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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b2f2e5e2",
+   "metadata": {},
+   "source": [
+    "## Тема “Обучение с учителем”"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16595a84",
+   "metadata": {},
+   "source": [
+    "### Задание 1\n",
+    "Импортируйте библиотеки pandas и numpy.\n",
+    "Загрузите \"Boston House Prices dataset\" из встроенных наборов данных библиотеки sklearn..\n",
+    "Разбейте эти датафреймы на тренировочные (X_train, y_train) и тестовые (X_test, y_test) с помощью\n",
+    "функции train_test_split так, чтобы размер тестовой выборки\n",
+    "составлял 30% от всех данных, при этом аргумент random state должен быть равен 42.\n",
+    "Создайте модель линейной регрессии под названием lr с помощью класса LinearRegression из модуля\n",
+    "sklearn.linear_model.\n",
+    "Обучите модель на тренировочных данных (используйте все признаки) и сделайте предсказание на\n",
+    "тестовых."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "274303e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "693a9c36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "pd.options.display.max_columns = 100\n",
+    "\n",
+    "from sklearn.datasets import load_boston"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a37f783d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boston = load_boston()\n",
+    "\n",
+    "feature_names = boston[\"feature_names\"]\n",
+    "\n",
+    "X = pd.DataFrame(boston[\"data\"], columns=feature_names)\n",
+    "y = pd.DataFrame(boston[\"target\"], columns=[\"price\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5a2e0780",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((354, 13), (152, 13))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
+    "\n",
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "96164976",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "lr = LinearRegression()\n",
+    "\n",
+    "lr.fit(X_train, y_train)\n",
+    "\n",
+    "lr_pred = lr.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a07cffa5",
+   "metadata": {},
+   "source": [
+    "### Задание 2\n",
+    "Создайте модель под названием model с помощью класса RandomForestRegressor из модуля\n",
+    "sklearn.ensemble.\n",
+    "Сделайте агрумент n_estimators равным 1000,\n",
+    "max_depth должен быть равен 12 и random_state сделайте равным 42.\n",
+    "Обучите модель на тренировочных данных аналогично тому, как вы обучали модель LinearRegression,\n",
+    "но при этом в метод fit вместо датафрейма y_train поставьте y_train.values[:, 0],\n",
+    "чтобы получить из датафрейма одномерный массив Numpy,\n",
+    "так как для класса RandomForestRegressor в данном методе для аргумента y предпочтительно\n",
+    "применение массивов вместо датафрейма.\n",
+    "Сделайте предсказание на тестовых данных и посчитайте R2. Сравните с результатом из\n",
+    "предыдущего задания.\n",
+    "Напишите в комментариях к коду, какая модель в данном случае работает лучше"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "64042e74",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "оценка R2 модели ансамбля случайного леса выше чем у линейной регрессии\n",
+      "RandomForestRegressor=0.8747\n",
+      "LinearRegression=0.7112\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import r2_score\n",
+    "\n",
+    "model = RandomForestRegressor(max_depth=12, n_estimators=1000, random_state=42)\n",
+    "\n",
+    "model.fit(X_train, y_train.values[:,0])\n",
+    "\n",
+    "rf_pred = model.predict(X_test)\n",
+    "\n",
+    "r2_lr = r2_score(y_test, lr_pred)\n",
+    "r2_rf = r2_score(y_test, rf_pred)\n",
+    "\n",
+    "print(f\"оценка R2 модели ансамбля случайного леса {'выше' if r2_rf > r2_lr else 'ниже' } \" \n",
+    "      f\"чем у линейной регрессии\\nRandomForestRegressor={r2_rf:.4f}\\nLinearRegression={r2_lr:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94b0548e",
+   "metadata": {},
+   "source": [
+    "### *Задание 3\n",
+    "Вызовите документацию для класса RandomForestRegressor,\n",
+    "найдите информацию об атрибуте feature_importances_.\n",
+    "С помощью этого атрибута найдите сумму всех показателей важности,\n",
+    "установите, какие два признака показывают наибольшую важность."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1327004d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "?RandomForestRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c8e1fab0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "сумма показателей важности признаков модели = 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"сумма показателей важности признаков модели = {np.sum(model.feature_importances_)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "744deb30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "первые два наиболее важные признака\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>feature</th>\n",
+       "      <th>importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>LSTAT</td>\n",
+       "      <td>0.415847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>RM</td>\n",
+       "      <td>0.402682</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   feature  importance\n",
+       "12   LSTAT    0.415847\n",
+       "5       RM    0.402682"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"первые два наиболее важные признака\")\n",
+    "pd.DataFrame({'feature': feature_names,'importance': model.feature_importances_}) \\\n",
+    ".sort_values('importance',ascending = False) \\\n",
+    ".head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9004884",
+   "metadata": {},
+   "source": [
+    "## *Задание 4\n",
+    "В этом задании мы будем работать с датасетом, с которым мы уже знакомы по домашнему заданию\n",
+    "по библиотеке Matplotlib, это датасет Credit Card Fraud Detection.Для этого датасета мы будем решать\n",
+    "задачу классификации - будем определять,какие из транзакции по кредитной карте являются\n",
+    "мошенническими.Данный датасет сильно несбалансирован (так как случаи мошенничества\n",
+    "относительно редки),так что применение метрики accuracy не принесет пользы и не поможет выбрать\n",
+    "лучшую модель.Мы будем вычислять AUC, то есть площадь под кривой ROC.\n",
+    "Импортируйте из соответствующих модулей RandomForestClassifier, GridSearchCV и train_test_split.\n",
+    "Загрузите датасет creditcard.csv и создайте датафрейм df."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a65b98a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset URL: https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud\n",
+      "License(s): DbCL-1.0\n",
+      "Downloading creditcardfraud.zip to W:\\Projects\\GB\\Python\\MLearning\\DSLibraries\\Lesson6\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
