AIM-PIbd-32-Kaznacheeva-E-K/lab_3/Lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Приступаем к работе...\n",
    "\n",
    "*Вариант задания:* Продажи домов в округе Кинг (вариант - 6)  \n",
    "Определим бизнес-цели и цели технического проекта  \n",
    "\n",
    "### Бизнес-цели:  \n",
    "1. Оптимизация процесса оценки стоимости дома  \n",
    "\n",
    "**Формулировка:** Разработать модель, которая позволяет автоматически и точно оценивать стоимость дома на основании его характеристик (таких как площадь, количество комнат, состояние, местоположение).  \n",
    "**Цель:** Увеличить точность оценки стоимости недвижимости для агенств и потенциальных покупателей, а также сократить время и затраты на оценку недвижимости, обеспечивая более точное предсказание цены.  \n",
    "\n",
    "**Ключевые показатели успеха (KPI):** \n",
    "*Точность модели прогнозирования* (RMSE): Минимизация среднеквадратичной ошибки до уровня ниже 10% от реальной цены, чтобы учитывать большие отклонения оценке.\n",
    "*Средная абсолютная ошибка* (MAE): Модель должна предсказать цену с минимальной ошибкой и снизить MAE до 5% или меньше учитывая большие отклонения в оценке.  \n",
    "*Скорость оценки:* Уменьшение времени на оценку стоимости дома, чтобы быстрее получать результат.\n",
    "*Доступность:* Внедрение модели в реальную систему для использования агентами недвижимости.\n",
    "\n",
    "2. Оптимизация затрат на ремонт перед продажей  \n",
    "\n",
    "**Формулировка:** Разработать модель, которая поможет продавцам домов и агентствам недвижимости определить, какие улучшения или реновации дадут наибольший прирост стоимости дома при минимальных затратах. Это поможет избежать ненужных расходов и максимизировать прибыль от продажи.  \n",
    "**Цель:** Снизить затраты на ремонт перед продажей, рекомендовать только те улучшения, которые максимально увеличат стоимость недвижимости, и сократить время на принятие решений по реновациям.  \n",
    "\n",
    "**Ключевые показатели успеха (KPI):**  \n",
    "*Возврат инвестиций* (ROI): Продавцы должны получать не менее 20% прироста стоимости дома на каждый вложенный доллар в реновацию. Например, если на ремонт было потрачено $10,000, цена дома должна увеличиться как минимум на $12,000.  \n",
    "*Средняя стоимость ремонта на 1 сделку* (CPA): Задача снизить расходы на ремонт, минимизировав ненужные траты. Например, оптимизация затрат до $5,000 на дом с учетом максимального прироста в цене.  \n",
    "*Сокращение времени на принятие решений:* Модель должна сокращать время, необходимое на оценку вариантов реноваций, до нескольких минут, что ускорит подготовку дома к продажи.\n",
    "\n",
    "### Технические цели проекта для каждой выделенной бизнес-цели\n",
    "\n",
    "1. **Создание модели для точной оценки стоимости дома.**  \n",
    "*Сбор и подготовка данных:* Очистка данных от пропусков, выбросов, дубликатов (аномальных значений в столбцах price, sqft_living, bedrooms). Преобразование категориальных переменных (view, condition, waterfront) в числовую форму с применением One-Hot-Encoding. Нормализация и стандартизация с применением методов масштабирования данных (нормировка, стандартизация для числовых признаков, чтобы привести их к 1ому масштабу). Разбиение набора данных на обучающую, контрольную и тестовую выборки для предотвращения утечек данных и переобучения.  \n",
    "*Разработка и обучение модели:* Исследование моделей машинного обучения, проводя эксперименты с различными алгоритмами (линейная регрессия, случайный лес, градиентный бустинг, деревья решений) для предсказания стоимости недвижимости. Обучение модели на обучающей выборке с использованием метрик оценки качества, таких как RMSE (Root Mean Square Error) и MAE (Mean Absolute Error). Оценка качества моделей на тестовой выборке, минимизируя MAE и RMSE для получения точных прогнозов стоимости.  \n",
    "*Развёртывание модели:* Интеграция модели в существующую систему или разработка API для доступа к модели с недвижимостью и частными продавцами. Создание веб-приложения или мобильного интерфейса для удобного использования модели и получения прогнозов в режиме реального времени.\n",
    "\n",
    "2. **Разработка модели для рекомендаций по реновациям.**  \n",
    "*Сбор и подготовка данных:* Сбор данных о типах и стоимости реноваций, а также их влияние на конечную стоимость дома. Очистка и устранение неточных или неполных данных о ремонтах. Преобразование категориальных признаков (реновации, например, обновление крыши, замена окон) в числовой формат для представления этих данных с применением One-Hot-Encoding. Разбиение данных на обучающую и тестовую выборки для обучения модели.  \n",
    "*Разработка и обучение модели:* Использование модели регрессий (линейная регрессия, случайный лес) для предсказания и моделирования влияния конкретных реноваций на увеличение стоимости недвижимости. Оценка метрики (CPA - Cost Per Acquisition) оценка затрат на реновацию одной продажи и (ROI - Return on Investment) расчёт возврата на инвестиции от реновации дома, прирост стоимости после реновации. Обучение модели с целью прогнозирования изменений, которые могут принести наибольшую пользу для стоимости домов и реноваций.  \n",
    "*Развёртывание модели:* Создание интерфейса, где пользователи смогут вводить информацию о текущем состоянии дома и получать рекомендации по реновациям с расчётом ROI. Создать рекомендационную систему для продавцов недвижимости, которая будет предлагать набор реноваций.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'date', 'price', 'bedrooms', 'bathrooms', 'sqft_living',\n",
      "       'sqft_lot', 'floors', 'waterfront', 'view', 'condition', 'grade',\n",
      "       'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode',\n",
      "       'lat', 'long', 'sqft_living15', 'sqft_lot15'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "\n",
    "# Подключим датафрейм и выгрузим данные\n",
    "df = pd.read_csv(\".//static//csv//kc_house_data.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
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       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
       "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
       "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Для наглядности\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
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       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>2.161300e+04</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "      <td>21613.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.580302e+09</td>\n",
       "      <td>5.400881e+05</td>\n",
       "      <td>3.370842</td>\n",
       "      <td>2.114757</td>\n",
       "      <td>2079.899736</td>\n",
       "      <td>1.510697e+04</td>\n",
       "      <td>1.494309</td>\n",
       "      <td>0.007542</td>\n",
       "      <td>0.234303</td>\n",
       "      <td>3.409430</td>\n",
       "      <td>7.656873</td>\n",
       "      <td>1788.390691</td>\n",
       "      <td>291.509045</td>\n",
       "      <td>1971.005136</td>\n",
       "      <td>84.402258</td>\n",
       "      <td>98077.939805</td>\n",
       "      <td>47.560053</td>\n",
       "      <td>-122.213896</td>\n",
       "      <td>1986.552492</td>\n",
       "      <td>12768.455652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.876566e+09</td>\n",
       "      <td>3.671272e+05</td>\n",
       "      <td>0.930062</td>\n",
       "      <td>0.770163</td>\n",
       "      <td>918.440897</td>\n",
       "      <td>4.142051e+04</td>\n",
       "      <td>0.539989</td>\n",
       "      <td>0.086517</td>\n",
       "      <td>0.766318</td>\n",
       "      <td>0.650743</td>\n",
       "      <td>1.175459</td>\n",
       "      <td>828.090978</td>\n",
       "      <td>442.575043</td>\n",
       "      <td>29.373411</td>\n",
       "      <td>401.679240</td>\n",
