{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Начинаем работу...  \n",
    "\n",
    "Датасет: Продажи домов в округе Кинг "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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",
    "from sklearn import set_config\n",
    "\n",
    "# Установим параметры для вывода\n",
    "set_config(transform_output=\"pandas\")\n",
    "\n",
    "random_state = 42\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": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <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>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": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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",
      "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\n"
     ]
    }
   ],
   "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",
    "print(df.isnull().sum())\n",
    "\n",
    "print(df.isnull().any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                 int64\n",
       "date              object\n",
       "price            float64\n",
       "bedrooms           int64\n",
       "bathrooms        float64\n",
       "sqft_living        int64\n",
       "sqft_lot           int64\n",
       "floors           float64\n",
       "waterfront         int64\n",
       "view               int64\n",
       "condition          int64\n",
       "grade              int64\n",
       "sqft_above         int64\n",
       "sqft_basement      int64\n",
       "yr_built           int64\n",
       "yr_renovated       int64\n",
       "zipcode            int64\n",
       "lat              float64\n",
       "long             float64\n",
       "sqft_living15      int64\n",
       "sqft_lot15         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка типов столбцов\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выбор бизнес-целей  \n",
    "Для датасета недвижимости предлагаются две бизнес-цели:\n",
    "\n",
    "*Задача регрессии* – предсказание цены дома (price). Это может помочь риэлторам и аналитикам определить справедливую рыночную стоимость недвижимости.  \n",
    "\n",
    "*Задача классификации* – определение вероятности того, что цена дома будет выше/ниже медианы рынка. Классифицировать дома по ценовым категориям (например, низкая, средняя, высокая цена). Это может помочь определить, какие дома популярны у покупателей.\n",
    "\n",
    "## Определение достижимого уровня качества модели  \n",
    "Для регрессии и классификации мы выберем метрики:  \n",
    "\n",
    "Для регрессии будем использовать метрики MAE (средняя абсолютная ошибка) и R^2 (коэффициент детерминации), стремясь к MAE ниже 10% от средней цены. А классификация будте ориентироваться на метрики accuracy и F1-score при целевом значении accuracy около 80%.\n",
    "\n",
    "## Ориентир для каждой задачи\n",
    "Для регрессии ориентиром будет медианная цена (price.median()), так как это стабильное значение. Для классификации ориентируемся на среднюю вероятность предсказания класса выше медианы.\n",
    "\n",
    "## Анализ алгоритмов машинного обучения  \n",
    "Рассмотрим для задачи регрессии:\n",
    "\n",
    "*Линейная регрессия:* подходит для простых линейных зависимостей.  \n",
    "*Дерево решений:* учитывает нелинейные зависимости, может учесть сложные закономерности.  \n",
    "*Случайный лес:* ансамблевый метод, обобщающий данные и эффективно обрабатывающий выбросы.  \n",
    "\n",
    "Для задачи классификации:  \n",
    "\n",
    "*Логистическая регрессия:* простая модель, подходящая для бинарной классификации.  \n",
    "*Метод опорных векторов (SVM):* работает хорошо на данных с четкими разделениями.  \n",
    "*Градиентный бустинг:* подходит для сложных и высокоразмерных данных, обеспечивает высокую точность.  \n",
    "\n",
    "## Выбор моделей  \n",
    "Выбираем по три модели для каждой задачи:\n",
    "\n",
    "*Регрессия:* Линейная регрессия, Дерево решений, Случайный лес.  \n",
    "*Классификация:* Логистическая регрессия, Метод опорных векторов (SVM), Градиентный бустинг.  \n",
    "\n",
    "\n",
    "## Построение конвейера и визуализации  \n",
    "Теперь напишем код для загрузки данных, анализа и подготовки моделей с визуализацией результатов.\n",
    "\n",
    "\n",
    "# Начнём с задачи классификации\n",
    "\n",
    "Целевой признак --> above_median_price\n",
    "\n",
    "Формируем выборки. Разделяем набор данных на обучающую и тестовые выборки (80/20) для задачи классификации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>above_median_price</th>\n",
       "      <th>price_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20962</th>\n",
       "      <td>1278000210</td>\n",
       "      <td>20150311T000000</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>828</td>\n",
       "      <td>4524</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1968</td>\n",
       "      <td>2007</td>\n",
       "      <td>98001</td>\n",
       "      <td>47.2655</td>\n",
       "      <td>-122.244</td>\n",
       "      <td>828</td>\n",
       "      <td>5402</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12284</th>\n",
       "      <td>2193300390</td>\n",
       "      <td>20140923T000000</td>\n",
       "      <td>624000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2810</td>\n",
       "      <td>11250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1130</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>98052</td>\n",
       "      <td>47.6920</td>\n",
       "      <td>-122.099</td>\n",
       "      <td>2110</td>\n",
       "      <td>11250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7343</th>\n",
       "      <td>4289900005</td>\n",
       "      <td>20141230T000000</td>\n",
       "      <td>1535000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2850</td>\n",
       "      <td>4100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1030</td>\n",
       "      <td>1908</td>\n",
       "      <td>2003</td>\n",
       "      <td>98122</td>\n",
       "      <td>47.6147</td>\n",
       "      <td>-122.285</td>\n",
       "      <td>2130</td>\n",
       "      <td>4200</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14247</th>\n",
       "      <td>316000145</td>\n",
       "      <td>20150325T000000</td>\n",
       "      <td>235000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1360</td>\n",
       "      <td>7132</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1941</td>\n",
       "      <td>0</td>\n",
       "      <td>98168</td>\n",
       "      <td>47.5054</td>\n",
       "      <td>-122.301</td>\n",
       "      <td>1280</td>\n",
       "      <td>7175</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16670</th>\n",
       "      <td>629400480</td>\n",
       "      <td>20140619T000000</td>\n",
       "      <td>775000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3010</td>\n",
       "      <td>15992</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>98075</td>\n",
       "      <td>47.5895</td>\n",
       "      <td>-121.994</td>\n",
       "      <td>3330</td>\n",
       "      <td>12333</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1332700270</td>\n",
       "      <td>20140519T000000</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1610</td>\n",
       "      <td>2040</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1979</td>\n",
       "      <td>0</td>\n",
       "      <td>98056</td>\n",
       "      <td>47.5180</td>\n",
       "      <td>-122.194</td>\n",
       "      <td>1950</td>\n",
       "      <td>2025</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15031</th>\n",
       "      <td>7129303070</td>\n",
       "      <td>20140820T000000</td>\n",
       "      <td>735000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>3040</td>\n",
       "      <td>2415</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "      <td>98118</td>\n",
       "      <td>47.5188</td>\n",
       "      <td>-122.256</td>\n",
       "      <td>2620</td>\n",
       "      <td>2433</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5234</th>\n",
       "      <td>2432000130</td>\n",
       "      <td>20150414T000000</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1660</td>\n",
       "      <td>9549</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1956</td>\n",
       "      <td>0</td>\n",
       "      <td>98033</td>\n",
       "      <td>47.6503</td>\n",
       "      <td>-122.198</td>\n",
       "      <td>2090</td>\n",
       "      <td>9549</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19980</th>\n",
       "      <td>774100475</td>\n",
       "      <td>20140627T000000</td>\n",
       "      <td>415000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2600</td>\n",
       "      <td>64626</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98014</td>\n",
       "      <td>47.7185</td>\n",
       "      <td>-121.405</td>\n",
       "      <td>1740</td>\n",
       "      <td>64626</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3671</th>\n",
       "      <td>8847400115</td>\n",
       "      <td>20140723T000000</td>\n",
       "      <td>590000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2420</td>\n",
       "      <td>208652</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>98010</td>\n",
       "      <td>47.3666</td>\n",
       "      <td>-121.978</td>\n",
       "      <td>3180</td>\n",
       "      <td>212137</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17290 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id             date      price  bedrooms  bathrooms  \\\n",
       "20962  1278000210  20150311T000000   110000.0         2       1.00   \n",
       "12284  2193300390  20140923T000000   624000.0         4       3.25   \n",
       "7343   4289900005  20141230T000000  1535000.0         4       3.25   \n",
       "14247   316000145  20150325T000000   235000.0         4       1.00   \n",
       "16670   629400480  20140619T000000   775000.0         4       2.75   \n",
       "...           ...              ...        ...       ...        ...   \n",
       "88     1332700270  20140519T000000   215000.0         2       2.25   \n",
       "15031  7129303070  20140820T000000   735000.0         4       2.75   \n",
       "5234   2432000130  20150414T000000   675000.0         3       1.75   \n",
       "19980   774100475  20140627T000000   415000.0         3       2.75   \n",
       "3671   8847400115  20140723T000000   590000.0         3       2.00   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  sqft_basement  \\\n",
       "20962          828      4524     1.0           0     0  ...              0   \n",
       "12284         2810     11250     1.0           0     0  ...           1130   \n",
       "7343          2850      4100     2.0           0     3  ...           1030   \n",
       "14247         1360      7132     1.5           0     0  ...              0   \n",
       "16670         3010     15992     2.0           0     0  ...              0   \n",
       "...            ...       ...     ...         ...   ...  ...            ...   \n",
       "88            1610      2040     2.0           0     0  ...              0   \n",
       "15031         3040      2415     2.0           1     4  ...              0   \n",
       "5234          1660      9549     1.0           0     0  ...              0   \n",
       "19980         2600     64626     1.5           0     0  ...              0   \n",
       "3671          2420    208652     1.5           0     0  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "20962      1968          2007    98001  47.2655 -122.244            828   \n",
       "12284      1980             0    98052  47.6920 -122.099           2110   \n",
       "7343       1908          2003    98122  47.6147 -122.285           2130   \n",
       "14247      1941             0    98168  47.5054 -122.301           1280   \n",
       "16670      1996             0    98075  47.5895 -121.994           3330   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "88         1979             0    98056  47.5180 -122.194           1950   \n",
       "15031      1966             0    98118  47.5188 -122.256           2620   \n",
       "5234       1956             0    98033  47.6503 -122.198           2090   \n",
       "19980      2009             0    98014  47.7185 -121.405           1740   \n",
       "3671       2005             0    98010  47.3666 -121.978           3180   \n",
       "\n",
       "       sqft_lot15  above_median_price  price_category  \n",
       "20962        5402                   0               0  \n",
       "12284       11250                   1               1  \n",
       "7343         4200                   1               2  \n",
       "14247        7175                   0               0  \n",
       "16670       12333                   1               2  \n",
       "...           ...                 ...             ...  \n",
       "88           2025                   0               0  \n",
       "15031        2433                   1               2  \n",
       "5234         9549                   1               1  \n",
       "19980       64626                   0               1  \n",
       "3671       212137                   1               1  \n",
       "\n",
       "[17290 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>above_median_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20962</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12284</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7343</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14247</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16670</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15031</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5234</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19980</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3671</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17290 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       above_median_price\n",
       "20962                   0\n",
       "12284                   1\n",
       "7343                    1\n",
       "14247                   0\n",
       "16670                   1\n",
       "...                   ...\n",
       "88                      0\n",
       "15031                   1\n",
       "5234                    1\n",
       "19980                   0\n",
       "3671                    1\n",
       "\n",
       "[17290 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'X_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>above_median_price</th>\n",
