AIM-PIbd-32-Shabunov-O-A/lab_5/lab5.ipynb

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    "# Лабораторная работа №5. Обучение без учителя.\n",
    "\n",
    "## Датасет \"Набор данных для анализа и прогнозирования сердечного приступа\".\n",
    "\n",
    "[**Ссылка**](https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease)\n",
    "\n",
    "### Описание датасета\n",
    "\n",
    "**Проблемная область**: Датасет связан с медицинской статистикой и направлен на анализ факторов, связанных с риском сердечного приступа. Это важно для прогнозирования и разработки стратегий профилактики сердечно-сосудистых заболеваний.\n",
    "\n",
    "**Актуальность**: Сердечно-сосудистые заболевания являются одной из ведущих причин смертности во всем мире. Анализ данных об образе жизни, состоянии здоровья и наследственных факторах позволяет выделить ключевые предикторы, влияющие на развитие сердечно-сосудистых заболеваний. Этот датасет предоставляет инструменты для анализа таких факторов и может быть полезен в создании прогнозных моделей, направленных на снижение рисков и своевременную диагностику.\n",
    "\n",
    "**Объекты наблюдения**: Каждая запись представляет собой данные о человеке, включая информацию об их состоянии здоровья, образе жизни, демографических характеристиках и наличию определенных заболеваний. Объекты наблюдений — это индивидуальные пациенты.\n",
    "\n",
    "**Атрибуты объектов:**\n",
    "- `HeartDisease` — наличие сердечного приступа (Yes/No).\n",
    "- `BMI` — индекс массы тела (Body Mass Index), числовой показатель.\n",
    "- `Smoking` — курение (Yes/No).\n",
    "- `AlcoholDrinking` — употребление алкоголя (Yes/No).\n",
    "- `Stroke` — наличие инсульта (Yes/No).\n",
    "- `PhysicalHealth` — количество дней в месяц, когда физическое здоровье было неудовлетворительным.\n",
    "- `MentalHealth` — количество дней в месяц, когда психическое здоровье было неудовлетворительным.\n",
    "- `DiffWalking` — трудности при ходьбе (Yes/No).\n",
    "- `Sex` — пол (Male/Female).\n",
    "- `AgeCategory` — возрастная категория (например, 55-59, 80 or older).\n",
    "- `Race` — расовая принадлежность (например, White, Black).\n",
    "- `Diabetic` — наличие диабета (Yes/No/No, borderline diabetes).\n",
    "- `PhysicalActivity` — физическая активность (Yes/No).\n",
    "- `GenHealth` — общее состояние здоровья (от Excellent до Poor).\n",
    "- `SleepTime` — среднее количество часов сна за сутки.\n",
    "- `Asthma` — наличие астмы (Yes/No).\n",
    "- `KidneyDisease` — наличие заболеваний почек (Yes/No).\n",
    "- `SkinCancer` — наличие кожного рака (Yes/No)."
   ]
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  {
   "cell_type": "code",
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       "\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>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>20.34</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>26.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>24.21</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>6.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>23.71</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8.0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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      "text/plain": [
       "  HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "0           No  16.60     Yes              No     No             3.0   \n",
       "1           No  20.34      No              No    Yes             0.0   \n",
       "2           No  26.58     Yes              No     No            20.0   \n",
       "3           No  24.21      No              No     No             0.0   \n",
       "4           No  23.71      No              No     No            28.0   \n",
       "\n",
       "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",
       "0          30.0          No  Female        55-59  White      Yes   \n",
       "1           0.0          No  Female  80 or older  White       No   \n",
       "2          30.0          No    Male        65-69  White      Yes   \n",
       "3           0.0          No  Female        75-79  White       No   \n",
       "4           0.0         Yes  Female        40-44  White       No   \n",
       "\n",
       "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
       "0              Yes  Very good        5.0    Yes            No        Yes  \n",
       "1              Yes  Very good        7.0     No            No         No  \n",
       "2              Yes       Fair        8.0    Yes            No         No  \n",
       "3               No       Good        6.0     No            No        Yes  \n",
       "4              Yes  Very good        8.0     No            No         No  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\".//static//csv//heart_2020_cleaned.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Бизнес-цель. Задача кластеризации\n",
    "\n",
    "### Описание бизнес-цели\n",
    "\n",
    "**Цель**: группировка пациентов для выявления скрытых паттернов в их состоянии здоровья. Необходимо кластеризовать пациентов на основе факторов риска (индекс массы тела, физическая активность, возраст, качество сна и т.д.), чтобы лучше понять, какие группы населения требуют большего внимания для профилактики сердечных заболеваний."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Уменьшение объема датасета\n",
