3193 lines
1.5 MiB
Plaintext
3193 lines
1.5 MiB
Plaintext
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{
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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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"### 1. Датасет: Диабет у индейцев Пима\n",
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"https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database\n",
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"##### О наборе данных: \n",
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"Этот набор данных был получен из Национального института диабета, болезней органов пищеварения и почек. Цель набора данных - диагностически предсказать, есть ли у пациента сахарный диабет, на основе определенных диагностических измерений, включенных в набор данных. На выбор этих образцов из более обширной базы данных было наложено несколько ограничений. В частности, все пациенты были женщинами в возрасте не менее 21 года, родом из племени пима.\n",
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"##### Таким образом:\n",
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"* Объект наблюдения - женщины племени пима, возрастом от 21 года.\n",
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"* Атрибуты: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome.\n",
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"* Проблемная область: Предсказание диабета у пациента на основе измерений."
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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": 1,
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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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"Количество колонок: 9\n",
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"Колонки: Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age, Outcome\n",
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"\n",
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 768 entries, 0 to 767\n",
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"Data columns (total 9 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Pregnancies 768 non-null int64 \n",
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" 1 Glucose 768 non-null int64 \n",
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" 2 BloodPressure 768 non-null int64 \n",
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" 3 SkinThickness 768 non-null int64 \n",
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" 4 Insulin 768 non-null int64 \n",
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" 5 BMI 768 non-null float64\n",
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" 6 DiabetesPedigreeFunction 768 non-null float64\n",
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" 7 Age 768 non-null int64 \n",
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" 8 Outcome 768 non-null int64 \n",
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"dtypes: float64(2), int64(7)\n",
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"memory usage: 54.1 KB\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Pregnancies</th>\n",
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" <th>Glucose</th>\n",
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" <th>BloodPressure</th>\n",
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" <th>SkinThickness</th>\n",
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" <th>Insulin</th>\n",
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" <th>BMI</th>\n",
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" <th>DiabetesPedigreeFunction</th>\n",
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" <th>Age</th>\n",
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" <th>Outcome</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>6</td>\n",
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" <td>148</td>\n",
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" <td>72</td>\n",
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" <td>35</td>\n",
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" <td>0</td>\n",
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" <td>33.6</td>\n",
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" <td>0.627</td>\n",
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" <td>50</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>85</td>\n",
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" <td>66</td>\n",
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" <td>29</td>\n",
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" <td>0</td>\n",
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" <td>26.6</td>\n",
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" <td>0.351</td>\n",
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" <td>31</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>8</td>\n",
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" <td>183</td>\n",
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" <td>64</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>23.3</td>\n",
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" <td>0.672</td>\n",
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" <td>32</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1</td>\n",
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" <td>89</td>\n",
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" <td>66</td>\n",
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" <td>23</td>\n",
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" <td>94</td>\n",
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" <td>28.1</td>\n",
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|
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" <td>0.167</td>\n",
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" <td>21</td>\n",
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|
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>0</td>\n",
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" <td>137</td>\n",
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" <td>40</td>\n",
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" <td>35</td>\n",
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" <td>168</td>\n",
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" <td>43.1</td>\n",
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" <td>2.288</td>\n",
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" <td>33</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
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"0 6 148 72 35 0 33.6 \n",
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"1 1 85 66 29 0 26.6 \n",
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"2 8 183 64 0 0 23.3 \n",
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"3 1 89 66 23 94 28.1 \n",
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"4 0 137 40 35 168 43.1 \n",
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"\n",
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" DiabetesPedigreeFunction Age Outcome \n",
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"0 0.627 50 1 \n",
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"1 0.351 31 0 \n",
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"2 0.672 32 1 \n",
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"3 0.167 21 0 \n",
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"4 2.288 33 1 "
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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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(\".//static//csv//diabetes.csv\", sep=\",\")\n",
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"print('Количество колонок: ' + str(df.columns.size)) \n",
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"print('Колонки: ' + ', '.join(df.columns)+'\\n')\n",
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"\n",
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"df.info()\n",
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"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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"#### Получение сведений о пропущенных данных\n",
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"Типы пропущенных данных:\n",
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"\n",
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"- None - представление пустых данных в Python\n",
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"- NaN - представление пустых данных в Pandas\n",
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"- '' - пустая строка"
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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": 2,
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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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"Pregnancies 0\n",
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"Glucose 0\n",
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"BloodPressure 0\n",
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"SkinThickness 0\n",
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"Insulin 0\n",
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"BMI 0\n",
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"DiabetesPedigreeFunction 0\n",
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"Age 0\n",
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"Outcome 0\n",
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"dtype: int64\n",
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"\n",
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"Pregnancies False\n",
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"Glucose False\n",
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"BloodPressure False\n",
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"SkinThickness False\n",
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"Insulin False\n",
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"BMI False\n",
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"DiabetesPedigreeFunction False\n",
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"Age False\n",
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"Outcome False\n",
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"dtype: bool\n",
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"\n",
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"Pregnancies процент пустых значений: %0.00\n",
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"Glucose процент пустых значений: %0.00\n",
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"BloodPressure процент пустых значений: %0.00\n",
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"SkinThickness процент пустых значений: %0.00\n",
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"Insulin процент пустых значений: %0.00\n",
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"BMI процент пустых значений: %0.00\n",
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"DiabetesPedigreeFunction процент пустых значений: %0.00\n",
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"Age процент пустых значений: %0.00\n",
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"Outcome процент пустых значений: %0.00\n"
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]
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}
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],
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"source": [
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"# Количество пустых значений признаков\n",
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"print(df.isnull().sum())\n",
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"print()\n",
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"\n",
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"# Есть ли пустые значения признаков\n",
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"print(df.isnull().any())\n",
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"print()\n",
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"\n",
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"# Процент пустых значений признаков\n",
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"for i in df.columns:\n",
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" null_rate = df[i].isnull().sum() / len(df) * 100\n",
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" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
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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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"##### Проверим выбросы и устраним их:"
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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": 3,
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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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"Колонка Pregnancies:\n",
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" Есть выбросы: Да\n",
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" Количество выбросов: 4\n",
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" Минимальное значение: 0.0\n",
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|
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" Максимальное значение: 13.5\n",
|
|||
|
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" 1-й квартиль (Q1): 1.0\n",
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" 3-й квартиль (Q3): 6.0\n",
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"\n",
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"Колонка Glucose:\n",
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|
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" Есть выбросы: Да\n",
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" Количество выбросов: 5\n",
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|||
|
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" Минимальное значение: 37.125\n",
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|
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" Максимальное значение: 199.0\n",
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|
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" 1-й квартиль (Q1): 99.0\n",
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" 3-й квартиль (Q3): 140.25\n",
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"\n",
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"Колонка BloodPressure:\n",
|
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|
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" Есть выбросы: Да\n",
|
|||
|
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" Количество выбросов: 45\n",
|
|||
|
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" Минимальное значение: 35.0\n",
|
|||
|
|
" Максимальное значение: 107.0\n",
|
|||
|
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" 1-й квартиль (Q1): 62.0\n",
|
|||
|
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" 3-й квартиль (Q3): 80.0\n",
|
|||
|