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+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "!kaggle datasets download -d mlg-ulb/creditcardfraud"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f7955e2",
+   "metadata": {},
+   "source": [
+    "С помощью метода value_counts с аргументом normalize=True убедитесь в том, что выборка\n",
+    "несбалансирована. Используя метод info, проверьте, все ли столбцы содержат числовые данные и нет\n",
+    "ли в них пропусков.Примените следующую настройку, чтобы можно было просматривать все столбцы\n",
+    "датафрейма: pd.options.display.max_columns = 100.\n",
+    "Просмотрите первые 10 строк датафрейма df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3e959e4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Time</th>\n",
+       "      <th>V1</th>\n",
+       "      <th>V2</th>\n",
+       "      <th>V3</th>\n",
+       "      <th>V4</th>\n",
+       "      <th>V5</th>\n",
+       "      <th>V6</th>\n",
+       "      <th>V7</th>\n",
+       "      <th>V8</th>\n",
+       "      <th>V9</th>\n",
+       "      <th>V10</th>\n",
+       "      <th>V11</th>\n",
+       "      <th>V12</th>\n",
+       "      <th>V13</th>\n",
+       "      <th>V14</th>\n",
+       "      <th>V15</th>\n",
+       "      <th>V16</th>\n",
+       "      <th>V17</th>\n",
+       "      <th>V18</th>\n",
+       "      <th>V19</th>\n",
+       "      <th>V20</th>\n",
+       "      <th>V21</th>\n",
+       "      <th>V22</th>\n",
+       "      <th>V23</th>\n",
+       "      <th>V24</th>\n",
+       "      <th>V25</th>\n",
+       "      <th>V26</th>\n",
+       "      <th>V27</th>\n",
+       "      <th>V28</th>\n",
+       "      <th>Amount</th>\n",
+       "      <th>Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.359807</td>\n",
+       "      <td>-0.072781</td>\n",
+       "      <td>2.536347</td>\n",
+       "      <td>1.378155</td>\n",
+       "      <td>-0.338321</td>\n",
+       "      <td>0.462388</td>\n",
+       "      <td>0.239599</td>\n",
+       "      <td>0.098698</td>\n",
+       "      <td>0.363787</td>\n",
+       "      <td>0.090794</td>\n",
+       "      <td>-0.551600</td>\n",
+       "      <td>-0.617801</td>\n",
+       "      <td>-0.991390</td>\n",
+       "      <td>-0.311169</td>\n",
+       "      <td>1.468177</td>\n",
+       "      <td>-0.470401</td>\n",
+       "      <td>0.207971</td>\n",
+       "      <td>0.025791</td>\n",
+       "      <td>0.403993</td>\n",
+       "      <td>0.251412</td>\n",
+       "      <td>-0.018307</td>\n",
+       "      <td>0.277838</td>\n",
+       "      <td>-0.110474</td>\n",
+       "      <td>0.066928</td>\n",
+       "      <td>0.128539</td>\n",
+       "      <td>-0.189115</td>\n",
+       "      <td>0.133558</td>\n",
+       "      <td>-0.021053</td>\n",
+       "      <td>149.62</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.191857</td>\n",
+       "      <td>0.266151</td>\n",
+       "      <td>0.166480</td>\n",
+       "      <td>0.448154</td>\n",
+       "      <td>0.060018</td>\n",
+       "      <td>-0.082361</td>\n",
+       "      <td>-0.078803</td>\n",
+       "      <td>0.085102</td>\n",
+       "      <td>-0.255425</td>\n",
+       "      <td>-0.166974</td>\n",
+       "      <td>1.612727</td>\n",
+       "      <td>1.065235</td>\n",
+       "      <td>0.489095</td>\n",
+       "      <td>-0.143772</td>\n",
+       "      <td>0.635558</td>\n",
+       "      <td>0.463917</td>\n",
+       "      <td>-0.114805</td>\n",
+       "      <td>-0.183361</td>\n",
+       "      <td>-0.145783</td>\n",
+       "      <td>-0.069083</td>\n",
+       "      <td>-0.225775</td>\n",
+       "      <td>-0.638672</td>\n",
+       "      <td>0.101288</td>\n",
+       "      <td>-0.339846</td>\n",
+       "      <td>0.167170</td>\n",
+       "      <td>0.125895</td>\n",
+       "      <td>-0.008983</td>\n",
+       "      <td>0.014724</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-1.358354</td>\n",
+       "      <td>-1.340163</td>\n",
+       "      <td>1.773209</td>\n",
+       "      <td>0.379780</td>\n",
+       "      <td>-0.503198</td>\n",
+       "      <td>1.800499</td>\n",
+       "      <td>0.791461</td>\n",
+       "      <td>0.247676</td>\n",
+       "      <td>-1.514654</td>\n",
+       "      <td>0.207643</td>\n",
+       "      <td>0.624501</td>\n",
+       "      <td>0.066084</td>\n",
+       "      <td>0.717293</td>\n",
+       "      <td>-0.165946</td>\n",
+       "      <td>2.345865</td>\n",
+       "      <td>-2.890083</td>\n",
+       "      <td>1.109969</td>\n",
+       "      <td>-0.121359</td>\n",
+       "      <td>-2.261857</td>\n",
+       "      <td>0.524980</td>\n",
+       "      <td>0.247998</td>\n",
+       "      <td>0.771679</td>\n",
+       "      <td>0.909412</td>\n",
+       "      <td>-0.689281</td>\n",
+       "      <td>-0.327642</td>\n",
+       "      <td>-0.139097</td>\n",
+       "      <td>-0.055353</td>\n",
+       "      <td>-0.059752</td>\n",
+       "      <td>378.66</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-0.966272</td>\n",
+       "      <td>-0.185226</td>\n",
+       "      <td>1.792993</td>\n",
+       "      <td>-0.863291</td>\n",
+       "      <td>-0.010309</td>\n",
+       "      <td>1.247203</td>\n",
+       "      <td>0.237609</td>\n",
+       "      <td>0.377436</td>\n",
+       "      <td>-1.387024</td>\n",
+       "      <td>-0.054952</td>\n",
+       "      <td>-0.226487</td>\n",
+       "      <td>0.178228</td>\n",
+       "      <td>0.507757</td>\n",
+       "      <td>-0.287924</td>\n",
+       "      <td>-0.631418</td>\n",
+       "      <td>-1.059647</td>\n",
+       "      <td>-0.684093</td>\n",
+       "      <td>1.965775</td>\n",
+       "      <td>-1.232622</td>\n",
+       "      <td>-0.208038</td>\n",
+       "      <td>-0.108300</td>\n",
+       "      <td>0.005274</td>\n",
+       "      <td>-0.190321</td>\n",
+       "      <td>-1.175575</td>\n",
+       "      <td>0.647376</td>\n",
+       "      <td>-0.221929</td>\n",
+       "      <td>0.062723</td>\n",
+       "      <td>0.061458</td>\n",
+       "      <td>123.50</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.158233</td>\n",
+       "      <td>0.877737</td>\n",
+       "      <td>1.548718</td>\n",
+       "      <td>0.403034</td>\n",
+       "      <td>-0.407193</td>\n",
+       "      <td>0.095921</td>\n",
+       "      <td>0.592941</td>\n",
+       "      <td>-0.270533</td>\n",
+       "      <td>0.817739</td>\n",
+       "      <td>0.753074</td>\n",
+       "      <td>-0.822843</td>\n",
+       "      <td>0.538196</td>\n",
+       "      <td>1.345852</td>\n",
+       "      <td>-1.119670</td>\n",
+       "      <td>0.175121</td>\n",
+       "      <td>-0.451449</td>\n",