       "      <td>53.505026</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.140828</td>\n",
       "      <td>685.391304</td>\n",
       "      <td>27304.179631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000102e+06</td>\n",
       "      <td>7.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>5.200000e+02</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98001.000000</td>\n",
       "      <td>47.155900</td>\n",
       "      <td>-122.519000</td>\n",
       "      <td>399.000000</td>\n",
       "      <td>651.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.123049e+09</td>\n",
       "      <td>3.219500e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>1427.000000</td>\n",
       "      <td>5.040000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1190.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1951.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98033.000000</td>\n",
       "      <td>47.471000</td>\n",
       "      <td>-122.328000</td>\n",
       "      <td>1490.000000</td>\n",
       "      <td>5100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.904930e+09</td>\n",
       "      <td>4.500000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1910.000000</td>\n",
       "      <td>7.618000e+03</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98065.000000</td>\n",
       "      <td>47.571800</td>\n",
       "      <td>-122.230000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>7620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.308900e+09</td>\n",
       "      <td>6.450000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>1.068800e+04</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2210.000000</td>\n",
       "      <td>560.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98118.000000</td>\n",
       "      <td>47.678000</td>\n",
       "      <td>-122.125000</td>\n",
       "      <td>2360.000000</td>\n",
       "      <td>10083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.900000e+09</td>\n",
       "      <td>7.700000e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13540.000000</td>\n",
       "      <td>1.651359e+06</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9410.000000</td>\n",
       "      <td>4820.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>47.777600</td>\n",
       "      <td>-121.315000</td>\n",
       "      <td>6210.000000</td>\n",
       "      <td>871200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         price      bedrooms     bathrooms   sqft_living  \\\n",
       "count  2.161300e+04  2.161300e+04  21613.000000  21613.000000  21613.000000   \n",
       "mean   4.580302e+09  5.400881e+05      3.370842      2.114757   2079.899736   \n",
       "std    2.876566e+09  3.671272e+05      0.930062      0.770163    918.440897   \n",
       "min    1.000102e+06  7.500000e+04      0.000000      0.000000    290.000000   \n",
       "25%    2.123049e+09  3.219500e+05      3.000000      1.750000   1427.000000   \n",
       "50%    3.904930e+09  4.500000e+05      3.000000      2.250000   1910.000000   \n",
       "75%    7.308900e+09  6.450000e+05      4.000000      2.500000   2550.000000   \n",
       "max    9.900000e+09  7.700000e+06     33.000000      8.000000  13540.000000   \n",
       "\n",
       "           sqft_lot        floors    waterfront          view     condition  \\\n",
       "count  2.161300e+04  21613.000000  21613.000000  21613.000000  21613.000000   \n",
       "mean   1.510697e+04      1.494309      0.007542      0.234303      3.409430   \n",
       "std    4.142051e+04      0.539989      0.086517      0.766318      0.650743   \n",
       "min    5.200000e+02      1.000000      0.000000      0.000000      1.000000   \n",
       "25%    5.040000e+03      1.000000      0.000000      0.000000      3.000000   \n",
       "50%    7.618000e+03      1.500000      0.000000      0.000000      3.000000   \n",
       "75%    1.068800e+04      2.000000      0.000000      0.000000      4.000000   \n",
       "max    1.651359e+06      3.500000      1.000000      4.000000      5.000000   \n",
       "\n",
       "              grade    sqft_above  sqft_basement      yr_built  yr_renovated  \\\n",
       "count  21613.000000  21613.000000   21613.000000  21613.000000  21613.000000   \n",
       "mean       7.656873   1788.390691     291.509045   1971.005136     84.402258   \n",
       "std        1.175459    828.090978     442.575043     29.373411    401.679240   \n",
       "min        1.000000    290.000000       0.000000   1900.000000      0.000000   \n",
       "25%        7.000000   1190.000000       0.000000   1951.000000      0.000000   \n",
       "50%        7.000000   1560.000000       0.000000   1975.000000      0.000000   \n",
       "75%        8.000000   2210.000000     560.000000   1997.000000      0.000000   \n",
       "max       13.000000   9410.000000    4820.000000   2015.000000   2015.000000   \n",
       "\n",
       "            zipcode           lat          long  sqft_living15     sqft_lot15  \n",
       "count  21613.000000  21613.000000  21613.000000   21613.000000   21613.000000  \n",
       "mean   98077.939805     47.560053   -122.213896    1986.552492   12768.455652  \n",
       "std       53.505026      0.138564      0.140828     685.391304   27304.179631  \n",
       "min    98001.000000     47.155900   -122.519000     399.000000     651.000000  \n",
       "25%    98033.000000     47.471000   -122.328000    1490.000000    5100.000000  \n",
       "50%    98065.000000     47.571800   -122.230000    1840.000000    7620.000000  \n",
       "75%    98118.000000     47.678000   -122.125000    2360.000000   10083.000000  \n",
       "max    98199.000000     47.777600   -121.315000    6210.000000  871200.000000  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Описание данных (основные статистические показатели)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id               0\n",
      "date             0\n",
      "price            0\n",
      "bedrooms         0\n",
      "bathrooms        0\n",
      "sqft_living      0\n",
      "sqft_lot         0\n",
      "floors           0\n",
      "waterfront       0\n",
      "view             0\n",
      "condition        0\n",
      "grade            0\n",
      "sqft_above       0\n",
      "sqft_basement    0\n",
      "yr_built         0\n",
      "yr_renovated     0\n",
      "zipcode          0\n",
      "lat              0\n",
      "long             0\n",
      "sqft_living15    0\n",
      "sqft_lot15       0\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "id               False\n",
       "date             False\n",
       "price            False\n",
       "bedrooms         False\n",
       "bathrooms        False\n",
       "sqft_living      False\n",
       "sqft_lot         False\n",
       "floors           False\n",
       "waterfront       False\n",
       "view             False\n",
       "condition        False\n",
       "grade            False\n",
       "sqft_above       False\n",
       "sqft_basement    False\n",
       "yr_built         False\n",
       "yr_renovated     False\n",
       "zipcode          False\n",
       "lat              False\n",
       "long             False\n",
       "sqft_living15    False\n",
       "sqft_lot15       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Процент пропущенных значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    if null_rate > 0:\n",
    "        print(f'{i} Процент пустых значений: %{null_rate:.2f}')\n",
    "\n",
    "# Проверка на пропущенные данные\n",
    "print(df.isnull().sum())\n",
    "\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ооо, пропущенных колонок нету :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разбиваем на выборки (обучающую, тестовую, контрольную)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки:  17290\n",