    "\n",
    "Так как исходный набор данных слишком объемный (более 300000 наблюдений), для дальнейшего удобства визуализации и скорости обработки снизим размер датасета до 10000 наблюдений:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Уменьшаем размер выборки, сохраняя пропорции классов целевой переменной\n",
    "df, _ = train_test_split(df, train_size=10000, stratify=df['HeartDisease'], random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Удаление целевого признака для кластеризации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(columns=['HeartDisease'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Предобработка данных\n",
    "\n",
    "Построим **конвейер для предобработки**, включающий *стандартизацию*, *унитарное кодирование* и *заполнение пропусков*, и применим его для набора данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>MentalHealth</th>\n",
       "      <th>SleepTime</th>\n",
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       "      <th>DiffWalking_Yes</th>\n",
       "      <th>Sex_Male</th>\n",
       "      <th>AgeCategory_25-29</th>\n",
       "      <th>...</th>\n",
       "      <th>Diabetic_Yes</th>\n",
       "      <th>Diabetic_Yes (during pregnancy)</th>\n",
       "      <th>PhysicalActivity_Yes</th>\n",
       "      <th>GenHealth_Fair</th>\n",
       "      <th>GenHealth_Good</th>\n",
       "      <th>GenHealth_Poor</th>\n",
       "      <th>GenHealth_Very good</th>\n",
       "      <th>Asthma_Yes</th>\n",
       "      <th>KidneyDisease_Yes</th>\n",
       "      <th>SkinCancer_Yes</th>\n",
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       "      <th>1</th>\n",
       "      <td>0.253157</td>\n",
       "      <td>-0.425660</td>\n",
       "      <td>-0.366659</td>\n",
       "      <td>0.646792</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.961511</td>\n",
       "      <td>-0.425660</td>\n",
       "      <td>-0.492347</td>\n",
       "      <td>-0.055403</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.749993</td>\n",
       "      <td>-0.175382</td>\n",
       "      <td>-0.492347</td>\n",
       "      <td>-0.757599</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.435763</td>\n",
       "      <td>-0.425660</td>\n",
       "      <td>-0.492347</td>\n",
       "      <td>0.646792</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>-0.959898</td>\n",
       "      <td>-0.425660</td>\n",
       "      <td>-0.492347</td>\n",
       "      <td>-0.055403</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>0.348330</td>\n",
       "      <td>-0.425660</td>\n",
       "      <td>-0.492347</td>\n",
       "      <td>1.348988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>-0.517907</td>\n",
       "      <td>-0.425660</td>\n",
       "      <td>-0.492347</td>\n",
       "      <td>-0.055403</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>0.646754</td>\n",
       "      <td>0.200034</td>\n",
       "      <td>0.387473</td>\n",
       "      <td>0.646792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>0.441890</td>\n",
       "      <td>-0.425660</td>\n",
       "      <td>-0.492347</td>\n",
       "      <td>-0.055403</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           BMI  PhysicalHealth  MentalHealth  SleepTime  Smoking_Yes  \\\n",
       "0    -0.392085        1.326284     -0.492347   1.348988          1.0   \n",
       "1     0.253157       -0.425660     -0.366659   0.646792          1.0   \n",
       "2    -0.961511       -0.425660     -0.492347  -0.055403          1.0   \n",
       "3     0.749993       -0.175382     -0.492347  -0.757599          1.0   \n",
       "4    -1.435763       -0.425660     -0.492347   0.646792          1.0   \n",
       "...        ...             ...           ...        ...          ...   \n",
       "9995 -0.959898       -0.425660     -0.492347  -0.055403          0.0   \n",
       "9996  0.348330       -0.425660     -0.492347   1.348988          0.0   \n",
       "9997 -0.517907       -0.425660     -0.492347  -0.055403          1.0   \n",
       "9998  0.646754        0.200034      0.387473   0.646792          0.0   \n",
       "9999  0.441890       -0.425660     -0.492347  -0.055403          1.0   \n",
       "\n",
       "      AlcoholDrinking_Yes  Stroke_Yes  DiffWalking_Yes  Sex_Male  \\\n",
       "0                     0.0         0.0              0.0       1.0   \n",
       "1                     0.0         0.0              0.0       1.0   \n",
       "2                     0.0         0.0              0.0       0.0   \n",
       "3                     0.0         0.0              0.0       0.0   \n",
       "4                     0.0         0.0              0.0       0.0   \n",
       "...                   ...         ...              ...       ...   \n",
       "9995                  0.0         0.0              0.0       1.0   \n",
       "9996                  0.0         0.0              0.0       1.0   \n",
       "9997                  0.0         0.0              0.0       0.0   \n",