       "      <th>price_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11592</th>\n",
       "      <td>2028701000</td>\n",
       "      <td>20140529T000000</td>\n",
       "      <td>635200.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1640</td>\n",
       "      <td>4240</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>720</td>\n",
       "      <td>1921</td>\n",
       "      <td>0</td>\n",
       "      <td>98117</td>\n",
       "      <td>47.6766</td>\n",
       "      <td>-122.368</td>\n",
       "      <td>1300</td>\n",
       "      <td>4240</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8984</th>\n",
       "      <td>9406500530</td>\n",
       "      <td>20140912T000000</td>\n",
       "      <td>249000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1090</td>\n",
       "      <td>1357</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7526</td>\n",
       "      <td>-122.244</td>\n",
       "      <td>1078</td>\n",
       "      <td>1318</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8280</th>\n",
       "      <td>8097000330</td>\n",
       "      <td>20140721T000000</td>\n",
       "      <td>359950.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2540</td>\n",
       "      <td>8604</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>98092</td>\n",
       "      <td>47.3209</td>\n",
       "      <td>-122.185</td>\n",
       "      <td>2260</td>\n",
       "      <td>7438</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>8081020370</td>\n",
       "      <td>20140709T000000</td>\n",
       "      <td>1355000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3550</td>\n",
       "      <td>11000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1290</td>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>98006</td>\n",
       "      <td>47.5506</td>\n",
       "      <td>-122.134</td>\n",
       "      <td>4100</td>\n",
       "      <td>10012</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10371</th>\n",
       "      <td>7518507580</td>\n",
       "      <td>20150502T000000</td>\n",
       "      <td>581000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1170</td>\n",
       "      <td>4080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1909</td>\n",
       "      <td>0</td>\n",
       "      <td>98117</td>\n",
       "      <td>47.6784</td>\n",
       "      <td>-122.386</td>\n",
       "      <td>1560</td>\n",
       "      <td>4586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16733</th>\n",
       "      <td>7212650950</td>\n",
       "      <td>20140708T000000</td>\n",
       "      <td>336000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2530</td>\n",
       "      <td>8169</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>98003</td>\n",
       "      <td>47.2634</td>\n",
       "      <td>-122.312</td>\n",
       "      <td>2220</td>\n",
       "      <td>8013</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13151</th>\n",
       "      <td>4365200620</td>\n",
       "      <td>20150312T000000</td>\n",
       "      <td>394000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1450</td>\n",
       "      <td>7930</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>300</td>\n",
       "      <td>1923</td>\n",
       "      <td>0</td>\n",
       "      <td>98126</td>\n",
       "      <td>47.5212</td>\n",
       "      <td>-122.371</td>\n",
       "      <td>1040</td>\n",
       "      <td>7740</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11667</th>\n",
       "      <td>4083304355</td>\n",
       "      <td>20150318T000000</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1530</td>\n",
       "      <td>3615</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1913</td>\n",
       "      <td>0</td>\n",
       "      <td>98103</td>\n",
       "      <td>47.6529</td>\n",
       "      <td>-122.334</td>\n",
       "      <td>1650</td>\n",
       "      <td>4200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>2891100820</td>\n",
       "      <td>20140825T000000</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1220</td>\n",
       "      <td>6000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1968</td>\n",
       "      <td>0</td>\n",
       "      <td>98002</td>\n",
       "      <td>47.3245</td>\n",
       "      <td>-122.209</td>\n",
       "      <td>1420</td>\n",
       "      <td>6000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12059</th>\n",
       "      <td>952000640</td>\n",
       "      <td>20141027T000000</td>\n",
       "      <td>715000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1670</td>\n",
       "      <td>5060</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1925</td>\n",
       "      <td>0</td>\n",
       "      <td>98126</td>\n",
       "      <td>47.5671</td>\n",
       "      <td>-122.379</td>\n",
       "      <td>1670</td>\n",
       "      <td>5118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4323 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id             date      price  bedrooms  bathrooms  \\\n",
       "11592  2028701000  20140529T000000   635200.0         4       1.75   \n",
       "8984   9406500530  20140912T000000   249000.0         2       2.00   \n",
       "8280   8097000330  20140721T000000   359950.0         3       2.75   \n",
       "792    8081020370  20140709T000000  1355000.0         4       3.50   \n",
       "10371  7518507580  20150502T000000   581000.0         2       1.00   \n",
       "...           ...              ...        ...       ...        ...   \n",
       "16733  7212650950  20140708T000000   336000.0         4       2.50   \n",
       "13151  4365200620  20150312T000000   394000.0         3       1.00   \n",
       "11667  4083304355  20150318T000000   675000.0         4       1.75   \n",
       "3683   2891100820  20140825T000000   213500.0         3       1.00   \n",
       "12059   952000640  20141027T000000   715000.0         3       1.50   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  sqft_basement  \\\n",
       "11592         1640      4240     1.0           0     0  ...            720   \n",
       "8984          1090      1357     2.0           0     0  ...              0   \n",
       "8280          2540      8604     2.0           0     0  ...              0   \n",
       "792           3550     11000     1.0           0     2  ...           1290   \n",
       "10371         1170      4080     1.0           0     0  ...              0   \n",
       "...            ...       ...     ...         ...   ...  ...            ...   \n",
       "16733         2530      8169     2.0           0     0  ...              0   \n",
       "13151         1450      7930     1.0           0     0  ...            300   \n",
       "11667         1530      3615     1.5           0     0  ...              0   \n",
       "3683          1220      6000     1.0           0     0  ...              0   \n",
       "12059         1670      5060     2.0           0     2  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "11592      1921             0    98117  47.6766 -122.368           1300   \n",
       "8984       1990             0    98028  47.7526 -122.244           1078   \n",
       "8280       1991             0    98092  47.3209 -122.185           2260   \n",
       "792        1999             0    98006  47.5506 -122.134           4100   \n",
       "10371      1909             0    98117  47.6784 -122.386           1560   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "16733      1993             0    98003  47.2634 -122.312           2220   \n",
       "13151      1923             0    98126  47.5212 -122.371           1040   \n",
       "11667      1913             0    98103  47.6529 -122.334           1650   \n",
       "3683       1968             0    98002  47.3245 -122.209           1420   \n",
       "12059      1925             0    98126  47.5671 -122.379           1670   \n",
       "\n",
       "       sqft_lot15  above_median_price  price_category  \n",
       "11592        4240                   1               1  \n",
       "8984         1318                   0               0  \n",
       "8280         7438                   0               1  \n",
       "792         10012                   1               2  \n",
       "10371        4586                   1               1  \n",
       "...           ...                 ...             ...  \n",
       "16733        8013                   0               1  \n",
       "13151        7740                   0               1  \n",
       "11667        4200                   1               1  \n",
       "3683         6000                   0               0  \n",
       "12059        5118                   1               2  \n",
       "\n",
       "[4323 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>above_median_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11592</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8984</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8280</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10371</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16733</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13151</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11667</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3683</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12059</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4323 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       above_median_price\n",
       "11592                   1\n",
       "8984                    0\n",
       "8280                    0\n",
       "792                     1\n",
       "10371                   1\n",
       "...                   ...\n",
       "16733                   0\n",
       "13151                   0\n",
       "11667                   1\n",
       "3683                    0\n",
       "12059                   1\n",
       "\n",
       "[4323 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                       int64\n",
      "date                    object\n",
      "price                  float64\n",
      "bedrooms                 int64\n",
      "bathrooms              float64\n",
      "sqft_living              int64\n",
      "sqft_lot                 int64\n",
      "floors                 float64\n",
      "waterfront               int64\n",
      "view                     int64\n",
      "condition                int64\n",
      "grade                    int64\n",
      "sqft_above               int64\n",
      "sqft_basement            int64\n",
      "yr_built                 int64\n",
      "yr_renovated             int64\n",
      "zipcode                  int64\n",
      "lat                    float64\n",
      "long                   float64\n",
      "sqft_living15            int64\n",
      "sqft_lot15               int64\n",
      "above_median_price       int64\n",
      "price_category        category\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from typing import Tuple\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "# Создание целевого признака\n",
    "median_price = df['price'].median()\n",
    "df['above_median_price'] = np.where(df['price'] > median_price, 1, 0)\n",
    "\n",
    "# Разделение на признаки и целевую переменную\n",
    "X = df.drop(columns=['id', 'date', 'price', 'above_median_price'])\n",
    "y = df['above_median_price']\n",
    "\n",
    "# Примерная категоризация\n",
    "df['price_category'] = pd.cut(df['price'], bins=[0, 300000, 700000, np.inf], labels=[0, 1, 2])\n",
    "\n",
    "# Выбор признаков и целевых переменных\n",
    "X = df.drop(columns=['id', 'date', 'price', 'price_category'])\n",
    "\n",
    "\n",
    "def split_stratified_into_train_val_test(\n",
    "    df_input,\n",
    "    stratify_colname=\"y\",\n",
    "    frac_train=0.6,\n",
    "    frac_val=0.15,\n",
    "    frac_test=0.25,\n",
    "    random_state=None,\n",
    ") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame, DataFrame, DataFrame]:\n",
    "   \n",
    "    if frac_train + frac_val + frac_test != 1.0:\n",
    "        raise ValueError(\n",
    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
    "            % (frac_train, frac_val, frac_test)\n",
    "        )\n",
    "    \n",
    "    if stratify_colname not in df_input.columns:\n",
    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
    "    X = df_input  # Contains all columns.\n",
    "    y = df_input[\n",
    "        [stratify_colname]\n",
    "    ]  # Dataframe of just the column on which to stratify.\n",
    "    \n",
    "    # Split original dataframe into train and temp dataframes.\n",
    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
    "        X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state\n",
    "    )\n",
    "\n",
    "    if frac_val <= 0:\n",
    "        assert len(df_input) == len(df_train) + len(df_temp)\n",
    "        return df_train, pd.DataFrame(), df_temp, y_train, pd.DataFrame(), y_temp\n",
    "    # Split the temp dataframe into val and test dataframes.\n",
    "    relative_frac_test = frac_test / (frac_val + frac_test)\n",
    "\n",
    "    df_val, df_test, y_val, y_test = train_test_split(\n",
    "        df_temp,\n",
    "        y_temp,\n",
    "        stratify=y_temp,\n",
    "        test_size=relative_frac_test,\n",
    "        random_state=random_state,\n",
    "    )\n",
    "\n",
    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
    "    return df_train, df_val, df_test, y_train, y_val, y_test\n",
    "\n",
    "X_train, X_val, X_test, y_train, y_val, y_test = split_stratified_into_train_val_test(\n",
    "    df, stratify_colname=\"above_median_price\", frac_train=0.80, frac_val=0, frac_test=0.20, random_state=42\n",
    ")\n",
    "\n",
    "display(\"X_train\", X_train)\n",
    "display(\"y_train\", y_train)\n",
    "\n",
    "display(\"X_test\", X_test)\n",
    "display(\"y_test\", y_test)\n",
    "\n",
    "\n",
    "# Проверка преобразования\n",
    "print(df.dtypes)\n",
    "\n",
    "# Визуализация распределения цен\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['price'], bins=50, kde=True)\n",
    "plt.title('Распределение цен на недвижимость')\n",
    "plt.xlabel('Цена')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Визуализация зависимости между ценой и количеством спален\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='bedrooms', y='price', data=df)\n",
    "plt.title('Зависимость цены от количества спален')\n",
