diff --git a/project/task_111.ipynb b/project/task_111.ipynb
new file mode 100644
index 00000000..2931317d
--- /dev/null
+++ b/project/task_111.ipynb
@@ -0,0 +1,561 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "cbdba796-14a2-4ab1-9e59-181f84e88b26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "data = pd.read_csv('winequality-white.csv', sep=';')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7aa78112-a530-43f6-a82a-1d071f062fa7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fixed acidity           0\n",
+      "volatile acidity        0\n",
+      "citric acid             0\n",
+      "residual sugar          0\n",
+      "chlorides               0\n",
+      "free sulfur dioxide     0\n",
+      "total sulfur dioxide    0\n",
+      "density                 0\n",
+      "pH                      0\n",
+      "sulphates               0\n",
+      "alcohol                 0\n",
+      "quality                 0\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "missing_values = data.isnull().sum()\n",
+    "print(missing_values)\n",
+    "\n",
+    "# 3. Разведочный анализ (EDA)\n",
+    "corr_matrix = data.corr()\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
+    "plt.title('Кореляционная матрица')\n",
+    "plt.show()\n",
+    "\n",
+    "# 4. Полезные преобразования данных (Feature Engineering)\n",
+    "threshold = 0.85\n",
+    "high_corr_features = [col for col in corr_matrix.columns if any(abs(corr_matrix[col]) > threshold) and col != 'quality']\n",
+    "features_to_keep = ['alcohol', 'density', 'volatile acidity']\n",
+    "features_to_drop = [col for col in high_corr_features if col not in features_to_keep]\n",
+    "data.drop(columns=features_to_drop, inplace=True)\n",
+    "\n",
+    "# 5. Подготовка данных для моделирования\n",
+    "X = data.drop(columns=['quality'])\n",
+    "y = data['quality']\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# 6. Масштабирование данных\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d970b72b-6732-4bd5-bb9a-9917880b7595",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 7. Построение моделей регрессии\n",
+    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "\n",
+    "# Линейная регрессия\n",
+    "lr = LinearRegression()\n",
+    "lr.fit(X_train_scaled, y_train)\n",
+    "y_pred_lr = lr.predict(X_test_scaled)\n",
+    "\n",
+    "# Полиномиальная регрессия\n",
+    "poly_model = make_pipeline(PolynomialFeatures(degree=2), LinearRegression())\n",
+    "poly_model.fit(X_train_scaled, y_train)\n",
+    "y_pred_poly = poly_model.predict(X_test_scaled)\n",
+    "\n",
+    "# Ridge регрессия\n",
+    "ridge_params = {'alpha': [0.1, 1.0, 10.0]}\n",
+    "ridge = Ridge()\n",
+    "ridge_grid = GridSearchCV(ridge, ridge_params, cv=5)\n",
+    "ridge_grid.fit(X_train_scaled, y_train)\n",
+    "y_pred_ridge = ridge_grid.predict(X_test_scaled)\n",
+    "\n",
+    "# Lasso регрессия\n",
+    "lasso_params = {'alpha': [0.01, 0.1, 1.0]}\n",
+    "lasso = Lasso()\n",
+    "lasso_grid = GridSearchCV(lasso, lasso_params, cv=5)\n",
+    "lasso_grid.fit(X_train_scaled, y_train)\n",
+    "y_pred_lasso = lasso_grid.predict(X_test_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "c472b46e-fdd8-4c6a-ae62-b09adb126000",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Linear Regression Performance:\n",
+      "Mean Squared Error (MSE): 0.59\n",
+      "Mean Absolute Error (MAE): 0.60\n",
+      "R^2 Score: 0.24\n",
+      "\n",
+      "Polynomial Regression Performance:\n",
+      "Mean Squared Error (MSE): 0.57\n",
+      "Mean Absolute Error (MAE): 0.59\n",
+      "R^2 Score: 0.26\n",
+      "\n",
+      "Ridge Regression Performance:\n",
+      "Mean Squared Error (MSE): 0.59\n",
+      "Mean Absolute Error (MAE): 0.60\n",
+      "R^2 Score: 0.24\n",
+      "\n",
+      "Lasso Regression Performance:\n",
+      "Mean Squared Error (MSE): 0.59\n",
+      "Mean Absolute Error (MAE): 0.60\n",
+      "R^2 Score: 0.24\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_model_performance(model_name, y_test, y_pred):\n",
+    "    mse = mean_squared_error(y_test, y_pred)\n",
+    "    mae = mean_absolute_error(y_test, y_pred)\n",
+    "    r2 = r2_score(y_test, y_pred)\n",
+    "    print(f\"\\n{model_name} Performance:\")\n",
+    "    print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
+    "    print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
+    "    print(f\"R^2 Score: {r2:.2f}\")\n",
+    "\n",
+    "print_model_performance('Linear Regression', y_test, y_pred_lr)\n",