       "9998                  0.0         0.0              1.0       0.0   \n",
       "9999                  0.0         0.0              0.0       0.0   \n",
       "\n",
       "      AgeCategory_25-29  ...  Diabetic_Yes  Diabetic_Yes (during pregnancy)  \\\n",
       "0                   0.0  ...           1.0                              0.0   \n",
       "1                   0.0  ...           0.0                              0.0   \n",
       "2                   0.0  ...           0.0                              0.0   \n",
       "3                   0.0  ...           0.0                              0.0   \n",
       "4                   0.0  ...           0.0                              0.0   \n",
       "...                 ...  ...           ...                              ...   \n",
       "9995                0.0  ...           0.0                              0.0   \n",
       "9996                0.0  ...           0.0                              0.0   \n",
       "9997                0.0  ...           0.0                              0.0   \n",
       "9998                0.0  ...           0.0                              0.0   \n",
       "9999                0.0  ...           0.0                              0.0   \n",
       "\n",
       "      PhysicalActivity_Yes  GenHealth_Fair  GenHealth_Good  GenHealth_Poor  \\\n",
       "0                      1.0             0.0             1.0             0.0   \n",
       "1                      1.0             0.0             0.0             0.0   \n",
       "2                      1.0             0.0             0.0             0.0   \n",
       "3                      1.0             0.0             1.0             0.0   \n",
       "4                      1.0             0.0             1.0             0.0   \n",
       "...                    ...             ...             ...             ...   \n",
       "9995                   1.0             0.0             0.0             0.0   \n",
       "9996                   1.0             1.0             0.0             0.0   \n",
       "9997                   1.0             0.0             0.0             0.0   \n",
       "9998                   1.0             0.0             1.0             0.0   \n",
       "9999                   1.0             0.0             0.0             0.0   \n",
       "\n",
       "      GenHealth_Very good  Asthma_Yes  KidneyDisease_Yes  SkinCancer_Yes  \n",
       "0                     0.0         0.0                1.0             1.0  \n",
       "1                     1.0         0.0                0.0             0.0  \n",
       "2                     0.0         0.0                0.0             0.0  \n",
       "3                     0.0         0.0                0.0             0.0  \n",
       "4                     0.0         0.0                0.0             0.0  \n",
       "...                   ...         ...                ...             ...  \n",
       "9995                  0.0         0.0                0.0             0.0  \n",
       "9996                  0.0         0.0                0.0             0.0  \n",
       "9997                  0.0         0.0                0.0             1.0  \n",
       "9998                  0.0         0.0                0.0             1.0  \n",
       "9999                  1.0         0.0                0.0             0.0  \n",
       "\n",
       "[10000 rows x 37 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Разделение признаков на числовые и категориальные\n",
    "num_columns = [\n",
    "    column\n",
    "    for column in df.columns\n",
    "    if df[column].dtype != \"object\"\n",
    "]\n",
    "cat_columns = [\n",
    "    column\n",
    "    for column in df.columns\n",
    "    if df[column].dtype == \"object\"\n",
    "]\n",
    "\n",
    "# Числовая обработка: заполнение пропусков медианой и стандартизация\n",
    "num_imputer = SimpleImputer(strategy=\"median\")\n",
    "num_scaler = StandardScaler()\n",
    "preprocessing_num = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", num_imputer),\n",
    "        (\"scaler\", num_scaler),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Категориальная обработка: заполнение пропусков значением \"unknown\" и унитарное кодирование\n",
    "cat_imputer = SimpleImputer(strategy=\"constant\", fill_value=\"unknown\")\n",
    "cat_encoder = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False, drop=\"first\")\n",
    "preprocessing_cat = Pipeline(\n",
    "    [\n",
    "        (\"imputer\", cat_imputer),\n",
    "        (\"encoder\", cat_encoder),\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Общий конвейер обработки признаков\n",
    "features_preprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"prepocessing_num\", preprocessing_num, num_columns),\n",
    "        (\"prepocessing_cat\", preprocessing_cat, cat_columns),\n",
    "    ],\n",
    "    remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "# Итоговый конвейер\n",
    "pipeline_end = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "    ]\n",
    ")\n",
    "\n",
    "preprocessing_result = pipeline_end.fit_transform(df)\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    ")\n",