      "Размер контрольной выборки:  4323\n",
      "Размер тестовой выборки:  4323\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тестовая)\n",
    "train_data, test_data = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение данных на обучающую и контрольную выборки (80% - обучение, 20% - контроль)\n",
    "train_data, val_data = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки: \", len(train_data))\n",
    "print(\"Размер контрольной выборки: \", len(val_data))\n",
    "print(\"Размер тестовой выборки: \", len(test_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя цена в обучающей выборке:  537768.04794679\n",
      "Средняя цена в контрольной выборке:  549367.443673375\n",
      "Средняя цена в тестовой выборке:  549367.443673375\n"
     ]
    }
   ],
   "source": [
    "# Оценка сбалансированности целевой переменной (цена)\n",
    "# Визуализация распределения цены в выборках (гистограмма)\n",
    "def plot_price_distribution(data, title):\n",
    "    sns.histplot(data['price'], kde=True)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Цена')\n",
    "    plt.ylabel('Частота')\n",
    "    plt.show()\n",
    "\n",
    "plot_price_distribution(train_data, 'Распределение цены в обучающей выборке')\n",
    "plot_price_distribution(val_data, 'Распределение цены в контрольной выборке')\n",
    "plot_price_distribution(test_data, 'Распределение цены в тестовой выборке')\n",
    "\n",
    "# Оценка сбалансированности данных по целевой переменной (price)\n",
    "print(\"Средняя цена в обучающей выборке: \", train_data['price'].mean())\n",
    "print(\"Средняя цена в контрольной выборке: \", val_data['price'].mean())\n",
    "print(\"Средняя цена в тестовой выборке: \", test_data['price'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки после oversampling и undersampling:  17620\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "# Преобразование целевой переменной (цены) в категориальные диапазоны с использованием квантилей\n",
    "train_data['price_category'] = pd.qcut(train_data['price'], q=4, labels=['low', 'medium', 'high', 'very_high'])\n",
    "\n",
    "# Визуализация распределения цен после преобразования в категории\n",
    "sns.countplot(x=train_data['price_category'])\n",
    "plt.title('Распределение категорий цены в обучающей выборке')\n",
    "plt.xlabel('Категория цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Балансировка категорий с помощью RandomOverSampler (увеличение меньшинств)\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_train = train_data.drop(columns=['price', 'price_category'])\n",
    "y_train = train_data['price_category']\n",
    "\n",
    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Визуализация распределения цен после oversampling\n",
    "sns.countplot(x=y_resampled)\n",
    "plt.title('Распределение категорий цены после oversampling')\n",
    "plt.xlabel('Категория цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Применение RandomUnderSampler для уменьшения большего класса\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "X_resampled, y_resampled = rus.fit_resample(X_resampled, y_resampled)\n",
    "\n",
    "# Визуализация распределения цен после undersampling\n",
    "sns.countplot(x=y_resampled)\n",
    "plt.title('Распределение категорий цены после undersampling')\n",
    "plt.xlabel('Категория цен')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Печать размеров выборки после балансировки\n",
    "print(\"Размер обучающей выборки после oversampling и undersampling: \", len(X_resampled))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование признаков  \n",
    "\n",
    "Теперь приступим к конструированию признаков для решения каждой задачи.\n",
    "\n",
    "**Процесс конструирования признаков**  \n",
    "Задача 1: Прогнозирование цен недвижимости. Цель технического проекта: Разработка модели машинного обучения для точного прогнозирования рыночной стоимости недвижимости.  \n",
    "Задача 2: Оптимизация затрат на ремонт перед продажей. Цель технического проекта: Разработка модели машинного обучения для точного прогнозирования по рекомендациям по реновациям.\n",
    "\n",
    "**Унитарное кодирование**  \n",
    "Унитарное кодирование категориальных признаков (one-hot encoding). Преобразование категориальных признаков в бинарные векторы.\n",
    "\n",
    "**Дискретизация числовых признаков**  \n",
    "Процесс преобразования непрерывных числовых значений в дискретные категории или интервалы (бины)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Столбцы train_data_encoded: ['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'price_category', 'date_20140502T000000', 'date_20140503T000000', 'date_20140504T000000', 'date_20140505T000000', 'date_20140506T000000', 'date_20140507T000000', 'date_20140508T000000', 'date_20140509T000000', 'date_20140510T000000', 'date_20140511T000000', 'date_20140512T000000', 'date_20140513T000000', 'date_20140514T000000', 'date_20140515T000000', 'date_20140516T000000', 'date_20140517T000000', 'date_20140518T000000', 'date_20140519T000000', 'date_20140520T000000', 'date_20140521T000000', 'date_20140522T000000', 'date_20140523T000000', 'date_20140524T000000', 'date_20140525T000000', 'date_20140526T000000', 'date_20140527T000000', 'date_20140528T000000', 'date_20140529T000000', 'date_20140530T000000', 'date_20140531T000000', 'date_20140601T000000', 'date_20140602T000000', 'date_20140603T000000', 'date_20140604T000000', 'date_20140605T000000', 'date_20140606T000000', 'date_20140607T000000', 'date_20140608T000000', 'date_20140609T000000', 'date_20140610T000000', 'date_20140611T000000', 'date_20140612T000000', 'date_20140613T000000', 'date_20140614T000000', 'date_20140615T000000', 'date_20140616T000000', 'date_20140617T000000', 'date_20140618T000000', 'date_20140619T000000', 'date_20140620T000000', 'date_20140621T000000', 'date_20140622T000000', 'date_20140623T000000', 'date_20140624T000000', 'date_20140625T000000', 'date_20140626T000000', 'date_20140627T000000', 'date_20140628T000000', 'date_20140629T000000', 'date_20140630T000000', 'date_20140701T000000', 'date_20140702T000000', 'date_20140703T000000', 'date_20140704T000000', 'date_20140705T000000', 'date_20140706T000000', 'date_20140707T000000', 'date_20140708T000000', 'date_20140709T000000', 'date_20140710T000000', 'date_20140711T000000', 'date_20140712T000000', 'date_20140713T000000', 'date_20140714T000000', 'date_20140715T000000', 'date_20140716T000000', 'date_20140717T000000', 'date_20140718T000000', 'date_20140719T000000', 'date_20140720T000000', 'date_20140721T000000', 'date_20140722T000000', 'date_20140723T000000', 'date_20140724T000000', 'date_20140725T000000', 'date_20140726T000000', 'date_20140728T000000', 'date_20140729T000000', 'date_20140730T000000', 'date_20140731T000000', 'date_20140801T000000', 'date_20140802T000000', 'date_20140804T000000', 'date_20140805T000000', 'date_20140806T000000', 'date_20140807T000000', 'date_20140808T000000', 'date_20140809T000000', 'date_20140810T000000', 'date_20140811T000000', 'date_20140812T000000', 'date_20140813T000000', 'date_20140814T000000', 'date_20140815T000000', 'date_20140816T000000', 'date_20140817T000000', 'date_20140818T000000', 'date_20140819T000000', 'date_20140820T000000', 'date_20140821T000000', 'date_20140822T000000', 'date_20140823T000000', 'date_20140824T000000', 'date_20140825T000000', 'date_20140826T000000', 'date_20140827T000000', 'date_20140828T000000', 'date_20140829T000000', 'date_20140830T000000', 'date_20140831T000000', 'date_20140901T000000', 'date_20140902T000000', 'date_20140903T000000', 'date_20140904T000000', 'date_20140905T000000', 'date_20140906T000000', 'date_20140907T000000', 'date_20140908T000000', 'date_20140909T000000', 'date_20140910T000000', 'date_20140911T000000', 'date_20140912T000000', 'date_20140913T000000', 'date_20140914T000000', 'date_20140915T000000', 