{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "74c43a64",
   "metadata": {},
   "source": [
    "\n",
    "# Кластеризация и снижение размерности по данным Mall Customers\n",
    "\n",
    "Задача:\n",
    "- оставить только содержательные непрерывные числовые переменные\n",
    "- решить задачу кластеризации\n",
    "- оценить качество кластеризации\n",
    "- выполнить снижение размерности\n",
    "- выделить 2 новых признака\n",
    "- построить кластеризацию по ним\n",
    "- визуализировать результат\n",
    "- сравнить качество до и после снижения размерности\n",
    "\n",
    "Ноутбук сделан без финального вывода.  \n",
    "После запуска можно будет добавить короткий итог по реальным результатам.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73796749",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5472cc3a",
   "metadata": {},
   "source": [
    "## 1. Загрузка данных и проверка столбцов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1badc89d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер датасета: (200, 5)\n",
      "\n",
      "Столбцы:\n",
      "- CustomerID\n",
      "- Gender\n",
      "- Age\n",
      "- Annual Income (k$)\n",
      "- Spending Score (1-100)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
       "0           1    Male   19                  15                      39\n",
       "1           2    Male   21                  15                      81\n",
       "2           3  Female   20                  16                       6\n",
       "3           4  Female   23                  16                      77\n",
       "4           5  Female   31                  17                      40"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CustomerID</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         dtype\n",
       "CustomerID               int64\n",
       "Gender                  object\n",
       "Age                      int64\n",
       "Annual Income (k$)       int64\n",
       "Spending Score (1-100)   int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv(\"Mall_Customers.csv\")\n",
    "df.columns = df.columns.str.strip()\n",
    "\n",
    "print(\"Размер датасета:\", df.shape)\n",
    "print(\"\\nСтолбцы:\")\n",
    "for col in df.columns:\n",
    "    print(\"-\", col)\n",
    "\n",
    "display(df.head())\n",
    "display(df.dtypes.to_frame(\"dtype\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e924f0e",
   "metadata": {},
   "source": [
    "\n",
    "## 2. Отбор содержательных непрерывных числовых переменных\n",
    "\n",
    "Из датасета исключаются:\n",
    "- идентификатор клиента\n",
    "- категориальные признаки\n",
    "\n",
    "Оставляются только содержательные числовые признаки:\n",
    "- `Age`\n",
    "- `Annual Income (k$)`\n",
    "- `Spending Score (1-100)`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c44f962",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер рабочей выборки: (200, 3)\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>Age</th>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.850000</td>\n",
       "      <td>60.560000</td>\n",
       "      <td>50.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.969007</td>\n",
       "      <td>26.264721</td>\n",
       "      <td>25.823522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.750000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>34.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>73.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  Annual Income (k$)  Spending Score (1-100)\n",
       "count  200.000000          200.000000              200.000000\n",
       "mean    38.850000           60.560000               50.200000\n",
       "std     13.969007           26.264721               25.823522\n",
       "min     18.000000           15.000000                1.000000\n",
       "25%     28.750000           41.500000               34.750000\n",
       "50%     36.000000           61.500000               50.000000\n",
       "75%     49.000000           78.000000               73.000000\n",
       "max     70.000000          137.000000               99.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "feature_cols = [\"Age\", \"Annual Income (k$)\", \"Spending Score (1-100)\"]\n",
    "data = df[feature_cols].dropna().copy()\n",
    "\n",
    "print(\"Размер рабочей выборки:\", data.shape)\n",
    "display(data.describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fac06db",
   "metadata": {},
   "source": [
    "## 3. Масштабирование"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7251319c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ba32e87",
   "metadata": {},
   "source": [