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"\n",
|
|||
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"Колонка SkinThickness:\n",
|
|||
|
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" Есть выбросы: Да\n",
|
|||
|
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" Количество выбросов: 1\n",
|
|||
|
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" Минимальное значение: 0.0\n",
|
|||
|
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" Максимальное значение: 80.0\n",
|
|||
|
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" 1-й квартиль (Q1): 0.0\n",
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|||
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" 3-й квартиль (Q3): 32.0\n",
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"\n",
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"Колонка Insulin:\n",
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" Есть выбросы: Да\n",
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|||
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" Количество выбросов: 34\n",
|
|||
|
|
" Минимальное значение: 0.0\n",
|
|||
|
|
" Максимальное значение: 318.125\n",
|
|||
|
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" 1-й квартиль (Q1): 0.0\n",
|
|||
|
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" 3-й квартиль (Q3): 127.25\n",
|
|||
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"\n",
|
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"Колонка BMI:\n",
|
|||
|
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" Есть выбросы: Да\n",
|
|||
|
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" Количество выбросов: 19\n",
|
|||
|
|
" Минимальное значение: 13.35\n",
|
|||
|
|
" Максимальное значение: 50.550000000000004\n",
|
|||
|
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" 1-й квартиль (Q1): 27.3\n",
|
|||
|
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" 3-й квартиль (Q3): 36.6\n",
|
|||
|
|
"\n",
|
|||
|
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"Колонка DiabetesPedigreeFunction:\n",
|
|||
|
|
" Есть выбросы: Да\n",
|
|||
|
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" Количество выбросов: 29\n",
|
|||
|
|
" Минимальное значение: 0.078\n",
|
|||
|
|
" Максимальное значение: 1.2\n",
|
|||
|
|
" 1-й квартиль (Q1): 0.24375\n",
|
|||
|
|
" 3-й квартиль (Q3): 0.62625\n",
|
|||
|
|
"\n",
|
|||
|
|
"Колонка Age:\n",
|
|||
|
|
" Есть выбросы: Да\n",
|
|||
|
|
" Количество выбросов: 9\n",
|
|||
|
|
" Минимальное значение: 21.0\n",
|
|||
|
|
" Максимальное значение: 66.5\n",
|
|||
|
|
" 1-й квартиль (Q1): 24.0\n",
|
|||
|
|
" 3-й квартиль (Q3): 41.0\n",
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"numeric_columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n",
|
|||
|
|
"for column in numeric_columns:\n",
|
|||
|
|
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
|
|||
|
|
" q1 = df[column].quantile(0.25) # Находим 1-й квартиль (Q1)\n",
|
|||
|
|
" q3 = df[column].quantile(0.75) # Находим 3-й квартиль (Q3)\n",
|
|||
|
|
" iqr = q3 - q1 # Вычисляем межквартильный размах (IQR)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Определяем границы для выбросов\n",
|
|||
|
|
" lower_bound = q1 - 1.5 * iqr # Нижняя граница\n",
|
|||
|
|
" upper_bound = q3 + 1.5 * iqr # Верхняя граница\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Подсчитываем количество выбросов\n",
|
|||
|
|
" outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
|
|||
|
|
" outlier_count = outliers.shape[0]\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
|
|||
|
|
" df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(f\"Колонка {column}:\")\n",
|
|||
|
|
" print(f\" Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
|
|||
|
|
" print(f\" Количество выбросов: {outlier_count}\")\n",
|
|||
|
|
" print(f\" Минимальное значение: {df[column].min()}\")\n",
|
|||
|
|
" print(f\" Максимальное значение: {df[column].max()}\")\n",
|
|||
|
|
" print(f\" 1-й квартиль (Q1): {q1}\")\n",
|
|||
|
|
" print(f\" 3-й квартиль (Q3): {q3}\\n\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Постараемся выявить зависимости Outcome от остальных колонок:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 4,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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"<Figure size 800x600 with 1 Axes>"
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]
|
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},
|
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|
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"metadata": {},
|
|||
|
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"output_type": "display_data"
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},
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{
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"data": {
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|
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"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
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]
|
|||
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|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
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|
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},
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|||
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{
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"data": {
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"text/plain": [
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"metadata": {},
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{
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"text/plain": [
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|||
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|
"<Figure size 800x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
" # Создание диаграмм зависимости\n",
|
|||
|
|
"for column in numeric_columns:\n",
|
|||
|
|
" plt.figure(figsize=(8, 6)) # Установка размера графика\n",
|
|||
|
|
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
|
|||
|
|
" # Проверяем, содержит ли колонка только два уникальных значения (0 и 1)\n",
|
|||
|
|
" if df[column].nunique() == 2 and set(df[column].unique()).issubset({0, 1}):\n",
|
|||
|
|
" # Если да, то строим столбчатую диаграмму\n",
|
|||
|
|
" counts = df[column].value_counts() # Считаем количество повторений каждого значения\n",
|
|||
|
|
" counts.plot(kind='bar') # Создаем столбчатую диаграмму\n",
|
|||
|
|
" plt.title(f'Количество значений для {column}')\n",
|
|||
|
|
" plt.xlabel(column)\n",
|
|||
|
|
" plt.ylabel('Количество повторений')\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" # Если колонка числовая, создаем диаграмму рассеяния\n",
|
|||
|
|
" plt.scatter(df['Outcome'], df[column], alpha=0.5) # Создаем диаграмму рассеяния\n",
|
|||
|
|
" plt.title(f'Зависимость {column} от Outcome')\n",
|
|||
|
|
" plt.xlabel('Outcome (0 = нет, 1 = да)')\n",
|
|||
|
|
" plt.ylabel(column)\n",
|
|||
|
|
" plt.xticks([0, 1]) # Установка меток по оси X\n",
|
|||
|
|
" plt.grid() # Добавление сетки для удобства восприятия\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" # Если колонка не числовая, строим столбчатую диаграмму\n",
|
|||
|
|
" counts = df[column].value_counts() # Считаем количество повторений каждого значения\n",
|
|||
|
|
" counts.plot(kind='bar') # Создаем столбчатую диаграмму\n",
|
|||
|
|
" plt.title(f'Количество значений для {column}')\n",
|
|||
|
|
" plt.xlabel(column)\n",
|
|||
|
|
" plt.ylabel('Количество повторений')\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.show() # Отображение графика"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Разобьем наш набор на выборки относительно параметра Outcome:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 5,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# Функция для создания выборок\n",
|
|||
|
|
"from sklearn.model_selection import train_test_split\n",
|
|||
|
|
"\n",
|
|||
|
|
"def split_stratified_into_train_val_test(\n",
|
|||
|
|
" df_input,\n",
|
|||
|
|
" stratify_colname=\"y\",\n",
|
|||
|
|
" frac_train=0.6,\n",
|
|||
|
|
" frac_val=0.15,\n",
|
|||
|
|
" frac_test=0.25,\n",
|
|||
|
|
" random_state=None,\n",
|
|||
|
|
"):\n",
|
|||
|
|
"\n",
|
|||
|
|
" if frac_train + frac_val + frac_test != 1.0:\n",
|
|||
|
|
" raise ValueError(\n",
|
|||
|
|
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
|
|||
|
|
" % (frac_train, frac_val, frac_test)\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" if stratify_colname not in df_input.columns:\n",
|
|||
|
|
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
|
|||
|
|
"\n",
|
|||
|
|
" X = df_input # Contains all columns.\n",
|
|||
|
|
" y = df_input[\n",
|
|||
|
|
" [stratify_colname]\n",
|
|||
|
|
" ] # Dataframe of just the column on which to stratify.\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Split original dataframe into train and temp dataframes.\n",
|
|||
|
|
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
|
|||
|
|
" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Split the temp dataframe into val and test dataframes.\n",
|
|||
|
|
" relative_frac_test = frac_test / (frac_val + frac_test)\n",
|
|||
|
|
" df_val, df_test, y_val, y_test = train_test_split(\n",
|
|||
|
|
" df_temp,\n",
|
|||
|
|
" y_temp,\n",
|
|||
|
|
" stratify=y_temp,\n",
|
|||
|
|
" test_size=relative_frac_test,\n",
|
|||
|
|
" random_state=random_state,\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return df_train, df_val, df_test"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Outcome\n",
|
|||
|
|
"0 500\n",
|
|||
|
|
"1 268\n",
|
|||
|
|
"Name: count, dtype: int64\n",
|
|||
|
|
"\n",
|
|||
|
|
"Обучающая выборка: (460, 9)\n",
|
|||
|
|
"Outcome\n",
|
|||
|
|
"0 299\n",
|
|||
|
|
"1 161\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAf8AAADECAYAAACROyhkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA4AklEQVR4nO3dd1wT9/8H8FcSAgTC3ooICqKCE0cdgAMHYl2t1GodqNXWttba1tZf3bb122Ld1lktKtY9WuvEusUtKFUUFBBR2SAEQiD5/P7ApISEKXhA3s/HIw/l8rnPvfO5y+d9d5+7C48xxkAIIYQQncHnOgBCCCGEvFmU/AkhhBAdQ8mfEEII0TGU/AkhhBAdQ8mfEEII0TGU/AkhhBAdQ8mfEEII0TGU/AkhhBAdQ8mfEEJeg0QiQWJiIjIzM7kOhdSwnJwcxMfHQyKRcB1KjaPkTwghVbR371707dsXJiYmEIvFcHJyws8//8x1WPVCbm4uVqxYofo7KysLa9eu5S6gEhhj2LhxI9566y0YGRnB1NQULi4u2LFjB9eh1bgqJf/ff/8dPB5P9TI0NESLFi3w6aefIjk5ubZiJERnXLp0CcOHD4ednR0MDAzg7OyMqVOn4smTJ9WuMy8vDwsWLMDZs2drLlAd9u233yIwMBAmJibYtGkTTp06hbCwMEybNo3r0OoFkUiEOXPmIDQ0FImJiViwYAH++usvrsMCAIwePRofffQRWrVqhe3bt6vW7YgRI7gOreaxKti6dSsDwBYtWsS2b9/ONm3axMaPH8/4fD5zcXFhEomkKtURQkpYtWoV4/F4rHnz5mzx4sVs8+bN7Msvv2RmZmbMzMyMXbp0qVr1pqamMgBs/vz5NRuwDjp79iwDwJYsWcJ1KPXa0qVLGZ/PZwCYqakpu3DhAtchsZCQEMbj8VhoaCjXobwR1Ur+169fV5s+c+ZMBoDt3LmzRoMjRFdcvHiR8fl85u3trbETHRsby+zs7JiDgwPLyMioct2U/GvO4MGDWffu3bkOo0FITExkly9fZpmZmVyHwhhjzNPTk40ePZrrMN6YGhnz79OnDwAgLi4OAJCRkYGvvvoKbdq0gVgshqmpKfz9/REZGakxr1QqxYIFC9CiRQsYGhrCwcEBI0aMwKNHjwAA8fHxakMNpV+9evVS1XX27FnweDzs3r0b//d//wd7e3sYGxtjyJAhSExM1Fj21atXMXDgQJiZmcHIyAi+vr64dOmS1s/Yq1cvrctfsGCBRtkdO3bAy8sLIpEIlpaWGDVqlNbll/fZSlIoFFixYgU8PDxgaGgIOzs7TJ06VeMCI2dnZwwePFhjOZ9++qlGndpiDw4O1mhTACgoKMD8+fPh6uoKAwMDNGnSBLNmzUJBQYHWtiqpV69eGvX98MMP4PP52LlzZ7XaY+nSpejevTusrKwgEong5eWFffv2aV3+jh070KVLFxgZGcHCwgI+Pj44efKkWpljx47B19cXJiYmMDU1RefOnTVi27t3r2qdWltb44MPPkBSUpJamQkTJqjFbGFhgV69euHChQsVttPixYvB4/EQEhICIyMjtfeaN2+On3/+Gc+fP8eGDRtU07W1rTIOZ2dnAMVtamNjAwBYuHCh1u02OjoagYGBsLGxgUgkgru7O7777ju1Om/fvg1/f3+YmppCLBajb9++uHLliloZ5bDgxYsXMX36dNjY2MDc3BxTp06FTCZDVlYWxo0bBwsLC1hYWGDWrFlgpX5UtLLbujav0/4A8M8//8Db2xvGxsYwNzfH0KFDcf/+fbUyV65cgaenJ0aNGgVLS0uIRCJ07twZhw4dUpXJzc2FsbExPv/8c41lPH36FAKBAEuWLFHFrFxXJZVeRwkJCZg2bRrc3d0hEolgZWWFkSNHIj4+Xm0+ZR9Ycojn+vXr6NevH0xMTGBsbKy1TZTr7saNG6ppaWlpWvuJwYMHa425Mv3pggULVN9nR0dHdOvWDXp6erC3t9eIWxvl/MqXiYkJunTpotb+QPF3w9PTs8x6lH3N77//DqD4os2oqCg0adIEAQEBMDU1LbOtAODx48cYOXIkLC0tYWRkhLfeegt///23Wpmq5KOq9JNVyVvl0avyHFooE7WVlRWA4oY5dOgQRo4cCRcXFyQnJ2PDhg3w9fXFvXv30KhRIwCAXC7H4MGDcfr0aYwaNQqff/45cnJycOrUKURFRaF58+aqZbz//vsYNGiQ2nJnz56tNZ4ffvgBPB4P33zzDVJSUrBixQr4+fkhIiICIpEIQPEX3d/fH15eXpg/fz74fD62bt2KPn364MKFC+jSpYtGvY6OjqovbW5uLj7++GOty547dy4CAwMxefJkpKamYvXq1fDx8cHt27dhbm6uMc+UKVPg7e0NADhw4AAOHjyo9v7UqVPx+++/IygoCNOnT0dcXBzWrFmD27dv49KlSxAKhVrboSqysrJUn60khUKBIUOG4OLFi5gyZQpatWqFu3fvYvny5Xj48KHGl64iW7duxZw5c/DLL79g9OjRWstU1B4rV67EkCFDMGbMGMhkMuzatQsjR47EkSNHEBAQoCq3cOFCLFiwAN27d8eiRYugr6+Pq1ev4p9//kH//v0BFHd6EydOhIeHB2bPng1zc3Pcvn0bx48fV8WnbPvOnTtjyZIlSE5OxsqVK3Hp0iWNdWptbY3ly5cDKO7oV65ciUGDBiExMVHrugeKx+RPnz4Nb29vuLi4aC3z3nvvYcqUKThy5Ai+/fbbihv6FRsbG6xbtw4ff/wxhg8frhq7bNu2LQDgzp078Pb2hlAoxJQpU+Ds7IxHjx7hr7/+wg8//AAA+Pfff+Ht7Q1TU1PMmjULQqEQGzZsQK9evXDu3Dl07dpVbZmfffYZ7O3tsXDhQly5cgUbN26Eubk5Ll++DCcnJ/z44484evQogoOD4enpiXHjxqnmfd1tvTrtDwBhYWHw9/dHs2bNsGDBAuTn52P16tXo0aMHbt26pUp26enp2LhxI8RisWoHZ8eOHRgxYgRCQ0Px/vvvQywWY/jw4di9ezeWLVsGgUCgWs4ff/wBxhjGjBlTuRX4yvXr13H58mWMGjUKjo6OiI+Px7p169CrVy/cu3dPY4dRKTY2Fr169YKRkRG+/vprGBkZYdOmTfDz88OpU6fg4+NTpTjKUp3+VOmXX36p8jVj27dvB1C8g/Lrr79i5MiRiIqKgru7e7XiT09PBwD89NNPsLe3x9dffw1DQ0OtbZWcnIzu3bsjLy8P06dPh5WVFUJCQjBkyBDs27cPw4cPV6u7MvmotLL6yddpZw1VOU2gPO0fFhbGUlNTWWJiItu1axezsrJiIpGIPX36lDHGmFQqZXK5XG3euLg4ZmBgwBYtWqSatmXLFgaALVu2TGNZCoVCNR8AFhwcrFHGw8OD+fr6qv4+c+YMA8AaN27MXr58qZq+Z88eBoCtXLlSVbebmxsbMGCAajmMMZaXl8dcXFxYv379NJbVvXt35unpqfpb26nU+Ph4JhAI2A8//KA27927d5menp7G9JiYGAaAhYSEqKbNnz+flVwtFy5cYAA0xqGOHz+uMb1p06YsICBAI/ZPPvmElV7VpWOfNWsWs7W1ZV5eXmptun37dsbn8zXG5NavX88AVDgO7evrq6rv77//Znp6euzLL7/UWrYy7cFY8XoqSSaTMU9PT9anTx+1uvh8Phs+fLjGtqhc51lZWczExIR17dqV5efnay0jk8mYra0t8/T0VCtz5MgRBoDNmzdPNW38+PGsadOmavVs3LiRAWDXrl3T+pkZYywiIoIBYJ9//nmZZRhjrG3btszS0lL1d8m2Lal0HOWd9vfx8WEmJiYsISFBbXrJ78WwYcOYvr4+e/TokWras2fPmImJCfPx8VFNU/YPpb9X3bp1Yzwej3300UeqaUVFRczR0VEt/qps69pUt/0ZY6x9+/bM1taWpaenq6ZFRkYyPp/Pxo0bp5oGgAFgZ8+eVU3Ly8tjrVq1Yvb29kwmkzHGGDtx4gQDwI4dO6a2nLZt26p95qCgIObk5KQRT+n1VXqbZ4yx8PBwBoBt27ZNNU3
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Контрольная выборка: (154, 9)\n",
|
|||
|
|
"Outcome\n",
|
|||
|
|
"0 101\n",
|
|||
|
|
"1 53\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Тестовая выборка: (154, 9)\n",
|
|||
|
|
"Outcome\n",
|
|||
|
|
"0 100\n",
|
|||
|
|