    "plt.xlabel('Количество спален')\n",
    "plt.ylabel('Цена')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Построение конвейеров предобработки  \n",
    "Создадим пайплайн для числовых и категориальных данных.  \n",
    "\n",
    "preprocessing_num -- конвейер для обработки числовых данных: заполнение пропущенных значений и стандартизация\n",
    "\n",
    "preprocessing_cat -- конвейер для обработки категориальных данных: заполнение пропущенных данных и унитарное кодирование\n",
    "\n",
    "features_preprocessing -- трансформер для предобработки признаков\n",
    "\n",
    "features_engineering -- трансформер для конструирования признаков\n",
    "\n",
    "drop_columns -- трансформер для удаления колонок\n",
    "\n",
    "pipeline_end -- основной конвейер предобработки данных и конструирования признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.discriminant_analysis import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline_end = StandardScaler()\n",
    "\n",
    "\n",
    "# Построение конвейеров предобработки\n",
    "\n",
    "class HouseFeatures(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        # Создание новых признаков\n",
    "        X = X.copy()\n",
    "        X[\"Living_area_to_Lot_ratio\"] = X[\"sqft_living\"] / X[\"sqft_lot\"]\n",
    "        return X\n",
    "    def get_feature_names_out(self, features_in):\n",
    "        # Добавление имен новых признаков\n",
    "        new_features = [\"Living_area_to_Lot_ratio\"]\n",
    "        return np.append(features_in, new_features, axis=0)\n",
    "\n",
    "\n",
    "# Обработка числовых данных. Числовой конвейр: заполнение пропущенных значений медианой и стандартизация\n",
    "preprocessing_num_class = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "preprocessing_cat_class = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])\n",
    "\n",
    "columns_to_drop = [\"date\"]\n",
    "numeric_columns = [\"sqft_living\", \"sqft_lot\", \"above_median_price\"]\n",
    "cat_columns = []\n",
    "\n",
    "features_preprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"prepocessing_num\", preprocessing_num_class, numeric_columns),\n",
    "        (\"prepocessing_cat\", preprocessing_cat_class, cat_columns),\n",
    "    ],\n",
    "    remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "drop_columns = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"drop_columns\", \"drop\", columns_to_drop),\n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "features_postprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        ('preprocessing_cat', preprocessing_cat_class, [\"price_category\"]),\n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "pipeline_end = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "        (\"custom_features\", HouseFeatures()),\n",
    "        (\"drop_columns\", drop_columns),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Демонстрация работы конвейра для предобработки данных при классификации**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>above_median_price</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</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",
       "      <th>price_category</th>\n",
       "      <th>Living_area_to_Lot_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20962</th>\n",
       "      <td>-1.360742</td>\n",
       "      <td>-0.262132</td>\n",
       "      <td>-0.994693</td>\n",
       "      <td>1278000210</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1968</td>\n",
       "      <td>2007</td>\n",
       "      <td>98001</td>\n",
       "      <td>47.2655</td>\n",
       "      <td>-122.244</td>\n",
       "      <td>828</td>\n",
       "      <td>5402</td>\n",
       "      <td>0</td>\n",
       "      <td>5.191063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12284</th>\n",
       "      <td>0.794390</td>\n",
       "      <td>-0.094121</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>2193300390</td>\n",
       "      <td>624000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1130</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>98052</td>\n",
       "      <td>47.6920</td>\n",
       "      <td>-122.099</td>\n",
       "      <td>2110</td>\n",
       "      <td>11250</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.440052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7343</th>\n",
       "      <td>0.837884</td>\n",
       "      <td>-0.272723</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>4289900005</td>\n",
       "      <td>1535000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1030</td>\n",
       "      <td>1908</td>\n",
       "      <td>2003</td>\n",
       "      <td>98122</td>\n",
       "      <td>47.6147</td>\n",
       "      <td>-122.285</td>\n",
       "      <td>2130</td>\n",
       "      <td>4200</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.072292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14247</th>\n",
       "      <td>-0.782270</td>\n",
       "      <td>-0.196986</td>\n",
       "      <td>-0.994693</td>\n",
       "      <td>316000145</td>\n",
       "      <td>235000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1941</td>\n",
       "      <td>0</td>\n",
       "      <td>98168</td>\n",
       "      <td>47.5054</td>\n",
       "      <td>-122.301</td>\n",
       "      <td>1280</td>\n",
       "      <td>7175</td>\n",
       "      <td>0</td>\n",
       "      <td>3.971201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16670</th>\n",
       "      <td>1.011860</td>\n",
       "      <td>0.024330</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>629400480</td>\n",
       "      <td>775000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>98075</td>\n",
       "      <td>47.5895</td>\n",
       "      <td>-121.994</td>\n",
       "      <td>3330</td>\n",
       "      <td>12333</td>\n",
       "      <td>2</td>\n",
       "      <td>41.589045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>-0.510432</td>\n",
       "      <td>-0.324180</td>\n",
       "      <td>-0.994693</td>\n",
       "      <td>1332700270</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1979</td>\n",
       "      <td>0</td>\n",
       "      <td>98056</td>\n",
       "      <td>47.5180</td>\n",
       "      <td>-122.194</td>\n",
       "      <td>1950</td>\n",
       "      <td>2025</td>\n",
       "      <td>0</td>\n",
       "      <td>1.574534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15031</th>\n",
       "      <td>1.044481</td>\n",
       "      <td>-0.314813</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>7129303070</td>\n",
       "      <td>735000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "      <td>98118</td>\n",
       "      <td>47.5188</td>\n",
       "      <td>-122.256</td>\n",
       "      <td>2620</td>\n",
       "      <td>2433</td>\n",
       "      <td>2</td>\n",
       "      <td>-3.317784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5234</th>\n",
       "      <td>-0.456065</td>\n",
       "      <td>-0.136611</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>2432000130</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1956</td>\n",
       "      <td>0</td>\n",
       "      <td>98033</td>\n",
       "      <td>47.6503</td>\n",
       "      <td>-122.198</td>\n",
       "      <td>2090</td>\n",
       "      <td>9549</td>\n",
       "      <td>1</td>\n",
       "      <td>3.338418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19980</th>\n",
       "      <td>0.566046</td>\n",
       "      <td>1.239169</td>\n",
       "      <td>-0.994693</td>\n",
       "      <td>774100475</td>\n",
       "      <td>415000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>98014</td>\n",
       "      <td>47.7185</td>\n",
       "      <td>-121.405</td>\n",
       "      <td>1740</td>\n",
       "      <td>64626</td>\n",
       "      <td>1</td>\n",
       "      <td>0.456795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3671</th>\n",
       "      <td>0.370323</td>\n",
       "      <td>4.836825</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>8847400115</td>\n",
       "      <td>590000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>98010</td>\n",
       "      <td>47.3666</td>\n",
       "      <td>-121.978</td>\n",
       "      <td>3180</td>\n",
       "      <td>212137</td>\n",
       "      <td>1</td>\n",
       "      <td>0.076563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17290 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sqft_living  sqft_lot  above_median_price          id      price  \\\n",
       "20962    -1.360742 -0.262132           -0.994693  1278000210   110000.0   \n",
       "12284     0.794390 -0.094121            1.005335  2193300390   624000.0   \n",
       "7343      0.837884 -0.272723            1.005335  4289900005  1535000.0   \n",
       "14247    -0.782270 -0.196986           -0.994693   316000145   235000.0   \n",
       "16670     1.011860  0.024330            1.005335   629400480   775000.0   \n",
       "...            ...       ...                 ...         ...        ...   \n",
       "88       -0.510432 -0.324180           -0.994693  1332700270   215000.0   \n",
       "15031     1.044481 -0.314813            1.005335  7129303070   735000.0   \n",
       "5234     -0.456065 -0.136611            1.005335  2432000130   675000.0   \n",
       "19980     0.566046  1.239169           -0.994693   774100475   415000.0   \n",
       "3671      0.370323  4.836825            1.005335  8847400115   590000.0   \n",
       "\n",
       "       bedrooms  bathrooms  floors  waterfront  view  ...  sqft_basement  \\\n",
       "20962         2       1.00     1.0           0     0  ...              0   \n",
       "12284         4       3.25     1.0           0     0  ...           1130   \n",
       "7343          4       3.25     2.0           0     3  ...           1030   \n",
       "14247         4       1.00     1.5           0     0  ...              0   \n",
       "16670         4       2.75     2.0           0     0  ...              0   \n",
       "...         ...        ...     ...         ...   ...  ...            ...   \n",
       "88            2       2.25     2.0           0     0  ...              0   \n",
       "15031         4       2.75     2.0           1     4  ...              0   \n",
       "5234          3       1.75     1.0           0     0  ...              0   \n",
       "19980         3       2.75     1.5           0     0  ...              0   \n",
       "3671          3       2.00     1.5           0     0  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "20962      1968          2007    98001  47.2655 -122.244            828   \n",
       "12284      1980             0    98052  47.6920 -122.099           2110   \n",
       "7343       1908          2003    98122  47.6147 -122.285           2130   \n",
       "14247      1941             0    98168  47.5054 -122.301           1280   \n",
       "16670      1996             0    98075  47.5895 -121.994           3330   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "88         1979             0    98056  47.5180 -122.194           1950   \n",
       "15031      1966             0    98118  47.5188 -122.256           2620   \n",
       "5234       1956             0    98033  47.6503 -122.198           2090   \n",
       "19980      2009             0    98014  47.7185 -121.405           1740   \n",
       "3671       2005             0    98010  47.3666 -121.978           3180   \n",
       "\n",
       "       sqft_lot15  price_category  Living_area_to_Lot_ratio  \n",
       "20962        5402               0                  5.191063  \n",
       "12284       11250               1                 -8.440052  \n",
       "7343         4200               2                 -3.072292  \n",
       "14247        7175               0                  3.971201  \n",
       "16670       12333               2                 41.589045  \n",
       "...           ...             ...                       ...  \n",
       "88           2025               0                  1.574534  \n",
       "15031        2433               2                 -3.317784  \n",
       "5234         9549               1                  3.338418  \n",
       "19980       64626               1                  0.456795  \n",
       "3671       212137               1                  0.076563  \n",
       "\n",
       "[17290 rows x 23 columns]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_result = pipeline_end.fit_transform(X_train)\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": [
    "## Формирование набора моделей для классификации\n",
    "\n",
    "logistic -- логистическая регрессия\n",
    "\n",
    "ridge -- гребневая регрессия\n",
    "\n",
    "decision_tree -- дерево решений\n",
    "\n",
    "knn -- k-ближайших соседей\n",
    "\n",
    "naive_bayes -- наивный Байесовский классификатор\n",
    "\n",
    "gradient_boosting -- метод градиентного бустинга (набор деревьев решений)\n",
    "\n",
    "random_forest -- метод случайного леса (набор деревьев решений)\n",
    "\n",
    "mlp -- многослойный персептрон (нейронная сеть)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import ensemble, linear_model, naive_bayes, neighbors, neural_network, tree, svm\n",
    "\n",
    "class_models = {\n",
    "    \"logistic\": {\"model\": linear_model.LogisticRegression(max_iter=150)},\n",
    "    \"ridge\": {\"model\": linear_model.RidgeClassifierCV(cv=5, class_weight=\"balanced\")},\n",
    "    \"ridge\": {\"model\": linear_model.LogisticRegression(max_iter=150, solver='lbfgs', penalty=\"l2\", class_weight=\"balanced\")},\n",
    "    \"decision_tree\": {\n",
    "        \"model\": tree.DecisionTreeClassifier(max_depth=5, min_samples_split=10, random_state=random_state)\n",
    "    },\n",
    "\n",
    "    \"knn\": {\"model\": neighbors.KNeighborsClassifier(n_neighbors=7)},\n",
    "    \"naive_bayes\": {\"model\": naive_bayes.GaussianNB()},\n",
    "    \"gradient_boosting\": {\n",
    "        \"model\": ensemble.GradientBoostingClassifier(n_estimators=210)\n",
    "    },\n",
    "\n",
    "    \"random_forest\": {\n",
    "        \"model\": ensemble.RandomForestClassifier(\n",
    "            max_depth=5, class_weight=\"balanced\", random_state=random_state\n",
    "        )\n",
    "    },\n",
    "\n",
    "    \"mlp\": {\n",
    "        \"model\": neural_network.MLPClassifier(\n",
    "            hidden_layer_sizes=(7,),\n",
    "            max_iter=200,\n",