+    "print_model_performance('Polynomial Regression', y_test, y_pred_poly)\n",
+    "print_model_performance('Ridge Regression', y_test, y_pred_ridge)\n",
+    "print_model_performance('Lasso Regression', y_test, y_pred_lasso)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1e597e06-639a-4deb-9278-2a8c27f5d5ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CustomLinearRegression:\n",
+    "    def __init__(self, learning_rate=0.01, n_iterations=1000):\n",
+    "        self.learning_rate = learning_rate\n",
+    "        self.n_iterations = n_iterations\n",
+    "        self.weights = None\n",
+    "        self.bias = None\n",
+    "\n",
+    "    def fit(self, X, y):\n",
+    "        n_samples, n_features = X.shape\n",
+    "        self.weights = np.zeros(n_features)\n",
+    "        self.bias = 0\n",
+    "        for _ in range(self.n_iterations):\n",
+    "            y_pred = np.dot(X, self.weights) + self.bias\n",
+    "            dw = (1 / n_samples) * np.dot(X.T, (y_pred - y))\n",
+    "            db = (1 / n_samples) * np.sum(y_pred - y)\n",
+    "            self.weights -= self.learning_rate * dw\n",
+    "            self.bias -= self.learning_rate * db\n",
+    "\n",
+    "    def predict(self, X):\n",
+    "        return np.dot(X, self.weights) + self.bias\n",
+    "\n",
+    "custom_lr = CustomLinearRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "22c052b9-8081-4000-b618-c661b6d355f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "\n",
+       "#h2o-table-11.h2o-container {\n",
+       "  overflow-x: auto;\n",
+       "}\n",
+       "#h2o-table-11 .h2o-table {\n",
+       "  /* width: 100%; */\n",
+       "  margin-top: 1em;\n",
+       "  margin-bottom: 1em;\n",
+       "}\n",
+       "#h2o-table-11 .h2o-table caption {\n",
+       "  white-space: nowrap;\n",
+       "  caption-side: top;\n",
+       "  text-align: left;\n",
+       "  /* margin-left: 1em; */\n",
+       "  margin: 0;\n",
+       "  font-size: larger;\n",
+       "}\n",
+       "#h2o-table-11 .h2o-table thead {\n",
+       "  white-space: nowrap; \n",
+       "  position: sticky;\n",
+       "  top: 0;\n",
+       "  box-shadow: 0 -1px inset;\n",
+       "}\n",
+       "#h2o-table-11 .h2o-table tbody {\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "#h2o-table-11 .h2o-table th,\n",
+       "#h2o-table-11 .h2o-table td {\n",
+       "  text-align: right;\n",
+       "  /* border: 1px solid; */\n",
+       "}\n",
+       "#h2o-table-11 .h2o-table tr:nth-child(even) {\n",
+       "  /* background: #F5F5F5 */\n",
+       "}\n",
+       "\n",
+       "</style>      \n",
+       "<div id=\"h2o-table-11\" class=\"h2o-container\">\n",
+       "  <table class=\"h2o-table\">\n",
+       "    <caption></caption>\n",
+       "    <thead></thead>\n",
+       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
+       "<td>30 mins 09 secs</td></tr>\n",
+       "<tr><td>H2O_cluster_timezone:</td>\n",
+       "<td>Europe/Moscow</td></tr>\n",
+       "<tr><td>H2O_data_parsing_timezone:</td>\n",
+       "<td>UTC</td></tr>\n",
+       "<tr><td>H2O_cluster_version:</td>\n",
+       "<td>3.46.0.6</td></tr>\n",
+       "<tr><td>H2O_cluster_version_age:</td>\n",
+       "<td>1 month and 17 days</td></tr>\n",
+       "<tr><td>H2O_cluster_name:</td>\n",
+       "<td>H2O_from_python_sofiasvorob_9c05q8</td></tr>\n",
+       "<tr><td>H2O_cluster_total_nodes:</td>\n",
+       "<td>1</td></tr>\n",
+       "<tr><td>H2O_cluster_free_memory:</td>\n",
+       "<td>1.906 Gb</td></tr>\n",
+       "<tr><td>H2O_cluster_total_cores:</td>\n",
+       "<td>8</td></tr>\n",
+       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
+       "<td>8</td></tr>\n",
+       "<tr><td>H2O_cluster_status:</td>\n",
+       "<td>locked, healthy</td></tr>\n",
+       "<tr><td>H2O_connection_url:</td>\n",
+       "<td>http://localhost:54321</td></tr>\n",
+       "<tr><td>H2O_connection_proxy:</td>\n",
+       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
+       "<tr><td>H2O_internal_security:</td>\n",
+       "<td>False</td></tr>\n",
+       "<tr><td>Python_version:</td>\n",
+       "<td>3.11.6 final</td></tr></tbody>\n",
+       "  </table>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "--------------------------  ----------------------------------\n",
+       "H2O_cluster_uptime:         30 mins 09 secs\n",
+       "H2O_cluster_timezone:       Europe/Moscow\n",
+       "H2O_data_parsing_timezone:  UTC\n",
+       "H2O_cluster_version:        3.46.0.6\n",
+       "H2O_cluster_version_age:    1 month and 17 days\n",