"1 54\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAekAAADECAYAAAC7i9nLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA1QklEQVR4nO3dd1xT1/sH8E8SEvbeKAKKiHug8lNkuEWsq9a6t1Kr1tZ+v7S2tY5qtdpq3drWPeqs2lonVr/uDbiQooIiKks2hITk/P6gSbkkICBwQ3jerxev1puTc597cnOfnHPPvVfAGGMghBBCiM4R8h0AIYQQQrSjJE0IIYToKErShBBCiI6iJE0IIYToKErShBBCiI6iJE0IIYToKErShBBCiI6iJE0IIYToKErShBBCajXGGF6/fo3Y2Fi+Q6lylKQJIYRode/ePRw+fFj978jISPz555/8BVRMdnY2vvrqKzRp0gQSiQS2trbw8vJCTEwM36FVqQol6a1bt0IgEKj/jIyM4OXlhenTpyMpKam6YiSkzrh06RIGDRoER0dHGBoawt3dHaGhoXj27Fml68zLy8O8efNw7ty5qguU1AnZ2dkIDQ3F1atXERsbi5kzZ+Lu3bt8h4W0tDR06tQJq1atwpAhQ3DkyBGcPn0a586dg7u7O9/hVSmDyrxpwYIF8PDwgFQqxcWLF7F+/XocO3YM9+7dg4mJSVXHSEidsHr1asycORMNGzbEjBkz4OzsjOjoaPzyyy/Yu3cvjh07hs6dO1e43ry8PMyfPx8AEBQUVMVRE33WqVMn9R8AeHl5YfLkyTxHBfz3v//Fy5cvceXKFTRv3pzvcKpVpZJ0cHAw2rdvDwCYNGkSbG1tsXz5chw5cgTDhw+v0gAJqQsuXbqEjz/+GF26dMGJEyc4P3anTp0KPz8/DBkyBPfv34e1tTWPkZK65vDhw3jw4AHy8/PRsmVLSCQSXuNJTk7Gtm3bsGHDBr1P0EAVnZPu1q0bACAuLg4A8Pr1a/znP/9By5YtYWZmBgsLCwQHByMqKkrjvVKpFPPmzYOXlxeMjIzg7OyMwYMH4/HjxwCA+Ph4zhB7yb/iPYNz585BIBBg7969+OKLL+Dk5ARTU1P0798fCQkJGuu+du0a+vTpA0tLS5iYmCAwMBCXLl3Suo1BQUFa1z9v3jyNsjt37oSPjw+MjY1hY2ODYcOGaV1/WdtWnFKpxI8//ojmzZvDyMgIjo6OCA0NRXp6Oqecu7s7+vXrp7Ge6dOna9SpLfZly5ZptCkAFBQUYO7cufD09IShoSFcXV0RFhaGgoICrW1VXFBQkEZ9ixYtglAoxO7duyvVHt9//z06d+4MW1tbGBsbw8fHBwcOHNC6/p07d6Jjx44wMTGBtbU1AgICcOrUKU6Z48ePIzAwEObm5rCwsECHDh00Ytu/f7/6M7Wzs8OoUaOQmJjIKTNu3DhOzNbW1ggKCsKFCxfe2E7ffPMNBAIBtm3bpjEa1ahRIyxduhQvX77Exo0b1cu1ta0qDtWQX3x8POzt7QEA8+fP17rfPnz4EEOHDoW9vT2MjY3RpEkTfPnll5w6IyIiEBwcDAsLC5iZmaF79+64evUqp4zqdNjFixfx0Ucfwd7eHlZWVggNDYVMJkNGRgbGjBkDa2trWFtbIywsDCUfwlfefV2byrZ/yfdp+4uPj1eXP378OPz9/WFqagpzc3OEhITg/v37GvWW1a7z5s174zqLn56o6v1v3bp1aN68OQwNDeHi4oJp06YhIyODU6b4/tWsWTP4+PggKipK63dSm5LHTDs7O4SEhODevXuccgKBANOnTy+1HtV+pfoMbty4AaVSCZlMhvbt28PIyAi2trYYPny41tNCf/31l/rzsrKywoABAxAdHc0po/o8VJ+ZhYUFbG1tMXPmTEilUo14i39/CgsL0bdvX9jY2ODBgwecsuXNBWWpVE+6JFVCtbW1BQA8efIEhw8fxnvvvQcPDw8kJSVh48aNCAwMxIMHD+Di4gIAUCgU6NevH86cOYNhw4Zh5syZyM7OxunTp3Hv3j00atRIvY7hw4ejb9++nPXOnj1bazyLFi2CQCDAZ599huTkZPz444/o0aMHIiMjYWxsDKDogwsODoaPjw/mzp0LoVCILVu2oFu3brhw4QI6duyoUW/9+vWxePFiAEBOTg6mTp2qdd1z5szB0KFDMWnSJKSkpGD16tUICAhAREQErKysNN4zZcoU+Pv7AwB+++03HDp0iPN6aGgotm7divHjx+Ojjz5CXFwc1qxZg4iICFy6dAlisVhrO1RERkaGetuKUyqV6N+/Py5evIgpU6agadOmuHv3LlasWIG///6bM6mkPLZs2YKvvvoKP/zwA0aMGKG1zJvaY+XKlejfvz9GjhwJmUyGPXv24L333sPRo0cREhKiLjd//nzMmzcPnTt3xoIFCyCRSHDt2jX89ddf6NWrF4CiA8CECRPQvHlzzJ49G1ZWVoiIiMCJEyfU8anavkOHDli8eDGSkpKwcuVKXLp0SeMztbOzw4oVKwAAz58/x8qVK9G3b18kJCRo/eyBouHoM2fOwN/fHx4eHlrLvP/++5gyZQqOHj2Kzz///M0N/Q97e3usX78eU6dOxaBBgzB48GAAQKtWrQAAd+7cgb+/P8RiMaZMmQJ3d3c8fvwYf/zxBxYtWgQAuH//Pvz9/WFhYYGwsDCIxWJs3LgRQUFB+N///gdfX1/OOmfMmAEnJyfMnz8fV69exU8//QQrKytcvnwZDRo0wLfffotjx45h2bJlaNGiBcaMGaN+79vu65Vp/9DQUPTo0UP979GjR3PaStWOALBjxw6MHTsWvXv3xnfffYe8vDysX78eXbp0QUREhPrH0ZvadfDgwfD09FTX/8knn6Bp06aYMmWKelnTpk0BVP3+N2/ePMyfPx89evTA1KlTERMTg/Xr1+PGjRtvbOPPPvuszPYvydvbG19++SUYY3j8+DGWL1+Ovn37vtUci7S0NABFnQ8fHx8sWbIEKSkpWLVqFS5evIiIiAjY2dkBAMLDwxEcHIyGDRti3rx5yM/Px+rVq+Hn54fbt29rnL8eOnQo3N3dsXjxYly9ehWrVq1Ceno6tm/fXmo8kyZNwrlz53D69Gk0a9ZMvbwyuUArVgFbtmxhAFh4eDhLSUlhCQkJbM+ePczW1pYZGxuz58+fM8YYk0qlTKFQcN4bFxfHDA0N2YIFC9TLNm/ezACw5cuXa6xLqVSq3weALVu2TKNM8+bNWWBgoPrfZ8+eZQBYvXr1WFZWlnr5vn37GAC2cuVKdd2NGzdmvXv3Vq+HMcby8vKYh4cH69mzp8a6OnfuzFq0aKH+d0pKCgPA5s6dq14WHx/PRCIRW7RoEee9d+/eZQYGBhrLY2NjGQC2bds29bK5c+ey4h/LhQsXGAC2a9cuzntPnDihsdzNzY2FhIRoxD5t2jRW8qMuGXtYWBhzcHBgPj4+nDbdsWMHEwqF7MKFC5z3b9iwgQFgly5d0lhfcYGBger6/vzzT2ZgYMA+/fRTrWXL0x6MFX1OxclkMtaiRQvWrVs3Tl1CoZANGjRIY19UfeYZGRnM3Nyc+fr6svz8fK1lZDIZc3BwYC1atOCUOXr0KAPAvv76a/WysWPHMjc3N049P/30EwPArl+/rnWbGWMsMjKSAWAzZ84stQxjjLVq1YrZ2Nio/128bYsrGYe2fVUlICCAmZubs6dPn3KWF/9eDBw4kEkkEvb48WP1shcvXjBzc3MWEBCgXqY6PpT8XnXq1IkJBAL2wQcfqJcVFhay+vXrc+KvyL6uTWXbv6TS2io7O5tZWVmxyZMnc5a/evWKWVpacpaXp12Lc3NzY2PHjtVYXtX7X3JyMpNIJKxXr16c78WaNWsYALZ582b1spL717FjxxgA1qdPH43vpDba9s8vvviCAWDJycnqZQDYtGnTSq1HtV/FxcVx/t2sWTPOsUB1/C9+fGnTpg1zcHBgaWlp6mVRUVFMKBSyMWPGqJepjjP9+/fnrPvDDz9kAFhUVBQnXtX+MXv2bCYSidjhw4c576toLihLpYa
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Вывод распределения количества наблюдений по меткам (классам)\n",
|
|||
|
|
"print(df.Outcome.value_counts())\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"\n",
|
|||
|
|
"data = df.copy()\n",
|
|||
|
|
"\n",
|
|||
|
|
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
|
|||
|
|
" data, stratify_colname=\"Outcome\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Обучающая выборка: \", df_train.shape)\n",
|
|||
|
|
"print(df_train.Outcome.value_counts())\n",
|
|||
|
|
"counts = df_train['Outcome'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))# Установка размера графика\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
|
|||
|
|
"plt.title('Распределение классов Outcome в обучающей выборке')# Добавление заголовка\n",
|
|||
|
|
"plt.show()# Отображение графика\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Контрольная выборка: \", df_val.shape)\n",
|
|||
|
|
"print(df_val.Outcome.value_counts())\n",
|
|||
|
|
"counts = df_val['Outcome'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов Outcome в контрольной выборке')\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Тестовая выборка: \", df_test.shape)\n",
|
|||
|
|
"print(df_test.Outcome.value_counts())\n",
|
|||
|
|
"counts = df_test['Outcome'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов Outcome в тестовой выборке')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Сбалансируем распределение:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"1. Балансировка данных оверсемплингом. Это метод, увеличивающий число наблюдений в меньшинственном классе для достижения более равномерного распределения классов."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Обучающая выборка: (460, 9)\n",
|
|||
|
|
"Outcome\n",
|
|||
|
|
"0 299\n",
|
|||
|
|
"1 161\n",
|
|||
|
|
"Name: count, dtype: int64\n",
|
|||
|
|
"Обучающая выборка после oversampling: (586, 9)\n",
|
|||
|
|
"Outcome\n",
|
|||
|
|
"0 299\n",
|
|||
|
|
"1 287\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
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|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Pregnancies</th>\n",
|
|||
|
|
" <th>Glucose</th>\n",
|
|||
|
|
" <th>BloodPressure</th>\n",
|
|||
|
|
" <th>SkinThickness</th>\n",
|
|||
|
|
" <th>Insulin</th>\n",
|
|||
|
|
" <th>BMI</th>\n",
|
|||
|
|
" <th>DiabetesPedigreeFunction</th>\n",
|
|||
|
|
" <th>Age</th>\n",
|
|||
|
|
" <th>Outcome</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>4.000000</td>\n",
|
|||
|
|
" <td>171.000000</td>\n",
|
|||
|
|
" <td>72.000000</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" <td>43.600000</td>\n",
|
|||
|
|
" <td>0.479000</td>\n",
|
|||
|
|
" <td>26.000000</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>4.000000</td>\n",
|
|||
|
|
" <td>110.000000</td>\n",
|
|||
|
|
" <td>76.000000</td>\n",
|
|||
|
|
" <td>20.000000</td>\n",
|
|||
|
|
" <td>100.000000</td>\n",
|
|||
|
|
" <td>28.400000</td>\n",
|
|||
|
|
" <td>0.118000</td>\n",
|
|||
|
|
" <td>27.000000</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>3.000000</td>\n",
|
|||
|
|
" <td>106.000000</td>\n",
|
|||
|
|
" <td>72.000000</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" <td>25.800000</td>\n",
|
|||
|
|
" <td>0.207000</td>\n",
|
|||
|
|
" <td>27.000000</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" <td>141.000000</td>\n",
|
|||
|
|
" <td>35.000000</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" <td>42.400000</td>\n",
|
|||
|
|
" <td>0.205000</td>\n",
|
|||
|
|
" <td>29.000000</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>1.000000</td>\n",
|
|||
|
|
" <td>100.000000</td>\n",
|
|||
|
|
" <td>66.000000</td>\n",
|
|||
|
|
" <td>15.000000</td>\n",
|
|||
|
|
" <td>56.000000</td>\n",
|
|||
|
|
" <td>23.600000</td>\n",
|
|||
|
|
" <td>0.666000</td>\n",
|
|||
|
|
" <td>26.000000</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>581</th>\n",
|
|||
|
|
" <td>4.223037</td>\n",
|
|||
|
|
" <td>113.446073</td>\n",
|
|||
|
|
" <td>77.243141</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" <td>0.000000</td>\n",
|
|||
|
|
" <td>30.687204</td>\n",
|
|||
|
|
" <td>0.404846</td>\n",
|
|||
|
|
" <td>46.797068</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>582</th>\n",
|
|||
|
|
" <td>3.000000</td>\n",
|
|||
|
|
" <td>78.972919</td>\n",
|
|||
|
|
" <td>65.566707</td>\n",
|
|||
|
|
" <td>31.513540</td>\n",
|
|||
|
|
" <td>79.243727</td>\n",
|
|||
|
|
" <td>32.556671</td>\n",
|
|||
|
|
" <td>0.711110</td>\n",
|
|||
|
|
" <td>26.486460</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>583</th>\n",
|
|||
|
|
" <td>3.000000</td>\n",
|
|||
|
|
" <td>79.630698</td>\n",
|
|||
|
|
" <td>76.091174</td>\n",
|
|||
|
|
" <td>31.184651</td>\n",
|
|||
|
|
" <td>73.323715</td>\n",
|
|||
|
|
" <td>33.609117</td>\n",
|
|||
|
|
" <td>1.024212</td>\n",
|
|||
|
|
" <td>26.815349</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>584</th>\n",
|
|||
|
|
" <td>2.984921</td>\n",
|
|||
|
|
" <td>95.708532</td>\n",
|
|||
|
|
" <td>66.462302</td>\n",
|
|||
|
|
" <td>28.552777</td>\n",
|
|||
|
|
" <td>159.015079</td>\n",
|
|||
|
|
" <td>36.005535</td>\n",
|
|||
|
|
" <td>0.685819</td>\n",
|
|||
|
|
" <td>27.432143</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>585</th>\n",
|
|||
|
|
" <td>10.312967</td>\n",
|
|||
|
|
" <td>142.374065</td>\n",
|
|||
|
|
" <td>77.082295</td>\n",
|
|||
|
|
" <td>41.915212</td>\n",
|
|||
|
|
" <td>124.164589</td>\n",
|
|||
|
|
" <td>37.666584</td>\n",
|
|||
|
|
" <td>0.495783</td>\n",
|
|||
|
|
" <td>40.875312</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>586 rows × 9 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Pregnancies Glucose BloodPressure SkinThickness Insulin \\\n",
|
|||
|
|
"0 4.000000 171.000000 72.000000 0.000000 0.000000 \n",
|
|||
|
|
"1 4.000000 110.000000 76.000000 20.000000 100.000000 \n",
|
|||
|
|
"2 3.000000 106.000000 72.000000 0.000000 0.000000 \n",
|
|||
|
|
"3 0.000000 141.000000 35.000000 0.000000 0.000000 \n",
|
|||
|
|
"4 1.000000 100.000000 66.000000 15.000000 56.000000 \n",
|
|||
|
|
".. ... ... ... ... ... \n",
|
|||
|
|
"581 4.223037 113.446073 77.243141 0.000000 0.000000 \n",
|
|||
|
|
"582 3.000000 78.972919 65.566707 31.513540 79.243727 \n",
|
|||
|
|
"583 3.000000 79.630698 76.091174 31.184651 73.323715 \n",
|
|||
|
|
"584 2.984921 95.708532 66.462302 28.552777 159.015079 \n",
|
|||
|
|
"585 10.312967 142.374065 77.082295 41.915212 124.164589 \n",
|
|||
|
|
"\n",
|
|||
|
|
" BMI DiabetesPedigreeFunction Age Outcome \n",
|
|||
|
|
"0 43.600000 0.479000 26.000000 1 \n",
|
|||
|
|
"1 28.400000 0.118000 27.000000 0 \n",
|
|||
|
|
"2 25.800000 0.207000 27.000000 0 \n",
|
|||
|
|
"3 42.400000 0.205000 29.000000 1 \n",
|
|||
|
|
"4 23.600000 0.666000 26.000000 0 \n",
|
|||
|
|
".. ... ... ... ... \n",
|
|||
|
|
"581 30.687204 0.404846 46.797068 1 \n",
|
|||
|
|
"582 32.556671 0.711110 26.486460 1 \n",
|
|||
|
|
"583 33.609117 1.024212 26.815349 1 \n",
|
|||
|
|
"584 36.005535 0.685819 27.432143 1 \n",
|
|||
|
|
"585 37.666584 0.495783 40.875312 1 \n",
|
|||
|
|
"\n",
|
|||
|
|
"[586 rows x 9 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from imblearn.over_sampling import ADASYN\n",
|
|||
|
|
"\n",
|
|||
|
|
"ada = ADASYN()\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Обучающая выборка: \", df_train.shape)\n",
|
|||
|
|
"print(df_train.Outcome.value_counts())\n",
|
|||
|
|
"\n",
|
|||
|
|
"X_resampled, y_resampled = ada.fit_resample(df_train, df_train[\"Outcome\"])\n",
|
|||
|
|
"df_train_adasyn = pd.DataFrame(X_resampled)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
|
|||
|
|
"print(df_train_adasyn.Outcome.value_counts())\n",
|
|||
|
|
"\n",
|
|||
|
|
"counts = df_train_adasyn['Outcome'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов Outcome в тренировочной выборке после ADASYN')\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"\n",
|
|||
|
|
"df_train_adasyn"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"2. Балансировка данных андерсемплингом. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести его в соответствие с классом меньшинства."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Обучающая выборка после undersampling: (322, 9)\n",
|
|||
|
|
"Outcome\n",
|
|||
|
|
"0 161\n",
|
|||
|
|
"1 161\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from imblearn.under_sampling import RandomUnderSampler\n",
|
|||
|
|
"\n",
|
|||
|
|
"rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Применение RandomUnderSampler\n",
|
|||
|
|
"X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['Outcome']), df_train['Outcome'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание нового DataFrame\n",
|
|||
|
|
"df_train_undersampled = pd.DataFrame(X_resampled)\n",
|
|||
|
|
"df_train_undersampled['Outcome'] = y_resampled # Добавление целевой переменной\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Вывод информации о новой выборке\n",
|
|||
|
|
"print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
|
|||
|
|
"print(df_train_undersampled['Outcome'].value_counts())\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация распределения классов\n",
|
|||
|
|
"counts = df_train_undersampled['Outcome'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов Outcome в тренировочной выборке после Undersampling')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"### 2. Датасет: Данные по инсультам\n",
|
|||
|
|
"https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset\n",
|
|||
|
|
"##### О наборе данных: \n",
|
|||
|
|
"По данным Всемирной организации здравоохранения (ВОЗ), инсульт является второй по значимости причиной смертности во всем мире, на его долю приходится примерно 11% от общего числа смертей.\n",
|
|||
|
|
"Этот набор данных используется для прогнозирования вероятности инсульта у пациента на основе входных параметров, таких как пол, возраст, различные заболевания и статус курильщика. Каждая строка в данных содержит соответствующую информацию о пациенте.\n",
|
|||
|
|
"\n",
|
|||
|
|
"Атрибуты:\n",
|
|||
|
|
"1) id: уникальный идентификатор\n",
|
|||
|
|
"2) gender: \"Male\", \"Female\" или \"Other\"\n",
|
|||
|
|
"3) age: возраст пациента\n",
|
|||
|
|
"4) hypertension: 0, если у пациента нет артериальной гипертензии, 1, если у пациента есть артериальная гипертензия\n",
|
|||
|
|
"5) heart_disease: 0, если у пациента нет сердечных заболеваний, 1, если у пациента есть сердечные заболевания\n",
|
|||
|
|
"6) ever_married: \"No\" или \"Yes\"\n",
|
|||
|
|
"7) work_type: \"children\", \"Govt_jov\", \"Never_worked\", \"Private\" or \"Self-employed\"\n",
|
|||
|
|
"8) Residence_type: \"Rural\" or \"Urban\"\n",
|
|||
|
|
"9) avg_glucose_level: средний уровень глюкозы в крови\n",
|
|||
|
|
"10) bmi: индекс массы тела\n",
|
|||
|
|
"11) smoking_status: \"formerly smoked\", \"never smoked\", \"smokes\" или \"Unknown\"*\n",
|
|||
|
|
"12) stroke: 1, если у пациента был инсульт, или 0, если нет.\n",
|
|||
|
|
"##### Таким образом:\n",
|
|||
|
|
"* Объект наблюдения - Реальные пациенты.\n",
|
|||
|
|
"* Атрибуты: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke.\n",
|
|||
|
|
"* Проблемная область: Прогнозирование вероятности инсульта у пациента."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Количество колонок: 12\n",
|
|||
|
|
"Колонки: id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke\n",
|
|||
|
|
"\n",
|
|||
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
|||
|
|
"RangeIndex: 5110 entries, 0 to 5109\n",
|
|||
|
|
"Data columns (total 12 columns):\n",
|
|||
|
|
" # Column Non-Null Count Dtype \n",
|
|||
|
|
"--- ------ -------------- ----- \n",
|
|||
|
|
" 0 id 5110 non-null int64 \n",
|
|||
|
|
" 1 gender 5110 non-null object \n",
|
|||
|
|
" 2 age 5110 non-null float64\n",
|
|||
|
|
" 3 hypertension 5110 non-null int64 \n",
|
|||
|
|
" 4 heart_disease 5110 non-null int64 \n",
|
|||
|
|
" 5 ever_married 5110 non-null object \n",
|
|||
|
|
" 6 work_type 5110 non-null object \n",
|
|||
|
|
" 7 Residence_type 5110 non-null object \n",
|
|||
|
|
" 8 avg_glucose_level 5110 non-null float64\n",
|
|||
|
|
" 9 bmi 4909 non-null float64\n",
|
|||
|
|
" 10 smoking_status 5110 non-null object \n",
|
|||