+       "      <td>-0.237033</td>\n",
+       "      <td>-0.038195</td>\n",
+       "      <td>0.803487</td>\n",
+       "      <td>0.408542</td>\n",
+       "      <td>-0.009431</td>\n",
+       "      <td>0.798278</td>\n",
+       "      <td>-0.137458</td>\n",
+       "      <td>0.141267</td>\n",
+       "      <td>-0.206010</td>\n",
+       "      <td>0.502292</td>\n",
+       "      <td>0.219422</td>\n",
+       "      <td>0.215153</td>\n",
+       "      <td>69.99</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-0.425966</td>\n",
+       "      <td>0.960523</td>\n",
+       "      <td>1.141109</td>\n",
+       "      <td>-0.168252</td>\n",
+       "      <td>0.420987</td>\n",
+       "      <td>-0.029728</td>\n",
+       "      <td>0.476201</td>\n",
+       "      <td>0.260314</td>\n",
+       "      <td>-0.568671</td>\n",
+       "      <td>-0.371407</td>\n",
+       "      <td>1.341262</td>\n",
+       "      <td>0.359894</td>\n",
+       "      <td>-0.358091</td>\n",
+       "      <td>-0.137134</td>\n",
+       "      <td>0.517617</td>\n",
+       "      <td>0.401726</td>\n",
+       "      <td>-0.058133</td>\n",
+       "      <td>0.068653</td>\n",
+       "      <td>-0.033194</td>\n",
+       "      <td>0.084968</td>\n",
+       "      <td>-0.208254</td>\n",
+       "      <td>-0.559825</td>\n",
+       "      <td>-0.026398</td>\n",
+       "      <td>-0.371427</td>\n",
+       "      <td>-0.232794</td>\n",
+       "      <td>0.105915</td>\n",
+       "      <td>0.253844</td>\n",
+       "      <td>0.081080</td>\n",
+       "      <td>3.67</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.229658</td>\n",
+       "      <td>0.141004</td>\n",
+       "      <td>0.045371</td>\n",
+       "      <td>1.202613</td>\n",
+       "      <td>0.191881</td>\n",
+       "      <td>0.272708</td>\n",
+       "      <td>-0.005159</td>\n",
+       "      <td>0.081213</td>\n",
+       "      <td>0.464960</td>\n",
+       "      <td>-0.099254</td>\n",
+       "      <td>-1.416907</td>\n",
+       "      <td>-0.153826</td>\n",
+       "      <td>-0.751063</td>\n",
+       "      <td>0.167372</td>\n",
+       "      <td>0.050144</td>\n",
+       "      <td>-0.443587</td>\n",
+       "      <td>0.002821</td>\n",
+       "      <td>-0.611987</td>\n",
+       "      <td>-0.045575</td>\n",
+       "      <td>-0.219633</td>\n",
+       "      <td>-0.167716</td>\n",
+       "      <td>-0.270710</td>\n",
+       "      <td>-0.154104</td>\n",
+       "      <td>-0.780055</td>\n",
+       "      <td>0.750137</td>\n",
+       "      <td>-0.257237</td>\n",
+       "      <td>0.034507</td>\n",
+       "      <td>0.005168</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>-0.644269</td>\n",
+       "      <td>1.417964</td>\n",
+       "      <td>1.074380</td>\n",
+       "      <td>-0.492199</td>\n",
+       "      <td>0.948934</td>\n",
+       "      <td>0.428118</td>\n",
+       "      <td>1.120631</td>\n",
+       "      <td>-3.807864</td>\n",
+       "      <td>0.615375</td>\n",
+       "      <td>1.249376</td>\n",
+       "      <td>-0.619468</td>\n",
+       "      <td>0.291474</td>\n",
+       "      <td>1.757964</td>\n",
+       "      <td>-1.323865</td>\n",
+       "      <td>0.686133</td>\n",
+       "      <td>-0.076127</td>\n",
+       "      <td>-1.222127</td>\n",
+       "      <td>-0.358222</td>\n",
+       "      <td>0.324505</td>\n",
+       "      <td>-0.156742</td>\n",
+       "      <td>1.943465</td>\n",
+       "      <td>-1.015455</td>\n",
+       "      <td>0.057504</td>\n",
+       "      <td>-0.649709</td>\n",
+       "      <td>-0.415267</td>\n",
+       "      <td>-0.051634</td>\n",
+       "      <td>-1.206921</td>\n",
+       "      <td>-1.085339</td>\n",
+       "      <td>40.80</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>-0.894286</td>\n",
+       "      <td>0.286157</td>\n",
+       "      <td>-0.113192</td>\n",
+       "      <td>-0.271526</td>\n",
+       "      <td>2.669599</td>\n",
+       "      <td>3.721818</td>\n",
+       "      <td>0.370145</td>\n",
+       "      <td>0.851084</td>\n",
+       "      <td>-0.392048</td>\n",
+       "      <td>-0.410430</td>\n",
+       "      <td>-0.705117</td>\n",
+       "      <td>-0.110452</td>\n",
+       "      <td>-0.286254</td>\n",
+       "      <td>0.074355</td>\n",
+       "      <td>-0.328783</td>\n",
+       "      <td>-0.210077</td>\n",
+       "      <td>-0.499768</td>\n",
+       "      <td>0.118765</td>\n",
+       "      <td>0.570328</td>\n",
+       "      <td>0.052736</td>\n",
+       "      <td>-0.073425</td>\n",
+       "      <td>-0.268092</td>\n",
+       "      <td>-0.204233</td>\n",
+       "      <td>1.011592</td>\n",
+       "      <td>0.373205</td>\n",
+       "      <td>-0.384157</td>\n",
+       "      <td>0.011747</td>\n",
+       "      <td>0.142404</td>\n",
+       "      <td>93.20</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>-0.338262</td>\n",
+       "      <td>1.119593</td>\n",
+       "      <td>1.044367</td>\n",
+       "      <td>-0.222187</td>\n",
+       "      <td>0.499361</td>\n",
+       "      <td>-0.246761</td>\n",
+       "      <td>0.651583</td>\n",
+       "      <td>0.069539</td>\n",
+       "      <td>-0.736727</td>\n",
+       "      <td>-0.366846</td>\n",
+       "      <td>1.017614</td>\n",
+       "      <td>0.836390</td>\n",
+       "      <td>1.006844</td>\n",
+       "      <td>-0.443523</td>\n",
+       "      <td>0.150219</td>\n",
+       "      <td>0.739453</td>\n",
+       "      <td>-0.540980</td>\n",
+       "      <td>0.476677</td>\n",
+       "      <td>0.451773</td>\n",
+       "      <td>0.203711</td>\n",
+       "      <td>-0.246914</td>\n",
+       "      <td>-0.633753</td>\n",
+       "      <td>-0.120794</td>\n",
+       "      <td>-0.385050</td>\n",
+       "      <td>-0.069733</td>\n",
+       "      <td>0.094199</td>\n",
+       "      <td>0.246219</td>\n",
+       "      <td>0.083076</td>\n",
+       "      <td>3.68</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
+       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
+       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
+       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
+       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
+       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
+       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
+       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
+       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
+       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
+       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \n",
+       "\n",
+       "         V8        V9       V10       V11       V12       V13       V14  \\\n",
+       "0  0.098698  0.363787  0.090794 -0.551600 -0.617801 -0.991390 -0.311169   \n",
