4zadaniepraktiki/week4_scikit_learn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e405e8d1",
   "metadata": {},
   "source": [
    "# Задание 4 — Clustering в scikit-learn\n",
    "\n",
    "**Автор: Шавлович Маргарита Михайловна**  \n",
    "**Группа: ИНБб-2301**  \n",
    "\n",
    "\n",
    "## Цель задачи\n",
    "Изучить применение алгоритмов кластеризации из библиотеки scikit-learn.\n",
    "Проверить работу метода KMeans на двух типах данных:\n",
    "1. на сгенерированном датасете;\n",
    "2. на внешнем датасете, загруженном из CSV-файла.\n",
    "\n",
    "## Используемый алгоритм\n",
    "KMeans — это алгоритм кластеризации без учителя, который делит объекты на группы\n",
    "по признаку близости к центрам кластеров.\n",
    "\n",
    "## План работы\n",
    "1. Подготовить данные;\n",
    "2. Выполнить предобработку;\n",
    "3. Обучить модель кластеризации;\n",
    "4. Визуализировать результаты;\n",
    "5. Провести интерпретацию результатов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99dd87e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caa9b8ee",
   "metadata": {},
   "source": [
    "## Часть 1. Кластеризация сгенерированного датасета\n",
    "\n",
    "Сначала проверим работу алгоритма KMeans на искусственно созданных данных.\n",
    "Для этого используем функцию `make_blobs`, которая генерирует несколько групп точек."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51815754",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размерность X: (400, 2)\n"
     ]
    }
   ],
   "source": [
    "X, y_true = make_blobs(\n",
    "    n_samples=400,\n",
    "    centers=3,\n",
    "    cluster_std=[1.0, 2.5, 0.8],\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "print(\"Размерность X:\", X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14b42e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X[:, 0], X[:, 1], s=30)\n",
    "plt.title(\"Сгенерированный датасет\")\n",
    "plt.xlabel(\"Признак 1\")\n",
    "plt.ylabel(\"Признак 2\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1fabfa4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первые 5 строк после масштабирования:\n",
      "[[-1.24796648 -1.02418907]\n",
      " [-0.69906013  1.2704844 ]\n",
      " [-1.08423598 -1.26725858]\n",
      " [ 1.71543511  0.0370654 ]\n",
      " [-0.06794571  1.31748889]]\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "print(\"Первые 5 строк после масштабирования:\")\n",
    "print(X_scaled[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9a55edd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первые 10 предсказанных кластеров:\n",
      "[1 0 1 2 0 0 0 1 0 2]\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)\n",
    "clusters = kmeans.fit_predict(X_scaled)\n",
    "\n",
    "print(\"Первые 10 предсказанных кластеров:\")\n",
    "print(clusters[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "920e5faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=clusters, cmap=\"viridis\", s=30)\n",
    "plt.title(\"Кластеры, найденные методом KMeans\")\n",
    "plt.xlabel(\"Признак 1 (scaled)\")\n",
    "plt.ylabel(\"Признак 2 (scaled)\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea993c91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score для сгенерированного датасета: 0.77\n"
     ]
    }
   ],
   "source": [
    "score = silhouette_score(X_scaled, clusters)\n",
    "print(\"Silhouette Score для сгенерированного датасета:\", round(score, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31f1767f",
   "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>Index</th>\n",
       "      <th>Organization Id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Website</th>\n",
       "      <th>Country</th>\n",
       "      <th>Description</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Number of employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>FAB0d41d5b5d22c</td>\n",
       "      <td>Ferrell LLC</td>\n",
       "      <td>https://price.net/</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>Horizontal empowering knowledgebase</td>\n",
       "      <td>1990</td>\n",
       "      <td>Plastics</td>\n",
       "      <td>3498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6A7EdDEA9FaDC52</td>\n",
       "      <td>Mckinney, Riley and Day</td>\n",
       "      <td>http://www.hall-buchanan.info/</td>\n",
       "      <td>Finland</td>\n",
       "      <td>User-centric system-worthy leverage</td>\n",
       "      <td>2015</td>\n",
       "      <td>Glass / Ceramics / Concrete</td>\n",
       "      <td>4952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0bFED1ADAE4bcC1</td>\n",
       "      <td>Hester Ltd</td>\n",
       "      <td>http://sullivan-reed.com/</td>\n",
       "      <td>China</td>\n",
       "      <td>Switchable scalable moratorium</td>\n",
       "      <td>1971</td>\n",
       "      <td>Public Safety</td>\n",
       "      <td>5287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2bFC1Be8a4ce42f</td>\n",
       "      <td>Holder-Sellers</td>\n",
       "      <td>https://becker.com/</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>De-engineered systemic artificial intelligence</td>\n",
       "      <td>2004</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9eE8A6a4Eb96C24</td>\n",
       "      <td>Mayer Group</td>\n",
       "      <td>http://www.brewer.com/</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>Synchronized needs-based challenge</td>\n",
       "      <td>1991</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>7870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index  Organization Id                     Name  \\\n",
       "0      1  FAB0d41d5b5d22c              Ferrell LLC   \n",