'date_20140916T000000', 'date_20140917T000000', 'date_20140918T000000', 'date_20140919T000000', 'date_20140920T000000', 'date_20140921T000000', 'date_20140922T000000', 'date_20140923T000000', 'date_20140924T000000', 'date_20140925T000000', 'date_20140926T000000', 'date_20140927T000000', 'date_20140928T000000', 'date_20140929T000000', 'date_20140930T000000', 'date_20141001T000000', 'date_20141002T000000', 'date_20141003T000000', 'date_20141004T000000', 'date_20141005T000000', 'date_20141006T000000', 'date_20141007T000000', 'date_20141008T000000', 'date_20141009T000000', 'date_20141010T000000', 'date_20141011T000000', 'date_20141012T000000', 'date_20141013T000000', 'date_20141014T000000', 'date_20141015T000000', 'date_20141016T000000', 'date_20141017T000000', 'date_20141018T000000', 'date_20141019T000000', 'date_20141020T000000', 'date_20141021T000000', 'date_20141022T000000', 'date_20141023T000000', 'date_20141024T000000', 'date_20141025T000000', 'date_20141026T000000', 'date_20141027T000000', 'date_20141028T000000', 'date_20141029T000000', 'date_20141030T000000', 'date_20141031T000000', 'date_20141101T000000', 'date_20141102T000000', 'date_20141103T000000', 'date_20141104T000000', 'date_20141105T000000', 'date_20141106T000000', 'date_20141107T000000', 'date_20141108T000000', 'date_20141109T000000', 'date_20141110T000000', 'date_20141111T000000', 'date_20141112T000000', 'date_20141113T000000', 'date_20141114T000000', 'date_20141115T000000', 'date_20141116T000000', 'date_20141117T000000', 'date_20141118T000000', 'date_20141119T000000', 'date_20141120T000000', 'date_20141121T000000', 'date_20141122T000000', 'date_20141123T000000', 'date_20141124T000000', 'date_20141125T000000', 'date_20141126T000000', 'date_20141128T000000', 'date_20141129T000000', 'date_20141130T000000', 'date_20141201T000000', 'date_20141202T000000', 'date_20141203T000000', 'date_20141204T000000', 'date_20141205T000000', 'date_20141206T000000', 'date_20141207T000000', 'date_20141208T000000', 'date_20141209T000000', 'date_20141210T000000', 'date_20141211T000000', 'date_20141212T000000', 'date_20141213T000000', 'date_20141214T000000', 'date_20141215T000000', 'date_20141216T000000', 'date_20141217T000000', 'date_20141218T000000', 'date_20141219T000000', 'date_20141220T000000', 'date_20141221T000000', 'date_20141222T000000', 'date_20141223T000000', 'date_20141224T000000', 'date_20141226T000000', 'date_20141227T000000', 'date_20141229T000000', 'date_20141230T000000', 'date_20141231T000000', 'date_20150102T000000', 'date_20150105T000000', 'date_20150106T000000', 'date_20150107T000000', 'date_20150108T000000', 'date_20150109T000000', 'date_20150110T000000', 'date_20150112T000000', 'date_20150113T000000', 'date_20150114T000000', 'date_20150115T000000', 'date_20150116T000000', 'date_20150117T000000', 'date_20150119T000000', 'date_20150120T000000', 'date_20150121T000000', 'date_20150122T000000', 'date_20150123T000000', 'date_20150124T000000', 'date_20150125T000000', 'date_20150126T000000', 'date_20150127T000000', 'date_20150128T000000', 'date_20150129T000000', 'date_20150130T000000', 'date_20150201T000000', 'date_20150202T000000', 'date_20150203T000000', 'date_20150204T000000', 'date_20150205T000000', 'date_20150206T000000', 'date_20150207T000000', 'date_20150209T000000', 'date_20150210T000000', 'date_20150211T000000', 'date_20150212T000000', 'date_20150213T000000', 'date_20150214T000000', 'date_20150215T000000', 'date_20150216T000000', 'date_20150217T000000', 'date_20150218T000000', 'date_20150219T000000', 'date_20150220T000000', 'date_20150221T000000', 'date_20150222T000000', 'date_20150223T000000', 'date_20150224T000000', 'date_20150225T000000', 'date_20150226T000000', 'date_20150227T000000', 'date_20150228T000000', 'date_20150301T000000', 'date_20150302T000000', 'date_20150303T000000', 'date_20150304T000000', 'date_20150305T000000', 'date_20150306T000000', 'date_20150307T000000', 'date_20150308T000000', 'date_20150309T000000', 'date_20150310T000000', 'date_20150311T000000', 'date_20150312T000000', 'date_20150313T000000', 'date_20150314T000000', 'date_20150315T000000', 'date_20150316T000000', 'date_20150317T000000', 'date_20150318T000000', 'date_20150319T000000', 'date_20150320T000000', 'date_20150321T000000', 'date_20150322T000000', 'date_20150323T000000', 'date_20150324T000000', 'date_20150325T000000', 'date_20150326T000000', 'date_20150327T000000', 'date_20150328T000000', 'date_20150329T000000', 'date_20150330T000000', 'date_20150331T000000', 'date_20150401T000000', 'date_20150402T000000', 'date_20150403T000000', 'date_20150404T000000', 'date_20150405T000000', 'date_20150406T000000', 'date_20150407T000000', 'date_20150408T000000', 'date_20150409T000000', 'date_20150410T000000', 'date_20150411T000000', 'date_20150412T000000', 'date_20150413T000000', 'date_20150414T000000', 'date_20150415T000000', 'date_20150416T000000', 'date_20150417T000000', 'date_20150418T000000', 'date_20150419T000000', 'date_20150420T000000', 'date_20150421T000000', 'date_20150422T000000', 'date_20150423T000000', 'date_20150424T000000', 'date_20150425T000000', 'date_20150426T000000', 'date_20150427T000000', 'date_20150428T000000', 'date_20150429T000000', 'date_20150430T000000', 'date_20150501T000000', 'date_20150502T000000', 'date_20150503T000000', 'date_20150504T000000', 'date_20150505T000000', 'date_20150506T000000', 'date_20150507T000000', 'date_20150508T000000', 'date_20150509T000000', 'date_20150510T000000', 'date_20150511T000000', 'date_20150512T000000', 'date_20150513T000000', 'date_20150514T000000', 'date_20150515T000000', 'date_20150524T000000', 'waterfront_0', 'waterfront_1', 'view_0', 'view_1', 'view_2', 'view_3', 'view_4', 'condition_1', 'condition_2', 'condition_3', 'condition_4', 'condition_5']\n",
      "Столбцы val_data_encoded: ['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'date_20140502T000000', 'date_20140503T000000', 'date_20140505T000000', 'date_20140506T000000', 'date_20140507T000000', 'date_20140508T000000', 'date_20140509T000000', 'date_20140510T000000', 'date_20140511T000000', 'date_20140512T000000', 'date_20140513T000000', 'date_20140514T000000', 'date_20140515T000000', 'date_20140516T000000', 'date_20140518T000000', 'date_20140519T000000', 'date_20140520T000000', 'date_20140521T000000', 'date_20140522T000000', 'date_20140523T000000', 'date_20140524T000000', 'date_20140525T000000', 'date_20140526T000000', 'date_20140527T000000', 'date_20140528T000000', 'date_20140529T000000', 'date_20140530T000000', 'date_20140531T000000', 'date_20140601T000000', 'date_20140602T000000', 'date_20140603T000000', 'date_20140604T000000', 'date_20140605T000000', 'date_20140606T000000', 'date_20140607T000000', 'date_20140609T000000', 'date_20140610T000000', 'date_20140611T000000', 'date_20140612T000000', 'date_20140613T000000', 'date_20140614T000000', 'date_20140615T000000', 'date_20140616T000000', 'date_20140617T000000', 'date_20140618T000000', 'date_20140619T000000', 'date_20140620T000000', 'date_20140621T000000', 'date_20140622T000000', 'date_20140623T000000', 'date_20140624T000000', 'date_20140625T000000', 'date_20140626T000000', 'date_20140627T000000', 'date_20140628T000000', 'date_20140629T000000', 'date_20140630T000000', 'date_20140701T000000', 'date_20140702T000000', 'date_20140703T000000', 'date_20140707T000000', 'date_20140708T000000', 'date_20140709T000000', 'date_20140710T000000', 'date_20140711T000000', 'date_20140712T000000', 'date_20140713T000000', 'date_20140714T000000', 'date_20140715T000000', 'date_20140716T000000', 'date_20140717T000000', 'date_20140718T000000', 'date_20140719T000000', 'date_20140721T000000', 'date_20140722T000000', 'date_20140723T000000', 'date_20140724T000000', 'date_20140725T000000', 'date_20140727T000000', 'date_20140728T000000', 'date_20140729T000000', 'date_20140730T000000', 'date_20140731T000000', 'date_20140801T000000', 'date_20140802T000000', 'date_20140803T000000', 