    "## 4. Подбор числа кластеров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "295116b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Лучшее число кластеров по silhouette: 6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "inertias = []\n",
    "sil_scores = []\n",
    "\n",
    "k_range = range(2, 8)\n",
    "for k in k_range:\n",
    "    km = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
    "    labels = km.fit_predict(X)\n",
    "    inertias.append(km.inertia_)\n",
    "    sil_scores.append(silhouette_score(X, labels))\n",
    "\n",
    "best_k = list(k_range)[int(np.argmax(sil_scores))]\n",
    "print(\"Лучшее число кластеров по silhouette:\", best_k)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(list(k_range), inertias, marker=\"o\")\n",
    "plt.title(\"Метод локтя\")\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"Inertia\")\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(list(k_range), sil_scores, marker=\"o\")\n",
    "plt.title(\"Silhouette score\")\n",
    "plt.xlabel(\"k\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a07c4e0e",
   "metadata": {},
   "source": [
    "## 5. Кластеризация по исходным признакам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2f5c2fb5",
   "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>Метрика</th>\n",
       "      <th>Значение</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Silhouette</td>\n",
       "      <td>0.428417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Calinski-Harabasz</td>\n",
       "      <td>135.102104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Davies-Bouldin</td>\n",
       "      <td>0.825354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Метрика    Значение\n",
       "0         Silhouette    0.428417\n",
       "1  Calinski-Harabasz  135.102104\n",
       "2     Davies-Bouldin    0.825354"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "kmeans_full = KMeans(n_clusters=best_k, random_state=42, n_init=10)\n",
    "labels_full = kmeans_full.fit_predict(X)\n",
    "\n",
    "metrics_full = pd.DataFrame({\n",
    "    \"Метрика\": [\"Silhouette\", \"Calinski-Harabasz\", \"Davies-Bouldin\"],\n",
    "    \"Значение\": [\n",
    "        silhouette_score(X, labels_full),\n",
    "        calinski_harabasz_score(X, labels_full),\n",
    "        davies_bouldin_score(X, labels_full)\n",
    "    ]\n",
    "})\n",
    "\n",
    "display(metrics_full)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79d8465",
   "metadata": {},
   "source": [
    "## 6. PCA до двух компонент"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9404c3a",
   "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>Компонента</th>\n",
       "      <th>Explained_variance_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC1</td>\n",
       "      <td>0.442662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PC2</td>\n",
       "      <td>0.333084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Компонента  Explained_variance_ratio\n",
       "0        PC1                  0.442662\n",
       "1        PC2                  0.333084"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Суммарная объяснённая дисперсия: 0.7757454566976747\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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.706382</td>\n",
       "      <td>0.030141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual Income (k$)</th>\n",
       "      <td>-0.048024</td>\n",
       "      <td>0.998832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spending Score (1-100)</th>\n",
       "      <td>-0.706199</td>\n",
       "      <td>-0.037775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             PC1       PC2\n",
       "Age                     0.706382  0.030141\n",
       "Annual Income (k$)     -0.048024  0.998832\n",
       "Spending Score (1-100) -0.706199 -0.037775"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "explained_variance = pd.DataFrame({\n",
    "    \"Компонента\": [\"PC1\", \"PC2\"],\n",
    "    \"Explained_variance_ratio\": pca.explained_variance_ratio_\n",
    "})\n",
    "\n",
    "display(explained_variance)\n",
    "print(\"Суммарная объяснённая дисперсия:\", pca.explained_variance_ratio_.sum())\n",
    "\n",
    "loadings = pd.DataFrame(\n",
    "    pca.components_.T,\n",
    "    index=feature_cols,\n",
    "    columns=[\"PC1\", \"PC2\"]\n",
    ")\n",
    "display(loadings)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a12e3b5",
   "metadata": {},
   "source": [
    "## 7. Кластеризация по PC1 и PC2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f081812",
   "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>Метрика</th>\n",
       "      <th>Значение</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Silhouette</td>\n",