       "1      2  6A7EdDEA9FaDC52  Mckinney, Riley and Day   \n",
       "2      3  0bFED1ADAE4bcC1               Hester Ltd   \n",
       "3      4  2bFC1Be8a4ce42f           Holder-Sellers   \n",
       "4      5  9eE8A6a4Eb96C24              Mayer Group   \n",
       "\n",
       "                          Website           Country  \\\n",
       "0              https://price.net/  Papua New Guinea   \n",
       "1  http://www.hall-buchanan.info/           Finland   \n",
       "2       http://sullivan-reed.com/             China   \n",
       "3             https://becker.com/      Turkmenistan   \n",
       "4          http://www.brewer.com/         Mauritius   \n",
       "\n",
       "                                      Description  Founded  \\\n",
       "0             Horizontal empowering knowledgebase     1990   \n",
       "1             User-centric system-worthy leverage     2015   \n",
       "2                  Switchable scalable moratorium     1971   \n",
       "3  De-engineered systemic artificial intelligence     2004   \n",
       "4              Synchronized needs-based challenge     1991   \n",
       "\n",
       "                      Industry  Number of employees  \n",
       "0                     Plastics                 3498  \n",
       "1  Glass / Ceramics / Concrete                 4952  \n",
       "2                Public Safety                 5287  \n",
       "3                   Automotive                  921  \n",
       "4               Transportation                 7870  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"organizations-100.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6646e35d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер таблицы: (100, 9)\n",
      "\n",
      "Информация о данных:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   Index                100 non-null    int64 \n",
      " 1   Organization Id      100 non-null    object\n",
      " 2   Name                 100 non-null    object\n",
      " 3   Website              100 non-null    object\n",
      " 4   Country              100 non-null    object\n",
      " 5   Description          100 non-null    object\n",
      " 6   Founded              100 non-null    int64 \n",
      " 7   Industry             100 non-null    object\n",
      " 8   Number of employees  100 non-null    int64 \n",
      "dtypes: int64(3), object(6)\n",
      "memory usage: 7.2+ KB\n",
      "None\n",
      "\n",
      "Количество пропусков:\n",
      "Index                  0\n",
      "Organization Id        0\n",
      "Name                   0\n",
      "Website                0\n",
      "Country                0\n",
      "Description            0\n",
      "Founded                0\n",
      "Industry               0\n",
      "Number of employees    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Размер таблицы:\", df.shape)\n",
    "print(\"\\nИнформация о данных:\")\n",
    "print(df.info())\n",
    "\n",
    "print(\"\\nКоличество пропусков:\")\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4d73fb9e",
   "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>Country</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Founded</th>\n",
       "      <th>Number of employees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>Plastics</td>\n",
       "      <td>1990</td>\n",
       "      <td>3498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Finland</td>\n",
       "      <td>Glass / Ceramics / Concrete</td>\n",
       "      <td>2015</td>\n",
       "      <td>4952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>China</td>\n",
       "      <td>Public Safety</td>\n",
       "      <td>1971</td>\n",
       "      <td>5287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>2004</td>\n",
       "      <td>921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mauritius</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>1991</td>\n",
       "      <td>7870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Country                     Industry  Founded  Number of employees\n",
       "0  Papua New Guinea                     Plastics     1990                 3498\n",
       "1           Finland  Glass / Ceramics / Concrete     2015                 4952\n",
       "2             China                Public Safety     1971                 5287\n",
       "3      Turkmenistan                   Automotive     2004                  921\n",
       "4         Mauritius               Transportation     1991                 7870"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[[\"Country\", \"Industry\", \"Founded\", \"Number of employees\"]].copy()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "850b7a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Founded</th>\n",
       "      <th>Number of employees</th>\n",
       "      <th>Country_Australia</th>\n",
       "      <th>Country_Bahamas</th>\n",
       "      <th>Country_Belarus</th>\n",
       "      <th>Country_Belgium</th>\n",
       "      <th>Country_Benin</th>\n",
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       "      <th>Country_Botswana</th>\n",
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       "      <th>...</th>\n",
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       "      <th>Industry_Venture Capital / VC</th>\n",
       "      <th>Industry_Wholesale</th>\n",
       "      <th>Industry_Wireless</th>\n",
       "      <th>Industry_Writing / Editing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990</td>\n",
       "      <td>3498</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>2015</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1971</td>\n",