+       "1  0.085102 -0.255425 -0.166974  1.612727  1.065235  0.489095 -0.143772   \n",
+       "2  0.247676 -1.514654  0.207643  0.624501  0.066084  0.717293 -0.165946   \n",
+       "3  0.377436 -1.387024 -0.054952 -0.226487  0.178228  0.507757 -0.287924   \n",
+       "4 -0.270533  0.817739  0.753074 -0.822843  0.538196  1.345852 -1.119670   \n",
+       "5  0.260314 -0.568671 -0.371407  1.341262  0.359894 -0.358091 -0.137134   \n",
+       "6  0.081213  0.464960 -0.099254 -1.416907 -0.153826 -0.751063  0.167372   \n",
+       "7 -3.807864  0.615375  1.249376 -0.619468  0.291474  1.757964 -1.323865   \n",
+       "8  0.851084 -0.392048 -0.410430 -0.705117 -0.110452 -0.286254  0.074355   \n",
+       "9  0.069539 -0.736727 -0.366846  1.017614  0.836390  1.006844 -0.443523   \n",
+       "\n",
+       "        V15       V16       V17       V18       V19       V20       V21  \\\n",
+       "0  1.468177 -0.470401  0.207971  0.025791  0.403993  0.251412 -0.018307   \n",
+       "1  0.635558  0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775   \n",
+       "2  2.345865 -2.890083  1.109969 -0.121359 -2.261857  0.524980  0.247998   \n",
+       "3 -0.631418 -1.059647 -0.684093  1.965775 -1.232622 -0.208038 -0.108300   \n",
+       "4  0.175121 -0.451449 -0.237033 -0.038195  0.803487  0.408542 -0.009431   \n",
+       "5  0.517617  0.401726 -0.058133  0.068653 -0.033194  0.084968 -0.208254   \n",
+       "6  0.050144 -0.443587  0.002821 -0.611987 -0.045575 -0.219633 -0.167716   \n",
+       "7  0.686133 -0.076127 -1.222127 -0.358222  0.324505 -0.156742  1.943465   \n",
+       "8 -0.328783 -0.210077 -0.499768  0.118765  0.570328  0.052736 -0.073425   \n",
+       "9  0.150219  0.739453 -0.540980  0.476677  0.451773  0.203711 -0.246914   \n",
+       "\n",
+       "        V22       V23       V24       V25       V26       V27       V28  \\\n",
+       "0  0.277838 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053   \n",
+       "1 -0.638672  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724   \n",
+       "2  0.771679  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752   \n",
+       "3  0.005274 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458   \n",
+       "4  0.798278 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   \n",
+       "5 -0.559825 -0.026398 -0.371427 -0.232794  0.105915  0.253844  0.081080   \n",
+       "6 -0.270710 -0.154104 -0.780055  0.750137 -0.257237  0.034507  0.005168   \n",
+       "7 -1.015455  0.057504 -0.649709 -0.415267 -0.051634 -1.206921 -1.085339   \n",
+       "8 -0.268092 -0.204233  1.011592  0.373205 -0.384157  0.011747  0.142404   \n",
+       "9 -0.633753 -0.120794 -0.385050 -0.069733  0.094199  0.246219  0.083076   \n",
+       "\n",
+       "   Amount  Class  \n",
+       "0  149.62      0  \n",
+       "1    2.69      0  \n",
+       "2  378.66      0  \n",
+       "3  123.50      0  \n",
+       "4   69.99      0  \n",
+       "5    3.67      0  \n",
+       "6    4.99      0  \n",
+       "7   40.80      0  \n",
+       "8   93.20      0  \n",
+       "9    3.68      0  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from zipfile import ZipFile\n",
+    "\n",
+    "ZipFile(\"creditcardfraud.zip\").extractall(\".\")\n",
+    "\n",
+    "df = pd.read_csv(\"creditcard.csv\")\n",
+    "\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "9302f8fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 284807 entries, 0 to 284806\n",
+      "Data columns (total 31 columns):\n",
+      " #   Column  Non-Null Count   Dtype  \n",
+      "---  ------  --------------   -----  \n",
+      " 0   Time    284807 non-null  float64\n",
+      " 1   V1      284807 non-null  float64\n",
+      " 2   V2      284807 non-null  float64\n",
+      " 3   V3      284807 non-null  float64\n",
+      " 4   V4      284807 non-null  float64\n",
+      " 5   V5      284807 non-null  float64\n",
+      " 6   V6      284807 non-null  float64\n",
+      " 7   V7      284807 non-null  float64\n",
+      " 8   V8      284807 non-null  float64\n",
+      " 9   V9      284807 non-null  float64\n",
+      " 10  V10     284807 non-null  float64\n",
+      " 11  V11     284807 non-null  float64\n",
+      " 12  V12     284807 non-null  float64\n",
+      " 13  V13     284807 non-null  float64\n",
+      " 14  V14     284807 non-null  float64\n",
+      " 15  V15     284807 non-null  float64\n",
+      " 16  V16     284807 non-null  float64\n",
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+      " 18  V18     284807 non-null  float64\n",
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+      " 20  V20     284807 non-null  float64\n",
+      " 21  V21     284807 non-null  float64\n",
+      " 22  V22     284807 non-null  float64\n",
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+      " 24  V24     284807 non-null  float64\n",
+      " 25  V25     284807 non-null  float64\n",
+      " 26  V26     284807 non-null  float64\n",
+      " 27  V27     284807 non-null  float64\n",
+      " 28  V28     284807 non-null  float64\n",
+      " 29  Amount  284807 non-null  float64\n",
+      " 30  Class   284807 non-null  int64  \n",
+      "dtypes: float64(30), int64(1)\n",
+      "memory usage: 67.4 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ba00248",
+   "metadata": {},
+   "source": [
+    "Создайте датафрейм X из датафрейма df, исключив столбец Class.\n",
+    "Создайте объект Series под названием y из столбца Class.\n",
+    "Разбейте X и y на тренировочный и тестовый наборы данных при помощи функции train_test_split,\n",
+    "используя аргументы: test_size=0.3, random_state=100, stratify=y.\n",
+    "У вас должны получиться объекты X_train, X_test, y_train и y_test.\n",
+    "Просмотрите информацию о их форме."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "59e1e34e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "выборка не сбалансированна, данных первого класса значительно меньше\n",
+      "0    0.998273\n",
+      "1    0.001727\n",
+      "Name: Class, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "((199364, 30), (85443, 30), (199364,), (85443,))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target = \"Class\"\n",
+    "\n",
+    "y = df[target]\n",
+    "X = df.drop(target, axis=1)\n",
+    "\n",
+    "print(f\"выборка не сбалансированна, данных первого класса значительно меньше\\n{y.value_counts(normalize=True)}\")\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100, stratify=y)\n",
+    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe7fae18",
+   "metadata": {},
+   "source": [
+    "Для поиска по сетке параметров задайте такие параметры:\n",
+    "parameters = [{'n_estimators': [10, 15],\n",
+    "'max_features': np.arange(3, 5),\n",