'date_20140804T000000', 'date_20140805T000000', 'date_20140806T000000', 'date_20140807T000000', 'date_20140808T000000', 'date_20140810T000000', 'date_20140811T000000', 'date_20140812T000000', 'date_20140813T000000', 'date_20140814T000000', 'date_20140815T000000', 'date_20140817T000000', 'date_20140818T000000', 'date_20140819T000000', 'date_20140820T000000', 'date_20140821T000000', 'date_20140822T000000', 'date_20140825T000000', 'date_20140826T000000', 'date_20140827T000000', 'date_20140828T000000', 'date_20140829T000000', 'date_20140831T000000', 'date_20140901T000000', 'date_20140902T000000', 'date_20140903T000000', 'date_20140904T000000', 'date_20140905T000000', 'date_20140907T000000', 'date_20140908T000000', 'date_20140909T000000', 'date_20140910T000000', 'date_20140911T000000', 'date_20140912T000000', 'date_20140913T000000', 'date_20140914T000000', 'date_20140915T000000', 'date_20140916T000000', 'date_20140917T000000', 'date_20140918T000000', 'date_20140919T000000', 'date_20140921T000000', 'date_20140922T000000', 'date_20140923T000000', 'date_20140924T000000', 'date_20140925T000000', 'date_20140926T000000', 'date_20140927T000000', 'date_20140929T000000', 'date_20140930T000000', 'date_20141001T000000', 'date_20141002T000000', 'date_20141003T000000', 'date_20141006T000000', 'date_20141007T000000', 'date_20141008T000000', 'date_20141009T000000', 'date_20141010T000000', 'date_20141012T000000', 'date_20141013T000000', 'date_20141014T000000', 'date_20141015T000000', 'date_20141016T000000', 'date_20141017T000000', 'date_20141018T000000', 'date_20141019T000000', 'date_20141020T000000', 'date_20141021T000000', 'date_20141022T000000', 'date_20141023T000000', 'date_20141024T000000', 'date_20141027T000000', 'date_20141028T000000', 'date_20141029T000000', 'date_20141030T000000', 'date_20141031T000000', 'date_20141101T000000', 'date_20141103T000000', 'date_20141104T000000', 'date_20141105T000000', 'date_20141106T000000', 'date_20141107T000000', 'date_20141108T000000', 'date_20141109T000000', 'date_20141110T000000', 'date_20141111T000000', 'date_20141112T000000', 'date_20141113T000000', 'date_20141114T000000', 'date_20141115T000000', 'date_20141116T000000', 'date_20141117T000000', 'date_20141118T000000', 'date_20141119T000000', 'date_20141120T000000', 'date_20141121T000000', 'date_20141122T000000', 'date_20141123T000000', 'date_20141124T000000', 'date_20141125T000000', 'date_20141126T000000', 'date_20141128T000000', 'date_20141201T000000', 'date_20141202T000000', 'date_20141203T000000', 'date_20141204T000000', 'date_20141205T000000', 'date_20141206T000000', 'date_20141208T000000', 'date_20141209T000000', 'date_20141210T000000', 'date_20141211T000000', 'date_20141212T000000', 'date_20141214T000000', 'date_20141215T000000', 'date_20141216T000000', 'date_20141217T000000', 'date_20141218T000000', 'date_20141219T000000', 'date_20141220T000000', 'date_20141222T000000', 'date_20141223T000000', 'date_20141224T000000', 'date_20141226T000000', 'date_20141227T000000', 'date_20141229T000000', 'date_20141230T000000', 'date_20141231T000000', 'date_20150102T000000', 'date_20150105T000000', 'date_20150106T000000', 'date_20150107T000000', 'date_20150108T000000', 'date_20150109T000000', 'date_20150112T000000', 'date_20150113T000000', 'date_20150114T000000', 'date_20150115T000000', 'date_20150116T000000', 'date_20150119T000000', 'date_20150120T000000', 'date_20150121T000000', 'date_20150122T000000', 'date_20150123T000000', 'date_20150124T000000', 'date_20150126T000000', 'date_20150127T000000', 'date_20150128T000000', 'date_20150129T000000', 'date_20150130T000000', 'date_20150131T000000', 'date_20150202T000000', 'date_20150203T000000', 'date_20150204T000000', 'date_20150205T000000', 'date_20150206T000000', 'date_20150207T000000', 'date_20150209T000000', 'date_20150210T000000', 'date_20150211T000000', 'date_20150212T000000', 'date_20150213T000000', 'date_20150214T000000', 'date_20150216T000000', 'date_20150217T000000', 'date_20150218T000000', 'date_20150219T000000', 'date_20150220T000000', 'date_20150221T000000', 'date_20150222T000000', 'date_20150223T000000', 'date_20150224T000000', 'date_20150225T000000', 'date_20150226T000000', 'date_20150227T000000', 'date_20150228T000000', 'date_20150301T000000', 'date_20150302T000000', 'date_20150303T000000', 'date_20150304T000000', 'date_20150305T000000', 'date_20150306T000000', 'date_20150307T000000', 'date_20150309T000000', 'date_20150310T000000', 'date_20150311T000000', 'date_20150312T000000', 'date_20150313T000000', 'date_20150315T000000', 'date_20150316T000000', 'date_20150317T000000', 'date_20150318T000000', 'date_20150319T000000', 'date_20150320T000000', 'date_20150321T000000', 'date_20150323T000000', 'date_20150324T000000', 'date_20150325T000000', 'date_20150326T000000', 'date_20150327T000000', 'date_20150328T000000', 'date_20150329T000000', 'date_20150330T000000', 'date_20150331T000000', 'date_20150401T000000', 'date_20150402T000000', 'date_20150403T000000', 'date_20150404T000000', 'date_20150406T000000', 'date_20150407T000000', 'date_20150408T000000', 'date_20150409T000000', 'date_20150410T000000', 'date_20150411T000000', 'date_20150412T000000', 'date_20150413T000000', 'date_20150414T000000', 'date_20150415T000000', 'date_20150416T000000', 'date_20150417T000000', 'date_20150419T000000', 'date_20150420T000000', 'date_20150421T000000', 'date_20150422T000000', 'date_20150423T000000', 'date_20150424T000000', 'date_20150425T000000', 'date_20150426T000000', 'date_20150427T000000', 'date_20150428T000000', 'date_20150429T000000', 'date_20150430T000000', 'date_20150501T000000', 'date_20150502T000000', 'date_20150503T000000', 'date_20150504T000000', 'date_20150505T000000', 'date_20150506T000000', 'date_20150507T000000', 'date_20150508T000000', 'date_20150509T000000', 'date_20150511T000000', 'date_20150512T000000', 'date_20150513T000000', 'date_20150514T000000', 'date_20150527T000000', 'waterfront_0', 'waterfront_1', 'view_0', 'view_1', 'view_2', 'view_3', 'view_4', 'condition_1', 'condition_2', 'condition_3', 'condition_4', 'condition_5']\n",
      "Столбцы test_data_encoded: ['id', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'grade', 'sqft_above', 'sqft_basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long', 'sqft_living15', 'sqft_lot15', 'date_20140502T000000', 'date_20140503T000000', 'date_20140505T000000', 'date_20140506T000000', 'date_20140507T000000', 'date_20140508T000000', 'date_20140509T000000', 'date_20140510T000000', 'date_20140511T000000', 'date_20140512T000000', 'date_20140513T000000', 'date_20140514T000000', 'date_20140515T000000', 'date_20140516T000000', 'date_20140518T000000', 'date_20140519T000000', 'date_20140520T000000', 'date_20140521T000000', 'date_20140522T000000', 'date_20140523T000000', 'date_20140524T000000', 'date_20140525T000000', 'date_20140526T000000', 'date_20140527T000000', 'date_20140528T000000', 'date_20140529T000000', 'date_20140530T000000', 'date_20140531T000000', 'date_20140601T000000', 'date_20140602T000000', 'date_20140603T000000', 'date_20140604T000000', 'date_20140605T000000', 'date_20140606T000000', 'date_20140607T000000', 'date_20140609T000000', 'date_20140610T000000', 'date_20140611T000000', 'date_20140612T000000', 'date_20140613T000000', 'date_20140614T000000', 'date_20140615T000000', 'date_20140616T000000', 'date_20140617T000000', 'date_20140618T000000', 'date_20140619T000000', 'date_20140620T000000', 'date_20140621T000000', 'date_20140622T000000', 'date_20140623T000000', 'date_20140624T000000', 'date_20140625T000000', 'date_20140626T000000', 'date_20140627T000000', 'date_20140628T000000', 'date_20140629T000000', 'date_20140630T000000', 'date_20140701T000000', 'date_20140702T000000', 'date_20140703T000000', 'date_20140707T000000', 'date_20140708T000000', 'date_20140709T000000', 'date_20140710T000000', 'date_20140711T000000', 'date_20140712T000000', 'date_20140713T000000', 