       "      <td>5287</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>921</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1991</td>\n",
       "      <td>7870</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 156 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Founded  Number of employees  Country_Australia  Country_Bahamas  \\\n",
       "0     1990                 3498              False            False   \n",
       "1     2015                 4952              False            False   \n",
       "2     1971                 5287              False            False   \n",
       "3     2004                  921              False            False   \n",
       "4     1991                 7870              False            False   \n",
       "\n",
       "   Country_Belarus  Country_Belgium  Country_Benin  Country_Bolivia  \\\n",
       "0            False            False          False            False   \n",
       "1            False            False          False            False   \n",
       "2            False            False          False            False   \n",
       "3            False            False          False            False   \n",
       "4            False            False          False            False   \n",
       "\n",
       "   Country_Botswana  Country_Bouvet Island (Bouvetoya)  ...  \\\n",
       "0             False                              False  ...   \n",
       "1             False                              False  ...   \n",
       "2             False                              False  ...   \n",
       "3             False                              False  ...   \n",
       "4             False                              False  ...   \n",
       "\n",
       "   Industry_Religious Institutions  Industry_Semiconductors  \\\n",
       "0                            False                    False   \n",
       "1                            False                    False   \n",
       "2                            False                    False   \n",
       "3                            False                    False   \n",
       "4                            False                    False   \n",
       "\n",
       "   Industry_Telecommunications  Industry_Textiles  Industry_Transportation  \\\n",
       "0                        False              False                    False   \n",
       "1                        False              False                    False   \n",
       "2                        False              False                    False   \n",
       "3                        False              False                    False   \n",
       "4                        False              False                     True   \n",
       "\n",
       "   Industry_Utilities  Industry_Venture Capital / VC  Industry_Wholesale  \\\n",
       "0               False                          False               False   \n",
       "1               False                          False               False   \n",
       "2               False                          False               False   \n",
       "3               False                          False               False   \n",
       "4               False                          False               False   \n",
       "\n",
       "   Industry_Wireless  Industry_Writing / Editing  \n",
       "0              False                       False  \n",
       "1              False                       False  \n",
       "2              False                       False  \n",
       "3              False                       False  \n",
       "4              False                       False  \n",
       "\n",
       "[5 rows x 156 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_encoded = pd.get_dummies(data, columns=[\"Country\", \"Industry\"], drop_first=True)\n",
    "data_encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53f0d884",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_real = StandardScaler()\n",
    "X_real_scaled = scaler_real.fit_transform(data_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6d14be66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>Industry</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ferrell LLC</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>Plastics</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mckinney, Riley and Day</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Glass / Ceramics / Concrete</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hester Ltd</td>\n",
       "      <td>China</td>\n",
       "      <td>Public Safety</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Holder-Sellers</td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>Automotive</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mayer Group</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>Transportation</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Henry-Thompson</td>\n",
       "      <td>Bahamas</td>\n",
       "      <td>Primary / Secondary Education</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hansen-Everett</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Publishing Industry</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mcintosh-Mora</td>\n",
       "      <td>Heard Island and McDonald Islands</td>\n",
       "      <td>Import / Export</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Carr Inc</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>Plastics</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Gaines Inc</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>Outsourcing / Offshoring</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Kidd Group</td>\n",
       "      <td>Bouvet Island (Bouvetoya)</td>\n",
       "      <td>Primary / Secondary Education</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crane-Clarke</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Food / Beverages</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Keller, Campos and Black</td>\n",
       "      <td>Liberia</td>\n",
       "      <td>Museums / Institutions</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Glover-Pope</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>Medical Practice</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pacheco-Spears</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Maritime</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Hodge-Ayers</td>\n",
       "      <td>Honduras</td>\n",
       "      <td>Facilities Services</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Bowers, Guerra and Krause</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>Primary / Secondary Education</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mckenzie-Melton</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>Investment Management / Hedge Fund / Private E...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Branch-Mann</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>Architecture / Planning</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Weiss and Sons</td>\n",
       "      <td>Korea</td>\n",
       "      <td>Plastics</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Name                            Country  \\\n",
       "0                 Ferrell LLC                   Papua New Guinea   \n",
       "1     Mckinney, Riley and Day                            Finland   \n",
       "2                  Hester Ltd                              China   \n",
       "3              Holder-Sellers                       Turkmenistan   \n",
       "4                 Mayer Group                          Mauritius   \n",
       "5              Henry-Thompson                            Bahamas   \n",
       "6              Hansen-Everett                           Pakistan   \n",
       "7               Mcintosh-Mora  Heard Island and McDonald Islands   \n",
       "8                    Carr Inc                             Kuwait   \n",
       "9                  Gaines Inc                         Uzbekistan   \n",
       "10                 Kidd Group          Bouvet Island (Bouvetoya)   \n",
       "11               Crane-Clarke                            Denmark   \n",
       "12   Keller, Campos and Black                            Liberia   \n",
       "13                Glover-Pope               United Arab Emirates   \n",
       "14             Pacheco-Spears                             Sweden   \n",
       "15                Hodge-Ayers                           Honduras   \n",
       "16  Bowers, Guerra and Krause                             Uganda   \n",
       "17            Mckenzie-Melton                          Hong Kong   \n",
       "18                Branch-Mann                           Botswana   \n",
       "19             Weiss and Sons                              Korea   \n",
       "\n",
       "                                             Industry  cluster  \n",
       "0                                            Plastics        0  \n",
       "1                         Glass / Ceramics / Concrete        1  \n",
       "2                                       Public Safety        2  \n",
       "3                                          Automotive        0  \n",
       "4                                      Transportation        2  \n",
       "5                       Primary / Secondary Education        2  \n",
       "6                                 Publishing Industry        2  \n",
       "7                                     Import / Export        2  \n",
       "8                                            Plastics        2  \n",
       "9                            Outsourcing / Offshoring        2  \n",
       "10                      Primary / Secondary Education        2  \n",
       "11                                   Food / Beverages        2  \n",
       "12                             Museums / Institutions        1  \n",
       "13                                   Medical Practice        2  \n",
       "14                                           Maritime        0  \n",
       "15                                Facilities Services        2  \n",
       "16                      Primary / Secondary Education        2  \n",
       "17  Investment Management / Hedge Fund / Private E...        2  \n",
       "18                            Architecture / Planning        2  \n",
       "19                                           Plastics        2  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_real = KMeans(n_clusters=3, random_state=42, n_init=10)\n",
    "real_clusters = kmeans_real.fit_predict(X_real_scaled)\n",
    "\n",
    "df[\"cluster\"] = real_clusters\n",
    "df[[\"Name\", \"Country\", \"Industry\", \"cluster\"]].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79a2f091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=2, random_state=42)\n",
    "X_pca = pca.fit_transform(X_real_scaled)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=real_clusters, s=60, cmap=\"viridis\")\n",
    "plt.title(\"Кластеризация организаций методом KMeans\")\n",
    "plt.xlabel(\"Первая главная компонента\")\n",
    "plt.ylabel(\"Вторая главная компонента\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "672121bd",
   "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>Founded</th>\n",
       "      <th>Number of employees</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990.520000</td>\n",
       "      <td>1596.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.705882</td>\n",