+       "H2O_cluster_name:           H2O_from_python_sofiasvorob_9c05q8\n",
+       "H2O_cluster_total_nodes:    1\n",
+       "H2O_cluster_free_memory:    1.906 Gb\n",
+       "H2O_cluster_total_cores:    8\n",
+       "H2O_cluster_allowed_cores:  8\n",
+       "H2O_cluster_status:         locked, healthy\n",
+       "H2O_connection_url:         http://localhost:54321\n",
+       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
+       "H2O_internal_security:      False\n",
+       "Python_version:             3.11.6 final\n",
+       "--------------------------  ----------------------------------"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+      "glm Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n",
+      "glm Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n",
+      "glm Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n",
+      "glm Model Build progress: |██████████████████████████████████████████████████████| (done) 100%\n",
+      "glm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
+      "Linear Regression Performance:\n",
+      "Mean Squared Error (MSE): 0.5910\n",
+      "Mean Absolute Error (MAE): 0.6002\n",
+      "R^2 Score: 0.2114\n",
+      "\n",
+      "glm prediction progress: |"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sofiasvorob/.pyenv/versions/3.11.6/lib/python3.11/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
+      "\n",
+      "  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n",
+      "/Users/sofiasvorob/.pyenv/versions/3.11.6/lib/python3.11/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
+      "\n",
+      "  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "███████████████████████████████████████████████████████| (done) 100%\n",
+      "Polynomial Regression Performance:\n",
+      "Mean Squared Error (MSE): 9.2665\n",
+      "Mean Absolute Error (MAE): 2.5288\n",
+      "R^2 Score: -11.3653\n",
+      "\n",
+      "glm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
+      "Ridge Regression Performance:\n",
+      "Mean Squared Error (MSE): 0.7502\n",
+      "Mean Absolute Error (MAE): 0.6529\n",
+      "R^2 Score: -0.0010\n",
+      "\n",
+      "glm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
+      "Lasso Regression Performance:\n",
+      "Mean Squared Error (MSE): 0.7502\n",
+      "Mean Absolute Error (MAE): 0.6529\n",
+      "R^2 Score: -0.0010\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/sofiasvorob/.pyenv/versions/3.11.6/lib/python3.11/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
+      "\n",
+      "  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n",
+      "/Users/sofiasvorob/.pyenv/versions/3.11.6/lib/python3.11/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
+      "\n",
+      "  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n",
+      "/Users/sofiasvorob/.pyenv/versions/3.11.6/lib/python3.11/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
+      "\n",
+      "  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n",
+      "/Users/sofiasvorob/.pyenv/versions/3.11.6/lib/python3.11/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
+      "\n",
+      "  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n",
+      "/Users/sofiasvorob/.pyenv/versions/3.11.6/lib/python3.11/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
+      "\n",
+      "  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n",
+      "/Users/sofiasvorob/.pyenv/versions/3.11.6/lib/python3.11/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n",
+      "\n",
+      "  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "import h2o\n",
+    "from h2o.automl import H2OAutoML\n",
+    "from h2o.estimators.glm import H2OGeneralizedLinearEstimator\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "\n",
+    "h2o.init()\n",
+    "\n",
+    "h2o_data = h2o.H2OFrame(data)\n",
+    "\n",
+    "train, test = h2o_data.split_frame(ratios=[0.8], seed=42)\n",
+    "\n",
+    "y = 'quality'\n",
+    "x = list(data.columns)\n",
+    "x.remove('quality')\n",
+    "\n",
+    "models = {}\n",
+    "\n",
+    "linear_model = H2OGeneralizedLinearEstimator(family=\"gaussian\", lambda_=0.0)  #линейная регрессия\n",
+    "linear_model.train(x=x, y=y, training_frame=train)\n",
+    "models['Linear Regression'] = linear_model\n",
+    "\n",
+    "for feature in x:\n",
+    "    valid_feature_name = feature.replace(' ', '_').replace('-', '_') \n",
+    "    poly_feature = train[feature] ** 2 \n",
+    "    poly_feature.set_names([f\"{valid_feature_name}_2\"]) \n",