+    "'max_depth': np.arange(4, 7)}]\n",
+    "Создайте модель GridSearchCV со следующими аргументами:\n",
+    "estimator=RandomForestClassifier(random_state=100),\n",
+    "param_grid=parameters,\n",
+    "scoring='roc_auc',\n",
+    "cv=3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "c034c2a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "parameters = {\n",
+    "    'n_estimators': [10, 15],\n",
+    "    'max_features': np.arange(3, 5),\n",
+    "    'max_depth': np.arange(4, 7),\n",
+    "}\n",
+    "\n",
+    "clf = GridSearchCV(\n",
+    "    estimator=RandomForestClassifier(random_state=100),\n",
+    "    param_grid=parameters,\n",
+    "    scoring='roc_auc',\n",
+    "    cv=3,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4308ac3",
+   "metadata": {},
+   "source": [
+    "Обучите модель на тренировочном наборе данных (может занять несколько минут).\n",
+    "Просмотрите параметры лучшей модели с помощью атрибута best_params_.\n",
+    "Предскажите вероятности классов с помощью полученной модели и метода predict_proba.\n",
+    "Из полученного результата (массив Numpy) выберите столбец с индексом 1 (вероятность класса 1) и\n",
+    "запишите в массив y_pred_proba. Из модуля sklearn.metrics импортируйте метрику roc_auc_score.\n",
+    "Вычислите AUC на тестовых данных и сравните с результатом,полученным на тренировочных данных,\n",
+    "используя в качестве аргументов массивы y_test и y_pred_proba"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "9b43d0b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=3, estimator=RandomForestClassifier(random_state=100),\n",
+       "             param_grid={'max_depth': array([4, 5, 6]),\n",
+       "                         'max_features': array([3, 4]),\n",
+       "                         'n_estimators': [10, 15]},\n",
+       "             scoring='roc_auc')"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "966c3d4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'max_depth': 6, 'max_features': 3, 'n_estimators': 15}"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "405d63d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "метрика AUC=0.9436 на тестовых данных меньше метрики AUC=0.9660 на обучающем наборе\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_auc_score\n",
+    "\n",
+    "model = RandomForestClassifier(max_depth=6, max_features=3, n_estimators=15)\n",
+    "\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
+    "\n",
+    "auc = roc_auc_score(y_test, y_pred_proba)\n",
+    "\n",
+    "print(f\"метрика AUC={auc:.4f} на тестовых данных {'больше' if auc > clf.best_score_ else 'меньше'} \"\n",
+    "      f\"метрики AUC={clf.best_score_:.4f} на обучающем наборе\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cb517be",
+   "metadata": {},
+   "source": [
+    "## *Дополнительные задания:\n",
+    "1). Загрузите датасет Wine из встроенных датасетов sklearn.datasets с помощью функции load_wine в\n",
+    "переменную data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "1dbcea26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_wine\n",
+    "\n",
+    "wine = load_wine()\n",
+    "\n",
+    "data = wine[\"data\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18dea80c",
+   "metadata": {},
+   "source": [
+    "2). Полученный датасет не является датафреймом. Это структура данных, имеющая ключи\n",
+    "аналогично словарю. Просмотрите тип данных этой структуры данных и создайте список data_keys,\n",
+    "содержащий ее ключи."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d5f5e52a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names'])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_keys = wine.keys()\n",
+    "data_keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73bff5cf",
+   "metadata": {},
+   "source": [
+    "3). Просмотрите данные, описание и названия признаков в датасете. Описание нужно вывести в виде\n",
+    "привычного, аккуратно оформленного текста, без обозначений переноса строки, но с самими\n",
+    "переносами и т.д."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "39433029",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".. _wine_dataset:\n",
+      "\n",
+      "Wine recognition dataset\n",
+      "------------------------\n",
+      "\n",
+      "**Data Set Characteristics:**\n",
+      "\n",
+      "    :Number of Instances: 178 (50 in each of three classes)\n",
+      "    :Number of Attributes: 13 numeric, predictive attributes and the class\n",
+      "    :Attribute Information:\n",
+      " \t\t- Alcohol\n",
+      " \t\t- Malic acid\n",
+      " \t\t- Ash\n",
+      "\t\t- Alcalinity of ash  \n",
+      " \t\t- Magnesium\n",
+      "\t\t- Total phenols\n",
+      " \t\t- Flavanoids\n",
+      " \t\t- Nonflavanoid phenols\n",
+      " \t\t- Proanthocyanins\n",
+      "\t\t- Color intensity\n",
+      " \t\t- Hue\n",
+      " \t\t- OD280/OD315 of diluted wines\n",
+      " \t\t- Proline\n",
+      "\n",
+      "    - class:\n",
+      "            - class_0\n",
+      "            - class_1\n",
+      "            - class_2\n",
+      "\t\t\n",
+      "    :Summary Statistics:\n",
+      "    \n",
+      "    ============================= ==== ===== ======= =====\n",
+      "                                   Min   Max   Mean     SD\n",
+      "    ============================= ==== ===== ======= =====\n",
+      "    Alcohol:                      11.0  14.8    13.0   0.8\n",
+      "    Malic Acid:                   0.74  5.80    2.34  1.12\n",
+      "    Ash:                          1.36  3.23    2.36  0.27\n",
+      "    Alcalinity of Ash:            10.6  30.0    19.5   3.3\n",
+      "    Magnesium:                    70.0 162.0    99.7  14.3\n",
+      "    Total Phenols:                0.98  3.88    2.29  0.63\n",
+      "    Flavanoids:                   0.34  5.08    2.03  1.00\n",
+      "    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\n",
+      "    Proanthocyanins:              0.41  3.58    1.59  0.57\n",
+      "    Colour Intensity:              1.3  13.0     5.1   2.3\n",
+      "    Hue:                          0.48  1.71    0.96  0.23\n",
+      "    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\n",
+      "    Proline:                       278  1680     746   315\n",
+      "    ============================= ==== ===== ======= =====\n",
+      "\n",
+      "    :Missing Attribute Values: None\n",
+      "    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\n",
+      "    :Creator: R.A. Fisher\n",
+      "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
+      "    :Date: July, 1988\n",
+      "\n",
+      "This is a copy of UCI ML Wine recognition datasets.\n",
+      "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\n",
+      "\n",