    "            early_stopping=True,\n",
    "            random_state=random_state,\n",
    "        )\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Обучение моделей на обучающем наборе данных и оценка на тестовом**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: logistic\n",
      "Model: ridge\n",
      "Model: decision_tree\n",
      "Model: knn\n",
      "Model: naive_bayes\n",
      "Model: gradient_boosting\n",
      "Model: random_forest\n",
      "Model: mlp\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "\n",
    "for model_name in class_models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    model = class_models[model_name][\"model\"]\n",
    "\n",
    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
    "    model_pipeline = model_pipeline.fit(X_train, y_train.values.ravel())\n",
    "\n",
    "    y_train_predict = model_pipeline.predict(X_train)\n",
    "    y_test_probs = model_pipeline.predict_proba(X_test)[:, 1]\n",
    "    y_test_predict = np.where(y_test_probs > 0.5, 1, 0)\n",
    "\n",
    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
    "    class_models[model_name][\"probs\"] = y_test_probs\n",
    "    class_models[model_name][\"preds\"] = y_test_predict\n",
    "\n",
    "    class_models[model_name][\"Precision_train\"] = metrics.precision_score(\n",
    "        y_train, y_train_predict, zero_division=1\n",
    "    )\n",
    "    class_models[model_name][\"Precision_test\"] = metrics.precision_score(\n",
    "        y_test, y_test_predict, zero_division=1\n",
    "    )\n",
    "    class_models[model_name][\"Recall_train\"] = metrics.recall_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Recall_test\"] = metrics.recall_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_train\"] = metrics.accuracy_score(\n",
    "        y_train, y_train_predict\n",
    "    )\n",
    "    class_models[model_name][\"Accuracy_test\"] = metrics.accuracy_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"ROC_AUC_test\"] = metrics.roc_auc_score(\n",
    "        y_test, y_test_probs\n",
    "    )\n",
    "    class_models[model_name][\"F1_train\"] = metrics.f1_score(y_train, y_train_predict)\n",
    "    class_models[model_name][\"F1_test\"] = metrics.f1_score(y_test, y_test_predict)\n",
    "    class_models[model_name][\"MCC_test\"] = metrics.matthews_corrcoef(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(\n",
    "        y_test, y_test_predict\n",
    "    )\n",
    "    class_models[model_name][\"Confusion_matrix\"] = metrics.confusion_matrix(\n",
    "        y_test, y_test_predict\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Сводная таблица оценок качества для использованных моделей классификации¶\n",
    "Матрица неточностей**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "_, ax = plt.subplots(int(len(class_models) / 2), 2, figsize=(12, 10), sharex=False, sharey=False)\n",
    "for index, key in enumerate(class_models.keys()):\n",
    "    c_matrix = class_models[key][\"Confusion_matrix\"]\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Less\", \"More\"]\n",
    "    ).plot(ax=ax.flat[index])\n",
    "    disp.ax_.set_title(key)\n",
    "\n",
    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Значение 2173 в желтом квадрате представляет собой количество объектов, относимых к классу \"Less\", которые модель правильно классифицировала. Это свидетельствует о высоком уровне точности в идентификации этого класса. Значение 2150 в жёлтом нижнем правом квадрате указывает на количество правильно классифицированных объектов класса \"More\". Хотя это также является положительным результатом, мы можем заметить, что он местами ниже, чем для класса \"Less\", а местами и выше.\n",
    "\n",
    "Точность, полнота, верность (аккуратность), F-мера"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c780a_row0_col0, #T_c780a_row0_col1, #T_c780a_row0_col2, #T_c780a_row0_col3, #T_c780a_row1_col0, #T_c780a_row1_col1, #T_c780a_row1_col2, #T_c780a_row1_col3, #T_c780a_row2_col0, #T_c780a_row2_col1, #T_c780a_row2_col2, #T_c780a_row2_col3, #T_c780a_row3_col0, #T_c780a_row3_col1, #T_c780a_row3_col2, #T_c780a_row3_col3, #T_c780a_row4_col0, #T_c780a_row4_col1, #T_c780a_row4_col2, #T_c780a_row4_col3, #T_c780a_row5_col0, #T_c780a_row5_col1 {\n",
       "  background-color: #a8db34;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c780a_row0_col4, #T_c780a_row0_col5, #T_c780a_row0_col6, #T_c780a_row0_col7, #T_c780a_row1_col4, #T_c780a_row1_col5, #T_c780a_row1_col6, #T_c780a_row1_col7, #T_c780a_row2_col4, #T_c780a_row2_col5, #T_c780a_row2_col6, #T_c780a_row2_col7, #T_c780a_row3_col4, #T_c780a_row3_col5, #T_c780a_row3_col6, #T_c780a_row3_col7, #T_c780a_row4_col4, #T_c780a_row4_col5, #T_c780a_row4_col6, #T_c780a_row4_col7 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row5_col2 {\n",
       "  background-color: #6ccd5a;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c780a_row5_col3 {\n",
       "  background-color: #6ece58;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c780a_row5_col4 {\n",
       "  background-color: #c43e7f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row5_col5 {\n",
       "  background-color: #c5407e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row5_col6, #T_c780a_row5_col7 {\n",
       "  background-color: #ce4b75;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row6_col0 {\n",
       "  background-color: #40bd72;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row6_col1 {\n",
       "  background-color: #38b977;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row6_col2 {\n",
       "  background-color: #7fd34e;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c780a_row6_col3 {\n",
       "  background-color: #75d054;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c780a_row6_col4 {\n",
       "  background-color: #be3885;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row6_col5 {\n",
       "  background-color: #b42e8d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row6_col6 {\n",
       "  background-color: #cc4977;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row6_col7 {\n",
       "  background-color: #c8437b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row7_col0, #T_c780a_row7_col1, #T_c780a_row7_col2, #T_c780a_row7_col3 {\n",
       "  background-color: #26818e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c780a_row7_col4, #T_c780a_row7_col5, #T_c780a_row7_col6, #T_c780a_row7_col7 {\n",
       "  background-color: #4e02a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c780a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c780a_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_c780a_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_c780a_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
       "      <th id=\"T_c780a_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
       "      <th id=\"T_c780a_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
       "      <th id=\"T_c780a_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
       "      <th id=\"T_c780a_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_c780a_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c780a_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
       "      <td id=\"T_c780a_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row0_col2\" class=\"data row0 col2\" >0.999767</td>\n",
       "      <td id=\"T_c780a_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row0_col4\" class=\"data row0 col4\" >0.999884</td>\n",
       "      <td id=\"T_c780a_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row0_col6\" class=\"data row0 col6\" >0.999884</td>\n",
       "      <td id=\"T_c780a_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c780a_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
       "      <td id=\"T_c780a_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row1_col2\" class=\"data row1 col2\" >0.999651</td>\n",
       "      <td id=\"T_c780a_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row1_col4\" class=\"data row1 col4\" >0.999826</td>\n",
       "      <td id=\"T_c780a_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row1_col6\" class=\"data row1 col6\" >0.999826</td>\n",
       "      <td id=\"T_c780a_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c780a_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
       "      <td id=\"T_c780a_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row2_col6\" class=\"data row2 col6\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row2_col7\" class=\"data row2 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c780a_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
       "      <td id=\"T_c780a_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row3_col5\" class=\"data row3 col5\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row3_col7\" class=\"data row3 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c780a_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
       "      <td id=\"T_c780a_row4_col0\" class=\"data row4 col0\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row4_col5\" class=\"data row4 col5\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row4_col6\" class=\"data row4 col6\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row4_col7\" class=\"data row4 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c780a_level0_row5\" class=\"row_heading level0 row5\" >naive_bayes</th>\n",
       "      <td id=\"T_c780a_row5_col0\" class=\"data row5 col0\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "      <td id=\"T_c780a_row5_col2\" class=\"data row5 col2\" >0.786719</td>\n",
       "      <td id=\"T_c780a_row5_col3\" class=\"data row5 col3\" >0.793953</td>\n",
       "      <td id=\"T_c780a_row5_col4\" class=\"data row5 col4\" >0.893927</td>\n",
       "      <td id=\"T_c780a_row5_col5\" class=\"data row5 col5\" >0.897525</td>\n",
       "      <td id=\"T_c780a_row5_col6\" class=\"data row5 col6\" >0.880630</td>\n",
       "      <td id=\"T_c780a_row5_col7\" class=\"data row5 col7\" >0.885144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c780a_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
       "      <td id=\"T_c780a_row6_col0\" class=\"data row6 col0\" >0.872486</td>\n",
       "      <td id=\"T_c780a_row6_col1\" class=\"data row6 col1\" >0.827473</td>\n",
       "      <td id=\"T_c780a_row6_col2\" class=\"data row6 col2\" >0.857774</td>\n",
       "      <td id=\"T_c780a_row6_col3\" class=\"data row6 col3\" >0.820930</td>\n",
       "      <td id=\"T_c780a_row6_col4\" class=\"data row6 col4\" >0.866917</td>\n",
       "      <td id=\"T_c780a_row6_col5\" class=\"data row6 col5\" >0.825815</td>\n",
       "      <td id=\"T_c780a_row6_col6\" class=\"data row6 col6\" >0.865068</td>\n",
       "      <td id=\"T_c780a_row6_col7\" class=\"data row6 col7\" >0.824189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c780a_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_c780a_row7_col0\" class=\"data row7 col0\" >0.687500</td>\n",
       "      <td id=\"T_c780a_row7_col1\" class=\"data row7 col1\" >0.615385</td>\n",
       "      <td id=\"T_c780a_row7_col2\" class=\"data row7 col2\" >0.002558</td>\n",
       "      <td id=\"T_c780a_row7_col3\" class=\"data row7 col3\" >0.003721</td>\n",
       "      <td id=\"T_c780a_row7_col4\" class=\"data row7 col4\" >0.503355</td>\n",
       "      <td id=\"T_c780a_row7_col5\" class=\"data row7 col5\" >0.503354</td>\n",
       "      <td id=\"T_c780a_row7_col6\" class=\"data row7 col6\" >0.005098</td>\n",
       "      <td id=\"T_c780a_row7_col7\" class=\"data row7 col7\" >0.007397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x234a486d7f0>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "        \"Accuracy_train\",\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_train\",\n",
    "        \"F1_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(\n",
    "    by=\"Accuracy_test\", ascending=False\n",
    ").style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Действительно, если модели, включая логистическую регрессию (есть исключения), ридж-регрессию (есть исключения), дерево решений, случайный лес и градиентный бустинг, показывают 100% точность на обучающей выборке, это может свидетельствовать о переобучении. Переобучение (overfitting) происходит, когда модель слишком хорошо подстраивается под обучающие данные, включая шум и случайные вариации, и начинает плохо работать на новых данных (например, на тестовой выборке).  \n",
    "\n",
    "ROC-кривая, каппа Коэна, коэффициент корреляции Мэтьюса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9fdc4_row0_col0, #T_9fdc4_row0_col1, #T_9fdc4_row1_col0, #T_9fdc4_row1_col1, #T_9fdc4_row2_col0, #T_9fdc4_row2_col1, #T_9fdc4_row3_col0, #T_9fdc4_row3_col1, #T_9fdc4_row4_col0, #T_9fdc4_row4_col1 {\n",
       "  background-color: #a8db34;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9fdc4_row0_col2, #T_9fdc4_row0_col3, #T_9fdc4_row0_col4, #T_9fdc4_row1_col2, #T_9fdc4_row1_col3, #T_9fdc4_row1_col4, #T_9fdc4_row2_col2, #T_9fdc4_row2_col3, #T_9fdc4_row2_col4, #T_9fdc4_row3_col2, #T_9fdc4_row3_col3, #T_9fdc4_row3_col4, #T_9fdc4_row4_col2, #T_9fdc4_row4_col3, #T_9fdc4_row4_col4, #T_9fdc4_row5_col2 {\n",
       "  background-color: #da5a6a;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9fdc4_row5_col0 {\n",
       "  background-color: #6ece58;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9fdc4_row5_col1 {\n",
       "  background-color: #86d549;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9fdc4_row5_col3 {\n",
       "  background-color: #c5407e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9fdc4_row5_col4 {\n",
       "  background-color: #c7427c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9fdc4_row6_col0 {\n",
       "  background-color: #4cc26c;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9fdc4_row6_col1 {\n",
       "  background-color: #75d054;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9fdc4_row6_col2 {\n",
       "  background-color: #c8437b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9fdc4_row6_col3, #T_9fdc4_row6_col4 {\n",
       "  background-color: #b42e8d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9fdc4_row7_col0, #T_9fdc4_row7_col1 {\n",
       "  background-color: #26818e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9fdc4_row7_col2, #T_9fdc4_row7_col3, #T_9fdc4_row7_col4 {\n",
