+2HoqW/bx46RJkxQTE6OwsDANHTpUBw8e1Pz58/XNN9/YfKZLNz/+K8h2Srb9bNOmTerQoYNCQkI0ceJEOTo6asmSJWrbtq2+/PJL68j6vXv3qn379vL391dMTIwyMzM1efJk6w+xN9q0aZNWrlyp4cOHq3LlyqpRo4aSk5PVvHlza4jj7e2tL774QgMGDFBKSor15itvvfWWnnnmGXXr1k3PPvusrly5ou+//15ff/219fvEkCFDtGrVKg0fPlzBwcE6e/astm/frp9++inP194wDHXu3FmbN2/WgAED1LBhQ61fv16jR4/W77//nmMU+fbt2/XRRx9p2LBhKleunN544w117dpVJ06cUKVKlfL1nPfs2VPvvfeeXnnlFTk4OOjMmTOKi4vTu+++m2told8+8fbbb2vw4MG6//77NXLkSP3888/q3LmzvLy8FBAQYF1fQd53ubVl6tSpevrpp9W0aVOlpKTo22+/1Z49exiNVZIZKDGWLFliSDK++eYbY86cOUa5cuWMS5cuGYZhGP/85z+NNm3aGIZhGNWrVzciIyOty3355ZeGJGPp0qU261u3bl2O6dnru97gwYMNDw8P48qVK9ZpDz74oCHJ+N///medlpaWZvj5+Rldu3bN1/Y8/PDDRosWLayP33zzTaNMmTLGqVOnbMr17dvXkGSMGDHCOi0rK8uIjIw0XFxcjNOnTxuGYRjPPvusYbFYjKtXr+ZZ58iRIw1JxpdffmmdduHCBSMwMNCoUaOGkZmZaRiGYRw7dsyQZCxZssRmmx988MEc6+zbt69RvXp16+PTp08bkoyJEyfmKNuuXTujXr16Ns9lVlaWcf/99xv33HNPnu3OJinPf8uXL7dpqyRjwYIFNstnb5fFYsnxPDds2NDw8fExzp49a5323XffGY6OjkafPn2s0yZOnGhIMnr27HnL9hqGYSxatMiQZPzwww855mX31atXrxp+fn7Giy++aBiGYezfv9+QZGzdutWm319v1apVhiTj8OHDhmEYRkpKiuHm5mbMnDkz123O619CQkKOdrm4uBhDhw695bbd+F4zDMOYNWuWIcl47733rNPS09ON0NBQw9PT00hJSbFpV26vxc3qCw8PN06fPm2cPn3a+OGHH4zevXsbkoyoqChrufy+55OSkowyZcoYXbp0sSk3adIkQ5LRt29f67Ts16Fly5Y277ELFy4YFSpUMAYOHGizjqSkJKN8+fLW6X/99ZchyXjttdfy3L6PP/4419f6Rje+v7p06WK4uLgYR48etU77448/jHLlyhmtWrXKsQ1hYWFGVlaWdfqoUaMMJycn49y5czetN7vvnz592vjXv/5l1KxZ0zrvH//4h9G/f39r+65/PfLbJz755BNDkjFt2jRruatXrxoPPPBAjv1RfvclmzdvNiQZmzdvNgzDMPbu3WtIMj744IObbisA+8reX23YsME4ffq08euvvxqrVq0yvL29DVdXV+PXX3+96fL52eeeOnXKcHFxMSIjI232if/+979zfAZk7//yauexY8es0wp6LHnjscq7775rODo62hyrGYZhLFiwwJBkfPXVV4ZhGEZiYqIhyRg2bJhNuV69euV5HHa9DRs2GJKMzz//PMe8Bx980KhVq5b18/bAgQPG6NGjDUk5Pvd37dpl+Pn5GR9//HGedQ0aNMhwd3e/aXsMw/ZzJlv26xQeHm49TjUMw5gzZ44hyVi8eLFNu3N7Tm9V38GDB43Tp08bx48fNxYvXmy4u7sb3t7eRmpqqmEY1z5f7rnnHiMiIsKmr1y6dMkIDAw0HnroIeu0Tp06GR4eHsbvv/9unXb48GGjTJkyOfqQJMPR0dHYt2+fzfQBAwYY/v7+xpkzZ2ym9+jRwyhfvry1jz3yyCNGnTp1brqN5cuXt/lMzs2Nx/LZn8dTpkyxKdetWzfDwcHBOHLkiM02uLi42Ez77rvvDEnG7Nmzb1pv9rHga6+9Zvz4448231Hmzp1reHp