|
|
" 11 stroke 5110 non-null int64 \n",
|
|||
|
|
"dtypes: float64(3), int64(4), object(5)\n",
|
|||
|
|
"memory usage: 479.2+ KB\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>id</th>\n",
|
|||
|
|
" <th>gender</th>\n",
|
|||
|
|
" <th>age</th>\n",
|
|||
|
|
" <th>hypertension</th>\n",
|
|||
|
|
" <th>heart_disease</th>\n",
|
|||
|
|
" <th>ever_married</th>\n",
|
|||
|
|
" <th>work_type</th>\n",
|
|||
|
|
" <th>Residence_type</th>\n",
|
|||
|
|
" <th>avg_glucose_level</th>\n",
|
|||
|
|
" <th>bmi</th>\n",
|
|||
|
|
" <th>smoking_status</th>\n",
|
|||
|
|
" <th>stroke</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>9046</td>\n",
|
|||
|
|
" <td>Male</td>\n",
|
|||
|
|
" <td>67.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Urban</td>\n",
|
|||
|
|
" <td>228.69</td>\n",
|
|||
|
|
" <td>36.6</td>\n",
|
|||
|
|
" <td>formerly smoked</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>51676</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>61.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Self-employed</td>\n",
|
|||
|
|
" <td>Rural</td>\n",
|
|||
|
|
" <td>202.21</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>never smoked</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>31112</td>\n",
|
|||
|
|
" <td>Male</td>\n",
|
|||
|
|
" <td>80.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Rural</td>\n",
|
|||
|
|
" <td>105.92</td>\n",
|
|||
|
|
" <td>32.5</td>\n",
|
|||
|
|
" <td>never smoked</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>60182</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>49.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Urban</td>\n",
|
|||
|
|
" <td>171.23</td>\n",
|
|||
|
|
" <td>34.4</td>\n",
|
|||
|
|
" <td>smokes</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>1665</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>79.0</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Self-employed</td>\n",
|
|||
|
|
" <td>Rural</td>\n",
|
|||
|
|
" <td>174.12</td>\n",
|
|||
|
|
" <td>24.0</td>\n",
|
|||
|
|
" <td>never smoked</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" id gender age hypertension heart_disease ever_married \\\n",
|
|||
|
|
"0 9046 Male 67.0 0 1 Yes \n",
|
|||
|
|
"1 51676 Female 61.0 0 0 Yes \n",
|
|||
|
|
"2 31112 Male 80.0 0 1 Yes \n",
|
|||
|
|
"3 60182 Female 49.0 0 0 Yes \n",
|
|||
|
|
"4 1665 Female 79.0 1 0 Yes \n",
|
|||
|
|
"\n",
|
|||
|
|
" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
|
|||
|
|
"0 Private Urban 228.69 36.6 formerly smoked \n",
|
|||
|
|
"1 Self-employed Rural 202.21 NaN never smoked \n",
|
|||
|
|
"2 Private Rural 105.92 32.5 never smoked \n",
|
|||
|
|
"3 Private Urban 171.23 34.4 smokes \n",
|
|||
|
|
"4 Self-employed Rural 174.12 24.0 never smoked \n",
|
|||
|
|
"\n",
|
|||
|
|
" stroke \n",
|
|||
|
|
"0 1 \n",
|
|||
|
|
"1 1 \n",
|
|||
|
|
"2 1 \n",
|
|||
|
|
"3 1 \n",
|
|||
|
|
"4 1 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"df = pd.read_csv(\".//static//csv//stroke.csv\", sep=\",\")\n",
|
|||
|
|
"print('Количество колонок: ' + str(df.columns.size)) \n",
|
|||
|
|
"print('Колонки: ' + ', '.join(df.columns)+'\\n')\n",
|
|||
|
|
"\n",
|
|||
|
|
"df.info()\n",
|
|||
|
|
"df.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Получение сведений о пропущенных данных\n",
|
|||
|
|
"Типы пропущенных данных:\n",
|
|||
|
|
"\n",
|
|||
|
|
"- None - представление пустых данных в Python\n",
|
|||
|
|
"- NaN - представление пустых данных в Pandas\n",
|
|||
|
|
"- '' - пустая строка"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"id 0\n",
|
|||
|
|
"gender 0\n",
|
|||
|
|
"age 0\n",
|
|||
|
|
"hypertension 0\n",
|
|||
|
|
"heart_disease 0\n",
|
|||
|
|
"ever_married 0\n",
|
|||
|
|
"work_type 0\n",
|
|||
|
|
"Residence_type 0\n",
|
|||
|
|
"avg_glucose_level 0\n",
|
|||
|
|
"bmi 201\n",
|
|||
|
|
"smoking_status 0\n",
|
|||
|
|
"stroke 0\n",
|
|||
|
|
"dtype: int64\n",
|
|||
|
|
"\n",
|
|||
|
|
"id False\n",
|
|||
|
|
"gender False\n",
|
|||
|
|
"age False\n",
|
|||
|
|
"hypertension False\n",
|
|||
|
|
"heart_disease False\n",
|
|||
|
|
"ever_married False\n",
|
|||
|
|
"work_type False\n",
|
|||
|
|
"Residence_type False\n",
|
|||
|
|
"avg_glucose_level False\n",
|
|||
|
|
"bmi True\n",
|
|||
|
|
"smoking_status False\n",
|
|||
|
|
"stroke False\n",
|
|||
|
|
"dtype: bool\n",
|
|||
|
|
"\n",
|
|||
|
|
"id процент пустых значений: %0.00\n",
|
|||
|
|
"gender процент пустых значений: %0.00\n",
|
|||
|
|
"age процент пустых значений: %0.00\n",
|
|||
|
|
"hypertension процент пустых значений: %0.00\n",
|
|||
|
|
"heart_disease процент пустых значений: %0.00\n",
|
|||
|
|
"ever_married процент пустых значений: %0.00\n",
|
|||
|
|
"work_type процент пустых значений: %0.00\n",
|
|||
|
|
"Residence_type процент пустых значений: %0.00\n",
|
|||
|
|
"avg_glucose_level процент пустых значений: %0.00\n",
|
|||
|
|
"bmi процент пустых значений: %3.93\n",
|
|||
|
|
"smoking_status процент пустых значений: %0.00\n",
|
|||
|
|
"stroke процент пустых значений: %0.00\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Количество пустых значений признаков\n",
|
|||
|
|
"print(df.isnull().sum())\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Есть ли пустые значения признаков\n",
|
|||
|
|
"print(df.isnull().any())\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Процент пустых значений признаков\n",
|
|||
|
|
"for i in df.columns:\n",
|
|||
|
|
" null_rate = df[i].isnull().sum() / len(df) * 100\n",
|
|||
|
|
" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"##### Пропущенные данные существуют. Необходимо заполнить пропуски медианными значениями.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Заполнение пропущенных данных:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"(5110, 12)\n",
|
|||
|
|
"id False\n",
|
|||
|
|
"gender False\n",
|
|||
|
|
"age False\n",
|
|||
|
|
"hypertension False\n",
|
|||
|
|
"heart_disease False\n",
|
|||
|
|
"ever_married False\n",
|
|||
|
|
"work_type False\n",
|
|||
|
|
"Residence_type False\n",
|
|||
|
|
"avg_glucose_level False\n",
|
|||
|
|
"bmi False\n",
|
|||
|
|
"smoking_status False\n",
|
|||
|
|
"stroke False\n",
|
|||
|
|
"dtype: bool\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>id</th>\n",
|
|||
|
|
" <th>gender</th>\n",
|
|||
|
|
" <th>age</th>\n",
|
|||
|
|
" <th>hypertension</th>\n",
|
|||
|
|
" <th>heart_disease</th>\n",
|
|||
|
|
" <th>ever_married</th>\n",
|
|||
|
|
" <th>work_type</th>\n",
|
|||
|
|
" <th>Residence_type</th>\n",
|
|||
|
|
" <th>avg_glucose_level</th>\n",
|
|||
|
|
" <th>bmi</th>\n",
|
|||
|
|
" <th>smoking_status</th>\n",
|
|||
|
|
" <th>stroke</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5105</th>\n",
|
|||
|
|
" <td>18234</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>80.0</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Urban</td>\n",
|
|||
|
|
" <td>83.75</td>\n",
|
|||
|
|
" <td>27.7</td>\n",
|
|||
|
|
" <td>never smoked</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5106</th>\n",
|
|||
|
|
" <td>44873</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>81.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Self-employed</td>\n",
|
|||
|
|
" <td>Urban</td>\n",
|
|||
|
|
" <td>125.20</td>\n",
|
|||
|
|
" <td>40.0</td>\n",
|
|||
|
|
" <td>never smoked</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5107</th>\n",
|
|||
|
|
" <td>19723</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>35.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Self-employed</td>\n",
|
|||
|
|
" <td>Rural</td>\n",
|
|||
|
|
" <td>82.99</td>\n",
|
|||
|
|
" <td>30.6</td>\n",
|
|||
|
|
" <td>never smoked</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5108</th>\n",
|
|||
|
|
" <td>37544</td>\n",
|
|||
|
|
" <td>Male</td>\n",
|
|||
|
|
" <td>51.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Rural</td>\n",
|
|||
|
|
" <td>166.29</td>\n",
|
|||
|
|
" <td>25.6</td>\n",
|
|||
|
|
" <td>formerly smoked</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5109</th>\n",
|
|||
|
|
" <td>44679</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>44.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Govt_job</td>\n",
|
|||
|
|
" <td>Urban</td>\n",
|
|||
|
|
" <td>85.28</td>\n",
|
|||
|
|
" <td>26.2</td>\n",
|
|||
|
|
" <td>Unknown</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" id gender age hypertension heart_disease ever_married \\\n",
|
|||
|
|
"5105 18234 Female 80.0 1 0 Yes \n",
|
|||
|
|
"5106 44873 Female 81.0 0 0 Yes \n",
|
|||
|
|
"5107 19723 Female 35.0 0 0 Yes \n",
|
|||
|
|
"5108 37544 Male 51.0 0 0 Yes \n",
|
|||
|
|
"5109 44679 Female 44.0 0 0 Yes \n",
|
|||
|
|
"\n",
|
|||
|
|
" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
|
|||
|
|
"5105 Private Urban 83.75 27.7 never smoked \n",
|
|||
|
|
"5106 Self-employed Urban 125.20 40.0 never smoked \n",
|
|||
|
|
"5107 Self-employed Rural 82.99 30.6 never smoked \n",
|
|||
|
|
"5108 Private Rural 166.29 25.6 formerly smoked \n",
|
|||
|
|
"5109 Govt_job Urban 85.28 26.2 Unknown \n",
|
|||
|
|
"\n",
|
|||
|
|
" stroke \n",
|
|||
|
|
"5105 0 \n",
|
|||
|
|
"5106 0 \n",
|
|||
|
|
"5107 0 \n",
|
|||
|
|
"5108 0 \n",
|
|||
|
|
"5109 0 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"fillna_df = df.fillna(0)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(fillna_df.shape)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(fillna_df.isnull().any())\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Замена пустых данных на 0\n",
|
|||
|
|
"df[\"bmi\"] = df[\"bmi\"].fillna(0)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Вычисляем медиану для колонки \"bmi\"\n",
|
|||
|
|
"median_bmi = df[\"bmi\"].median()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Заменяем значения 0 на медиану\n",
|
|||
|
|
"df.loc[df[\"bmi\"] == 0, \"bmi\"] = median_bmi\n",
|
|||
|
|
"\n",
|
|||
|
|
"df.tail()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"Удалим наблюдения с пропусками:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"(5110, 12)\n",
|
|||
|
|
"id False\n",
|
|||
|
|
"gender False\n",
|
|||
|
|
"age False\n",
|
|||
|
|
"hypertension False\n",
|
|||
|
|
"heart_disease False\n",
|
|||
|
|
"ever_married False\n",
|
|||
|
|
"work_type False\n",
|
|||
|
|
"Residence_type False\n",
|
|||
|
|
"avg_glucose_level False\n",
|
|||
|
|
"bmi False\n",
|
|||
|
|
"smoking_status False\n",
|
|||
|
|
"stroke False\n",
|
|||
|
|
"dtype: bool\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>id</th>\n",
|
|||
|
|
" <th>gender</th>\n",
|
|||
|
|
" <th>age</th>\n",
|
|||
|
|
" <th>hypertension</th>\n",
|
|||
|
|
" <th>heart_disease</th>\n",
|
|||
|
|
" <th>ever_married</th>\n",
|
|||
|
|
" <th>work_type</th>\n",
|
|||
|
|
" <th>Residence_type</th>\n",
|
|||
|
|
" <th>avg_glucose_level</th>\n",
|
|||
|
|
" <th>bmi</th>\n",
|
|||
|
|
" <th>smoking_status</th>\n",
|
|||
|
|
" <th>stroke</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5105</th>\n",
|
|||
|
|
" <td>18234</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>80.0</td>\n",
|
|||
|
|
" <td>1</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Urban</td>\n",
|
|||
|
|
" <td>83.75</td>\n",
|
|||
|
|
" <td>27.7</td>\n",
|
|||
|
|
" <td>never smoked</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5106</th>\n",
|
|||
|
|
" <td>44873</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>81.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Self-employed</td>\n",
|
|||
|
|
" <td>Urban</td>\n",
|
|||
|
|
" <td>125.20</td>\n",
|
|||
|
|
" <td>40.0</td>\n",
|
|||
|
|
" <td>never smoked</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5107</th>\n",
|
|||
|
|
" <td>19723</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>35.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Self-employed</td>\n",
|
|||
|
|
" <td>Rural</td>\n",
|
|||
|
|
" <td>82.99</td>\n",
|
|||
|
|
" <td>30.6</td>\n",
|
|||
|
|
" <td>never smoked</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5108</th>\n",
|
|||
|
|
" <td>37544</td>\n",
|
|||
|
|
" <td>Male</td>\n",
|
|||
|
|
" <td>51.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Rural</td>\n",
|
|||
|
|
" <td>166.29</td>\n",
|
|||
|
|
" <td>25.6</td>\n",
|
|||
|
|
" <td>formerly smoked</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5109</th>\n",
|
|||
|
|
" <td>44679</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>44.0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Govt_job</td>\n",
|
|||
|
|
" <td>Urban</td>\n",
|
|||
|
|
" <td>85.28</td>\n",
|
|||
|
|
" <td>26.2</td>\n",
|
|||
|
|
" <td>Unknown</td>\n",
|
|||
|
|
" <td>0</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" id gender age hypertension heart_disease ever_married \\\n",
|
|||
|
|
"5105 18234 Female 80.0 1 0 Yes \n",
|
|||
|
|
"5106 44873 Female 81.0 0 0 Yes \n",
|
|||
|
|
"5107 19723 Female 35.0 0 0 Yes \n",
|
|||
|
|
"5108 37544 Male 51.0 0 0 Yes \n",
|
|||
|
|
"5109 44679 Female 44.0 0 0 Yes \n",
|
|||
|
|
"\n",
|
|||
|
|
" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
|
|||
|
|
"5105 Private Urban 83.75 27.7 never smoked \n",
|
|||
|
|
"5106 Self-employed Urban 125.20 40.0 never smoked \n",
|
|||
|
|
"5107 Self-employed Rural 82.99 30.6 never smoked \n",
|
|||
|
|
"5108 Private Rural 166.29 25.6 formerly smoked \n",
|
|||
|
|
"5109 Govt_job Urban 85.28 26.2 Unknown \n",
|
|||
|
|
"\n",
|
|||
|
|
" stroke \n",
|
|||
|
|
"5105 0 \n",
|
|||
|
|
"5106 0 \n",
|
|||
|
|
"5107 0 \n",
|
|||
|
|
"5108 0 \n",
|
|||
|
|
"5109 0 "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"dropna_df = df.dropna()\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(dropna_df.shape)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(fillna_df.isnull().any())\n",
|
|||
|
|
"df.tail()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"##### Проверим выбросы и усредним их:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Колонка age:\n",
|
|||
|
|
" Есть выбросы: Нет\n",
|
|||
|
|
" Количество выбросов: 0\n",
|
|||
|
|
" Минимальное значение: 0.08\n",
|
|||
|
|
" Максимальное значение: 82.0\n",
|
|||
|
|
" 1-й квартиль (Q1): 25.0\n",
|
|||
|
|
" 3-й квартиль (Q3): 61.0\n",
|
|||
|
|
"\n",
|
|||
|
|
"Колонка avg_glucose_level:\n",
|
|||
|
|
" Есть выбросы: Нет\n",
|
|||
|
|
" Количество выбросов: 0\n",
|
|||
|
|
" Минимальное значение: 55.12\n",
|
|||
|
|
" Максимальное значение: 169.35750000000002\n",
|
|||
|
|
" 1-й квартиль (Q1): 77.245\n",
|
|||
|
|
" 3-й квартиль (Q3): 114.09\n",
|
|||
|
|
"\n",
|
|||
|
|
"Колонка bmi:\n",
|
|||
|
|
" Есть выбросы: Нет\n",
|
|||
|
|
" Количество выбросов: 0\n",
|
|||
|
|
" Минимальное значение: 10.300000000000006\n",
|
|||
|
|
" Максимальное значение: 46.29999999999999\n",
|
|||
|
|
" 1-й квартиль (Q1): 23.8\n",
|
|||
|
|
" 3-й квартиль (Q3): 32.8\n",
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"numeric_columns = ['age', 'avg_glucose_level', 'bmi']\n",
|
|||
|
|
"for column in numeric_columns:\n",
|
|||
|
|
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
|
|||
|
|
" q1 = df[column].quantile(0.25) # Находим 1-й квартиль (Q1)\n",
|
|||
|
|
" q3 = df[column].quantile(0.75) # Находим 3-й квартиль (Q3)\n",
|
|||
|
|
" iqr = q3 - q1 # Вычисляем межквартильный размах (IQR)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Определяем границы для выбросов\n",