       "  background-color: #4e02a2;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9fdc4\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9fdc4_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_9fdc4_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_9fdc4_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_9fdc4_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_9fdc4_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9fdc4_level0_row0\" class=\"row_heading level0 row0\" >logistic</th>\n",
       "      <td id=\"T_9fdc4_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9fdc4_level0_row1\" class=\"row_heading level0 row1\" >ridge</th>\n",
       "      <td id=\"T_9fdc4_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9fdc4_level0_row2\" class=\"row_heading level0 row2\" >decision_tree</th>\n",
       "      <td id=\"T_9fdc4_row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row2_col4\" class=\"data row2 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9fdc4_level0_row3\" class=\"row_heading level0 row3\" >gradient_boosting</th>\n",
       "      <td id=\"T_9fdc4_row3_col0\" class=\"data row3 col0\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row3_col4\" class=\"data row3 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9fdc4_level0_row4\" class=\"row_heading level0 row4\" >random_forest</th>\n",
       "      <td id=\"T_9fdc4_row4_col0\" class=\"data row4 col0\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row4_col2\" class=\"data row4 col2\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row4_col3\" class=\"data row4 col3\" >1.000000</td>\n",
       "      <td id=\"T_9fdc4_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9fdc4_level0_row5\" class=\"row_heading level0 row5\" >naive_bayes</th>\n",
       "      <td id=\"T_9fdc4_row5_col0\" class=\"data row5 col0\" >0.897525</td>\n",
       "      <td id=\"T_9fdc4_row5_col1\" class=\"data row5 col1\" >0.885144</td>\n",
       "      <td id=\"T_9fdc4_row5_col2\" class=\"data row5 col2\" >0.999566</td>\n",
       "      <td id=\"T_9fdc4_row5_col3\" class=\"data row5 col3\" >0.794820</td>\n",
       "      <td id=\"T_9fdc4_row5_col4\" class=\"data row5 col4\" >0.812098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9fdc4_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
       "      <td id=\"T_9fdc4_row6_col0\" class=\"data row6 col0\" >0.825815</td>\n",
       "      <td id=\"T_9fdc4_row6_col1\" class=\"data row6 col1\" >0.824189</td>\n",
       "      <td id=\"T_9fdc4_row6_col2\" class=\"data row6 col2\" >0.910823</td>\n",
       "      <td id=\"T_9fdc4_row6_col3\" class=\"data row6 col3\" >0.651606</td>\n",
       "      <td id=\"T_9fdc4_row6_col4\" class=\"data row6 col4\" >0.651627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9fdc4_level0_row7\" class=\"row_heading level0 row7\" >mlp</th>\n",
       "      <td id=\"T_9fdc4_row7_col0\" class=\"data row7 col0\" >0.503354</td>\n",
       "      <td id=\"T_9fdc4_row7_col1\" class=\"data row7 col1\" >0.007397</td>\n",
       "      <td id=\"T_9fdc4_row7_col2\" class=\"data row7 col2\" >0.497071</td>\n",
       "      <td id=\"T_9fdc4_row7_col3\" class=\"data row7 col3\" >0.001427</td>\n",
       "      <td id=\"T_9fdc4_row7_col4\" class=\"data row7 col4\" >0.012966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2349e152690>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_metrics = pd.DataFrame.from_dict(class_models, \"index\")[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "]\n",
    "class_metrics.sort_values(by=\"ROC_AUC_test\", ascending=False).style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'logistic'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = str(class_metrics.sort_values(by=\"MCC_test\", ascending=False).iloc[0].name)\n",
    "\n",
    "display(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод данных с ошибкой предсказания для оценки**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Error items count: 0'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Predicted</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>...</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",
       "      <th>above_median_price</th>\n",
       "      <th>price_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, Predicted, date, price, bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, grade, sqft_above, sqft_basement, yr_built, yr_renovated, zipcode, lat, long, sqft_living15, sqft_lot15, above_median_price, price_category]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 24 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing_result = pipeline_end.transform(X_test)\n",
    "preprocessed_df = pd.DataFrame(\n",
    "    preprocessing_result,\n",
    "    columns=pipeline_end.get_feature_names_out(),\n",
    ")\n",
    "\n",
    "y_pred = class_models[best_model][\"preds\"]\n",
    "\n",
    "error_index = y_test[y_test[\"above_median_price\"] != y_pred].index.tolist()\n",
    "display(f\"Error items count: {len(error_index)}\")\n",
    "\n",
    "error_predicted = pd.Series(y_pred, index=y_test.index).loc[error_index]\n",
    "error_df = X_test.loc[error_index].copy()\n",
    "error_df.insert(loc=1, column=\"Predicted\", value=error_predicted)\n",
    "error_df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>above_median_price</th>\n",
       "      <th>price_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6863</th>\n",
       "      <td>1124000050</td>\n",
       "      <td>20140729T000000</td>\n",
       "      <td>461000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1260</td>\n",
       "      <td>8505</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1951</td>\n",
       "      <td>0</td>\n",
       "      <td>98177</td>\n",
       "      <td>47.7181</td>\n",
       "      <td>-122.371</td>\n",
       "      <td>1480</td>\n",
       "      <td>8100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id             date     price bedrooms bathrooms sqft_living  \\\n",
       "6863  1124000050  20140729T000000  461000.0        4       1.0        1260   \n",
       "\n",
       "     sqft_lot floors waterfront view  ... sqft_basement yr_built yr_renovated  \\\n",
       "6863     8505    1.5          0    0  ...             0     1951            0   \n",
       "\n",
       "     zipcode      lat     long sqft_living15 sqft_lot15 above_median_price  \\\n",
       "6863   98177  47.7181 -122.371          1480       8100                  1   \n",
       "\n",
       "     price_category  \n",
       "6863              1  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>above_median_price</th>\n",
       "      <th>id</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</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",
       "      <th>price_category</th>\n",
       "      <th>Living_area_to_Lot_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6863</th>\n",
       "      <td>-0.891006</td>\n",
       "      <td>-0.162689</td>\n",
       "      <td>1.005335</td>\n",
       "      <td>1.124000e+09</td>\n",
       "      <td>461000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98177.0</td>\n",
       "      <td>47.7181</td>\n",
       "      <td>-122.371</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.476729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      sqft_living  sqft_lot  above_median_price            id     price  \\\n",
       "6863    -0.891006 -0.162689            1.005335  1.124000e+09  461000.0   \n",
       "\n",
       "      bedrooms  bathrooms  floors  waterfront  view  ...  sqft_basement  \\\n",
       "6863       4.0        1.0     1.5         0.0   0.0  ...            0.0   \n",
       "\n",
       "      yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "6863    1951.0           0.0  98177.0  47.7181 -122.371         1480.0   \n",
       "\n",
       "      sqft_lot15  price_category  Living_area_to_Lot_ratio  \n",
       "6863      8100.0             1.0                  5.476729  \n",
       "\n",
       "[1 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'predicted: 1 (proba: [0. 1.])'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'real: 1'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = class_models[best_model][\"pipeline\"]\n",
    "\n",
    "example_id = 6863\n",
    "test = pd.DataFrame(X_test.loc[example_id, :]).T\n",
    "test_preprocessed = pd.DataFrame(preprocessed_df.loc[example_id, :]).T\n",
    "display(test)\n",
    "display(test_preprocessed)\n",
    "result_proba = model.predict_proba(test)[0]\n",
    "result = model.predict(test)[0]\n",
    "real = int(y_test.loc[example_id].values[0])\n",
    "display(f\"predicted: {result} (proba: {result_proba})\")\n",
    "display(f\"real: {real}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Подбор гиперпараметров методом поиска по сетке**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\numpy\\ma\\core.py:2881: RuntimeWarning: invalid value encountered in cast\n",
      "  _data = np.array(data, dtype=dtype, copy=copy,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'model__criterion': 'gini',\n",
       " 'model__max_depth': 5,\n",
       " 'model__max_features': 'sqrt',\n",
       " 'model__n_estimators': 10}"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "optimized_model_type = \"random_forest\"\n",
    "\n",
    "random_forest_model = class_models[optimized_model_type][\"pipeline\"]\n",
    "\n",
    "param_grid = {\n",
    "    \"model__n_estimators\": [10, 50, 100],\n",
    "    \"model__max_features\": [\"sqrt\", \"log2\"],\n",
    "    \"model__max_depth\": [5, 7, 10],\n",
    "    \"model__criterion\": [\"gini\", \"entropy\"],\n",
    "}\n",
    "\n",
    "gs_optomizer = GridSearchCV(\n",
    "    estimator=random_forest_model, param_grid=param_grid, n_jobs=-1\n",
    ")\n",
    "gs_optomizer.fit(X_train, y_train.values.ravel())\n",
    "gs_optomizer.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Обучение модели с новыми гиперпараметрами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_model = ensemble.RandomForestClassifier(\n",
    "    random_state=random_state,\n",
    "    criterion=\"gini\",\n",
    "    max_depth=5,\n",
    "    max_features=\"log2\",\n",
    "    n_estimators=10,\n",
    ")\n",
    "\n",
    "result = {}\n",
    "\n",
    "result[\"pipeline\"] = Pipeline([(\"pipeline\", pipeline_end), (\"model\", optimized_model)]).fit(X_train, y_train.values.ravel())\n",
    "result[\"train_preds\"] = result[\"pipeline\"].predict(X_train)\n",
    "result[\"probs\"] = result[\"pipeline\"].predict_proba(X_test)[:, 1]\n",
    "result[\"preds\"] = np.where(result[\"probs\"] > 0.5, 1, 0)\n",
    "\n",
    "result[\"Precision_train\"] = metrics.precision_score(y_train, result[\"train_preds\"])\n",
    "result[\"Precision_test\"] = metrics.precision_score(y_test, result[\"preds\"])\n",
    "result[\"Recall_train\"] = metrics.recall_score(y_train, result[\"train_preds\"])\n",
    "result[\"Recall_test\"] = metrics.recall_score(y_test, result[\"preds\"])\n",
    "result[\"Accuracy_train\"] = metrics.accuracy_score(y_train, result[\"train_preds\"])\n",
    "result[\"Accuracy_test\"] = metrics.accuracy_score(y_test, result[\"preds\"])\n",
    "result[\"ROC_AUC_test\"] = metrics.roc_auc_score(y_test, result[\"probs\"])\n",
    "result[\"F1_train\"] = metrics.f1_score(y_train, result[\"train_preds\"])\n",
    "result[\"F1_test\"] = metrics.f1_score(y_test, result[\"preds\"])\n",
    "result[\"MCC_test\"] = metrics.matthews_corrcoef(y_test, result[\"preds\"])\n",
    "result[\"Cohen_kappa_test\"] = metrics.cohen_kappa_score(y_test, result[\"preds\"])\n",
    "result[\"Confusion_matrix\"] = metrics.confusion_matrix(y_test, result[\"preds\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Формирование данных для оценки старой и новой версии модели**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimized_metrics = pd.DataFrame(columns=list(result.keys()))\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=class_models[optimized_model_type]\n",
    ")\n",
    "optimized_metrics.loc[len(optimized_metrics)] = pd.Series(\n",
    "    data=result\n",
    ")\n",
    "optimized_metrics.insert(loc=0, column=\"Name\", value=[\"Old\", \"New\"])\n",
    "optimized_metrics = optimized_metrics.set_index(\"Name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Оценка параметров старой и новой модели**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_1e498_row0_col0, #T_1e498_row0_col1, #T_1e498_row0_col2, #T_1e498_row0_col3, #T_1e498_row1_col0, #T_1e498_row1_col1, #T_1e498_row1_col2, #T_1e498_row1_col3 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1e498_row0_col4, #T_1e498_row0_col5, #T_1e498_row0_col6, #T_1e498_row0_col7, #T_1e498_row1_col4, #T_1e498_row1_col5, #T_1e498_row1_col6, #T_1e498_row1_col7 {\n",
       "  background-color: #0d0887;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_1e498\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1e498_level0_col0\" class=\"col_heading level0 col0\" >Precision_train</th>\n",
       "      <th id=\"T_1e498_level0_col1\" class=\"col_heading level0 col1\" >Precision_test</th>\n",
       "      <th id=\"T_1e498_level0_col2\" class=\"col_heading level0 col2\" >Recall_train</th>\n",
       "      <th id=\"T_1e498_level0_col3\" class=\"col_heading level0 col3\" >Recall_test</th>\n",
       "      <th id=\"T_1e498_level0_col4\" class=\"col_heading level0 col4\" >Accuracy_train</th>\n",
       "      <th id=\"T_1e498_level0_col5\" class=\"col_heading level0 col5\" >Accuracy_test</th>\n",
       "      <th id=\"T_1e498_level0_col6\" class=\"col_heading level0 col6\" >F1_train</th>\n",
       "      <th id=\"T_1e498_level0_col7\" class=\"col_heading level0 col7\" >F1_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1e498_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
       "      <td id=\"T_1e498_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row0_col6\" class=\"data row0 col6\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row0_col7\" class=\"data row0 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1e498_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
       "      <td id=\"T_1e498_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
       "      <td id=\"T_1e498_row1_col7\" class=\"data row1 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x234c7e2ab40>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_metrics[\n",
    "    [\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "        \"Accuracy_train\",\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_train\",\n",
    "        \"F1_test\",\n",
    "    ]\n",
    "].style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\"Accuracy_train\", \"Accuracy_test\", \"F1_train\", \"F1_test\"],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Precision_train\",\n",