6GqmpqUbfvn2NsmXLWpfLb59IT083fHx8jIYNGxppaWnWcm+++aYhyeZ7TX7fd4Zx7Xj0+v1DgwYNcrwvUPJx6lwJlX2q2erVq3XhwgWtXr06z1M2PvjgA5UvX14PPfSQzpw5Y/0XEhIiT09Pm3PG3d3drX9fuHBBZ86c0QMPPKBLly7ZnCYiSZ6ennryySetj11cXNS0aVP9/PPPt2z/2bNntX79evXs2dM6rWvXrnJwcNDKlStzXeb6uzhl/8KRnp6uDRs2SLo2MiU1NfWmwzTXrl2rpk2b2pzq5enpqUGDBun48eMFPm2pIP78809t2rRJ3bt3tz63Z86c0dmzZxUREaHDhw/n604yjzzyiOLj43P8a9OmjU05V1dX9e/fP9d1dO3a1eaXpZMnTyoxMVH9+vWzGVFTv359PfTQQ1q7dm2OdeT3Lndnz56VdPNfX52cnNS9e3frdW+WLl2qgIAA69Dt3CxdulRNmjSxXiwz+3StvE6fGzRoUK7PW3BwcI6yFStWzHX0U36sXbtWfn5+Nn3b2dlZzzzzjC5evKitW7falL/xtbiVuLg4eXt7y9vbW/Xq1dO7776r/v372/xKnN/3/MaNG3X16lUNGzbMpo4RI0bkWf/AgQPl5ORkfRwfH69z586pZ8+eNnU5OTmpWbNm1rrc3d3l4uKiLVu25Dh9L1v2L8SrV69WRkZGvp6PzMxMxcXFqUuXLrr77rut0/39/dWrVy9t375dKSkpNssMGjTIZvj+Aw88oMzMzALd2rpXr146cuSIvvnmG+v/ee2D89sn1q5dqzJlymjo0KHWck5OTjlej7+zL8kesbR+/foivxYKgIILCwuTt7e3AgIC1K1bN5UtW1afffaZzanLucnPPnfDhg1KT0/PcZHq7NEit6sgx5K5Hat88MEHql27tmrVqmXzudK2bVtJsn6uZB+bPPPMMzbL57f9tzo+OXDggPXztlatWnrttdfUuXNnxcbG2pTr0qWLHBwcNGvWLLVu3VqPPPJIjnVVrFhRly9fvq39bvbrNHLkSJvrPw0cOFAWiyXH5R9udvyXl/vuu0/e3t6qUaOGnnrqKdWsWVNffPGFPDw8JF27aPrhw4fVq1cvnT171vqapKamql27dtq2bZuysrKUmZmpDRs2qEuXLqpSpYp1/TVr1szzbIcHH3zQ5ljMMAx9+OGH6tSpkwzDsOkDEREROn/+vPW0twoVKui3337TN998k+e2VahQQV9//bX++OOPfD8fa9eulZOTU46+9dxzz8kwjBx3yg4LC7OOyJeuHT9bLJZ8fSfKVqdOHdWvX996LLxs2TI98sgj1tfgevntE99++61OnTqlIUOG2Jz1kH2K/vXy+77LTYUKFbRv3z4dPnw439uL4o9T50oob29vhYWFadmyZbp06ZIyMzPVrVu3XMsePnxY58+fl4+PT67zrz/PfN++fRo/frw2bdqU48vZjdfvqFq1ao5zrStWrKjvv//+lu1fsWKFMjIy1KhRIx05csQ6vVmzZlq6dKmioqJsyjs6Otp8iZT+/+KL2cN7hw0bppUrV6pDhw666667FB4eru7du6t9+/bWZX755Zccp9FI106Fyp5ft27dW7b/dhw5ckSGYeiFF17QCy+8kGuZU6dO5XrB7OtVrVo1z+sdXe+uu+6y+VC5XmBgoM3j7C/Y9913X46ytWvX1vr165WamqqyZcvmuY5bMW5xce1evXrpjTfe0Hfffadly5apR48eeV4z4dy5c1q7dq2GDx9u039atGihDz/8UIcOHcpxF7577rknX89bdlvzc72G3Pzyyy+65557clwM9Po+dr2CPo/NmjXTlClTlJmZqR9//FFTpkzRX3/9ZfNa5/c9n92WG+9s4+XlleeB943tzT6oyD4QuZHFYpF07cD31Vdf1XPPPSdfX181b95cDz/8sPr06WM91fHBBx9U165dFRMTo5kzZ6p169bq0qWLevXqledFOk+fPq1Lly7l2XezsrL066+/2lz0vVq1ajblsrc1ry9juWnUqJFq1aqlZcuWqUKFCvLz88vzOchvn/jll1/k7++f4xbGN27b39mXBAYGKjo6WjNmzNDSpUv1wAMPqHPnznryySc5bQ64A82dO1f33nuvzp8/r8WLF2vbtm02+8P09HT9+eefNst4e3vna5+bve+55557ciyfn1Pz8lKQY8ncjlUOHz6sn376Kc8fYa7/DHN0dLT5gi/lfixzM3kdn9SoUcN6N7KjR4/qpZde0unTp3PcASw/PxJm13E7xxZ5HaO5uLjo7rvvznFccbPjv7x8+OGHslgsOn36tN544w0dO3bMJjDM/qzP7VTubOfPn9eVK1d0+fLlXO+Yl9dd9G48rjh9+rTOnTunN998U2+++Wauy2T3gbFjx2rDhg1q2rSpatasqfDwcPXq1UstWrSwlp02bZr69u2rgIAAhYSEqGPHjurTp0+O7xXX++WXX1SlSpUcdzLO61juxuMK6dqxRUGOK6Rrx8LTp0/XqFGjtGPHjjyvr5XfPpHXe9zZ2TnH9uf3fZebyZMn65FHHtG9996runXrqn379urdu7f11E+UTARNJVivXr00cOBAJSUlqUOHDnneoSIrK0s+Pj55jvTI3qGcO3dODz74oCwWiyZPnqygoCC5ublpz549Gjt2bI7bfl4/quF6twoUJFnbcv0HwfV+/vnnm34A5MbHx0eJiYlav369vvjiC33xxRdasmSJ+vTpk+tFdwvKwcEh12278eKWecl+/v71r3/leYFrM29le/0BQkHmmbH+62Wfm/7XX3/d9BfYZs2aKSgoSCNHjtSxY8fyHB0iXfvVJS0tTdOnT9f06dNzzF+6dKliYmLy1b7cnDt3TpUrV77t5QuioK9F5cqVrYFZRESEatWqpYcfflivv/66oqOjJeX/PW9Ge7P79bvvvpvrtbGuv/7VyJEj1alTJ33yySdav369XnjhBU2dOlWbNm1So0aN5ODgoFWrVmnnzp36/PPPtX79ej311FOaPn26du7cmSOAuV1/Z991vV69emn+/PkqV66cHn/88RxBUmH5u/uS6dOnq1+/fvr0008VFxenZ555RlOnTtXOnTtvOUoCQNFq2rSpmjRpIunayJmWLVuqV69eOnjwoDw9PbVjx44cI5qPHTumGjVq3HKfWxB5BSQ3HgMV9Fgyt8/ArKws1atXTzNmzMi1zuuvK/N3XH98kpuyZcva/EDVokULNW7cWP/+979zvR7qzfz111/y8PAw5fjrVm6njlatWlmPezp16qR69erpiSee0O7du+Xo6Gh93V577TU1bNgw13V4enre1h3j8jquePLJJ/MMtrIDjNq1a+vgwYNavXq11q1bpw8//FDz5s3ThAkTrMeB3bt31wMPPKCPP/5YcXFxeu211/Tqq6/qo48+ytc1ZfPDrOOKnj17aty4cRo4cKAqVaqk8PBwM5qXL3/nfdeqVSsdPXrUelyxaNEizZw5UwsWLNDTTz9dWE2GnRE0lWCPPvqoBg8erJ07d970IrZBQUHasGGDWrRocdMPny1btujs2bP66KOP1KpVK+v0Y8eOmdruY8eOaceOHRo+fLgefPBBm3lZWVnq3bu3li1bpvHjx9tM//nnn21GqRw6dEiSbC5S7eLiok6dOqlTp07KysrSsGHDtHDhQr3wwguqWbOmqlevroMHD+ZoU/ZQ7urVq+fZ7ooVK+Y6BPbGXzXyOhjLDs6cnZ3zPbKmqGRvd17PTeXKlW1GMxVErVq1JF173evVq3fTsj179tSUKVNUu3btPA9kpGtBUt26da13Z7newoULtWzZstsOmn7//Xelp6dbf7UqqOrVq+v7779XVlaWTfCQnz52OyIjI/Xggw/q5Zdf1uDBg1W2bNl8v+ez23LkyBGbXxTPnj2b71/hsn9J9vHxyVe/DgoK0nPPPafnnntOhw8fVsOGDTV9+nS999571jLNmzdX8+bN9dJLL2nZsmV64okn9P777