|
|||
|
|
" lower_bound = q1 - 1.5 * iqr # Нижняя граница\n",
|
|||
|
|
" upper_bound = q3 + 1.5 * iqr # Верхняя граница\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Подсчитываем количество выбросов\n",
|
|||
|
|
" outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
|
|||
|
|
" outlier_count = outliers.shape[0]\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(f\"Колонка {column}:\")\n",
|
|||
|
|
" print(f\" Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
|
|||
|
|
" print(f\" Количество выбросов: {outlier_count}\")\n",
|
|||
|
|
" print(f\" Минимальное значение: {df[column].min()}\")\n",
|
|||
|
|
" print(f\" Максимальное значение: {df[column].max()}\")\n",
|
|||
|
|
" print(f\" 1-й квартиль (Q1): {q1}\")\n",
|
|||
|
|
" print(f\" 3-й квартиль (Q3): {q3}\\n\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
|
|||
|
|
" df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Постараемся выявить зависимости Stroke от остальных колонок:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Разобьем наш набор на выборки относительно параметра Stroke:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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"metadata": {},
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{
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"text/plain": [
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|||
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"<Figure size 800x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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},
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"metadata": {},
|
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 800x600 with 1 Axes>"
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]
|
|||
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},
|
|||
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"metadata": {},
|
|||
|
|
"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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},
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"metadata": {},
|
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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|||
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"<Figure size 800x600 with 1 Axes>"
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]
|
|||
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},
|
|||
|
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"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
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},
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{
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"data": {
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"text/plain": [
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"metadata": {},
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{
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"text/plain": [
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|||
|
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"<Figure size 800x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsAAAAKLCAYAAAD4sD5AAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABvjklEQVR4nO3deXRM9+PG8WcSWQRJbFlUBLFHbKGaql0TS6mlrbW0pZbaSquqVXuttVVbLV9LtZRSpS21hKC1FbHvexShKJEgJLm/P3rMr9MEmZgYMu/XOXOO+dxPZp5JRjzufO69JsMwDAEAAAAOwsneAQAAAIBHiQIMAAAAh0IBBgAAgEOhAAMAAMChUIABAADgUCjAAAAAcCgUYAAAADgUCjAAAAAcCgUYAAAADoUCDAAOKj4+XpMmTTLfv3r1qj7//HP7BcpiTCaTevTocd85p06dkslk0uzZsx9NKACSKMCAVWbPni2TyaTt27en2jZ9+nSZTCY1bdpUycnJdkgHWCd79uwaOHCg5s6dqzNnzmjIkCH6+eef7R0Lj6lNmzZpyJAhunr1aoYf44svvqDs47GQzd4BgKzgxx9/VLdu3VS9enXNnz9fzs7O9o4EPJCzs7OGDh2q9u3bKyUlRZ6enlq2bJm9YzmUwMBA3bx5Uy4uLvaO8kCbNm3S0KFD9dprr8nb2ztDj/HFF18oX758eu2112yaDbAWBRh4SOvWrVPr1q1VpkwZ/fzzz3J3d7d3JCDd3nnnHbVs2VJnzpxR6dKlM1xskDEmk4nfGYAdsAQCeAi7du3Siy++KH9/f61cuVJeXl6p5ixcuFChoaHKnj278uXLp3bt2uns2bNpPp7JZErzdurUKYs5Q4YMsfi6cePGyWQyqVatWuaxIUOGyGQypXqOwoULp9r7cvXqVb399tsKCAiQm5ubihUrpjFjxiglJcViXkpKiiZPnqyQkBC5u7srf/78ql+/vnlJyL3y373dzbdu3TqLcTc3N5UoUUKjRo2SYRgWz7lz5041aNBAnp6eypkzp+rWrastW7ak+f37t8OHD6tOnTry8/OTm5ubAgIC1LVrV125csU8526ORYsWpfr6nDlzWnyfrly5onfffVchISHKmTOnPD091aBBA+3evdvi62z9fb+7RvSTTz5J9Zhly5a1+JnffT3r1q2zmNeoUaNU75t/5yxYsKDCwsKULVs2+fn5pfkY91KrVq00f9b/fY9K/7+E6F7vi7t27typ+vXrK3/+/BbzXnjhhQfmmT9/vkJDQ5UrVy55enoqJCREkydPTpXh999/V69evZQ/f355e3urS5cuun37tq5evar27dsrd+7cyp07t957771U78mEhAS988475p9byZIl9cknn6Sal5YRI0bIyclJU6ZMkZT2GuDXXntNOXPm1NmzZ9W0aVPlzJlT+fPn17vvvptqedXly5f16quvytPTU97e3urQoYN2796doXXFU6ZMUXBwsDw8PJQ7d25VrlxZ8+bNk/TP+6Vfv36SpCJFiqT63TRr1izVqVNHPj4+cnNzU5kyZTR16lSLxy9cuLD279+v9evXp/rZ3+vvzd2f179/B27fvl0RERHKly+fsmfPriJFiuiNN96w6rUC7AEGMuj48eOqX7++3NzctHLlSvn7+6eaM3v2bL3++uuqUqWKRo0apQsXLmjy5MnauHGjdu7cmebetmbNmql58+aSpN9++03Tpk27b46rV69q1KhRGX4dN27cUM2aNXX27Fl16dJFhQoV0qZNmzRgwACdP3/e4iCpjh07avbs2WrQoIE6deqkpKQk/fbbb9qyZYsqV66sb775xjz3bvaJEycqX758kiRfX1+L5/7ggw9UunRp3bx5UwsWLNAHH3wgHx8fdezYUZK0f/9+Va9eXZ6ennrvvffk4uKir776SrVq1dL69etVtWrVe76uhIQEFSxYUI0bN5anp6f27dunzz//XGfPns3QOtcTJ05oyZIlevnll1WkSBFduHBBX331lWrWrKkDBw6oQIECVj2eNd/3h7FhwwYtX748XXPHjx+vCxcuWP0cBQsWNL8H4+Pj1a1bt/vO//d74uOPP7bYdu3aNTVo0ECGYahv374KCAiQJPXp0+eBOVavXq3WrVurbt26GjNmjCTp4MGD2rhxo3r37m0xt2fPnvLz89PQoUO1ZcsWTZs2Td7e3tq0aZMKFSqkkSNHavny5Ro3bpzKli2r9u3bS5IMw1CTJk0UFRWljh07qkKFClq5cqX69euns2fPauLEiffMN3DgQI0cOVJfffWV3nzzzfu+luTkZEVERKhq1ar65JNPFBkZqfHjxysoKMj8/U1JSVHjxo31xx9/qFu3bipVqpSWLl2qDh06PPB79V/Tp09Xr1699NJLL6l37966deuW9uzZo61bt6pNmzZq3ry5jhw5ou+++87i55c/f35J0tSpUxUcHKwmTZooW7Zs+vnnn/XWW28pJSVF3bt3lyRNmjRJPXv2VM6cOfXhhx9KSv074UEuXryo8PBw5c+fX++//768vb116tQpLV682OrXDAdnAEi3WbNmGZKMX375xQgKCjIkGeHh4WnOvX37tuHj42OULVvWuHnzpnn8l19+MSQZgwYNsph/584dQ5IxdOjQVM938uRJ85gkY/Dgweb77733nuHj42OEhoYaNWvWNI8PHTrUkGSkpKRYPE9gYKDRoUMH8/3hw4cbOXLkMI4cOWIx7/333zecnZ2NmJgYwzAMY+3atYYko1evXqle63+f417Z74qKijIkGVFRUeaxW7duGU5OTsZbb71lHmvatKnh6upqHD9+3Dx27tw5I1euXEaNGjVSPe6DvPXWW0bOnDlT5Vi4cGGquTly5LD4Pt26dctITk62mHPy5EnDzc3NGDZsmHnM1t/3kydPGpKMcePGpcoYHBxs8TNP6/tatWpVo0GDBqneN4MHDzb+/U/AxYsXjVy5cpnn/vsx7ufZZ581ypYta77/119/pXquu6ZPn25IMk6fPm0eq1mzpsVrWLlypSHJ+O677yy+NjAw0GjUqNF9s/Tu3dvw9PQ0kpKS7jnn7vsyIiLC4mcUFhZmmEwmo2vXruaxpKQko2DBghb5lixZYkgyRowYYfG4L730kmEymYxjx46ZxyQZ3bt3NwzDMN555x3DycnJmD17tsXX3f35zpo1yzzWoUMHQ5LF+8owDKNixYpGaGio+f4PP/xgSDImTZpkHktOTjbq1KmT6jEf5MUXXzSCg4PvO2fcuHH3/Dt948aNVGMRERFG0aJFLcb++56967/vx7v++3vkxx9/NCQZ27Ztu29W4EFYAgFkwGuvvaYzZ86oTZs2WrVqlRYuXJhqzvbt23Xx4kW99dZbFmv8GjVqpFKlSqU62Oj27duSJDc3t3TnOHv2rKZMmaKPPvpIOXPmtNjm4+MjSfrzzz/v+xgLFy5U9erVlTt3bl26dMl8q1evnpKTk7VhwwZJ0g8//CCTyaTBgweneoy0PrpMj2vXrunSpUuKiYnR2LFjlZKSojp16kj6Zw/YqlWr1LRpUxUtWtT8Nf7+/mrTpo1+//13xcXFpes5Lly4oDVr1mjZsmWqUaNGqjnXr1+3eO2XLl1KNcfNzU1OTk7mbJcvX1bOnDlVsmRJRUdHm+fZ+vt+140bN1JlfNDZRhYvXqxt27Zp9OjR950nScOHD5eXl5d69er1wLn/duvWrXSvYU3Pe/z69euSpLx581qVQ5K8vb2VkJCg1atXP3Bux44dLd63VatWlWEY5k8fpH8OEqxcubJOnDhhHlu+fLmcnZ1TfZ/eeecdGYahX3/91WLcMAz16NFDkydP1rfffmvV3tmuXbta3K9evbpFlhUrVsjFxcVib7KTk5N5j6s1vL299eeff2rbtm1Wf630zxlF7rr797pmzZo6ceKErl27lqHHTMvdT81++eUX3blzx2aPC8fDEgggA65cuaL58+erWbNmOnDggHr37q3w8HCLNcCnT5+WJJUsWTLV15cqVUq///67xdjdUwv9t8jez+DBg1WgQAF16dIl1TrWsLAwmUwmDRgwQCNGjDA
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"# Список колонок для построения графиков\n",
|
|||
|
|
"columns = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
|
|||
|
|
" 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
|
|||
|
|
" 'smoking_status']\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание диаграмм зависимости\n",
|
|||
|
|
"for column in columns:\n",
|
|||
|
|
" plt.figure(figsize=(8, 6)) # Установка размера графика\n",
|
|||
|
|
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
|
|||
|
|
" # Проверяем, содержит ли колонка только два уникальных значения (0 и 1)\n",
|
|||
|
|
" if df[column].nunique() == 2 and set(df[column].unique()).issubset({0, 1}):\n",
|
|||
|
|
" # Если да, то строим столбчатую диаграмму\n",
|
|||
|
|
" counts = df[column].value_counts() # Считаем количество повторений каждого значения\n",
|
|||
|
|
" counts.plot(kind='bar') # Создаем столбчатую диаграмму\n",
|
|||
|
|
" plt.title(f'Количество значений для {column}')\n",
|
|||
|
|
" plt.xlabel(column)\n",
|
|||
|
|
" plt.ylabel('Количество повторений')\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" # Если колонка числовая, создаем диаграмму рассеяния\n",
|
|||
|
|
" plt.scatter(df['stroke'], df[column], alpha=0.5) # Создаем диаграмму рассеяния\n",
|
|||
|
|
" plt.title(f'Зависимость {column} от stroke')\n",
|
|||
|
|
" plt.xlabel('stroke (0 = нет, 1 = да)')\n",
|
|||
|
|
" plt.ylabel(column)\n",
|
|||
|
|
" plt.xticks([0, 1]) # Установка меток по оси X\n",
|
|||
|
|
" plt.grid() # Добавление сетки для удобства восприятия\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" # Если колонка не числовая, строим столбчатую диаграмму\n",
|
|||
|
|
" counts = df[column].value_counts() # Считаем количество повторений каждого значения\n",
|
|||
|
|
" counts.plot(kind='bar') # Создаем столбчатую диаграмму\n",
|
|||
|
|
" plt.title(f'Количество значений для {column}')\n",
|
|||
|
|
" plt.xlabel(column)\n",
|
|||
|
|
" plt.ylabel('Количество повторений')\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.show() # Отображение графика"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# Функция для создания выборок\n",
|
|||
|
|
"from sklearn.model_selection import train_test_split\n",
|
|||
|
|
"\n",
|
|||
|
|
"def split_stratified_into_train_val_test(\n",
|
|||
|
|
" df_input,\n",
|
|||
|
|
" stratify_colname=\"y\",\n",
|
|||
|
|
" frac_train=0.6,\n",
|
|||
|
|
" frac_val=0.15,\n",
|
|||
|
|
" frac_test=0.25,\n",
|
|||
|
|
" random_state=None,\n",
|
|||
|
|
"):\n",
|
|||
|
|
"\n",
|
|||
|
|
" if frac_train + frac_val + frac_test != 1.0:\n",
|
|||
|
|
" raise ValueError(\n",
|
|||
|
|
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
|
|||
|
|
" % (frac_train, frac_val, frac_test)\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" if stratify_colname not in df_input.columns:\n",
|
|||
|
|
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
|
|||
|
|
"\n",
|
|||
|
|
" X = df_input # Contains all columns.\n",
|
|||
|
|
" y = df_input[\n",
|
|||
|
|
" [stratify_colname]\n",
|
|||
|
|
" ] # Dataframe of just the column on which to stratify.\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Split original dataframe into train and temp dataframes.\n",
|
|||
|
|
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
|
|||
|
|
" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Split the temp dataframe into val and test dataframes.\n",
|
|||
|
|
" relative_frac_test = frac_test / (frac_val + frac_test)\n",
|
|||
|
|
" df_val, df_test, y_val, y_test = train_test_split(\n",
|
|||
|
|
" df_temp,\n",
|
|||
|
|
" y_temp,\n",
|
|||
|
|
" stratify=y_temp,\n",
|
|||
|
|
" test_size=relative_frac_test,\n",
|
|||
|
|
" random_state=random_state,\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return df_train, df_val, df_test"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"stroke\n",
|
|||
|
|
"0 4861\n",
|
|||
|
|
"1 249\n",
|
|||
|
|
"Name: count, dtype: int64\n",
|
|||
|
|
"\n",
|
|||
|
|
"Обучающая выборка: (3066, 12)\n",
|
|||
|
|
"stroke\n",
|
|||
|
|
"0 2917\n",
|
|||
|
|
"1 149\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Контрольная выборка: (1022, 12)\n",
|
|||
|
|
"stroke\n",
|
|||
|
|
"0 972\n",
|
|||
|
|
"1 50\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Тестовая выборка: (1022, 12)\n",
|
|||
|
|
"stroke\n",
|
|||
|
|
"0 972\n",
|
|||
|
|
"1 50\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Вывод распределения количества наблюдений по меткам (классам)\n",
|
|||
|
|
"print(df.stroke.value_counts())\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"\n",
|
|||
|
|
"data = df.copy()\n",
|
|||
|
|
"\n",
|
|||
|
|
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
|
|||
|
|
" data, stratify_colname=\"stroke\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Обучающая выборка: \", df_train.shape)\n",
|
|||
|
|
"print(df_train.stroke.value_counts())\n",
|
|||
|
|
"counts = df_train['stroke'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))# Установка размера графика\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
|
|||
|
|
"plt.title('Распределение классов stroke в обучающей выборке')# Добавление заголовка\n",
|
|||
|
|
"plt.show()# Отображение графика\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Контрольная выборка: \", df_val.shape)\n",
|
|||
|
|
"print(df_val.stroke.value_counts())\n",
|
|||
|
|
"counts = df_val['stroke'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов stroke в контрольной выборке')\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Тестовая выборка: \", df_test.shape)\n",
|
|||
|
|
"print(df_test.stroke.value_counts())\n",
|
|||
|
|
"counts = df_test['stroke'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов stroke в тестовой выборке')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Сбалансируем распределение:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"1. Балансировка данных оверсемплингом. Это метод, увеличивающий число наблюдений в меньшинственном классе для достижения более равномерного распределения классов."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Обучающая выборка после oversampling: (5811, 18)\n",
|
|||
|
|
"stroke\n",
|
|||
|
|
"0 2917\n",
|
|||
|
|
"1 2894\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 600x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from imblearn.over_sampling import ADASYN\n",
|
|||
|
|
"from sklearn.compose import ColumnTransformer\n",
|
|||
|
|
"from sklearn.preprocessing import OneHotEncoder\n",
|
|||
|
|
"\n",
|
|||
|
|
"categorical_features = ['gender', 'ever_married', 'work_type', 'Residence_type'] # Ваши категориальные признаки\n",
|
|||
|
|
"numeric_features = ['age', 'hypertension', 'heart_disease', 'avg_glucose_level', 'bmi'] # Ваши числовые признаки\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание пайплайна для обработки категориальных данных\n",
|
|||
|
|
"preprocessor = ColumnTransformer(\n",
|
|||
|
|
" transformers=[\n",
|
|||
|
|
" ('cat', OneHotEncoder(), categorical_features), # OneHotEncoder для категориальных данных\n",
|
|||
|
|
" ('num', 'passthrough', numeric_features) # Оставляем числовые колонки без изменений\n",
|
|||
|
|
" ]\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание экземпляра ADASYN\n",
|
|||
|
|
"ada = ADASYN()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Преобразование данных с помощью пайплайна\n",
|
|||
|
|
"X = preprocessor.fit_transform(df_train.drop(columns=['stroke']))\n",
|
|||
|
|
"y = df_train['stroke']\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Применение ADASYN\n",
|
|||
|
|
"X_resampled, y_resampled = ada.fit_resample(X, y)\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание нового DataFrame\n",
|
|||
|
|
"df_train_adasyn = pd.DataFrame(X_resampled)\n",
|
|||
|
|
"# Восстанавливаем названия столбцов для DataFrame\n",
|
|||
|
|
"ohe_columns = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features)\n",
|
|||
|
|
"new_column_names = list(ohe_columns) + numeric_features\n",
|
|||
|
|