    "        \"Precision_test\",\n",
    "        \"Recall_train\",\n",
    "        \"Recall_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как для обучающей (Precision_train), так и для тестовой (Precision_test) выборки обе модели достигли идеальных значений 1.000000. Это указывает на то, что модели очень точно классифицируют положительные образцы, не пропуская их."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_1a980_row0_col0, #T_1a980_row0_col1, #T_1a980_row1_col0, #T_1a980_row1_col1 {\n",
       "  background-color: #440154;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_1a980_row0_col2, #T_1a980_row0_col3, #T_1a980_row0_col4, #T_1a980_row1_col2, #T_1a980_row1_col3, #T_1a980_row1_col4 {\n",
       "  background-color: #0d0887;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_1a980\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1a980_level0_col0\" class=\"col_heading level0 col0\" >Accuracy_test</th>\n",
       "      <th id=\"T_1a980_level0_col1\" class=\"col_heading level0 col1\" >F1_test</th>\n",
       "      <th id=\"T_1a980_level0_col2\" class=\"col_heading level0 col2\" >ROC_AUC_test</th>\n",
       "      <th id=\"T_1a980_level0_col3\" class=\"col_heading level0 col3\" >Cohen_kappa_test</th>\n",
       "      <th id=\"T_1a980_level0_col4\" class=\"col_heading level0 col4\" >MCC_test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1a980_level0_row0\" class=\"row_heading level0 row0\" >Old</th>\n",
       "      <td id=\"T_1a980_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_1a980_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_1a980_row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "      <td id=\"T_1a980_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_1a980_row0_col4\" class=\"data row0 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1a980_level0_row1\" class=\"row_heading level0 row1\" >New</th>\n",
       "      <td id=\"T_1a980_row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "      <td id=\"T_1a980_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_1a980_row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "      <td id=\"T_1a980_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_1a980_row1_col4\" class=\"data row1 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x234a466bbc0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimized_metrics[\n",
    "    [\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "        \"ROC_AUC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "        \"MCC_test\",\n",
    "    ]\n",
    "].style.background_gradient(\n",
    "    cmap=\"plasma\",\n",
    "    low=0.3,\n",
    "    high=1,\n",
    "    subset=[\n",
    "        \"ROC_AUC_test\",\n",
    "        \"MCC_test\",\n",
    "        \"Cohen_kappa_test\",\n",
    "    ],\n",
    ").background_gradient(\n",
    "    cmap=\"viridis\",\n",
    "    low=1,\n",
    "    high=0.3,\n",
    "    subset=[\n",
    "        \"Accuracy_test\",\n",
    "        \"F1_test\",\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оба варианта модели продемонстрировали безупречную точность классификации, достигнув значения 1.000000. Это свидетельствует о том, что модели точно классифицировали все тестовые примеры, не допустив никаких ошибок в предсказаниях."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=False, sharey=False\n",
    ")\n",
    "\n",
    "for index in range(0, len(optimized_metrics)):\n",
    "    c_matrix = optimized_metrics.iloc[index][\"Confusion_matrix\"]\n",
    "    disp = ConfusionMatrixDisplay(\n",
    "        confusion_matrix=c_matrix, display_labels=[\"Less\", \"More\"]\n",
    "    ).plot(ax=ax.flat[index])\n",
    "\n",
    "plt.subplots_adjust(top=1, bottom=0, hspace=0.4, wspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В желтом квадрате мы видим значение 2173, что обозначает количество правильно классифицированных объектов, отнесенных к классу \"Less\". Это свидетельствует о том, что модель успешно идентифицирует объекты этого класса, минимизируя количество ложных положительных срабатываний.\n",
    "\n",
    "В правом нижнем жёлтом квадрате значение 2150 указывает на количество правильно классифицированных объектов, отнесенных к классу \"More\". Это также является показателем высокой точности модели в определении объектов данного класса."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задача регресии: предсказание цены дома (price).\n",
    "\n",
    "Описание: Оценить, какая будет цена дома (price) на основе исторических данных о характеристиках домов, таких как площадь. Целевая переменная: Цена дома (price). (среднее значение)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Среднее значение поля: 2079.8997362698374\n",
      "           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  ...  yr_built  yr_renovated  zipcode  \\\n",
      "0      5650     1.0           0     0  ...      1955             0    98178   \n",
      "1      7242     2.0           0     0  ...      1951          1991    98125   \n",
      "2     10000     1.0           0     0  ...      1933             0    98028   \n",
      "3      5000     1.0           0     0  ...      1965             0    98136   \n",
      "4      8080     1.0           0     0  ...      1987             0    98074   \n",
      "\n",
      "       lat     long  sqft_living15  sqft_lot15  above_median_price  \\\n",
      "0  47.5112 -122.257           1340        5650                   0   \n",
      "1  47.7210 -122.319           1690        7639                   1   \n",
      "2  47.7379 -122.233           2720        8062                   0   \n",
      "3  47.5208 -122.393           1360        5000                   1   \n",
      "4  47.6168 -122.045           1800        7503                   1   \n",
      "\n",
      "   price_category  average_price  \n",
      "0               0              0  \n",
      "1               1              1  \n",
      "2               0              0  \n",
      "3               1              0  \n",
      "4               1              0  \n",
      "\n",
      "[5 rows x 24 columns]\n",
      "Статистическое описание DataFrame:\n",
      "                 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",
      "       ...  sqft_basement      yr_built  yr_renovated       zipcode  \\\n",
      "count  ...   21613.000000  21613.000000  21613.000000  21613.000000   \n",
      "mean   ...     291.509045   1971.005136     84.402258  98077.939805   \n",
      "std    ...     442.575043     29.373411    401.679240     53.505026   \n",
      "min    ...       0.000000   1900.000000      0.000000  98001.000000   \n",
      "25%    ...       0.000000   1951.000000      0.000000  98033.000000   \n",
      "50%    ...       0.000000   1975.000000      0.000000  98065.000000   \n",
      "75%    ...     560.000000   1997.000000      0.000000  98118.000000   \n",
      "max    ...    4820.000000   2015.000000   2015.000000  98199.000000   \n",
      "\n",
      "                lat          long  sqft_living15     sqft_lot15  \\\n",
      "count  21613.000000  21613.000000   21613.000000   21613.000000   \n",
      "mean      47.560053   -122.213896    1986.552492   12768.455652   \n",
      "std        0.138564      0.140828     685.391304   27304.179631   \n",
      "min       47.155900   -122.519000     399.000000     651.000000   \n",
      "25%       47.471000   -122.328000    1490.000000    5100.000000   \n",
      "50%       47.571800   -122.230000    1840.000000    7620.000000   \n",
      "75%       47.678000   -122.125000    2360.000000   10083.000000   \n",
      "max       47.777600   -121.315000    6210.000000  871200.000000   \n",
      "\n",
      "       above_median_price  average_price  \n",
      "count        21613.000000    21613.00000  \n",
      "mean             0.497340        0.42752  \n",
      "std              0.500004        0.49473  \n",
      "min              0.000000        0.00000  \n",
      "25%              0.000000        0.00000  \n",
      "50%              0.000000        0.00000  \n",
      "75%              1.000000        1.00000  \n",
      "max              1.000000        1.00000  \n",
      "\n",
      "[8 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import set_config\n",
    "\n",
    "set_config(transform_output=\"pandas\")\n",
    "\n",
    "# Опция для настройки генерации случайных чисел (если это нужно для других частей кода)\n",
    "random_state = 42\n",
    "\n",
    "# Вычисление среднего значения поля \"Close\"\n",
    "average_price = df['sqft_living'].mean()\n",
    "print(f\"Среднее значение поля: {average_price}\")\n",
    "\n",
    "# Создание новой колонки, указывающей, выше или ниже среднего значение цена закрытия\n",
    "df['average_price'] = (df['sqft_living'] > average_price).astype(int)\n",
    "\n",
    "# Удаление последней строки, где нет значения для следующего дня\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "# Вывод DataFrame с новой колонкой\n",
    "print(df.head())\n",
    "\n",
    "# Примерный анализ данных\n",
    "print(\"Статистическое описание DataFrame:\")\n",
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>above_median_price</th>\n",
       "      <th>price_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6325</th>\n",
       "      <td>5467910190</td>\n",
       "      <td>20140527T000000</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1780</td>\n",
       "      <td>13095</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1983</td>\n",
       "      <td>0</td>\n",
       "      <td>98042</td>\n",
       "      <td>47.3670</td>\n",
       "      <td>-122.152</td>\n",
       "      <td>2750</td>\n",
       "      <td>13095</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13473</th>\n",
       "      <td>9331800580</td>\n",
       "      <td>20150310T000000</td>\n",
       "      <td>257000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1000</td>\n",
       "      <td>3700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>1929</td>\n",
       "      <td>0</td>\n",
       "      <td>98118</td>\n",
       "      <td>47.5520</td>\n",
       "      <td>-122.290</td>\n",
       "      <td>1270</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17614</th>\n",
       "      <td>2407000405</td>\n",
       "      <td>20150226T000000</td>\n",
       "      <td>228500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1080</td>\n",
       "      <td>7486</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>1942</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.4838</td>\n",
       "      <td>-122.335</td>\n",
       "      <td>1170</td>\n",
       "      <td>7800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>5466700290</td>\n",
       "      <td>20150108T000000</td>\n",
       "      <td>288000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2090</td>\n",
       "      <td>7500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>810</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>98031</td>\n",
       "      <td>47.3951</td>\n",
       "      <td>-122.172</td>\n",
       "      <td>1800</td>\n",
       "      <td>7350</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20868</th>\n",
       "      <td>3026059361</td>\n",
       "      <td>20150417T000000</td>\n",
       "      <td>479000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1741</td>\n",
       "      <td>1439</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>295</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>98034</td>\n",
       "      <td>47.7043</td>\n",
       "      <td>-122.209</td>\n",
       "      <td>2090</td>\n",
       "      <td>10454</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11964</th>\n",
       "      <td>5272200045</td>\n",
       "      <td>20141113T000000</td>\n",
       "      <td>378000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1000</td>\n",
       "      <td>6914</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>0</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7144</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1000</td>\n",
       "      <td>6947</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21575</th>\n",
       "      <td>9578500790</td>\n",
       "      <td>20141111T000000</td>\n",
       "      <td>399950.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3087</td>\n",
       "      <td>5002</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>98023</td>\n",
       "      <td>47.2974</td>\n",
       "      <td>-122.349</td>\n",
       "      <td>2927</td>\n",
       "      <td>5183</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>7202350480</td>\n",
       "      <td>20140930T000000</td>\n",
       "      <td>575000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2120</td>\n",
       "      <td>4780</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6810</td>\n",
       "      <td>-122.032</td>\n",
       "      <td>1690</td>\n",
       "      <td>2650</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>1723049033</td>\n",
       "      <td>20140620T000000</td>\n",
       "      <td>245000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.75</td>\n",
       "      <td>380</td>\n",
       "      <td>15000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>98168</td>\n",
       "      <td>47.4810</td>\n",
       "      <td>-122.323</td>\n",
       "      <td>1170</td>\n",
       "      <td>15000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>6147650280</td>\n",
       "      <td>20150325T000000</td>\n",
       "      <td>315000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3130</td>\n",
       "      <td>5999</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>98042</td>\n",
       "      <td>47.3837</td>\n",
       "      <td>-122.099</td>\n",
       "      <td>3020</td>\n",
       "      <td>5997</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17290 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id             date     price  bedrooms  bathrooms  \\\n",
       "6325   5467910190  20140527T000000  325000.0         3       1.75   \n",
       "13473  9331800580  20150310T000000  257000.0         2       1.00   \n",
       "17614  2407000405  20150226T000000  228500.0         3       1.00   \n",
       "16970  5466700290  20150108T000000  288000.0         3       2.25   \n",
       "20868  3026059361  20150417T000000  479000.0         2       2.50   \n",
       "...           ...              ...       ...       ...        ...   \n",
       "11964  5272200045  20141113T000000  378000.0         3       1.50   \n",