'date_20140714T000000', 'date_20140715T000000', 'date_20140716T000000', 'date_20140717T000000', 'date_20140718T000000', 'date_20140719T000000', 'date_20140721T000000', 'date_20140722T000000', 'date_20140723T000000', 'date_20140724T000000', 'date_20140725T000000', 'date_20140727T000000', 'date_20140728T000000', 'date_20140729T000000', 'date_20140730T000000', 'date_20140731T000000', 'date_20140801T000000', 'date_20140802T000000', 'date_20140803T000000', 'date_20140804T000000', 'date_20140805T000000', 'date_20140806T000000', 'date_20140807T000000', 'date_20140808T000000', 'date_20140810T000000', 'date_20140811T000000', 'date_20140812T000000', 'date_20140813T000000', 'date_20140814T000000', 'date_20140815T000000', 'date_20140817T000000', 'date_20140818T000000', 'date_20140819T000000', 'date_20140820T000000', 'date_20140821T000000', 'date_20140822T000000', 'date_20140825T000000', 'date_20140826T000000', 'date_20140827T000000', 'date_20140828T000000', 'date_20140829T000000', 'date_20140831T000000', 'date_20140901T000000', 'date_20140902T000000', 'date_20140903T000000', 'date_20140904T000000', 'date_20140905T000000', 'date_20140907T000000', 'date_20140908T000000', 'date_20140909T000000', 'date_20140910T000000', 'date_20140911T000000', 'date_20140912T000000', 'date_20140913T000000', 'date_20140914T000000', 'date_20140915T000000', 'date_20140916T000000', 'date_20140917T000000', 'date_20140918T000000', 'date_20140919T000000', 'date_20140921T000000', 'date_20140922T000000', 'date_20140923T000000', 'date_20140924T000000', 'date_20140925T000000', 'date_20140926T000000', 'date_20140927T000000', 'date_20140929T000000', 'date_20140930T000000', 'date_20141001T000000', 'date_20141002T000000', 'date_20141003T000000', 'date_20141006T000000', 'date_20141007T000000', 'date_20141008T000000', 'date_20141009T000000', 'date_20141010T000000', 'date_20141012T000000', 'date_20141013T000000', 'date_20141014T000000', 'date_20141015T000000', 'date_20141016T000000', 'date_20141017T000000', 'date_20141018T000000', 'date_20141019T000000', 'date_20141020T000000', 'date_20141021T000000', 'date_20141022T000000', 'date_20141023T000000', 'date_20141024T000000', 'date_20141027T000000', 'date_20141028T000000', 'date_20141029T000000', 'date_20141030T000000', 'date_20141031T000000', 'date_20141101T000000', 'date_20141103T000000', 'date_20141104T000000', 'date_20141105T000000', 'date_20141106T000000', 'date_20141107T000000', 'date_20141108T000000', 'date_20141109T000000', 'date_20141110T000000', 'date_20141111T000000', 'date_20141112T000000', 'date_20141113T000000', 'date_20141114T000000', 'date_20141115T000000', 'date_20141116T000000', 'date_20141117T000000', 'date_20141118T000000', 'date_20141119T000000', 'date_20141120T000000', 'date_20141121T000000', 'date_20141122T000000', 'date_20141123T000000', 'date_20141124T000000', 'date_20141125T000000', 'date_20141126T000000', 'date_20141128T000000', 'date_20141201T000000', 'date_20141202T000000', 'date_20141203T000000', 'date_20141204T000000', 'date_20141205T000000', 'date_20141206T000000', 'date_20141208T000000', 'date_20141209T000000', 'date_20141210T000000', 'date_20141211T000000', 'date_20141212T000000', 'date_20141214T000000', 'date_20141215T000000', 'date_20141216T000000', 'date_20141217T000000', 'date_20141218T000000', 'date_20141219T000000', 'date_20141220T000000', 'date_20141222T000000', 'date_20141223T000000', 'date_20141224T000000', 'date_20141226T000000', 'date_20141227T000000', 'date_20141229T000000', 'date_20141230T000000', 'date_20141231T000000', 'date_20150102T000000', 'date_20150105T000000', 'date_20150106T000000', 'date_20150107T000000', 'date_20150108T000000', 'date_20150109T000000', 'date_20150112T000000', 'date_20150113T000000', 'date_20150114T000000', 'date_20150115T000000', 'date_20150116T000000', 'date_20150119T000000', 'date_20150120T000000', 'date_20150121T000000', 'date_20150122T000000', 'date_20150123T000000', 'date_20150124T000000', 'date_20150126T000000', 'date_20150127T000000', 'date_20150128T000000', 'date_20150129T000000', 'date_20150130T000000', 'date_20150131T000000', 'date_20150202T000000', 'date_20150203T000000', 'date_20150204T000000', 'date_20150205T000000', 'date_20150206T000000', 'date_20150207T000000', 'date_20150209T000000', 'date_20150210T000000', 'date_20150211T000000', 'date_20150212T000000', 'date_20150213T000000', 'date_20150214T000000', 'date_20150216T000000', 'date_20150217T000000', 'date_20150218T000000', 'date_20150219T000000', 'date_20150220T000000', 'date_20150221T000000', 'date_20150222T000000', 'date_20150223T000000', 'date_20150224T000000', 'date_20150225T000000', 'date_20150226T000000', 'date_20150227T000000', 'date_20150228T000000', 'date_20150301T000000', 'date_20150302T000000', 'date_20150303T000000', 'date_20150304T000000', 'date_20150305T000000', 'date_20150306T000000', 'date_20150307T000000', 'date_20150309T000000', 'date_20150310T000000', 'date_20150311T000000', 'date_20150312T000000', 'date_20150313T000000', 'date_20150315T000000', 'date_20150316T000000', 'date_20150317T000000', 'date_20150318T000000', 'date_20150319T000000', 'date_20150320T000000', 'date_20150321T000000', 'date_20150323T000000', 'date_20150324T000000', 'date_20150325T000000', 'date_20150326T000000', 'date_20150327T000000', 'date_20150328T000000', 'date_20150329T000000', 'date_20150330T000000', 'date_20150331T000000', 'date_20150401T000000', 'date_20150402T000000', 'date_20150403T000000', 'date_20150404T000000', 'date_20150406T000000', 'date_20150407T000000', 'date_20150408T000000', 'date_20150409T000000', 'date_20150410T000000', 'date_20150411T000000', 'date_20150412T000000', 'date_20150413T000000', 'date_20150414T000000', 'date_20150415T000000', 'date_20150416T000000', 'date_20150417T000000', 'date_20150419T000000', 'date_20150420T000000', 'date_20150421T000000', 'date_20150422T000000', 'date_20150423T000000', 'date_20150424T000000', 'date_20150425T000000', 'date_20150426T000000', 'date_20150427T000000', 'date_20150428T000000', 'date_20150429T000000', 'date_20150430T000000', 'date_20150501T000000', 'date_20150502T000000', 'date_20150503T000000', 'date_20150504T000000', 'date_20150505T000000', 'date_20150506T000000', 'date_20150507T000000', 'date_20150508T000000', 'date_20150509T000000', 'date_20150511T000000', 'date_20150512T000000', 'date_20150513T000000', 'date_20150514T000000', 'date_20150527T000000', 'waterfront_0', 'waterfront_1', 'view_0', 'view_1', 'view_2', 'view_3', 'view_4', 'condition_1', 'condition_2', 'condition_3', 'condition_4', 'condition_5']\n"
     ]
    }
   ],
   "source": [
    "# Конструирование признаков\n",
    "# Унитарное кодирование категориальных признаков (применение one-hot encoding)\n",
    "\n",
    "# Пример категориальных признаков\n",
    "categorical_features = ['date', 'waterfront', 'view', 'condition']\n",
    "\n",
    "# Применение one-hot encoding\n",
    "train_data_encoded = pd.get_dummies(train_data, columns=categorical_features)\n",
    "val_data_encoded = pd.get_dummies(val_data, columns=categorical_features)\n",
    "test_data_encoded = pd.get_dummies(test_data, columns=categorical_features)\n",
    "df_encoded = pd.get_dummies(df, columns=categorical_features)\n",
    "\n",
    "print(\"Столбцы train_data_encoded:\", train_data_encoded.columns.tolist())\n",
    "print(\"Столбцы val_data_encoded:\", val_data_encoded.columns.tolist())\n",
    "print(\"Столбцы test_data_encoded:\", test_data_encoded.columns.tolist())\n",
    "\n",
    "\n",
    "# Дискретизация числовых признаков (цены). Например, можно разделить площадь жилья на категории\n",
    "# Пример дискретизации признака 'Общая площадь'\n",
    "train_data_encoded['sqtf'] = pd.cut(train_data_encoded['sqft_living'], bins=5, labels=False)\n",
    "val_data_encoded['sqtf'] = pd.cut(val_data_encoded['sqft_living'], bins=5, labels=False)\n",
    "test_data_encoded['sqtf'] = pd.cut(test_data_encoded['sqft_living'], bins=5, labels=False)\n",