       "      <td>3248.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992.448276</td>\n",
       "      <td>6919.896552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Founded  Number of employees\n",
       "cluster                                  \n",
       "0        1990.520000          1596.640000\n",
       "1        2012.705882          3248.000000\n",
       "2        1992.448276          6919.896552"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_clusters = df.copy()\n",
    "df_with_clusters[\"cluster\"] = real_clusters\n",
    "\n",
    "summary = df_with_clusters.groupby(\"cluster\")[[\"Founded\", \"Number of employees\"]].mean()\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8f6c779d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Кластер 0:\n",
      "['Ferrell LLC', 'Holder-Sellers', 'Pacheco-Spears', 'Jenkins Inc', 'Dickson, Richmond and Clay', 'Prince-David', 'Sloan, Mays and Whitehead', 'Pineda-Cox', 'Walls LLC', 'Walton-Barnett']\n",
      "\n",
      "Кластер 1:\n",
      "['Mckinney, Riley and Day', 'Keller, Campos and Black', 'Harrell LLC', 'Greene, Benjamin and Novak', 'Rivas Group', 'Glass, Barrera and Turner', 'Baker, Mccann and Macdonald', 'Hahn PLC', 'Valentine, Ferguson and Kramer', 'Mitchell, Warren and Schneider']\n",
      "\n",
      "Кластер 2:\n",
      "['Hester Ltd', 'Mayer Group', 'Henry-Thompson', 'Hansen-Everett', 'Mcintosh-Mora', 'Carr Inc', 'Gaines Inc', 'Kidd Group', 'Crane-Clarke', 'Glover-Pope']\n"
     ]
    }
   ],
   "source": [
    "for c in sorted(df[\"cluster\"].unique()):\n",
    "    print(f\"\\nКластер {c}:\")\n",
    "    print(df[df[\"cluster\"] == c][\"Name\"].tolist()[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "10679002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cluster_counts = df[\"cluster\"].value_counts().sort_index()\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "cluster_counts.plot(kind=\"bar\")\n",
    "plt.title(\"Количество организаций в каждом кластере\")\n",
    "plt.xlabel(\"Номер кластера\")\n",
    "plt.ylabel(\"Количество организаций\")\n",
    "plt.grid(axis=\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3705b26d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "employees_by_cluster = df.groupby(\"cluster\")[\"Number of employees\"].mean()\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "employees_by_cluster.plot(kind=\"bar\")\n",
    "plt.title(\"Среднее число сотрудников по кластерам\")\n",
    "plt.xlabel(\"Кластер\")\n",
    "plt.ylabel(\"Среднее число сотрудников\")\n",
    "plt.grid(axis=\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "824e65db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score для внешнего датасета: 0.005\n"
     ]
    }
   ],
   "source": [
    "score_real = silhouette_score(X_real_scaled, real_clusters)\n",
    "print(\"Silhouette Score для внешнего датасета:\", round(score_real, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bff952f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inertia_values = []\n",
    "\n",
    "for k in range(1, 7):\n",
    "    model = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
    "    model.fit(X_real_scaled)\n",
    "    inertia_values.append(model.inertia_)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.plot(range(1, 7), inertia_values, marker=\"o\")\n",
    "plt.title(\"Метод локтя для выбора числа кластеров\")\n",
    "plt.xlabel(\"Количество кластеров\")\n",
    "plt.ylabel(\"Inertia\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b785d450",
   "metadata": {},
   "source": [
    "## Часть 2. Кластеризация внешнего датасета\n",
    "\n",
    "Во второй части работы используется внешний датасет `organizations-100.csv`,\n",
    "загруженный с помощью `pandas.read_csv`.\n",
    "\n",
    "Датасет содержит информацию об организациях:\n",
    "название, страну, отрасль, год основания и число сотрудников."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "631caeae",
   "metadata": {},
   "source": [
    "## Подготовка и предобработка внешнего датасета\n",
    "\n",
    "Для кластеризации были выбраны признаки:\n",
    "- страна;\n",
    "- отрасль;\n",
    "- год основания;\n",
    "- число сотрудников.\n",
    "\n",
    "Категориальные признаки `Country` и `Industry` были преобразованы\n",
    "в числовой формат с помощью one-hot encoding.\n",
    "\n",
    "После этого все признаки были масштабированы методом `StandardScaler`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "087ed460",
   "metadata": {},
   "source": [
    "## Интерпретация результатов\n",
    "\n",
    "После кластеризации организации были разделены на несколько групп.\n",
    "\n",
    "Кластеры можно интерпретировать как группы компаний,\n",
    "схожих по размеру, времени основания, стране и отрасли.\n",
    "\n",
    "Одни кластеры могут объединять более крупные организации,\n",
    "другие — компании с меньшим числом сотрудников\n",
    "или компании из определённых отраслей."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1648ae99",
   "metadata": {},
   "source": [
    "## Вывод\n",
    "\n",
    "В ходе выполнения работы был изучен метод кластеризации KMeans\n",
    "из библиотеки scikit-learn.\n",
    "\n",
    "Модель была применена:\n",
    "1. к сгенерированному датасету;\n",
    "2. к внешнему датасету `organizations-100.csv`.\n",
    "\n",
    "На обоих типах данных алгоритм успешно выполнил кластеризацию объектов.\n",
    "\n",
    "Было установлено, что метод KMeans позволяет находить скрытые группы данных\n",
    "на основе их признаков и может использоваться для исследовательского анализа\n",
    "и сегментации объектов."
   ]
  }
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