       "21575  9578500790  20141111T000000  399950.0         3       2.50   \n",
       "5390   7202350480  20140930T000000  575000.0         3       2.50   \n",
       "860    1723049033  20140620T000000  245000.0         1       0.75   \n",
       "15795  6147650280  20150325T000000  315000.0         4       2.50   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  sqft_basement  \\\n",
       "6325          1780     13095     1.0           0     0  ...              0   \n",
       "13473         1000      3700     1.0           0     0  ...            200   \n",
       "17614         1080      7486     1.5           0     0  ...             90   \n",
       "16970         2090      7500     1.0           0     0  ...            810   \n",
       "20868         1741      1439     2.0           0     0  ...            295   \n",
       "...            ...       ...     ...         ...   ...  ...            ...   \n",
       "11964         1000      6914     1.0           0     0  ...              0   \n",
       "21575         3087      5002     2.0           0     0  ...              0   \n",
       "5390          2120      4780     2.0           0     0  ...              0   \n",
       "860            380     15000     1.0           0     0  ...              0   \n",
       "15795         3130      5999     2.0           0     0  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "6325       1983             0    98042  47.3670 -122.152           2750   \n",
       "13473      1929             0    98118  47.5520 -122.290           1270   \n",
       "17614      1942             0    98146  47.4838 -122.335           1170   \n",
       "16970      1977             0    98031  47.3951 -122.172           1800   \n",
       "20868      2007             0    98034  47.7043 -122.209           2090   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "11964      1947             0    98125  47.7144 -122.319           1000   \n",
       "21575      2014             0    98023  47.2974 -122.349           2927   \n",
       "5390       2004             0    98053  47.6810 -122.032           1690   \n",
       "860        1963             0    98168  47.4810 -122.323           1170   \n",
       "15795      2006             0    98042  47.3837 -122.099           3020   \n",
       "\n",
       "       sqft_lot15  above_median_price  price_category  \n",
       "6325        13095                   0               1  \n",
       "13473        5000                   0               0  \n",
       "17614        7800                   0               0  \n",
       "16970        7350                   0               0  \n",
       "20868       10454                   1               1  \n",
       "...           ...                 ...             ...  \n",
       "11964        6947                   0               1  \n",
       "21575        5183                   0               1  \n",
       "5390         2650                   1               1  \n",
       "860         15000                   0               0  \n",
       "15795        5997                   0               1  \n",
       "\n",
       "[17290 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_train'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6325</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13473</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17614</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16970</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20868</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11964</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21575</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17290 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_price\n",
       "6325               0\n",
       "13473              0\n",
       "17614              0\n",
       "16970              1\n",
       "20868              0\n",
       "...              ...\n",
       "11964              0\n",
       "21575              1\n",
       "5390               1\n",
       "860                0\n",
       "15795              1\n",
       "\n",
       "[17290 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'X_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>above_median_price</th>\n",
       "      <th>price_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>2591820310</td>\n",
       "      <td>20141006T000000</td>\n",
       "      <td>365000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2070</td>\n",
       "      <td>8893</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>98058</td>\n",
       "      <td>47.4388</td>\n",
       "      <td>-122.162</td>\n",
       "      <td>2390</td>\n",
       "      <td>7700</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2830</th>\n",
       "      <td>7974200820</td>\n",
       "      <td>20140821T000000</td>\n",
       "      <td>865000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2900</td>\n",
       "      <td>6730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1070</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>98115</td>\n",
       "      <td>47.6784</td>\n",
       "      <td>-122.285</td>\n",
       "      <td>2370</td>\n",
       "      <td>6283</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4106</th>\n",
       "      <td>7701450110</td>\n",
       "      <td>20140815T000000</td>\n",
       "      <td>1038000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3770</td>\n",
       "      <td>10893</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>98006</td>\n",
       "      <td>47.5646</td>\n",
       "      <td>-122.129</td>\n",
       "      <td>3710</td>\n",
       "      <td>9685</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16218</th>\n",
       "      <td>9522300010</td>\n",
       "      <td>20150331T000000</td>\n",
       "      <td>1490000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.50</td>\n",
       "      <td>4560</td>\n",
       "      <td>14608</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>98034</td>\n",
       "      <td>47.6995</td>\n",
       "      <td>-122.228</td>\n",
       "      <td>4050</td>\n",
       "      <td>14226</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19964</th>\n",
       "      <td>9510861140</td>\n",
       "      <td>20140714T000000</td>\n",
       "      <td>711000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2550</td>\n",
       "      <td>5376</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>98052</td>\n",
       "      <td>47.6647</td>\n",
       "      <td>-122.083</td>\n",
       "      <td>2250</td>\n",
       "      <td>4050</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13674</th>\n",
       "      <td>6163900333</td>\n",
       "      <td>20141110T000000</td>\n",
       "      <td>338000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1250</td>\n",
       "      <td>7710</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1947</td>\n",
       "      <td>0</td>\n",
       "      <td>98155</td>\n",
       "      <td>47.7623</td>\n",
       "      <td>-122.317</td>\n",
       "      <td>1340</td>\n",
       "      <td>7710</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20377</th>\n",
       "      <td>3528960020</td>\n",
       "      <td>20140708T000000</td>\n",
       "      <td>673000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2830</td>\n",
       "      <td>3496</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>98029</td>\n",
       "      <td>47.5606</td>\n",
       "      <td>-122.011</td>\n",
       "      <td>2160</td>\n",
       "      <td>3501</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8805</th>\n",
       "      <td>1687000220</td>\n",
       "      <td>20141016T000000</td>\n",
       "      <td>285000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2434</td>\n",
       "      <td>4400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>98001</td>\n",
       "      <td>47.2874</td>\n",
       "      <td>-122.283</td>\n",
       "      <td>2434</td>\n",
       "      <td>4400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10168</th>\n",
       "      <td>4141400030</td>\n",
       "      <td>20141201T000000</td>\n",
       "      <td>605000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2250</td>\n",
       "      <td>10108</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1967</td>\n",
       "      <td>0</td>\n",
       "      <td>98008</td>\n",
       "      <td>47.5922</td>\n",
       "      <td>-122.118</td>\n",
       "      <td>2050</td>\n",
       "      <td>9750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2522</th>\n",
       "      <td>1822500160</td>\n",
       "      <td>20141212T000000</td>\n",
       "      <td>356500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2570</td>\n",
       "      <td>11473</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2008</td>\n",
       "      <td>0</td>\n",
       "      <td>98003</td>\n",
       "      <td>47.2809</td>\n",
       "      <td>-122.296</td>\n",
       "      <td>2430</td>\n",
       "      <td>5997</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4323 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               id             date      price  bedrooms  bathrooms  \\\n",
       "735    2591820310  20141006T000000   365000.0         4       2.25   \n",
       "2830   7974200820  20140821T000000   865000.0         5       3.00   \n",
       "4106   7701450110  20140815T000000  1038000.0         4       2.50   \n",
       "16218  9522300010  20150331T000000  1490000.0         3       3.50   \n",
       "19964  9510861140  20140714T000000   711000.0         3       2.50   \n",
       "...           ...              ...        ...       ...        ...   \n",
       "13674  6163900333  20141110T000000   338000.0         3       1.75   \n",
       "20377  3528960020  20140708T000000   673000.0         3       2.75   \n",
       "8805   1687000220  20141016T000000   285000.0         4       2.50   \n",
       "10168  4141400030  20141201T000000   605000.0         4       1.75   \n",
       "2522   1822500160  20141212T000000   356500.0         4       2.50   \n",
       "\n",
       "       sqft_living  sqft_lot  floors  waterfront  view  ...  sqft_basement  \\\n",
       "735           2070      8893     2.0           0     0  ...              0   \n",
       "2830          2900      6730     1.0           0     0  ...           1070   \n",
       "4106          3770     10893     2.0           0     2  ...              0   \n",
       "16218         4560     14608     2.0           0     2  ...              0   \n",
       "19964         2550      5376     2.0           0     0  ...              0   \n",
       "...            ...       ...     ...         ...   ...  ...            ...   \n",
       "13674         1250      7710     1.0           0     0  ...              0   \n",
       "20377         2830      3496     2.0           0     0  ...              0   \n",
       "8805          2434      4400     2.0           0     0  ...              0   \n",
       "10168         2250     10108     1.0           0     0  ...              0   \n",
       "2522          2570     11473     2.0           0     0  ...              0   \n",
       "\n",
       "       yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "735        1986             0    98058  47.4388 -122.162           2390   \n",
       "2830       1977             0    98115  47.6784 -122.285           2370   \n",
       "4106       1997             0    98006  47.5646 -122.129           3710   \n",
       "16218      1990             0    98034  47.6995 -122.228           4050   \n",
       "19964      2004             0    98052  47.6647 -122.083           2250   \n",
       "...         ...           ...      ...      ...      ...            ...   \n",
       "13674      1947             0    98155  47.7623 -122.317           1340   \n",
       "20377      2012             0    98029  47.5606 -122.011           2160   \n",
       "8805       2007             0    98001  47.2874 -122.283           2434   \n",
       "10168      1967             0    98008  47.5922 -122.118           2050   \n",
       "2522       2008             0    98003  47.2809 -122.296           2430   \n",
       "\n",
       "       sqft_lot15  above_median_price  price_category  \n",
       "735          7700                   0               1  \n",
       "2830         6283                   1               2  \n",
       "4106         9685                   1               2  \n",
       "16218       14226                   1               2  \n",
       "19964        4050                   1               2  \n",
       "...           ...                 ...             ...  \n",
       "13674        7710                   0               1  \n",
       "20377        3501                   1               1  \n",
       "8805         4400                   0               0  \n",
       "10168        9750                   1               1  \n",
       "2522         5997                   0               1  \n",
       "\n",
       "[4323 rows x 23 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y_test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2830</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4106</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16218</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19964</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13674</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20377</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8805</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10168</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2522</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4323 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       average_price\n",
       "735                0\n",
       "2830               1\n",
       "4106               1\n",
       "16218              1\n",
       "19964              1\n",
       "...              ...\n",
       "13674              0\n",
       "20377              1\n",
       "8805               1\n",
       "10168              1\n",
       "2522               1\n",
       "\n",
       "[4323 rows x 1 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from typing import Tuple\n",
    "from pandas import DataFrame\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def split_into_train_test(\n",
    "    df_input: DataFrame,\n",
    "    target_colname: str = \"average_price\",\n",