+      "The data is the results of a chemical analysis of wines grown in the same\n",
+      "region in Italy by three different cultivators. There are thirteen different\n",
+      "measurements taken for different constituents found in the three types of\n",
+      "wine.\n",
+      "\n",
+      "Original Owners: \n",
+      "\n",
+      "Forina, M. et al, PARVUS - \n",
+      "An Extendible Package for Data Exploration, Classification and Correlation. \n",
+      "Institute of Pharmaceutical and Food Analysis and Technologies,\n",
+      "Via Brigata Salerno, 16147 Genoa, Italy.\n",
+      "\n",
+      "Citation:\n",
+      "\n",
+      "Lichman, M. (2013). UCI Machine Learning Repository\n",
+      "[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n",
+      "School of Information and Computer Science. \n",
+      "\n",
+      ".. topic:: References\n",
+      "\n",
+      "  (1) S. Aeberhard, D. Coomans and O. de Vel, \n",
+      "  Comparison of Classifiers in High Dimensional Settings, \n",
+      "  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \n",
+      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
+      "  (Also submitted to Technometrics). \n",
+      "\n",
+      "  The data was used with many others for comparing various \n",
+      "  classifiers. The classes are separable, though only RDA \n",
+      "  has achieved 100% correct classification. \n",
+      "  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \n",
+      "  (All results using the leave-one-out technique) \n",
+      "\n",
+      "  (2) S. Aeberhard, D. Coomans and O. de Vel, \n",
+      "  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \n",
+      "  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \n",
+      "  Mathematics and Statistics, James Cook University of North Queensland. \n",
+      "  (Also submitted to Journal of Chemometrics).\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(wine[\"DESCR\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "772359a0",
+   "metadata": {},
+   "source": [
+    "4). Сколько классов содержит целевая переменная датасета? Выведите названия классов."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "183b0c76",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['class_0' 'class_1' 'class_2']\n",
+      "кол-во:3\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_class = wine[\"target_names\"]\n",
+    "\n",
+    "print(f\"{target_class}\\nкол-во:{len(target_class)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41d6690b",
+   "metadata": {},
+   "source": [
+    "5). На основе данных датасета (они содержатся в двумерном массиве Numpy) и названий признаков\n",
+    "создайте датафрейм под названием X."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "15557fbe",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>14.23</td>\n",
+       "      <td>1.71</td>\n",
+       "      <td>2.43</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>2.29</td>\n",
+       "      <td>5.64</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>3.92</td>\n",
+       "      <td>1065.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13.20</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2.14</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>1.28</td>\n",
+       "      <td>4.38</td>\n",
+       "      <td>1.05</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>1050.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>13.16</td>\n",
+       "      <td>2.36</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>18.6</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>2.81</td>\n",
+       "      <td>5.68</td>\n",
+       "      <td>1.03</td>\n",
+       "      <td>3.17</td>\n",
+       "      <td>1185.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14.37</td>\n",
+       "      <td>1.95</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>3.85</td>\n",
+       "      <td>3.49</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>2.18</td>\n",
+       "      <td>7.80</td>\n",
+       "      <td>0.86</td>\n",
+       "      <td>3.45</td>\n",
+       "      <td>1480.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.24</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>2.87</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>2.69</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>1.82</td>\n",
+       "      <td>4.32</td>\n",
+       "      <td>1.04</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>735.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
+       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
+       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
+       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
+       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
+       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
+       "\n",
+       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
+       "0        3.06                  0.28             2.29             5.64  1.04   \n",
+       "1        2.76                  0.26             1.28             4.38  1.05   \n",
+       "2        3.24                  0.30             2.81             5.68  1.03   \n",
+       "3        3.49                  0.24             2.18             7.80  0.86   \n",
+       "4        2.69                  0.39             1.82             4.32  1.04   \n",
+       "\n",
+       "   od280/od315_of_diluted_wines  proline  \n",
+       "0                          3.92   1065.0  \n",
+       "1                          3.40   1050.0  \n",
+       "2                          3.17   1185.0  \n",
+       "3                          3.45   1480.0  \n",
+       "4                          2.93    735.0  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_names = wine[\"feature_names\"]\n",
+    "\n",
+    "X = pd.DataFrame(data, columns=feature_names)\n",
+    "\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9ef2d4b",
+   "metadata": {},
+   "source": [
+    "6). Выясните размер датафрейма X и установите, имеются ли в нем пропущенные значения."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9f2ee54a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 178 entries, 0 to 177\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column                        Non-Null Count  Dtype  \n",
+      "---  ------                        --------------  -----  \n",
+      " 0   alcohol                       178 non-null    float64\n",
+      " 1   malic_acid                    178 non-null    float64\n",
+      " 2   ash                           178 non-null    float64\n",
+      " 3   alcalinity_of_ash             178 non-null    float64\n",
+      " 4   magnesium                     178 non-null    float64\n",
+      " 5   total_phenols                 178 non-null    float64\n",
