diff --git a/lab_3/lab3.ipynb b/lab_3/lab3.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Начало лабораторной работы"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "*Вариант 3:* Диабет у индейцев Пима\n",
+ "- Определим бизнес-цели и цели технического проекта "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
+ " 'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
+ " dtype='object')\n"
+ ]
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "df = pd.read_csv(\"C:/Users/TIGR228/Desktop/МИИ/Lab1/AIM-PIbd-31-Afanasev-S-S/static/csv/diabetes.csv\")\n",
+ "print(df.columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Определение бизнес целей:\n",
+ "1. Прогнозирование риска развития диабета\n",
+ "2. Оценка факторов, влияющих на развитие диабета"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Определение целей технического проекта:\n",
+ "1. Построить модель машинного обучения для классификации, которая будет прогнозировать вероятность развития диабета у индейцев Пима на основе предоставленных данных о их характеристиках.\n",
+ "2. Провести анализ данных для выявления ключевых факторов, влияющих на развитие диабета у индейцев Пима."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Pregnancies | \n",
+ " Glucose | \n",
+ " BloodPressure | \n",
+ " SkinThickness | \n",
+ " Insulin | \n",
+ " BMI | \n",
+ " DiabetesPedigreeFunction | \n",
+ " Age | \n",
+ " Outcome | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 6 | \n",
+ " 148 | \n",
+ " 72 | \n",
+ " 35 | \n",
+ " 0 | \n",
+ " 33.6 | \n",
+ " 0.627 | \n",
+ " 50 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 85 | \n",
+ " 66 | \n",
+ " 29 | \n",
+ " 0 | \n",
+ " 26.6 | \n",
+ " 0.351 | \n",
+ " 31 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 8 | \n",
+ " 183 | \n",
+ " 64 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 23.3 | \n",
+ " 0.672 | \n",
+ " 32 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 1 | \n",
+ " 89 | \n",
+ " 66 | \n",
+ " 23 | \n",
+ " 94 | \n",
+ " 28.1 | \n",
+ " 0.167 | \n",
+ " 21 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 137 | \n",
+ " 40 | \n",
+ " 35 | \n",
+ " 168 | \n",
+ " 43.1 | \n",
+ " 2.288 | \n",
+ " 33 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n",
+ "0 6 148 72 35 0 33.6 \n",
+ "1 1 85 66 29 0 26.6 \n",
+ "2 8 183 64 0 0 23.3 \n",
+ "3 1 89 66 23 94 28.1 \n",
+ "4 0 137 40 35 168 43.1 \n",
+ "\n",
+ " DiabetesPedigreeFunction Age Outcome \n",
+ "0 0.627 50 1 \n",
+ "1 0.351 31 0 \n",
+ "2 0.672 32 1 \n",
+ "3 0.167 21 0 \n",
+ "4 2.288 33 1 "
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Pregnancies 0\n",
+ "Glucose 0\n",
+ "BloodPressure 0\n",
+ "SkinThickness 0\n",
+ "Insulin 0\n",
+ "BMI 0\n",
+ "DiabetesPedigreeFunction 0\n",
+ "Age 0\n",
+ "Outcome 0\n",
+ "dtype: int64\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Pregnancies False\n",
+ "Glucose False\n",
+ "BloodPressure False\n",
+ "SkinThickness False\n",
+ "Insulin False\n",
+ "BMI False\n",
+ "DiabetesPedigreeFunction False\n",
+ "Age False\n",
+ "Outcome False\n",
+ "dtype: bool"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Процент пропущенных значений признаков\n",
+ "for i in df.columns:\n",
+ " null_rate = df[i].isnull().sum() / len(df) * 100\n",
+ " if null_rate > 0:\n",
+ " print(f'{i} Процент пустых значений: %{null_rate:.2f}')\n",
+ "\n",
+ "# Проверка на пропущенные данные\n",
+ "print(df.isnull().sum())\n",
+ "\n",
+ "df.isnull().any()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Пропущенных колонок нету, что не может не радовать "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Размер обучающей выборки: 614\n",
+ "Размер контрольной выборки: 154\n",
+ "Размер тестовой выборки: 154\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тестовая)\n",
+ "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
+ "\n",
+ "# Разделение данных на обучающую и контрольную выборки (80% - обучение, 20% - контроль)\n",
+ "train_data, val_data = train_test_split(df, test_size=0.2, random_state=42)\n",
+ "\n",
+ "print(\"Размер обучающей выборки: \", len(train_data))\n",
+ "print(\"Размер контрольной выборки: \", len(val_data))\n",
+ "print(\"Размер тестовой выборки: \", len(test_data))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "