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Date: Mon, 20 Apr 2026 23:17:52 +0300
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---
 week4_scikit_learn.ipynb | 43 ++++++++++++++++++++++++++++++++++++----
 1 file changed, 39 insertions(+), 4 deletions(-)

diff --git a/week4_scikit_learn.ipynb b/week4_scikit_learn.ipynb
index 757f8e4..d89b8d9 100644
--- a/week4_scikit_learn.ipynb
+++ b/week4_scikit_learn.ipynb
@@ -1,17 +1,52 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "88b1e95e",
+   "metadata": {},
+   "source": [
+    "# Задание 4 — Gaussian Mixture Models в scikit-learn\n",
+    "\n",
+    "## Цель задачи\n",
+    "Изучить применение модели Gaussian Mixture Model (GMM) для кластеризации данных.\n",
+    "Проверить работу алгоритма на двух типах данных:\n",
+    "1. на сгенерированном датасете;\n",
+    "2. на внешнем датасете, загруженном из CSV-файла.\n",
+    "\n",
+    "## Используемый алгоритм\n",
+    "Gaussian Mixture Model (GMM) — это вероятностная модель, которая предполагает,\n",
+    "что данные являются смесью нескольких гауссовых распределений.\n",
+    "\n",
+    "## План работы\n",
+    "1. Подготовить данные;\n",
+    "2. Выполнить предобработку;\n",
+    "3. Обучить модель GMM;\n",
+    "4. Визуализировать результаты;\n",
+    "5. Провести интерпретацию результатов.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e2f5a915-2115-48ab-84ae-052a73df6074",
+   "execution_count": 2,
+   "id": "b535e17e",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.datasets import make_blobs\n",
+    "from sklearn.mixture import GaussianMixture\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.metrics import silhouette_score"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": ".venv",
    "language": "python",
    "name": "python3"
   },