"df_train_adasyn.columns = new_column_names\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Добавление целевой переменной\n",
|
|||
|
|
"df_train_adasyn['stroke'] = y_resampled\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Вывод информации о новой выборке\n",
|
|||
|
|
"print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
|
|||
|
|
"print(df_train_adasyn['stroke'].value_counts())\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация\n",
|
|||
|
|
"counts = df_train_adasyn['stroke'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(6, 6))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов Stroke в тренировочной выборке после ADASYN')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"2. Балансировка данных андерсемплингом. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести его в соответствие с классом меньшинства."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Обучающая выборка после undersampling: (298, 12)\n",
|
|||
|
|
"stroke\n",
|
|||
|
|
"0 149\n",
|
|||
|
|
"1 149\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from imblearn.under_sampling import RandomUnderSampler\n",
|
|||
|
|
"\n",
|
|||
|
|
"rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Применение RandomUnderSampler\n",
|
|||
|
|
"X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['stroke']), df_train['stroke'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание нового DataFrame\n",
|
|||
|
|
"df_train_undersampled = pd.DataFrame(X_resampled)\n",
|
|||
|
|
"df_train_undersampled['stroke'] = y_resampled # Добавление целевой переменной\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Вывод информации о новой выборке\n",
|
|||
|
|
"print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
|
|||
|
|
"print(df_train_undersampled['stroke'].value_counts())\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация распределения классов\n",
|
|||
|
|
"counts = df_train_undersampled['stroke'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов stroke в тренировочной выборке после Undersampling')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"### 3. Датасет: Набор данных для анализа и прогнозирования сердечного приступа\n",
|
|||
|
|
"https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease\n",
|
|||
|
|
"##### О наборе данных: \n",
|
|||
|
|
"По данным CDC, болезни сердца являются основной причиной смерти представителей большинства рас в США (афроамериканцев, американских индейцев и коренных жителей Аляски, а также белых). Около половины всех американцев (47%) имеют по крайней мере 1 из 3 основных факторов риска сердечно-сосудистых заболеваний: высокое кровяное давление, высокий уровень холестерина и курение. Другие ключевые показатели включают сахарный диабет, ожирение (высокий ИМТ), недостаточную физическую активность или чрезмерное употребление алкоголя. Выявление и профилактика факторов, оказывающих наибольшее влияние на сердечно-сосудистые заболевания, очень важны в здравоохранении. В свою очередь, достижения в области вычислительной техники позволяют применять методы машинного обучения для выявления \"закономерностей\" в данных, которые позволяют предсказать состояние пациента.\n",
|
|||
|
|
"\n",
|
|||
|
|
"##### Таким образом:\n",
|
|||
|
|
"* Объект наблюдения - представители большинства рас в США\n",
|
|||
|
|
"* Атрибуты: HeartDisease, BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth(как много дней за месяц вы чувствовали себя плохо), MentalHealth(как много дней за месяц вы чувствовали себя ментально плохо), DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer.\n",
|
|||
|
|
"* Проблемная область: прогнозирование сердечного приступа у человека."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Количество колонок: 18\n",
|
|||
|
|
"Колонки: HeartDisease, BMI, Smoking, AlcoholDrinking, Stroke, PhysicalHealth, MentalHealth, DiffWalking, Sex, AgeCategory, Race, Diabetic, PhysicalActivity, GenHealth, SleepTime, Asthma, KidneyDisease, SkinCancer\n",
|
|||
|
|
"\n",
|
|||
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
|||
|
|
"RangeIndex: 319795 entries, 0 to 319794\n",
|
|||
|
|
"Data columns (total 18 columns):\n",
|
|||
|
|
" # Column Non-Null Count Dtype \n",
|
|||
|
|
"--- ------ -------------- ----- \n",
|
|||
|
|
" 0 HeartDisease 319795 non-null object \n",
|
|||
|
|
" 1 BMI 319795 non-null float64\n",
|
|||
|
|
" 2 Smoking 319795 non-null object \n",
|
|||
|
|
" 3 AlcoholDrinking 319795 non-null object \n",
|
|||
|
|
" 4 Stroke 319795 non-null object \n",
|
|||
|
|
" 5 PhysicalHealth 319795 non-null float64\n",
|
|||
|
|
" 6 MentalHealth 319795 non-null float64\n",
|
|||
|
|
" 7 DiffWalking 319795 non-null object \n",
|
|||
|
|
" 8 Sex 319795 non-null object \n",
|
|||
|
|
" 9 AgeCategory 319795 non-null object \n",
|
|||
|
|
" 10 Race 319795 non-null object \n",
|
|||
|
|
" 11 Diabetic 319795 non-null object \n",
|
|||
|
|
" 12 PhysicalActivity 319795 non-null object \n",
|
|||
|
|
" 13 GenHealth 319795 non-null object \n",
|
|||
|
|
" 14 SleepTime 319795 non-null float64\n",
|
|||
|
|
" 15 Asthma 319795 non-null object \n",
|
|||
|
|
" 16 KidneyDisease 319795 non-null object \n",
|
|||
|
|
" 17 SkinCancer 319795 non-null object \n",
|
|||
|
|
"dtypes: float64(4), object(14)\n",
|
|||
|
|
"memory usage: 43.9+ MB\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>HeartDisease</th>\n",
|
|||
|
|
" <th>BMI</th>\n",
|
|||
|
|
" <th>Smoking</th>\n",
|
|||
|
|
" <th>AlcoholDrinking</th>\n",
|
|||
|
|
" <th>Stroke</th>\n",
|
|||
|
|
" <th>PhysicalHealth</th>\n",
|
|||
|
|
" <th>MentalHealth</th>\n",
|
|||
|
|
" <th>DiffWalking</th>\n",
|
|||
|
|
" <th>Sex</th>\n",
|
|||
|
|
" <th>AgeCategory</th>\n",
|
|||
|
|
" <th>Race</th>\n",
|
|||
|
|
" <th>Diabetic</th>\n",
|
|||
|
|
" <th>PhysicalActivity</th>\n",
|
|||
|
|
" <th>GenHealth</th>\n",
|
|||
|
|
" <th>SleepTime</th>\n",
|
|||
|
|
" <th>Asthma</th>\n",
|
|||
|
|
" <th>KidneyDisease</th>\n",
|
|||
|
|
" <th>SkinCancer</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>16.60</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>3.0</td>\n",
|
|||
|
|
" <td>30.0</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>55-59</td>\n",
|
|||
|
|
" <td>White</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Very good</td>\n",
|
|||
|
|
" <td>5.0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>20.34</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>80 or older</td>\n",
|
|||
|
|
" <td>White</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Very good</td>\n",
|
|||
|
|
" <td>7.0</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>26.58</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>20.0</td>\n",
|
|||
|
|
" <td>30.0</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Male</td>\n",
|
|||
|
|
" <td>65-69</td>\n",
|
|||
|
|
" <td>White</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Fair</td>\n",
|
|||
|
|
" <td>8.0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>24.21</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>75-79</td>\n",
|
|||
|
|
" <td>White</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Good</td>\n",
|
|||
|
|
" <td>6.0</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>23.71</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>28.0</td>\n",
|
|||
|
|
" <td>0.0</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>40-44</td>\n",
|
|||
|
|
" <td>White</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Yes</td>\n",
|
|||
|
|
" <td>Very good</td>\n",
|
|||
|
|
" <td>8.0</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n",
|
|||
|
|
"0 No 16.60 Yes No No 3.0 \n",
|
|||
|
|
"1 No 20.34 No No Yes 0.0 \n",
|
|||
|
|
"2 No 26.58 Yes No No 20.0 \n",
|
|||
|
|
"3 No 24.21 No No No 0.0 \n",
|
|||
|
|
"4 No 23.71 No No No 28.0 \n",
|
|||
|
|
"\n",
|
|||
|
|
" MentalHealth DiffWalking Sex AgeCategory Race Diabetic \\\n",
|
|||
|
|
"0 30.0 No Female 55-59 White Yes \n",
|
|||
|
|
"1 0.0 No Female 80 or older White No \n",
|
|||
|
|
"2 30.0 No Male 65-69 White Yes \n",
|
|||
|
|
"3 0.0 No Female 75-79 White No \n",
|
|||
|
|
"4 0.0 Yes Female 40-44 White No \n",
|
|||
|
|
"\n",
|
|||
|
|
" PhysicalActivity GenHealth SleepTime Asthma KidneyDisease SkinCancer \n",
|
|||
|
|
"0 Yes Very good 5.0 Yes No Yes \n",
|
|||
|
|
"1 Yes Very good 7.0 No No No \n",
|
|||
|
|
"2 Yes Fair 8.0 Yes No No \n",
|
|||
|
|
"3 No Good 6.0 No No Yes \n",
|
|||
|
|
"4 Yes Very good 8.0 No No No "
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"df = pd.read_csv(\".//static//csv//heart.csv\", sep=\",\")\n",
|
|||
|
|
"print('Количество колонок: ' + str(df.columns.size)) \n",
|
|||
|
|
"print('Колонки: ' + ', '.join(df.columns)+'\\n')\n",
|
|||
|
|
"\n",
|
|||
|
|
"df.info()\n",
|
|||
|
|
"df.head()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Получение сведений о пропущенных данных\n",
|
|||
|
|
"Типы пропущенных данных:\n",
|
|||
|
|
"\n",
|
|||
|
|
"- None - представление пустых данных в Python\n",
|
|||
|
|
"- NaN - представление пустых данных в Pandas\n",
|
|||
|
|
"- '' - пустая строка"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 3,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"HeartDisease 0\n",
|
|||
|
|
"BMI 0\n",
|
|||
|
|
"Smoking 0\n",
|
|||
|
|
"AlcoholDrinking 0\n",
|
|||
|
|
"Stroke 0\n",
|
|||
|
|
"PhysicalHealth 0\n",
|
|||
|
|
"MentalHealth 0\n",
|
|||
|
|
"DiffWalking 0\n",
|
|||
|
|
"Sex 0\n",
|
|||
|
|
"AgeCategory 0\n",
|
|||
|
|
"Race 0\n",
|
|||
|
|
"Diabetic 0\n",
|
|||
|
|
"PhysicalActivity 0\n",
|
|||
|
|
"GenHealth 0\n",
|
|||
|
|
"SleepTime 0\n",
|
|||
|
|
"Asthma 0\n",
|
|||
|
|
"KidneyDisease 0\n",
|
|||
|
|
"SkinCancer 0\n",
|
|||
|
|
"dtype: int64\n",
|
|||
|
|
"\n",
|
|||
|
|
"HeartDisease False\n",
|
|||
|
|
"BMI False\n",
|
|||
|
|
"Smoking False\n",
|
|||
|
|
"AlcoholDrinking False\n",
|
|||
|
|
"Stroke False\n",
|
|||
|
|
"PhysicalHealth False\n",
|
|||
|
|
"MentalHealth False\n",
|
|||
|
|
"DiffWalking False\n",
|
|||
|
|
"Sex False\n",
|
|||
|
|
"AgeCategory False\n",
|
|||
|
|
"Race False\n",
|
|||
|
|
"Diabetic False\n",
|
|||
|
|
"PhysicalActivity False\n",
|
|||
|
|
"GenHealth False\n",
|
|||
|
|
"SleepTime False\n",
|
|||
|
|
"Asthma False\n",
|
|||
|
|
"KidneyDisease False\n",
|
|||
|
|
"SkinCancer False\n",
|
|||
|
|
"dtype: bool\n",
|
|||
|
|
"\n",
|
|||
|
|
"HeartDisease процент пустых значений: %0.00\n",
|
|||
|
|
"BMI процент пустых значений: %0.00\n",
|
|||
|
|
"Smoking процент пустых значений: %0.00\n",
|
|||
|
|
"AlcoholDrinking процент пустых значений: %0.00\n",
|
|||
|
|
"Stroke процент пустых значений: %0.00\n",
|
|||
|
|
"PhysicalHealth процент пустых значений: %0.00\n",
|
|||
|
|
"MentalHealth процент пустых значений: %0.00\n",
|
|||
|
|
"DiffWalking процент пустых значений: %0.00\n",
|
|||
|
|
"Sex процент пустых значений: %0.00\n",
|
|||
|
|
"AgeCategory процент пустых значений: %0.00\n",
|
|||
|
|
"Race процент пустых значений: %0.00\n",
|
|||
|
|
"Diabetic процент пустых значений: %0.00\n",
|
|||
|
|
"PhysicalActivity процент пустых значений: %0.00\n",
|
|||
|
|
"GenHealth процент пустых значений: %0.00\n",
|
|||
|
|
"SleepTime процент пустых значений: %0.00\n",
|
|||
|
|
"Asthma процент пустых значений: %0.00\n",
|
|||
|
|
"KidneyDisease процент пустых значений: %0.00\n",
|
|||
|
|
"SkinCancer процент пустых значений: %0.00\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Количество пустых значений признаков\n",
|
|||
|
|
"print(df.isnull().sum())\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Есть ли пустые значения признаков\n",
|
|||
|
|
"print(df.isnull().any())\n",
|
|||
|
|
"print()\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Процент пустых значений признаков\n",
|
|||
|
|
"for i in df.columns:\n",
|
|||
|
|
" null_rate = df[i].isnull().sum() / len(df) * 100\n",
|
|||
|
|
" print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"##### Пропущенные данные отсутствуют.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"##### Проверим выбросы и усредним их:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Колонка BMI:\n",
|
|||
|
|
" Есть выбросы: Да\n",
|
|||
|
|
" Количество выбросов: 10396\n",
|
|||
|
|
" Минимальное значение: 12.02\n",
|
|||
|
|
" Максимальное значение: 94.85\n",
|
|||
|
|
" 1-й квартиль (Q1): 24.03\n",
|
|||
|
|
" 3-й квартиль (Q3): 31.42\n",
|
|||
|
|
"\n",
|
|||
|
|
"Колонка PhysicalHealth:\n",
|
|||
|
|
" Есть выбросы: Да\n",
|
|||
|
|
" Количество выбросов: 47146\n",
|
|||
|
|
" Минимальное значение: 0.0\n",
|
|||
|
|
" Максимальное значение: 30.0\n",
|
|||
|
|
" 1-й квартиль (Q1): 0.0\n",
|
|||
|
|
" 3-й квартиль (Q3): 2.0\n",
|
|||
|
|
"\n",
|
|||
|
|
"Колонка MentalHealth:\n",
|
|||
|
|
" Есть выбросы: Да\n",
|
|||
|
|
" Количество выбросов: 51576\n",
|
|||
|
|
" Минимальное значение: 0.0\n",
|
|||
|
|
" Максимальное значение: 30.0\n",
|
|||
|
|
" 1-й квартиль (Q1): 0.0\n",
|
|||
|
|
" 3-й квартиль (Q3): 3.0\n",
|
|||
|
|
"\n",
|
|||
|
|
"Колонка SleepTime:\n",
|
|||
|
|
" Есть выбросы: Да\n",
|
|||
|
|
" Количество выбросов: 4543\n",
|
|||
|
|
" Минимальное значение: 1.0\n",
|
|||
|
|
" Максимальное значение: 24.0\n",
|
|||
|
|
" 1-й квартиль (Q1): 6.0\n",
|
|||
|
|
" 3-й квартиль (Q3): 8.0\n",
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"numeric_columns = ['BMI', 'PhysicalHealth', 'MentalHealth', 'AgeCategory', 'SleepTime']\n",
|
|||
|
|
"for column in numeric_columns:\n",
|
|||
|
|
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
|
|||
|
|
" q1 = df[column].quantile(0.25) # Находим 1-й квартиль (Q1)\n",
|
|||
|
|
" q3 = df[column].quantile(0.75) # Находим 3-й квартиль (Q3)\n",
|
|||
|
|
" iqr = q3 - q1 # Вычисляем межквартильный размах (IQR)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Определяем границы для выбросов\n",
|
|||
|
|
" lower_bound = q1 - 1.5 * iqr # Нижняя граница\n",
|
|||
|
|
" upper_bound = q3 + 1.5 * iqr # Верхняя граница\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Подсчитываем количество выбросов\n",
|
|||
|
|
" outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
|
|||
|
|
" outlier_count = outliers.shape[0]\n",
|
|||
|
|
"\n",
|
|||
|
|
" print(f\"Колонка {column}:\")\n",
|
|||
|
|
" print(f\" Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
|
|||
|
|
" print(f\" Количество выбросов: {outlier_count}\")\n",
|
|||
|
|
" print(f\" Минимальное значение: {df[column].min()}\")\n",
|
|||
|
|
" print(f\" Максимальное значение: {df[column].max()}\")\n",
|
|||
|
|
" print(f\" 1-й квартиль (Q1): {q1}\")\n",
|
|||
|
|
" print(f\" 3-й квартиль (Q3): {q3}\\n\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
|
|||
|
|
" df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Постараемся выявить зависимости HeartDisease от остальных колонок:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Разобьем наш набор на выборки относительно параметра HeartDisease:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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"text/plain": [
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},
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"metadata": {},
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{
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"data": {
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"text/plain": [
|
|||