+    "    train = train.cbind(poly_feature)  \n",
+    "\n",
+    "x_poly = x + [f\"{feature.replace(' ', '_').replace('-', '_')}_2\" for feature in x]  # Список признаков теперь включает квадраты\n",
+    "\n",
+    "#полиномиальная регрессия\n",
+    "poly_model = H2OGeneralizedLinearEstimator(family=\"gaussian\", lambda_=0.0)\n",
+    "poly_model.train(x=x_poly, y=y, training_frame=train)\n",
+    "models['Polynomial Regression'] = poly_model\n",
+    "\n",
+    "#3.Ridge Regression (L2-регуляризация)\n",
+    "ridge_model = H2OGeneralizedLinearEstimator(family=\"gaussian\", lambda_=1.0)  # L2-регуляризация\n",
+    "ridge_model.train(x=x, y=y, training_frame=train)\n",
+    "models['Ridge Regression'] = ridge_model\n",
+    "\n",
+    "#4.Lasso Regression (L1-регуляризация)\n",
+    "lasso_model = H2OGeneralizedLinearEstimator(family=\"gaussian\", lambda_=1.0, alpha=1.0)  # L1-регуляризация\n",
+    "lasso_model.train(x=x, y=y, training_frame=train)\n",
+    "models['Lasso Regression'] = lasso_model\n",
+    "\n",
+    "for model_name, model in models.items():\n",
+    "    preds_h2o = model.predict(test)\n",
+    "    h2o_y_test = test[y].as_data_frame().values.flatten()\n",
+    "    h2o_y_pred = preds_h2o.as_data_frame().values.flatten()\n",
+    "    \n",
+    "    mse = mean_squared_error(h2o_y_test, h2o_y_pred)\n",
+    "    mae = mean_absolute_error(h2o_y_test, h2o_y_pred)\n",
+    "    r2 = r2_score(h2o_y_test, h2o_y_pred)\n",
+    "  \n",
+    "    print(f\"{model_name} Performance:\")\n",
+    "    print(f\"Mean Squared Error (MSE): {mse:.4f}\")\n",
+    "    print(f\"Mean Absolute Error (MAE): {mae:.4f}\")\n",
+    "    print(f\"R^2 Score: {r2:.4f}\\n\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "b6caeb95-b4ea-465e-bc9e-71c10fb99006",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'autosklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[36], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mautosklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mregression\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodel_selection\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m train_test_split\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mean_squared_error, mean_absolute_error, r2_score\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'autosklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "import autosklearn.regression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "X = data.drop('quality', axis=1)\n",
+    "y = data['quality']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "models = {}\n",
+    "\n",
+    "\n",
+    "linear_model = autosklearn.regression.AutoSklearnRegressor(time_left_for_this_task=300, per_run_time_limit=60)\n",
+    "linear_model.fit(X_train, y_train)\n",
+    "models['Linear Regression'] = linear_model\n",
+    "\n",
+    "poly = PolynomialFeatures(degree=2, include_bias=False)\n",
+    "X_train_poly = poly.fit_transform(X_train)\n",
+    "X_test_poly = poly.transform(X_test)\n",
+    "\n",
+    "poly_model = LinearRegression()\n",
+    "poly_model.fit(X_train_poly, y_train)\n",
+    "models['Polynomial Regression'] = poly_model\n",
+    "\n",
+    "ridge_model = autosklearn.regression.AutoSklearnRegressor(time_left_for_this_task=300, per_run_time_limit=60)\n",
+    "ridge_model.fit(X_train, y_train)\n",
+    "models['Ridge Regression'] = ridge_model\n",
+    "\n",
+    "lasso_model = autosklearn.regression.AutoSklearnRegressor(time_left_for_this_task=300, per_run_time_limit=60)\n",
+    "lasso_model.fit(X_train, y_train)\n",
+    "models['Lasso Regression'] = lasso_model\n",
+    "\n",
+    "for model_name, model in models.items():\n",
+    "\n",
+    "    if model_name == 'Polynomial Regression':\n",
+    "        predictions = model.predict(X_test_poly)\n",
+    "    else:\n",
+    "        predictions = model.predict(X_test)\n",
+    "    \n",
+    "    mse = mean_squared_error(y_test, predictions)\n",
+    "    mae = mean_absolute_error(y_test, predictions)\n",
+    "    r2 = r2_score(y_test, predictions)\n",
+    "\n",
+    "    print(f\"{model_name} Performance:\")\n",
+    "    print(f\"Mean Squared Error (MSE): {mse:.4f}\")\n",
+    "    print(f\"Mean Absolute Error (MAE): {mae:.4f}\")\n",
+    "    print(f\"R^2 Score: {r2:.4f}\\n\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a175ea87-ee70-48b9-b6aa-99becf110577",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.6",
+   "language": "python",
+   "name": "python3.11.6"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}