    "\n",
    "# Пример дискретизации признака 'sqft_living' на 5 категорий\n",
    "df_encoded['sqtf'] = pd.cut(df_encoded['sqft_living'], bins=5, labels=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ручной синтез\n",
    "Создание новых признаков на основе экспертных знаний и логики предметной области. К примеру, для данных о продаже домов можно создать признак цена за квадратный фут."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ручной синтез признаков\n",
    "train_data_encoded['price_per_sqft'] = df['price'] / df['sqft_living']\n",
    "val_data_encoded['price_per_sqft'] = df['price'] / df['sqft_living']\n",
    "test_data_encoded['price_per_sqft'] = df['price'] / df['sqft_living']\n",
    "\n",
    "# Пример создания нового признака - цена за квадратный фут\n",
    "df_encoded['price_per_sqft'] = df_encoded['price'] / df_encoded['sqft_living']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Масштабирование признаков - это процесс преобразования числовых признаков таким образом, чтобы они имели одинаковый масштаб. Это важно для многих алгоритмов машинного обучения, которые чувствительны к масштабу признаков, таких как линейная регрессия, метод опорных векторов (SVM) и нейронные сети."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "# Пример масштабирования числовых признаков\n",
    "numerical_features = ['bedrooms', 'sqft_living']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "train_data_encoded[numerical_features] = scaler.fit_transform(train_data_encoded[numerical_features])\n",
    "val_data_encoded[numerical_features] = scaler.transform(val_data_encoded[numerical_features])\n",
    "test_data_encoded[numerical_features] = scaler.transform(test_data_encoded[numerical_features])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Конструирование признаков с применением фреймворка Featuretools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               id     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
      "9876   1219000473  164950.0 -0.395263       1.75    -0.555396     15330   \n",
      "14982  6308000010  585000.0 -0.395263       2.50     0.238192      5089   \n",
      "1464   3630120700  757000.0 -0.395263       3.25     1.230177      5283   \n",
      "19209  1901600090  359000.0  1.752138       1.75    -0.147580      6654   \n",
      "2039   3395040550  320000.0 -0.395263       2.50    -0.599484      2890   \n",
      "...           ...       ...       ...        ...          ...       ...   \n",
      "13184  1523049207  220000.0  0.678437       2.00    -0.412109      8043   \n",
      "5759   1954420170  580000.0 -0.395263       2.50     0.083883      7484   \n",
      "8433   1721801010  225000.0 -0.395263       1.00    -0.312911      6120   \n",
      "10253  2422049104   85000.0 -1.468964       1.00    -1.371028      9000   \n",
      "11363  7701960990  870000.0  0.678437       2.50     1.230177     14565   \n",
      "\n",
      "       floors  grade  sqft_above  sqft_basement  ...  view_2  view_3  view_4  \\\n",
      "9876      1.0      7        1080            490  ...   False   False   False   \n",
      "14982     2.0      9        2290              0  ...   False   False   False   \n",
      "1464      2.0      9        3190              0  ...   False   False   False   \n",
      "19209     1.5      7        1940              0  ...   False   False   False   \n",
      "2039      2.0      7        1530              0  ...   False   False   False   \n",
      "...       ...    ...         ...            ...  ...     ...     ...     ...   \n",
      "13184     1.0      7         850            850  ...   False   False   False   \n",
      "5759      2.0      8        2150              0  ...   False   False   False   \n",
      "8433      1.0      6        1790              0  ...   False   False   False   \n",
      "10253     1.0      6         830              0  ...   False   False   False   \n",
      "11363     2.0     11        3190              0  ...   False   False   False   \n",
      "\n",
      "       condition_1  condition_2  condition_3  condition_4 condition_5  sqtf  \\\n",
      "9876         False        False         True        False       False     0   \n",
      "14982        False        False         True        False       False     0   \n",
      "1464         False        False         True        False       False     1   \n",
      "19209        False        False        False         True       False     0   \n",
      "2039         False        False         True        False       False     0   \n",
      "...            ...          ...          ...          ...         ...   ...   \n",
      "13184        False        False         True        False       False     0   \n",
      "5759         False        False         True        False       False     0   \n",
      "8433         False        False         True        False       False     0   \n",
      "10253        False        False         True        False       False     0   \n",
      "11363        False        False         True        False       False     1   \n",
      "\n",
      "       price_per_sqft  \n",
      "9876       105.063694  \n",
      "14982      255.458515  \n",
      "1464       237.304075  \n",
      "19209      185.051546  \n",
      "2039       209.150327  \n",
      "...               ...  \n",
      "13184      129.411765  \n",
      "5759       269.767442  \n",
      "8433       125.698324  \n",
      "10253      102.409639  \n",
      "11363      272.727273  \n",
      "\n",
      "[224 rows x 400 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               price  bedrooms  bathrooms  sqft_living  sqft_lot  floors  \\\n",
      "id                                                                         \n",
      "7129300520  221900.0         3       1.00         1180      5650     1.0   \n",
      "6414100192  538000.0         3       2.25         2570      7242     2.0   \n",
      "5631500400  180000.0         2       1.00          770     10000     1.0   \n",
      "2487200875  604000.0         4       3.00         1960      5000     1.0   \n",
      "1954400510  510000.0         3       2.00         1680      8080     1.0   \n",
      "\n",
      "            grade  sqft_above  sqft_basement  yr_built  ...  view_2  view_3  \\\n",
      "id                                                      ...                   \n",
      "7129300520      7        1180              0      1955  ...   False   False   \n",
      "6414100192      7        2170            400      1951  ...   False   False   \n",
      "5631500400      6         770              0      1933  ...   False   False   \n",
      "2487200875      7        1050            910      1965  ...   False   False   \n",
      "1954400510      8        1680              0      1987  ...   False   False   \n",
      "\n",
      "            view_4  condition_1  condition_2  condition_3  condition_4  \\\n",
      "id                                                                       \n",
      "7129300520   False        False        False         True        False   \n",
      "6414100192   False        False        False         True        False   \n",
      "5631500400   False        False        False         True        False   \n",
      "2487200875   False        False        False        False        False   \n",
      "1954400510   False        False        False         True        False   \n",
      "\n",
      "            condition_5  sqtf  price_per_sqft  \n",
      "id                                             \n",
      "7129300520        False     0      188.050847  \n",
      "6414100192        False     0      209.338521  \n",
      "5631500400        False     0      233.766234  \n",
      "2487200875         True     0      308.163265  \n",