       "      <td>0.377470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Calinski-Harabasz</td>\n",
       "      <td>184.466755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Davies-Bouldin</td>\n",
       "      <td>0.856393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Метрика    Значение\n",
       "0         Silhouette    0.377470\n",
       "1  Calinski-Harabasz  184.466755\n",
       "2     Davies-Bouldin    0.856393"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "kmeans_pca = KMeans(n_clusters=best_k, random_state=42, n_init=10)\n",
    "labels_pca = kmeans_pca.fit_predict(X_pca)\n",
    "\n",
    "metrics_pca = pd.DataFrame({\n",
    "    \"Метрика\": [\"Silhouette\", \"Calinski-Harabasz\", \"Davies-Bouldin\"],\n",
    "    \"Значение\": [\n",
    "        silhouette_score(X_pca, labels_pca),\n",
    "        calinski_harabasz_score(X_pca, labels_pca),\n",
    "        davies_bouldin_score(X_pca, labels_pca)\n",
    "    ]\n",
    "})\n",
    "\n",
    "display(metrics_pca)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f78bca",
   "metadata": {},
   "source": [
    "## 8. Визуализация кластеров по двум новым признакам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "127daed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=labels_pca, alpha=0.7)\n",
    "centers_pca = kmeans_pca.cluster_centers_\n",
    "plt.scatter(centers_pca[:, 0], centers_pca[:, 1], marker=\"x\", s=150, linewidths=3)\n",
    "plt.xlabel(\"PC1\")\n",
    "plt.ylabel(\"PC2\")\n",
    "plt.title(\"Кластеризация в пространстве PC1–PC2\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec601e92",
   "metadata": {},
   "source": [
    "## 9. Сравнение качества кластеризации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44fba293",
   "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>Метрика</th>\n",
       "      <th>Исходные признаки</th>\n",
       "      <th>PC1 + PC2</th>\n",
       "      <th>Изменение</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Silhouette</td>\n",
       "      <td>0.428417</td>\n",
       "      <td>0.377470</td>\n",
       "      <td>-0.050947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Calinski-Harabasz</td>\n",
       "      <td>135.102104</td>\n",
       "      <td>184.466755</td>\n",
       "      <td>49.364651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Davies-Bouldin</td>\n",
       "      <td>0.825354</td>\n",
       "      <td>0.856393</td>\n",
       "      <td>0.031040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Метрика  Исходные признаки   PC1 + PC2  Изменение\n",
       "0         Silhouette           0.428417    0.377470  -0.050947\n",
       "1  Calinski-Harabasz         135.102104  184.466755  49.364651\n",
       "2     Davies-Bouldin           0.825354    0.856393   0.031040"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "compare = pd.DataFrame({\n",
    "    \"Метрика\": [\"Silhouette\", \"Calinski-Harabasz\", \"Davies-Bouldin\"],\n",
    "    \"Исходные признаки\": [\n",
    "        silhouette_score(X, labels_full),\n",
    "        calinski_harabasz_score(X, labels_full),\n",
    "        davies_bouldin_score(X, labels_full)\n",
    "    ],\n",
    "    \"PC1 + PC2\": [\n",
    "        silhouette_score(X_pca, labels_pca),\n",
    "        calinski_harabasz_score(X_pca, labels_pca),\n",
    "        davies_bouldin_score(X_pca, labels_pca)\n",
    "    ]\n",
    "})\n",
    "\n",
    "compare[\"Изменение\"] = compare[\"PC1 + PC2\"] - compare[\"Исходные признаки\"]\n",
    "display(compare)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da2f7983",
   "metadata": {},
   "source": [
    "## 10. Итог "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f287ee",
   "metadata": {},
   "source": [
    "\n",
    "Для анализа использованы признаки: Age, Annual Income (k$), Spending Score (1-100).\n",
    "\n",
    "Кластеризация по исходным признакам\n",
    "\n",
    "Оптимальное число кластеров: 6.\n",
    "\n",
    "Качество кластеризации:\n",
    "\n",
    "Silhouette = 0.428\n",
    "\n",
    "Calinski–Harabasz = 135.10\n",
    "\n",
    "Davies–Bouldin = 0.825\n",
    "\n",
    "Снижение размерности (PCA)\n",
    "\n",
    "Две компоненты объясняют 77.6% дисперсии.\n",
    "\n",
    "Кластеризация по PC1 и PC2:\n",
    "\n",
    "Silhouette = 0.377\n",
    "\n",
    "Calinski–Harabasz = 184.47\n",
    "\n",
    "Davies–Bouldin = 0.856\n",
    "\n",
    "Вывод\n",
    "\n",
    "После снижения размерности кластеризация немного ухудшилась: Silhouette снизился, а Davies–Bouldin вырос.\n",
    "Следовательно, кластеризация по исходным признакам более точная, хотя PCA сохраняет большую часть информации и удобен для визуализации кластеров. "
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}