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"<Figure size 800x600 with 1 Axes>"
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]
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},
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"metadata": {},
|
|||
|
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"output_type": "display_data"
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},
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|||
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{
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"data": {
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"text/plain": [
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},
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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|||
|
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"<Figure size 800x600 with 1 Axes>"
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]
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},
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"metadata": {},
|
|||
|
|
"output_type": "display_data"
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|||
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},
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|||
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{
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"data": {
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"text/plain": [
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"<Figure size 800x600 with 1 Axes>"
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"data": {
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|||
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"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
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]
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},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
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},
|
|||
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{
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"data": {
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"text/plain": [
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"<Figure size 800x600 with 1 Axes>"
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]
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},
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"metadata": {},
|
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},
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{
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"data": {
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"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
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]
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},
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"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
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},
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|||
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{
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"data": {
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"text/plain": [
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},
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"metadata": {},
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{
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"data": {
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"text/plain": [
|
|||
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"<Figure size 800x600 with 1 Axes>"
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]
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},
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|||
|
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"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
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{
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"data": {
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"text/plain": [
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"<Figure size 800x600 with 1 Axes>"
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},
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
|
|||
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|
"<Figure size 800x600 with 1 Axes>"
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]
|
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},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
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},
|
|||
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{
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"data": {
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"text/plain": [
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},
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"metadata": {},
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{
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"data": {
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"text/plain": [
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|||
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|
"<Figure size 800x600 with 1 Axes>"
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]
|
|||
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},
|
|||
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"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
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},
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{
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"data": {
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|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtEAAAIrCAYAAAApwMV4AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABXeElEQVR4nO3deXgO9/7/8dedkASRWCNURIgt1iM0UqWlIUjtPbW1YmmVoiWtrVVBq0iPWmrrdqpajq2WorYTazWWInaOajRKE0uaRIIgmd8ffrm/7ibIpCEJz8d13dflnnnPzPseN14mn/mMxTAMQwAAAACyzC63GwAAAADyG0I0AAAAYBIhGgAAADCJEA0AAACYRIgGAAAATCJEAwAAACYRogEAAACTCNEAAACASYRoAAAAwCRCNAAAAGASIRoAkCckJSVp2rRp1vfx8fGaNWtW7jX0EPXq1UvOzs73rXv22Wf17LPPPviGANwXIRrIx+bNmyeLxaKff/45w7rPP/9cFotFHTp0UGpqai50B5hTqFAhjR49WgsWLNDZs2c1duxYrV69Orfb+luSkpIUGhqqWrVqqUiRIipZsqTq1aunN998U+fPn8+VnrZu3apOnTrJ3d1dDg4OcnNzU9u2bbV8+fJc6QfIrwrkdgMAct6KFSs0YMAANWnSRIsWLZK9vX1utwTcl729vcaNG6eePXsqLS1NLi4uWrt2bW63lW03b95U06ZNdeLECQUHB2vw4MFKSkrS0aNHtXDhQnXs2FHlypUztc+NGzf+rZ5CQ0M1fvx4ValSRa+99po8PT11+fJl/fDDD+rcubMWLFig7t27/61jAI8LQjTwiNm6dau6desmHx8frV69Wk5OTrndEpBlb731lrp06aKzZ8+qRo0aKlasWG63lG0rV67UgQMHMg2m169f140bN0zv08HBIdv9LFu2TOPHj9cLL7yghQsXqmDBgtZ1w4YN04YNG3Tz5s1s7z+3Xb9+XQ4ODrKz44fseDj4pgGPkMjISLVv315ly5bVhg0b5OrqmqFm6dKl8vX1VaFChVSqVCm99NJLOnfuXKb7s1gsmb7OnDljUzN27Fib7T766CNZLBabsZtjx46VxWLJcIyKFSuqV69eNsvi4+M1ZMgQeXh4yNHRUd7e3po8ebLS0tJs6tLS0jR9+nTVrl1bTk5OKl26tFq1amUd3nK3/tNf6f1t3brVZrmjo6OqVq2qiRMnyjAMm2MeOHBArVu3louLi5ydnfXcc89p165dmZ6/O508eVLNmzeXu7u7HB0d5eHhof79+ysuLs5ak97HsmXLMmzv7Oxsc57i4uL09ttvq3bt2nJ2dpaLi4tat26tgwcP2myX0+f9zJkzslgs+te//pVhn7Vq1bL5PU//PFu3brWpCwoKyvC9ubPP8uXLy9/fXwUKFJC7u3um+7ibZ599NtPf679+R6X/Gw51t+9FugMHDqhVq1YqXbq0Td3zzz9/z15Onz4tSWrcuHGGdU5OTnJxcbnn9pGRkSpdurSeffZZJSUlWT9fZud4yZIlmjBhgsqXLy8nJyc999xz+uWXX2z2995776lEiRL697//bROg0wUGBlo/040bNzRmzBj5+vrK1dVVRYoUUZMmTbRlyxabbe78Pnz22WeqXLmyHB0d1bBhQ+3duzfDMU6cOKEXX3xRpUuXVqFChVStWjW9++67NjXnzp1Tnz59VKZMGTk6OqpmzZr697//bVOT/rkXLVqk0aNH64knnlDhwoWVmJh4z3MK5CSuRAOPiNOnT6tVq1ZydHTUhg0bVLZs2Qw18+bNU+/evdWwYUNNnDhRsbGxmj59unbu3KkDBw5ketWvY8eO6tSpkyRpx44d+uyzz+7ZR3x8vCZOnJjtz3H16lU988wzOnfunF577TVVqFBBP/30k0aNGqU//vjD5sazvn37at68eWrdurVeeeUV3bp1Szt27NCuXbvUoEEDffPNN9ba9N6nTp2qUqVKSZLKlCljc+x33nlHNWrU0LVr17R48WK98847cnNzU9++fSVJR48eVZMmTeTi4qLhw4erYMGC+vTTT/Xss89q27Zt8vPzu+vnSk5OVvny5dW2bVu5uLjoyJEjmjVrls6dO5etcb+//vqrVq5cqX/+85/y8vJSbGysPv30Uz3zzDM6duyY6WECZs7737F9+3b98MMPWaqdMmWKYmNjTR+jfPny1u9gUlKSBgwYcM/6O78TEyZMsFmXkJCg1q1byzAMhYSEyMPDQ5I0dOjQ+/bh6ekpSZo/f75Gjx6d6X9m7mbv3r0KDAxUgwYNtGrVKhUqVOie9ZMmTZKdnZ3efvttJSQkKCwsTD169NDu3bslSadOndKJEyfUp08fFS1a9L7HT0xM1BdffKFu3brp1Vdf1ZUrV/Tll18qMDBQe/bsUb169WzqFy5cqCtXrui1116TxWJRWFiYOnXqpF9//dUa2A8dOqQmTZqoYMGC6tevnypWrKjTp09r9erV1vMeGxurRo0ayWKxaNCgQSpdurTWrVunvn37KjExUUOGDLE57vvvvy8HBwe9/fbbSklJ+VtX6gHTDAD51ldffWVIMtasWWNUrlzZkGS0bNky09obN24Ybm5uRq1atYxr165Zl69Zs8aQZIwZM8am/ubNm4YkY9y4cRmOFxUVZV0myQgNDbW+Hz58uOHm5mb4+voazzzzjHX5uHHjDElGWlqazXE8PT2N4OBg6/v333/fKFKkiPG///3Ppm7kyJGGvb29ER0dbRiGYWzevNmQZLzxxhsZPutfj3G33tNt2bLFkGRs2bLFuuz69euGnZ2d8frrr1uXdejQwXBwcDBOnz5tXXb+/HmjaNGiRtOmTTPs935ef/11w9nZOUMfS5cuzVBbpEgRm/N0/fp1IzU11aYmKirKcHR0NMaPH29dltPnPSoqypBkfPTRRxl6rFmzps3veWbn1c/Pz2jdunWG701oaKhx5z9JFy5cMIoWLWqtvXMf9/LUU08ZtWrVsr6/ePFihmOl+/zzzw1Jxm+//WZd9swzz9h8hg0bNhiSjP/85z8223p6ehpBQUH37OXq1atGtWrVDEmGp6en0atXL+PLL780YmNjM9QGBwcbRYoUMQzDMH788UfDxcXFCAoKMq5fv25T99f+0s9xjRo1jJSUFOvy6dOnG5KMw4cPG4ZhGKtWrTIkGVOnTr1nz+lu3bplsz/DMIw///zTKFOmjNGnTx/rsvTvQ8mSJY24uDjr8vTjrV692rqsadOmRtGiRW3Ot2HY/nnt27evUbZsWePSpUs2NV27djVcXV2Nq1ev2nzuSpUqWZcBDxvDOYBHQK9evXT27Fl1795dGzdu1NKlSzPU/Pzzz7pw4YJef/11m3HSQUFBql69eoYbuNLHazo6Oma5j3PnzumTTz7Re++9l2G6Ljc3N0nS77//fs99LF26VE2aNFHx4sV16dIl6ysgIECpqanavn27JOm7776TxWJRaGhohn2YueJ3p4SEBF26dEnR0dEKCwtTWlqamjdvLklKTU3Vxo0b1aFDB1WqVMm6TdmyZdW9e3f9+OOPWfpRckJCgmJjYxUeHq61a9eqadOmGWquXLli89kvXbqUocbR0dE69jM1NVWXL1+Ws7OzqlWrpv3791vrcvq8p7t69WqGHu83C8zy5cu1d+9eTZo06Z510u0rjK6urnrjjTfuW3un69evZ/k+gKx8x69cuSJJKlmypKk+pNuzjezevVvDhg2TdPsnQX379lXZsmU1ePBgpaSkZNhmy5YtCgwM1HPPPafly5dn+c9f7969ba7CNmnSRNLtn1hIsn43s3IVWrp9k2f6/tLS0hQXF6dbt26pQYMGNt+vdF26dFHx4sXvevyLFy9q+/bt6tOnjypUqGCzbfqfV8Mw9N1336lt27YyDMPmuxUYGKiEhIQMxw4ODr7vVXrgQWE4B/AIiIuL06JFi9SxY0cdO3ZMb775plq2bGkzJvq3336TJFWrVi3D9tWrV9ePP/5osyw+Pl6SsjR3bbrQ0FCVK1dOr732WoZxvf7+/rJYLBo1apQ++OAD637/Os751KlTOnT
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 800x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"# Список колонок для построения графиков\n",
|
|||
|
|
"columns = ['BMI', 'Smoking', 'AlcoholDrinking', 'Stroke',\n",
|
|||
|
|
" 'PhysicalHealth', 'MentalHealth', 'DiffWalking', 'Sex',\n",
|
|||
|
|
" 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'SleepTime', 'Asthma', 'KidneyDisease', 'SkinCancer']\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание диаграмм зависимости\n",
|
|||
|
|
"for column in columns:\n",
|
|||
|
|
" plt.figure(figsize=(8, 6)) # Установка размера графика\n",
|
|||
|
|
" if pd.api.types.is_numeric_dtype(df[column]): # Проверяем, является ли колонка числовой\n",
|
|||
|
|
" # Проверяем, содержит ли колонка только два уникальных значения (0 и 1)\n",
|
|||
|
|
" if df[column].nunique() == 2 and set(df[column].unique()).issubset({0, 1}):\n",
|
|||
|
|
" # Если да, то строим столбчатую диаграмму\n",
|
|||
|
|
" counts = df[column].value_counts() # Считаем количество повторений каждого значения\n",
|
|||
|
|
" counts.plot(kind='bar') # Создаем столбчатую диаграмму\n",
|
|||
|
|
" plt.title(f'Количество значений для {column}')\n",
|
|||
|
|
" plt.xlabel(column)\n",
|
|||
|
|
" plt.ylabel('Количество повторений')\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" # Если колонка числовая, создаем диаграмму рассеяния\n",
|
|||
|
|
" plt.scatter(df['HeartDisease'], df[column], alpha=0.5) # Создаем диаграмму рассеяния\n",
|
|||
|
|
" plt.title(f'Зависимость HeartDisease от {column}')\n",
|
|||
|
|
" plt.xlabel('HeartDisease (0 = нет, 1 = да)')\n",
|
|||
|
|
" plt.ylabel(column)\n",
|
|||
|
|
" plt.xticks([0, 1]) # Установка меток по оси X\n",
|
|||
|
|
" plt.grid() # Добавление сетки для удобства восприятия\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" # Если колонка не числовая, строим столбчатую диаграмму\n",
|
|||
|
|
" counts = df[column].value_counts() # Считаем количество повторений каждого значения\n",
|
|||
|
|
" counts.plot(kind='bar') # Создаем столбчатую диаграмму\n",
|
|||
|
|
" plt.title(f'Количество значений для {column}')\n",
|
|||
|
|
" plt.xlabel(column)\n",
|
|||
|
|
" plt.ylabel('Количество повторений')\n",
|
|||
|
|
"\n",
|
|||
|
|
" plt.show() # Отображение графика"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# Функция для создания выборок\n",
|
|||
|
|
"from sklearn.model_selection import train_test_split\n",
|
|||
|
|
"\n",
|
|||
|
|
"def split_stratified_into_train_val_test(\n",
|
|||
|
|
" df_input,\n",
|
|||
|
|
" stratify_colname=\"y\",\n",
|
|||
|
|
" frac_train=0.6,\n",
|
|||
|
|
" frac_val=0.15,\n",
|
|||
|
|
" frac_test=0.25,\n",
|
|||
|
|
" random_state=None,\n",
|
|||
|
|
"):\n",
|
|||
|
|
"\n",
|
|||
|
|
" if frac_train + frac_val + frac_test != 1.0:\n",
|
|||
|
|
" raise ValueError(\n",
|
|||
|
|
" \"fractions %f, %f, %f do not add up to 1.0\"\n",
|
|||
|
|
" % (frac_train, frac_val, frac_test)\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" if stratify_colname not in df_input.columns:\n",
|
|||
|
|
" raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
|
|||
|
|
"\n",
|
|||
|
|
" X = df_input # Contains all columns.\n",
|
|||
|
|
" y = df_input[\n",
|
|||
|
|
" [stratify_colname]\n",
|
|||
|
|
" ] # Dataframe of just the column on which to stratify.\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Split original dataframe into train and temp dataframes.\n",
|
|||
|
|
" df_train, df_temp, y_train, y_temp = train_test_split(\n",
|
|||
|
|
" X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Split the temp dataframe into val and test dataframes.\n",
|
|||
|
|
" relative_frac_test = frac_test / (frac_val + frac_test)\n",
|
|||
|
|
" df_val, df_test, y_val, y_test = train_test_split(\n",
|
|||
|
|
" df_temp,\n",
|
|||
|
|
" y_temp,\n",
|
|||
|
|
" stratify=y_temp,\n",
|
|||
|
|
" test_size=relative_frac_test,\n",
|
|||
|
|
" random_state=random_state,\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
" assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
|
|||
|
|
"\n",
|
|||
|
|
" return df_train, df_val, df_test"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"HeartDisease\n",
|
|||
|
|
"0 53\n",
|
|||
|
|
"1 47\n",
|
|||
|
|
"Name: count, dtype: int64\n",
|
|||
|
|
"\n",
|
|||
|
|
"Обучающая выборка: (60, 2)\n",
|
|||
|
|
"HeartDisease\n",
|
|||
|
|
"0 32\n",
|
|||
|
|
"1 28\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Контрольная выборка: (20, 2)\n",
|
|||
|
|
"HeartDisease\n",
|
|||
|
|
"0 11\n",
|
|||
|
|
"1 9\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Тестовая выборка: (20, 2)\n",
|
|||
|
|
"HeartDisease\n",
|
|||
|
|
"1 10\n",
|
|||
|
|
"0 10\n",
|
|||
|
|
"Name: count, dtype: int64\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
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|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
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|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
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"output_type": "display_data"
|
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|
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}
|
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|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Вывод распределения количества наблюдений по меткам (классам)\n",
|
|||
|
|
"print(df.HeartDisease.value_counts())\n",