+      " 6   flavanoids                    178 non-null    float64\n",
+      " 7   nonflavanoid_phenols          178 non-null    float64\n",
+      " 8   proanthocyanins               178 non-null    float64\n",
+      " 9   color_intensity               178 non-null    float64\n",
+      " 10  hue                           178 non-null    float64\n",
+      " 11  od280/od315_of_diluted_wines  178 non-null    float64\n",
+      " 12  proline                       178 non-null    float64\n",
+      "dtypes: float64(13)\n",
+      "memory usage: 18.2 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "X.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "90a743fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "размер = (178, 13)\n",
+      "кол-во пустых значений\n",
+      "alcohol                         0\n",
+      "malic_acid                      0\n",
+      "ash                             0\n",
+      "alcalinity_of_ash               0\n",
+      "magnesium                       0\n",
+      "total_phenols                   0\n",
+      "flavanoids                      0\n",
+      "nonflavanoid_phenols            0\n",
+      "proanthocyanins                 0\n",
+      "color_intensity                 0\n",
+      "hue                             0\n",
+      "od280/od315_of_diluted_wines    0\n",
+      "proline                         0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"размер = {X.shape}\\nкол-во пустых значений\\n{X.isnull().sum(axis=0)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf169b47",
+   "metadata": {},
+   "source": [
+    "7). Добавьте в датафрейм поле с классами вин в виде чисел, имеющих тип данных numpy.int64.\n",
+    "Название поля - 'target'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "3883dbe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X[\"target\"] = wine[\"target\"].astype(\"int64\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5918c4e",
+   "metadata": {},
+   "source": [
+    "8). Постройте матрицу корреляций для всех полей X. Дайте полученному датафрейму название\n",
+    "X_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "d4c9b5e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_corr = X.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43fd36d4",
+   "metadata": {},
+   "source": [
+    "9). Создайте список high_corr из признаков, корреляция которых с полем target по абсолютному\n",
+    "значению превышает 0.5 (причем, само поле target не должно входить в этот список)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "a788a848",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['alcalinity_of_ash',\n",
+       " 'total_phenols',\n",
+       " 'flavanoids',\n",
+       " 'hue',\n",
+       " 'od280/od315_of_diluted_wines',\n",
+       " 'proline']"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "high_corr = [item for item in X_corr[abs(X_corr.target) > 0.5].index if item != \"target\"]\n",
+    "high_corr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beab1b7d",
+   "metadata": {},
+   "source": [
+    "10). Удалите из датафрейма X поле с целевой переменной. Для всех признаков, названия которых\n",
+    "содержатся в списке high_corr, вычислите квадрат их значений и добавьте в датафрейм X\n",
+    "соответствующие поля с суффиксом '_2', добавленного к первоначальному названию признака.\n",
+    "Итоговый датафрейм должен содержать все поля, которые, были в нем изначально, а также поля с\n",
+    "признаками из списка high_corr, возведенными в квадрат. Выведите описание полей датафрейма X с\n",
+    "помощью метода describe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "426afeb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X.drop(\"target\", axis = 1, inplace = True) \n",
+    "\n",
+    "for item in high_corr:\n",
+    "    X[item+\"_2\"] = X[item] ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "382274e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "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>alcohol</th>\n",
+       "      <th>malic_acid</th>\n",
+       "      <th>ash</th>\n",
+       "      <th>alcalinity_of_ash</th>\n",
+       "      <th>magnesium</th>\n",
+       "      <th>total_phenols</th>\n",
+       "      <th>flavanoids</th>\n",
+       "      <th>nonflavanoid_phenols</th>\n",
+       "      <th>proanthocyanins</th>\n",
+       "      <th>color_intensity</th>\n",
+       "      <th>hue</th>\n",
+       "      <th>od280/od315_of_diluted_wines</th>\n",
+       "      <th>proline</th>\n",
+       "      <th>alcalinity_of_ash_2</th>\n",
+       "      <th>total_phenols_2</th>\n",
+       "      <th>flavanoids_2</th>\n",
+       "      <th>hue_2</th>\n",
+       "      <th>od280/od315_of_diluted_wines_2</th>\n",
+       "      <th>proline_2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>178.000000</td>\n",
+       "      <td>1.780000e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>13.000618</td>\n",
+       "      <td>2.336348</td>\n",
+       "      <td>2.366517</td>\n",
+       "      <td>19.494944</td>\n",
+       "      <td>99.741573</td>\n",
+       "      <td>2.295112</td>\n",
+       "      <td>2.029270</td>\n",
+       "      <td>0.361854</td>\n",
+       "      <td>1.590899</td>\n",
+       "      <td>5.058090</td>\n",
+       "      <td>0.957449</td>\n",
+       "      <td>2.611685</td>\n",
+       "      <td>746.893258</td>\n",
+       "      <td>391.142865</td>\n",
+       "      <td>5.657030</td>\n",
+       "      <td>5.110049</td>\n",
+       "      <td>0.968661</td>\n",
+       "      <td>7.322155</td>\n",
+       "      <td>6.564591e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.811827</td>\n",
+       "      <td>1.117146</td>\n",
+       "      <td>0.274344</td>\n",
+       "      <td>3.339564</td>\n",
+       "      <td>14.282484</td>\n",
+       "      <td>0.625851</td>\n",
+       "      <td>0.998859</td>\n",
+       "      <td>0.124453</td>\n",
+       "      <td>0.572359</td>\n",
+       "      <td>2.318286</td>\n",
+       "      <td>0.228572</td>\n",
+       "      <td>0.709990</td>\n",
+       "      <td>314.907474</td>\n",
+       "      <td>133.671775</td>\n",
+       "      <td>2.936294</td>\n",
+       "      <td>4.211441</td>\n",
+       "      <td>0.443798</td>\n",
+       "      <td>3.584316</td>\n",
+       "      <td>5.558591e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>11.030000</td>\n",
+       "      <td>0.740000</td>\n",
+       "      <td>1.360000</td>\n",
+       "      <td>10.600000</td>\n",
+       "      <td>70.000000</td>\n",
+       "      <td>0.980000</td>\n",
+       "      <td>0.340000</td>\n",
+       "      <td>0.130000</td>\n",
+       "      <td>0.410000</td>\n",
+       "      <td>1.280000</td>\n",
+       "      <td>0.480000</td>\n",