+d6sOLt7S0PD488+66jo6NpX0pu1KtXL02YMEEnT57Uu+++m2e5/PaJ6tWra+PGjbp48aJNqHbjtpmxL6lXr57q1aun8ePHa8eOHWrRooUWLFigKVOm3Nb6ABQ+JycnTZ06VW3atNGcOXP0/PPPq0GDBjkuGXB96H+zfW72vufw4cM2P+6dPn06x2dA9ginc+fO2fyweeMxkBnHkkFBQfruu+/Url27m44Aql69unW00fUjO3L7PMjN9ccn+VG/fn09+eSTWrhwof71r3/lOoolL8eOHftbxxXSte26/nVKT0/XsWPHTD+m9PT01MSJE9W/f3+tXLlSPXr0sH7WWyyWm9bn4+MjNzc3m9Hm2XKblpvsOwhmZmbma9vKli2rxx9/XI8//rjS09P12GOP6aWXXtK4ceOso8/8/f01bNgwDRs2TKdOnVLjxo310ksv5Rk0Va9eXRs2bNCFCxdsRjUV1rFctmrVqqlFixbasmWLhg4davNj3Y3tk27dJ65/j18/6jojI0PHjh1TgwYNrNPy+77Li5eXl/r376/+/fvr4sWLatWqlSZNmkTQVIJxjaYSzNPTU/Pnz9ekSZPUqVOnPMt1795dmZmZevHFF3PMu3r1qvU2mNlp/PXpe3p6uubNm2dqu7NHWYwZM0bdunWz+de9e3c9+OCDuY7EmDNnjvVvwzA0Z84cOTs7q127dpL+/1z7bI6OjtZfPLJvy9mxY0ft2rVLCQkJ1nKpqal68803VaNGjVyv15MtKChIBw4csLkN+nfffZfjLiLZ51LfeHtRHx8ftW7dWgsXLtTJkydzrL8gt1c3m7+/vxo2bKh33nnHpt0//vij4uLi1LFjx9ted0hIiFxcXPTtt9/esuzTTz+tiRMn5jpKKduvv/6qbdu2qXv37jn6T7du3dS/f38dOXJEX3/99W21d/fu3ZKk+++//7aW79ixo5KSkmzC36tXr2r27Nny9PTMEa6aYezYsTp79qzeeustSfl/z7dr105lypTR/Pnzbcpc/167lYiICFksFr388su5Xlcpu19funQpxy+dQUFBKleunPX9+ddff+X49S87cMzr1rpOTk4KDw/Xp59+anN77eTkZC1btkwtW7a0nr5ntqCgIM2aNUtTp0613m0nN/ntEx07dtTVq1dtXo/MzEzNnj3bZn1/Z1+SkpKiq1ev2kyrV6+eHB0db3n7YgD217p1azVt2lSzZs3SlStXVLFiRYWFhdn8c3Nzy9c+NywsTM7Ozpo9e7bNvnfWrFk56s0OGrZt22adlpqammPEuBnHkt27d9fvv/9u/Uy73uXLl613PM0OCW4cXZRb+3Nz1113KSAgIF/HJ9nGjBmjjIyMPEd95GXPnj23fVwRFhYmFxcXvfHGGzbP69tvv63z58/n+y57BfHEE0+oatWq1rs4h4SEKCgoSP/973918eLFHOWzP3ecnJwUFhamTz75xOaaSEeOHMlxXaO8ODk5qWvXrvrwww/1448/5lmXlPPY38XFRcHBwTIMQxkZGcrMzMxxuqaPj4+qVKly08+8jh07KjMzM8fx0MyZM+Xg4GDaSKjcTJkyRRMnTrzp9TLz2yeaNGkib29vLViwwOZOkrGxsTm+p+T3fZebG18HT09P1axZk+OKEo4RTSXczc6Vzvbggw9q8ODBmjp1qhITExUeHi5nZ2cdPnxYH3zwgV5//XV169ZN999/vypWrKi+ffvqmWeekYODg959990CD/u8laVLl6phw4Z5jjLo3LmzRowYoT179lhvO+rm5qZ169apb9++atasmb744gutWbNG//73v62nAT399NP6888/1bZtW1WtWlW//PKLZs+erYYNG1p/RXr++ee1fPlydejQQc8884y8vLz0zjvv6NixY/rwww9veurLU089pRkzZigiIkIDBgzQqVOntGDBAtWpU8fmGgTu7u4KDg7WihUrdO+998rLy0t169ZV3bp1NXfuXLVs2VL16tXTwIEDdffddys5OVkJCQn67bff9N13393y+Tt06JDN6I9svr6+f+sWoq+99po6dOig0NBQDRgwQJcvX9bs2bNVvnx5TZo06bbX6+bmpvDwcG3YsMF6K/i8VK9e/ZZ1LVu2zHrb2dx07NhRZcqU0dKlS22ux7Vnz55cn7egoCCFhoZaH8fHx6tatWoFPq0g26BBg7Rw4UL169dPu3fvVo0aNbRq1Sp99dVXmjVrVo7z/c3QoUMH1a1bVzNmzFBUVFS+3/O+vr569tlnNX36dHXu3Fnt27fXd999py+++EKVK1fO1y9aFotF8+fPV+/evdW4cWP16NFD3t7eOnHihNasWaMWLVpozpw5OnTokNq1a6fu3bsrODhYZcqU0ccff6zk5GT16NFDkvTOO+9o3rx5evTRRxUUFKQLFy7orbfeksViuWnYOWXKFMXHx6tly5YaNmyYypQpo4ULFyotLU3Tpk0z7XnOzbPPPnvLMvntE506dVKLFi30/PPP6/jx4woODtZHH32U4yBZ0m3vSzZt2qThw4frn//8p+69915dvXpV7777rvXAHsCdb/To0frnP/+p2NjYPG/MkZ99rre3t/71r39p6tSpevjhh9WxY0ft3bvX+hlwvfDwcFWrVk0DBgzQ6NGj5eTkpMWLF1v399nMOJbs3bu3Vq5cqSFDhmjz5s1q0aKFMjMzdeDAAa1cuVLr169XkyZN1LBhQ/Xs2VPz5s3T+fPndf/992vjxo35HjkjXbvByscff5zvazMGBwerY8eOWrRokV544YV83bp+9+7d+vPPP3O9SHh+eHt7a9y4cYqJiVH79u3VuXNnHTx4UPPmzdM//vEPmxvzmMXZ2VnPPvusRo8erXXr1ql9+/ZatGiROnTooDp16qh///6666679Pvvv2vz5s2yWCz6/PPPJV271X1cXJxatGihoUOHWgObunXrKjExMV/1v/LKK9q8ebOaNWumgQMHKjg4WH/++af27NmjDRs2WK9LFh4eLj8/P7Vo0UK+vr766aefNGfOHEVGRqpcuXI6d+6cqlatqm7duqlBgwby9PTUhg0b9M0339z0R81OnTqpTZs2+s9//qPjx4+rQYMGiouL06effqqRI0fmuC6YmR588MFb/iiZ3z7h7OysKVOmaPDgwWrbtq0ef/xxHTt2TEuWLMlxiZL8vu9yExwcrNatWyskJEReXl769ttvtWrVKpsbOaEEKrL726HQ5XWb9xvldst1w7h2K8uQkBDD3d3dKFeunFGvXj1jzJgxxh9//GEt89VXXxnNmzc33N3djSpVqhhjxowx1q9fb3N7bMO4dvvU3G4neuPtQW+0e/duQ5Lxwgsv5Fnm+PHjhiRj1KhR1nWWLVvWOHr0qBEeHm54eHgYvr6+xsSJE21u6blq1SojPDzc8PHxMVxcXIxq1aoZgwcPNk6ePGmz/qNHjxrdunUzKlSoYLi5uRlNmzY1Vq9ebVMm+3aj199O3DAM47333jPuvvtuw8XFxWjYsKGxfv36XLd5x44dRkhIiOHi4pLjFrtHjx41+vTpY/j5+RnOzs7GXXfdZTz88MPGqlWr8nxOsknK89/1tyjN6/W5/jaqudmwYYPRokULw93d3bBYLEanTp2M/fv325TJ7da7t/LRRx8ZDg4OxokTJ2ym59VXr3djv69Xr55RrVq1my7TunVrw8f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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x1200 with 4 Axes>"
|
||
]
|
||
},
|
||
"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(\"data/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": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|