      "1954400510        False     0      303.571429  \n",
      "\n",
      "[5 rows x 402 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "# Предобработка данных (например, кодирование категориальных признаков, удаление дубликатов)\n",
    "# Удаление дубликатов по идентификатору\n",
    "df = df.drop_duplicates(subset='id')\n",
    "duplicates = train_data_encoded[train_data_encoded['id'].duplicated(keep=False)]\n",
    "\n",
    "# Удаление дубликатов из столбца \"id\", сохранив первое вхождение\n",
    "df_encoded = df_encoded.drop_duplicates(subset='id', keep='first')\n",
    "\n",
    "print(duplicates)\n",
    "\n",
    "\n",
    "# Создание EntitySet\n",
    "es = ft.EntitySet(id='house_data')\n",
    "\n",
    "# Добавление датафрейма с домами\n",
    "es = es.add_dataframe(dataframe_name='houses', dataframe=df_encoded, index='id')\n",
    "\n",
    "# Генерация признаков с помощью глубокой синтезы признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='houses', max_depth=2)\n",
    "\n",
    "# Выводим первые 5 строк сгенерированного набора признаков\n",
    "print(feature_matrix.head())\n",
    "\n",
    "train_data_encoded = train_data_encoded.drop_duplicates(subset='id')\n",
    "train_data_encoded = train_data_encoded.drop_duplicates(subset='id', keep='first')  # or keep='last'\n",
    "\n",
    "# Определение сущностей (Создание EntitySet)\n",
    "es = ft.EntitySet(id='house_data')\n",
    "\n",
    "es = es.add_dataframe(dataframe_name='houses', dataframe=train_data_encoded, index='id')\n",
    "\n",
    "# Генерация признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='houses', max_depth=2)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=val_data_encoded.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=test_data_encoded.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценка качества каждого набора признаков  \n",
    "\n",
    "*Предсказательная способность Метрики:* RMSE, MAE, R²  \n",
    "\n",
    "*Методы:* Обучение модели на обучающей выборке и оценка на контрольной и тестовой выборках.  \n",
    "\n",
    "*Скорость вычисления Методы:* Измерение времени выполнения генерации признаков и обучения модели.  \n",
    "\n",
    "*Надежность Методы:* Кросс-валидация, анализ чувствительности модели к изменениям в данных.  \n",
    "\n",
    "*Корреляция Методы:* Анализ корреляционной матрицы признаков, удаление мультиколлинеарных признаков.  \n",
    "\n",
    "*Цельность Методы:* Проверка логической связи между признаками и целевой переменной, интерпретация результатов модели.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Время обучения модели: 5.18 секунд\n",
      "Среднеквадратичная ошибка: 125198557176601739264.00\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Разделение данных на обучающую и валидационную выборки. Удаляем целевую переменную\n",
    "X = feature_matrix.drop('price', axis=1)\n",
    "y = feature_matrix['price']\n",
    "\n",
    "# One-hot encoding для категориальных переменных (преобразование категориальных объектов в числовые)\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "# Проверяем, есть ли пропущенные значения, и заполняем их медианой или другим подходящим значением\n",
    "X.fillna(X.median(), inplace=True)\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model = LinearRegression()\n",
    "\n",
    "# Начинаем отсчет времени\n",
    "start_time = time.time()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# Предсказания и оценка модели и вычисляем среднеквадратичную ошибку\n",
    "predictions = model.predict(X_val)\n",
    "mse = mean_squared_error(y_val, predictions)\n",
    "\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE: 17870.38470608543\n",
      "R²: 0.9973762630189477\n",
      "MAE: 5924.569330616996 \n",
      "\n",
      "Кросс-валидация RMSE: 34577.766841359786 \n",
      "\n",
      "Train RMSE: 12930.759734777745\n",
      "Train R²: 0.9987426148033223\n",
      "Train MAE: 2495.3698282637165\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score, mean_absolute_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "\n",
    "# Удаление строк с NaN\n",
    "feature_matrix = feature_matrix.dropna()\n",
    "val_feature_matrix = val_feature_matrix.dropna()\n",
    "test_feature_matrix = test_feature_matrix.dropna()\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "X_train = feature_matrix.drop('price', axis=1)\n",
    "y_train = feature_matrix['price']\n",
    "X_val = val_feature_matrix.drop('price', axis=1)\n",
    "y_val = val_feature_matrix['price']\n",
    "X_test = test_feature_matrix.drop('price', axis=1)\n",
    "y_test = test_feature_matrix['price']\n",
    "\n",
    "X_test = X_test.reindex(columns=X_train.columns, fill_value=0)  \n",
    "\n",
    "# Кодирования категориальных переменных с использованием одноразового кодирования\n",
    "X = pd.get_dummies(X, drop_first=True)\n",
    "\n",
    "# Разобьём тренировочный тест и примерку модели\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Выбор модели\n",
    "model = RandomForestRegressor(random_state=42)\n",
    "\n",
    "# Обучение модели\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Предсказание и оценка\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "\n",
    "print()\n",
    "print(f\"RMSE: {rmse}\")\n",
    "print(f\"R²: {r2}\")\n",
    "print(f\"MAE: {mae} \\n\")\n",
    "\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "\n",
    "# Проверка на переобучение\n",
    "y_train_pred = model.predict(X_train)\n",
    "\n",
    "rmse_train = mean_squared_error(y_train, y_train_pred, squared=False)\n",
    "r2_train = r2_score(y_train, y_train_pred)\n",
    "mae_train = mean_absolute_error(y_train, y_train_pred)\n",
    "\n",
    "print(f\"Train RMSE: {rmse_train}\")\n",
    "print(f\"Train R²: {r2_train}\")\n",
    "print(f\"Train MAE: {mae_train}\")\n",
    "print()\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выводы и итог  \n",
    "\n",
    "**Модель случайного леса (RandomForestRegressor)** показала удовлетворительные результаты при прогнозировании цен на недвижимость. Метрики качества и кросс-валидация позволяют предположить, что модель не сильно переобучена и может быть использована для практических целей.  \n",
    "\n",
    "*Точность предсказаний:* Модель демонстрирует довольно высокий R² (0.9987), что указывает на большую часть вариации целевого признака (цены недвижимости). Однако, значения RMSE и MAE остаются высоки (12930 и 2495), что свидетельствует о том, что модель не всегда точно предсказывает значения, особенно для объектов с высокими или низкими ценами.  \n",
    "\n",
    "*Переобучение:* Разница между RMSE на обучающей и тестовой выборках незначительна, что указывает на то, что модель не склонна к переобучению. Однако в будущем стоит следить за этой метрикой при добавлении новых признаков или усложнении модели, чтобы избежать излишней подгонки под тренировочные данные. Также стоит быть осторожным и продолжать мониторинг этого показателя.  \n",
    "\n",
    "*Кросс-валидация:* При кросс-валидации наблюдается небольшое увеличение ошибки RMSE по сравнению с тестовой выборкой (рост на 2-3%). Это может указывать на небольшую нестабильность модели при использовании разных подвыборок данных. Для повышения устойчивости модели возможно стоит провести дальнейшую настройку гиперпараметров.  \n",
    "\n",
    "*Рекомендации:* Следует уделить внимание дополнительной обработке категориальных признаков, улучшению метода feature engineering, а также возможной оптимизации модели (например, через подбор гиперпараметров) для повышения точности предсказаний на экстремальных значениях.\n",
    "\n",
    "Кажется на этом закончили :)"
   ]
  }
 ],
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   "display_name": "mai",
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    "name": "ipython",
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