    "\n",
    "preprocessed_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Понижение размерности признаков и визуализация данных\n",
    "\n",
    "Используем алгоритм снижения размерности **PCA** (*метод главных компонент*), чтобы уменьшить количество признаков до 2 и отобразить данные на графике:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "data_pca = pca.fit_transform(preprocessed_df)\n",
    "\n",
    "# Преобразуем результат в DataFrame для визуализации через scatterplot\n",
    "df_pca = pd.DataFrame(data_pca, columns=['Principal Component 1', 'Principal Component 2'])\n",
    "\n",
    "# Визуализация данных после PCA\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(\n",
    "    x='Principal Component 1',\n",
    "    y='Principal Component 2',\n",
    "    data=df_pca,\n",
    "    alpha=0.6\n",
    ")\n",
    "plt.title('Визуализация данных после PCA', fontsize=14)\n",
    "plt.xlabel('Главная компонента 1')\n",
    "plt.ylabel('Главная компонента 2')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выбор количества кластеров\n",
    "\n",
    "Для определения оптимального числа кластеров используется:\n",
    "1. **Инерция** (*метод локтя*): показывает, как внутрикластерная вариация уменьшается с увеличением числа кластеров.\n",
    "2. **Коэффициент силуэта**: измеряет, насколько хорошо каждый объект кластеризован."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "inertia = []\n",
    "silhouette_scores = []\n",
    "k_range = range(2, 11)\n",
    "\n",
    "for k in k_range:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    kmeans.fit(data_pca)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "    silhouette_scores.append(silhouette_score(data_pca, kmeans.labels_))\n",
    "\n",
    "# Графики для определения оптимального числа кластеров\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(k_range, inertia, marker='o')\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(k_range, silhouette_scores, marker='o')\n",
    "plt.title('Silhouette Scores')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Silhouette Score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Судя по графику инерции, в качестве оптимального числа кластеров можно выбрать k=8, так как в этой точке происходит резкое улучшение метрики (спад инерции) и скорость уменьшения инерции заметно замедляется."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Кластерный анализ\n",
    "\n",
    "Используются два подхода:\n",
    "1. **Иерархическая кластеризация** - агломеративная кластеризация.\n",
    "2. **Неиерархическая кластеризация** - алгоритм k-means."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Неиерархическая кластеризация:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Используем неиерархическую кластеризацию KMeans с оптимальным количеством кластеров 8\n",
    "kmeans = KMeans(n_clusters=8, random_state=42)\n",
    "kmeans_labels = kmeans.fit_predict(data_pca)\n",
    "\n",
    "# Визуализация кластеров KMeans\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(data_pca[:, 0], data_pca[:, 1], c=kmeans_labels, cmap='viridis', alpha=0.6)\n",
    "plt.title('KMeans Clustering')\n",
    "plt.xlabel('PCA 1')\n",
    "plt.ylabel('PCA 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Иерархическая кластеризация:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "\n",
    "# Иерархическая кластеризация с оптимальным количеством кластеров 8\n",
    "hierarchical = AgglomerativeClustering(n_clusters=8)\n",
    "hierarchical_labels = hierarchical.fit_predict(data_pca)\n",
    "\n",
    "# Визуализация с помощью дендрограммы\n",
    "plt.figure(figsize=(10, 7))\n",
    "linkage_matrix = linkage(data_pca, method='ward')\n",
    "dendrogram(linkage_matrix, truncate_mode='level', p=5)\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализация с помощью диаграммы рассеяния\n",
    "\n",
    "plt.scatter(data_pca[:, 0], data_pca[:, 1], c=hierarchical_labels, cmap='viridis', alpha=0.6)\n",
    "plt.title('Hierarchical Clustering')\n",
    "plt.xlabel('PCA 1')\n",
    "plt.ylabel('PCA 2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценка качества кластеризации\n",
    "\n",
    "Используемая метрика - **коэффициент силуэта**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score for KMeans: 0.33\n",
      "Silhouette Score for Hierarchical Clustering: 0.26\n"
     ]
    }
   ],
   "source": [
    "silhouette_kmeans = silhouette_score(data_pca, kmeans_labels)\n",
    "silhouette_hierarchical = silhouette_score(data_pca, hierarchical_labels)\n",
    "\n",
    "print(f'Silhouette Score for KMeans: {silhouette_kmeans:.2f}')\n",
    "print(f'Silhouette Score for Hierarchical Clustering: {silhouette_hierarchical:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "K-Means показал лучшие результаты и может быть рекомендован для дальнейшей работы с данными. Однако качество кластеризации в целом среднее, что указывает на необходимость дополнительной оптимизации модели или работы с признаками."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "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": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}