+       "      <td>1.270000</td>\n",
+       "      <td>278.000000</td>\n",
+       "      <td>112.360000</td>\n",
+       "      <td>0.960400</td>\n",
+       "      <td>0.115600</td>\n",
+       "      <td>0.230400</td>\n",
+       "      <td>1.612900</td>\n",
+       "      <td>7.728400e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>12.362500</td>\n",
+       "      <td>1.602500</td>\n",
+       "      <td>2.210000</td>\n",
+       "      <td>17.200000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>1.742500</td>\n",
+       "      <td>1.205000</td>\n",
+       "      <td>0.270000</td>\n",
+       "      <td>1.250000</td>\n",
+       "      <td>3.220000</td>\n",
+       "      <td>0.782500</td>\n",
+       "      <td>1.937500</td>\n",
+       "      <td>500.500000</td>\n",
+       "      <td>295.840000</td>\n",
+       "      <td>3.036325</td>\n",
+       "      <td>1.452100</td>\n",
+       "      <td>0.612325</td>\n",
+       "      <td>3.754075</td>\n",
+       "      <td>2.505010e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>13.050000</td>\n",
+       "      <td>1.865000</td>\n",
+       "      <td>2.360000</td>\n",
+       "      <td>19.500000</td>\n",
+       "      <td>98.000000</td>\n",
+       "      <td>2.355000</td>\n",
+       "      <td>2.135000</td>\n",
+       "      <td>0.340000</td>\n",
+       "      <td>1.555000</td>\n",
+       "      <td>4.690000</td>\n",
+       "      <td>0.965000</td>\n",
+       "      <td>2.780000</td>\n",
+       "      <td>673.500000</td>\n",
+       "      <td>380.250000</td>\n",
+       "      <td>5.546050</td>\n",
+       "      <td>4.558250</td>\n",
+       "      <td>0.931250</td>\n",
+       "      <td>7.728400</td>\n",
+       "      <td>4.536045e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>13.677500</td>\n",
+       "      <td>3.082500</td>\n",
+       "      <td>2.557500</td>\n",
+       "      <td>21.500000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>2.875000</td>\n",
+       "      <td>0.437500</td>\n",
+       "      <td>1.950000</td>\n",
+       "      <td>6.200000</td>\n",
+       "      <td>1.120000</td>\n",
+       "      <td>3.170000</td>\n",
+       "      <td>985.000000</td>\n",
+       "      <td>462.250000</td>\n",
+       "      <td>7.840000</td>\n",
+       "      <td>8.265700</td>\n",
+       "      <td>1.254400</td>\n",
+       "      <td>10.048900</td>\n",
+       "      <td>9.702250e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>14.830000</td>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>3.230000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>162.000000</td>\n",
+       "      <td>3.880000</td>\n",
+       "      <td>5.080000</td>\n",
+       "      <td>0.660000</td>\n",
+       "      <td>3.580000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>1.710000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1680.000000</td>\n",
+       "      <td>900.000000</td>\n",
+       "      <td>15.054400</td>\n",
+       "      <td>25.806400</td>\n",
+       "      <td>2.924100</td>\n",
+       "      <td>16.000000</td>\n",
+       "      <td>2.822400e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
+       "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
+       "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
+       "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
+       "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
+       "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
+       "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
+       "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
+       "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
+       "\n",
+       "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
+       "count     178.000000  178.000000            178.000000       178.000000   \n",
+       "mean        2.295112    2.029270              0.361854         1.590899   \n",
+       "std         0.625851    0.998859              0.124453         0.572359   \n",
+       "min         0.980000    0.340000              0.130000         0.410000   \n",
+       "25%         1.742500    1.205000              0.270000         1.250000   \n",
+       "50%         2.355000    2.135000              0.340000         1.555000   \n",
+       "75%         2.800000    2.875000              0.437500         1.950000   \n",
+       "max         3.880000    5.080000              0.660000         3.580000   \n",
+       "\n",
+       "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \\\n",
+       "count       178.000000  178.000000                    178.000000   178.000000   \n",
+       "mean          5.058090    0.957449                      2.611685   746.893258   \n",
+       "std           2.318286    0.228572                      0.709990   314.907474   \n",
+       "min           1.280000    0.480000                      1.270000   278.000000   \n",
+       "25%           3.220000    0.782500                      1.937500   500.500000   \n",
+       "50%           4.690000    0.965000                      2.780000   673.500000   \n",
+       "75%           6.200000    1.120000                      3.170000   985.000000   \n",
+       "max          13.000000    1.710000                      4.000000  1680.000000   \n",
+       "\n",
+       "       alcalinity_of_ash_2  total_phenols_2  flavanoids_2       hue_2  \\\n",
+       "count           178.000000       178.000000    178.000000  178.000000   \n",
+       "mean            391.142865         5.657030      5.110049    0.968661   \n",
+       "std             133.671775         2.936294      4.211441    0.443798   \n",
+       "min             112.360000         0.960400      0.115600    0.230400   \n",
+       "25%             295.840000         3.036325      1.452100    0.612325   \n",
+       "50%             380.250000         5.546050      4.558250    0.931250   \n",
+       "75%             462.250000         7.840000      8.265700    1.254400   \n",
+       "max             900.000000        15.054400     25.806400    2.924100   \n",
+       "\n",
+       "       od280/od315_of_diluted_wines_2     proline_2  \n",
+       "count                      178.000000  1.780000e+02  \n",
+       "mean                         7.322155  6.564591e+05  \n",
+       "std                          3.584316  5.558591e+05  \n",
+       "min                          1.612900  7.728400e+04  \n",
+       "25%                          3.754075  2.505010e+05  \n",
+       "50%                          7.728400  4.536045e+05  \n",
+       "75%                         10.048900  9.702250e+05  \n",
+       "max                         16.000000  2.822400e+06  "
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1245b68",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}