|
|||
|
|
"print()\n",
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"\n",
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|||
|
|
"data = df.copy()\n",
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|
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"\n",
|
|||
|
|
"df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
|
|||
|
|
" data, stratify_colname=\"HeartDisease\", frac_train=0.60, frac_val=0.20, frac_test=0.20\n",
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|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Обучающая выборка: \", df_train.shape)\n",
|
|||
|
|
"print(df_train.HeartDisease.value_counts())\n",
|
|||
|
|
"counts = df_train['HeartDisease'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))# Установка размера графика\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)# Построение круговой диаграммы\n",
|
|||
|
|
"plt.title('Распределение классов HeartDisease в обучающей выборке')# Добавление заголовка\n",
|
|||
|
|
"plt.show()# Отображение графика\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Контрольная выборка: \", df_val.shape)\n",
|
|||
|
|
"print(df_val.HeartDisease.value_counts())\n",
|
|||
|
|
"counts = df_val['HeartDisease'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов HeartDisease в контрольной выборке')\n",
|
|||
|
|
"plt.show()\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Тестовая выборка: \", df_test.shape)\n",
|
|||
|
|
"print(df_test.HeartDisease.value_counts())\n",
|
|||
|
|
"counts = df_test['HeartDisease'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов HeartDisease в тестовой выборке')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"#### Сбалансируем распределение:"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"1. Балансировка данных оверсемплингом. Это метод, увеличивающий число наблюдений в меньшинственном классе для достижения более равномерного распределения классов."
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"ename": "ValueError",
|
|||
|
|
"evalue": "A given column is not a column of the dataframe",
|
|||
|
|
"output_type": "error",
|
|||
|
|
"traceback": [
|
|||
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|||
|
|
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"File \u001b[1;32mc:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
|
|||
|
|
"File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
|
|||
|
|
"File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
|
|||
|
|
"File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
|
|||
|
|
"File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
|
|||
|
|
"\u001b[1;31mKeyError\u001b[0m: 'Smoking'",
|
|||
|
|
"\nThe above exception was the direct cause of the following exception:\n",
|
|||
|
|
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"File \u001b[1;32mc:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\utils\\_indexing.py:361\u001b[0m, in \u001b[0;36m_get_column_indices\u001b[1;34m(X, key)\u001b[0m\n\u001b[0;32m 360\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m columns:\n\u001b[1;32m--> 361\u001b[0m col_idx \u001b[38;5;241m=\u001b[39m \u001b[43mall_columns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcol\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(col_idx, numbers\u001b[38;5;241m.\u001b[39mIntegral):\n",
|
|||
|
|
"File \u001b[1;32mc:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n",
|
|||
|
|
"\u001b[1;31mKeyError\u001b[0m: 'Smoking'",
|
|||
|
|
"\nThe above exception was the direct cause of the following exception:\n",
|
|||
|
|
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
|||
|
|
"Cell \u001b[1;32mIn[25], line 20\u001b[0m\n\u001b[0;32m 17\u001b[0m ada \u001b[38;5;241m=\u001b[39m ADASYN()\n\u001b[0;32m 19\u001b[0m \u001b[38;5;66;03m# Преобразование данных с помощью пайплайна\u001b[39;00m\n\u001b[1;32m---> 20\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mpreprocessor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mHeartDisease\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 21\u001b[0m y \u001b[38;5;241m=\u001b[39m df_train[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHeartDisease\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m 23\u001b[0m \u001b[38;5;66;03m# Применение ADASYN\u001b[39;00m\n",
|
|||
|
|
"File \u001b[1;32mc:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\utils\\_set_output.py:316\u001b[0m, in \u001b[0;36m_wrap_method_output.<locals>.wrapped\u001b[1;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[0;32m 314\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[0;32m 315\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 316\u001b[0m data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m 318\u001b[0m \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[0;32m 319\u001b[0m return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 320\u001b[0m _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m 321\u001b[0m \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[0;32m 322\u001b[0m )\n",
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"File \u001b[1;32mc:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\base.py:1473\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1466\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m 1469\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m 1470\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 1471\u001b[0m )\n\u001b[0;32m 1472\u001b[0m ):\n\u001b[1;32m-> 1473\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[1;32mc:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\compose\\_column_transformer.py:968\u001b[0m, in \u001b[0;36mColumnTransformer.fit_transform\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m 965\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_transformers()\n\u001b[0;32m 966\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(X)\n\u001b[1;32m--> 968\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_column_callables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 969\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_remainder(X)\n\u001b[0;32m 971\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _routing_enabled():\n",
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"File \u001b[1;32mc:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\compose\\_column_transformer.py:536\u001b[0m, in \u001b[0;36mColumnTransformer._validate_column_callables\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 534\u001b[0m columns \u001b[38;5;241m=\u001b[39m columns(X)\n\u001b[0;32m 535\u001b[0m all_columns\u001b[38;5;241m.\u001b[39mappend(columns)\n\u001b[1;32m--> 536\u001b[0m transformer_to_input_indices[name] \u001b[38;5;241m=\u001b[39m \u001b[43m_get_column_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 538\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_columns \u001b[38;5;241m=\u001b[39m all_columns\n\u001b[0;32m 539\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transformer_to_input_indices \u001b[38;5;241m=\u001b[39m transformer_to_input_indices\n",
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"File \u001b[1;32mc:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\utils\\_indexing.py:369\u001b[0m, in \u001b[0;36m_get_column_indices\u001b[1;34m(X, key)\u001b[0m\n\u001b[0;32m 366\u001b[0m column_indices\u001b[38;5;241m.\u001b[39mappend(col_idx)\n\u001b[0;32m 368\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m--> 369\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA given column is not a column of the dataframe\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m 371\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m column_indices\n",
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"\u001b[1;31mValueError\u001b[0m: A given column is not a column of the dataframe"
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]
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}
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],
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"source": [
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"from imblearn.over_sampling import ADASYN\n",
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"from sklearn.compose import ColumnTransformer\n",
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"from sklearn.preprocessing import OneHotEncoder\n",
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"\n",
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"categorical_features = ['Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking', 'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity', 'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer'] # Ваши категориальные признаки\n",
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"numeric_features = ['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime'] # Ваши числовые признаки\n",
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"\n",
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"# Создание пайплайна для обработки категориальных данных\n",
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"preprocessor = ColumnTransformer(\n",
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" transformers=[\n",
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" ('cat', OneHotEncoder(), categorical_features), # OneHotEncoder для категориальных данных\n",
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" ('num', 'passthrough', numeric_features) # Оставляем числовые колонки без изменений\n",
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" ]\n",
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")\n",
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"\n",
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"# Создание экземпляра ADASYN\n",
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"ada = ADASYN()\n",
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"\n",
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"# Преобразование данных с помощью пайплайна\n",
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"X = preprocessor.fit_transform(df_train.drop(columns=['HeartDisease']))\n",
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"y = df_train['HeartDisease']\n",
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"\n",
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"# Применение ADASYN\n",
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"X_resampled, y_resampled = ada.fit_resample(X, y)\n",
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"\n",
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"# Создание нового DataFrame\n",
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"df_train_adasyn = pd.DataFrame(X_resampled)\n",
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"# Восстанавливаем названия столбцов для DataFrame\n",
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"ohe_columns = preprocessor.named_transformers_['cat'].get_feature_names_out(categorical_features)\n",
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"new_column_names = list(ohe_columns) + numeric_features\n",
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"df_train_adasyn.columns = new_column_names\n",
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"\n",
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"# Добавление целевой переменной\n",
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"df_train_adasyn['HeartDisease'] = y_resampled\n",
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"\n",
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"# Вывод информации о новой выборке\n",
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"print(\"Обучающая выборка после oversampling: \", df_train_adasyn.shape)\n",
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"print(df_train_adasyn['HeartDisease'].value_counts())\n",
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"\n",
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"# Визуализация\n",
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"counts = df_train_adasyn['HeartDisease'].value_counts()\n",
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"plt.figure(figsize=(6, 6))\n",
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"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
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"plt.title('Распределение классов HeartDisease в тренировочной выборке после ADASYN')\n",
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"plt.show()"
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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. Балансировка данных андерсемплингом. Этот метод помогает сбалансировать выборку, уменьшая количество экземпляров класса большинства, чтобы привести его в соответствие с классом меньшинства."
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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": 56,
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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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"Обучающая выборка после undersampling: (32848, 18)\n",
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"HeartDisease\n",
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"No 16424\n",
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"Yes 16424\n",
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"Name: count, dtype: int64\n"
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]
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},
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{
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"data": {
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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 200x200 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"from imblearn.under_sampling import RandomUnderSampler\n",
|
|||
|
|
"\n",
|
|||
|
|
"rus = RandomUnderSampler()# Создание экземпляра RandomUnderSampler\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Применение RandomUnderSampler\n",
|
|||
|
|
"X_resampled, y_resampled = rus.fit_resample(df_train.drop(columns=['HeartDisease']), df_train['HeartDisease'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Создание нового DataFrame\n",
|
|||
|
|
"df_train_undersampled = pd.DataFrame(X_resampled)\n",
|
|||
|
|
"df_train_undersampled['HeartDisease'] = y_resampled # Добавление целевой переменной\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Вывод информации о новой выборке\n",
|
|||
|
|
"print(\"Обучающая выборка после undersampling: \", df_train_undersampled.shape)\n",
|
|||
|
|
"print(df_train_undersampled['HeartDisease'].value_counts())\n",
|
|||
|
|
"\n",
|
|||
|
|
"# Визуализация распределения классов\n",
|
|||
|
|
"counts = df_train_undersampled['HeartDisease'].value_counts()\n",
|
|||
|
|
"plt.figure(figsize=(2, 2))\n",
|
|||
|
|
"plt.pie(counts, labels=counts.index, autopct='%1.1f%%', startangle=90)\n",
|
|||
|
|
"plt.title('Распределение классов HeartDisease в тренировочной выборке после Undersampling')\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "aimenv",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.12.5"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|