diff --git a/.ipynb_checkpoints/week4_scikit_learn.ipynb-checkpoint.ipynb b/.ipynb_checkpoints/week4_scikit_learn.ipynb-checkpoint.ipynb
new file mode 100644
index 0000000..f5de917
--- /dev/null
+++ b/.ipynb_checkpoints/week4_scikit_learn.ipynb-checkpoint.ipynb
@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6af36011-9a8c-4dc1-85c5-910263c2d25e",
+   "metadata": {},
+   "source": [
+    "Базовая нейросеть"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2115d6e8-d6d0-4025-9ee0-32c46b20fe45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        10\n",
+      "           1       1.00      1.00      1.00         9\n",
+      "           2       1.00      1.00      1.00        11\n",
+      "\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "# Загрузка и разбиение данных\n",
+    "X, y = load_iris(return_X_y=True)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Нормализация данных\n",
+    "scaler = StandardScaler()\n",
+    "X_train = scaler.fit_transform(X_train)\n",
+    "X_test = scaler.transform(X_test)\n",
+    "\n",
+    "# Модель MLP — многослойный перцептрон\n",
+    "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500, learning_rate_init=0.001, random_state=42)\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "# Отчёт о точности\n",
+    "print(classification_report(y_test, clf.predict(X_test)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "125b3f46-cc81-4341-94f3-9de6dee8aff5",
+   "metadata": {},
+   "source": [
+    "Модель работает очень хорошо и достигла 100% точности на тестовых данных, что является отличным результатом для этого набора данных."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d032afd-fc98-48cd-9ec8-7a742fcf8a50",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62c7892d-b296-4f0c-8886-514b4ee2bad6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/week4_scikit_learn.ipynb.ipynb b/week4_scikit_learn.ipynb.ipynb
new file mode 100644
index 0000000..f5de917
--- /dev/null
+++ b/week4_scikit_learn.ipynb.ipynb
@@ -0,0 +1,104 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6af36011-9a8c-4dc1-85c5-910263c2d25e",
+   "metadata": {},
+   "source": [
+    "Базовая нейросеть"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2115d6e8-d6d0-4025-9ee0-32c46b20fe45",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        10\n",
+      "           1       1.00      1.00      1.00         9\n",
+      "           2       1.00      1.00      1.00        11\n",
+      "\n",
+      "    accuracy                           1.00        30\n",
+      "   macro avg       1.00      1.00      1.00        30\n",
+      "weighted avg       1.00      1.00      1.00        30\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.neural_network import MLPClassifier\n",
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "# Загрузка и разбиение данных\n",
+    "X, y = load_iris(return_X_y=True)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Нормализация данных\n",
+    "scaler = StandardScaler()\n",
+    "X_train = scaler.fit_transform(X_train)\n",
+    "X_test = scaler.transform(X_test)\n",
+    "\n",
+    "# Модель MLP — многослойный перцептрон\n",
+    "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500, learning_rate_init=0.001, random_state=42)\n",
+    "clf.fit(X_train, y_train)\n",
+    "\n",
+    "# Отчёт о точности\n",
+    "print(classification_report(y_test, clf.predict(X_test)))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "125b3f46-cc81-4341-94f3-9de6dee8aff5",
+   "metadata": {},
+   "source": [
+    "Модель работает очень хорошо и достигла 100% точности на тестовых данных, что является отличным результатом для этого набора данных."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d032afd-fc98-48cd-9ec8-7a742fcf8a50",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62c7892d-b296-4f0c-8886-514b4ee2bad6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}