    "    frac_train: float = 0.8,\n",
    "    random_state: int = None,\n",
    ") -> Tuple[DataFrame, DataFrame, DataFrame, DataFrame]:\n",
    "    \n",
    "    if not (0 < frac_train < 1):\n",
    "        raise ValueError(\"Fraction must be between 0 and 1.\")\n",
    "    \n",
    "    # Проверка наличия целевого признака\n",
    "    if target_colname not in df_input.columns:\n",
    "        raise ValueError(f\"{target_colname} is not a column in the DataFrame.\")\n",
    "    \n",
    "    # Разделяем данные на признаки и целевую переменную\n",
    "    X = df_input.drop(columns=[target_colname])  # Признаки\n",
    "    y = df_input[[target_colname]]  # Целевая переменная\n",
    "\n",
    "    # Разделяем данные на обучающую и тестовую выборки\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y,\n",
    "        test_size=(1.0 - frac_train),\n",
    "        random_state=random_state\n",
    "    )\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test\n",
    "\n",
    "# Применение функции для разделения данных\n",
    "X_train, X_test, y_train, y_test = split_into_train_test(\n",
    "    df, \n",
    "    target_colname=\"average_price\", \n",
    "    frac_train=0.8, \n",
    "    random_state=42  # Убедитесь, что вы задали нужное значение random_state\n",
    ")\n",
    "\n",
    "# Для отображения результатов\n",
    "display(\"X_train\", X_train)\n",
    "display(\"y_train\", y_train)\n",
    "\n",
    "display(\"X_test\", X_test)\n",
    "display(\"y_test\", y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Формирование конвейера для решения задачи регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor  # Пример регрессионной модели\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "class HouseFeatures(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    def transform(self, X, y=None):\n",
    "        # Создание новых признаков\n",
    "        X = X.copy()\n",
    "        X[\"Square\"] = X[\"sqft_living\"] / X[\"sqft_lot\"]\n",
    "        return X\n",
    "    def get_feature_names_out(self, features_in):\n",
    "        # Добавление имен новых признаков\n",
    "        new_features = [\"Square\"]\n",
    "        return np.append(features_in, new_features, axis=0)\n",
    "\n",
    "# Указываем столбцы, которые нужно удалить и обрабатывать\n",
    "columns_to_drop = [\"date\"]\n",
    "num_columns = [\"bathrooms\", \"floors\", \"waterfront\", \"view\"]\n",
    "cat_columns = []  \n",
    "\n",
    "# Определяем предобработку для численных данных\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",
    "# Определяем предобработку для категориальных данных\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",
    "# Подготовка признаков с использованием ColumnTransformer\n",
    "features_preprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"preprocessing_num\", preprocessing_num, num_columns),\n",
    "        (\"preprocessing_cat\", preprocessing_cat, cat_columns),\n",
    "    ],\n",
    "    remainder=\"passthrough\"\n",
    ")\n",
    "\n",
    "# Удаление нежелательных столбцов\n",
    "drop_columns = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"drop_columns\", \"drop\", columns_to_drop),\n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "# Постобработка признаков\n",
    "features_postprocessing = ColumnTransformer(\n",
    "    verbose_feature_names_out=False,\n",
    "    transformers=[\n",
    "        (\"preprocessing_cat\", preprocessing_cat, [\"price_category\"]),  \n",
    "    ],\n",
    "    remainder=\"passthrough\",\n",
    ")\n",
    "\n",
    "# Создание окончательного конвейера\n",
    "pipeline = Pipeline(\n",
    "    [\n",
    "        (\"features_preprocessing\", features_preprocessing),\n",
    "        (\"drop_columns\", drop_columns),\n",
    "        (\"custom_features\", HouseFeatures()),\n",
    "        (\"model\", RandomForestRegressor())  # Выбор модели для обучения\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Использование конвейера\n",
    "def train_pipeline(X, y):\n",
    "    pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Формирование набора моделей для регрессии  \n",
    "Определение перечня алгоритмов решения задачи аппроксимации (регрессии)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model, tree, neighbors, ensemble, neural_network\n",
    "\n",
    "random_state = 9\n",
    "\n",
    "models = {\n",
    "    \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n",
    "    \"linear_poly\": {\n",
    "        \"model\": make_pipeline(\n",
    "            PolynomialFeatures(degree=2),\n",
    "            linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
    "        )\n",
    "    },\n",
    "    \"linear_interact\": {\n",
    "        \"model\": make_pipeline(\n",
    "            PolynomialFeatures(interaction_only=True),\n",
    "            linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
    "        )\n",
    "    },\n",
    "    \"ridge\": {\"model\": linear_model.RidgeCV()},\n",
    "    \"decision_tree\": {\n",
    "        \"model\": tree.DecisionTreeRegressor(max_depth=7, random_state=random_state)\n",
    "    },\n",
    "    \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n",
    "    \"random_forest\": {\n",
    "        \"model\": ensemble.RandomForestRegressor(\n",
    "            max_depth=7, random_state=random_state, n_jobs=-1\n",
    "        )\n",
    "    },\n",
    "    \"mlp\": {\n",
    "        \"model\": neural_network.MLPRegressor(\n",
    "            activation=\"tanh\",\n",
    "            hidden_layer_sizes=(3,),\n",
    "            max_iter=500,\n",
    "            early_stopping=True,\n",
    "            random_state=random_state,\n",
    "        )\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Формирование набора моделей для регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest: Mean Score = 1.0, Standard Deviation = 0.0\n",
      "Linear Regression: Mean Score = 0.6396438910587428, Standard Deviation = 0.006348300027629372\n",
      "Gradient Boosting: Mean Score = 0.9999999992943781, Standard Deviation = 6.609300428326041e-14\n",
      "Support Vector Regression: Mean Score = -0.4335265257004087, Standard Deviation = 0.012071668862264313\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def train_multiple_models(X, y, models):\n",
    "    results = {}\n",
    "\n",
    "     # Преобразуем y в одномерный массив numpy только при необходимости\n",
    "    if hasattr(y, 'values'):\n",
    "        y = y.values.ravel()  # Если y - DataFrame, преобразуем в numpy array\n",
    "    else:\n",
    "        y = y.ravel()  # Если y - numpy array, просто используем ravel()\n",
    "\n",
    "    for model_name, model in models.items():\n",
    "        # Создаем конвейер для каждой модели\n",
    "        model_pipeline = Pipeline(\n",
    "            [\n",
    "                (\"features_preprocessing\", features_preprocessing),\n",
    "                (\"drop_columns\", drop_columns),\n",
    "                (\"model\", model)  # Используем текущую модель\n",
    "            ]\n",
    "        )\n",
    "        \n",
    "        # Обучаем модель и вычисляем кросс-валидацию\n",
    "        scores = cross_val_score(model_pipeline, X, y, cv=5, error_score='raise')  # 5-кратная кросс-валидация\n",
    "        results[model_name] = {\n",
    "            \"mean_score\": scores.mean(),\n",
    "            \"std_dev\": scores.std()\n",
    "        }\n",
    "    \n",
    "    return results\n",
    "\n",
    "models = {\n",
    "    \"Random Forest\": RandomForestRegressor(),\n",
    "    \"Linear Regression\": LinearRegression(),\n",
    "    \"Gradient Boosting\": GradientBoostingRegressor(),\n",
    "    \"Support Vector Regression\": SVR()\n",
    "}\n",
    "\n",
    "results = train_multiple_models(X_train, y_train, models)\n",
    "\n",
    "# Вывод результатов\n",
    "for model_name, scores in results.items():\n",
    "    print(f\"{model_name}: Mean Score = {scores['mean_score']}, Standard Deviation = {scores['std_dev']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: logistic\n",
      "MSE (train): 0.24060150375939848\n",
      "MSE (test): 0.23455933379597502\n",
      "MAE (train): 0.24060150375939848\n",
      "MAE (test): 0.23455933379597502\n",
      "R2 (train): 0.015780807725750634\n",
      "R2 (test): 0.045807954005714024\n",
      "STD (train): 0.48387852043102103\n",
      "STD (test): 0.4780359236045559\n",
      "----------------------------------------\n",
      "Model: ridge\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE (train): 0.11596298438403702\n",
      "MSE (test): 0.11265325005783021\n",
      "MAE (train): 0.11596298438403702\n",
      "MAE (test): 0.11265325005783021\n",
      "R2 (train): 0.5256347402620505\n",
      "R2 (test): 0.541724332939628\n",
      "STD (train): 0.3405113334365492\n",
      "STD (test): 0.3356321137822519\n",
      "----------------------------------------\n",
      "Model: decision_tree\n",
      "MSE (train): 0.0\n",
      "MSE (test): 0.0\n",
      "MAE (train): 0.0\n",
      "MAE (test): 0.0\n",
      "R2 (train): 1.0\n",
      "R2 (test): 1.0\n",
      "STD (train): 0.0\n",
      "STD (test): 0.0\n",
      "----------------------------------------\n",
      "Model: knn\n",
      "MSE (train): 0.1949681897050318\n",
      "MSE (test): 0.27989821882951654\n",
      "MAE (train): 0.1949681897050318\n",
      "MAE (test): 0.27989821882951654\n",
      "R2 (train): 0.20245122664507342\n",
      "R2 (test): -0.13863153417464114\n",
      "STD (train): 0.43948973967967464\n",
      "STD (test): 0.5264647910268833\n",
      "----------------------------------------\n",
      "Model: naive_bayes\n",
      "MSE (train): 0.26928860613071137\n",
      "MSE (test): 0.2690261392551469\n",
      "MAE (train): 0.26928860613071137\n",
      "MAE (test): 0.2690261392551469\n",
      "R2 (train): -0.10156840366079445\n",
      "R2 (test): -0.09440369772322943\n",
      "STD (train): 0.47316941542228536\n",
      "STD (test): 0.47206502931490235\n",
      "----------------------------------------\n",
      "Model: gradient_boosting\n",
      "MSE (train): 0.0\n",
      "MSE (test): 0.0\n",
      "MAE (train): 0.0\n",
      "MAE (test): 0.0\n",
      "R2 (train): 1.0\n",
      "R2 (test): 1.0\n",
      "STD (train): 0.0\n",
      "STD (test): 0.0\n",
      "----------------------------------------\n",
      "Model: random_forest\n",
      "MSE (train): 0.0\n",
      "MSE (test): 0.0\n",
      "MAE (train): 0.0\n",
      "MAE (test): 0.0\n",
      "R2 (train): 1.0\n",
      "R2 (test): 1.0\n",
      "STD (train): 0.0\n",
      "STD (test): 0.0\n",
      "----------------------------------------\n",
      "Model: mlp\n",
      "MSE (train): 0.4253903990746096\n",
      "MSE (test): 0.4353458246588018\n",
      "MAE (train): 0.4253903990746096\n",
      "MAE (test): 0.4353458246588018\n",
      "R2 (train): -0.7401279228791116\n",
      "R2 (test): -0.7709954936501442\n",
      "STD (train): 0.4959884986820156\n",
      "STD (test): 0.49782384226978177\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Проверка наличия необходимых переменных\n",
    "if 'class_models' not in locals():\n",
    "    raise ValueError(\"class_models is not defined\")\n",
    "if 'X_train' not in locals() or 'X_test' not in locals() or 'y_train' not in locals() or 'y_test' not in locals():\n",
    "    raise ValueError(\"Train/test data is not defined\")\n",
    "\n",
    "\n",
    "y_train = np.ravel(y_train)  \n",
    "y_test = np.ravel(y_test)      \n",
    "\n",
    "# Инициализация списка для хранения результатов\n",
    "results = []\n",
    "\n",
    "# Проход по моделям и оценка их качества\n",
    "for model_name in class_models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    \n",
    "    # Извлечение модели из словаря\n",
    "    model = class_models[model_name][\"model\"]\n",
    "    \n",
    "    # Создание пайплайна\n",
    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
    "    \n",
    "    # Обучение модели\n",
    "    model_pipeline.fit(X_train, y_train)\n",
    "\n",
    "    # Предсказание для обучающей и тестовой выборки\n",
    "    y_train_predict = model_pipeline.predict(X_train)\n",
    "    y_test_predict = model_pipeline.predict(X_test)\n",
    "\n",
    "    # Сохранение пайплайна и предсказаний\n",
    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
    "    class_models[model_name][\"preds\"] = y_test_predict\n",
    "\n",
    "    # Вычисление метрик для регрессии\n",
    "    class_models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_predict)\n",
    "    class_models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_predict)\n",
    "    class_models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_predict)\n",
    "    class_models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_predict)\n",
    "    class_models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_predict)\n",
    "    class_models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_predict)\n",
    "\n",
    "    # Дополнительные метрики\n",
    "    class_models[model_name][\"STD_train\"] = np.std(y_train - y_train_predict)\n",
    "    class_models[model_name][\"STD_test\"] = np.std(y_test - y_test_predict)\n",
    "\n",
    "    # Вывод результатов для текущей модели\n",
    "    print(f\"MSE (train): {class_models[model_name]['MSE_train']}\")\n",
    "    print(f\"MSE (test): {class_models[model_name]['MSE_test']}\")\n",
    "    print(f\"MAE (train): {class_models[model_name]['MAE_train']}\")\n",
    "    print(f\"MAE (test): {class_models[model_name]['MAE_test']}\")\n",
    "    print(f\"R2 (train): {class_models[model_name]['R2_train']}\")\n",
    "    print(f\"R2 (test): {class_models[model_name]['R2_test']}\")\n",
    "    print(f\"STD (train): {class_models[model_name]['STD_train']}\")\n",
    "    print(f\"STD (test): {class_models[model_name]['STD_test']}\")\n",
    "    print(\"-\" * 40)  # Разделитель для разных моделей"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import linear_model, tree, neighbors, ensemble, neural_network\n",
    "\n",
    "random_state = 9\n",
    "\n",
    "models = {\n",
    "    \"linear\": {\"model\": linear_model.LinearRegression(n_jobs=-1)},\n",
    "    \"linear_poly\": {\n",
    "        \"model\": make_pipeline(\n",
    "            PolynomialFeatures(degree=2),\n",
    "            linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
    "        )\n",
    "    },\n",
    "    \"linear_interact\": {\n",
    "        \"model\": make_pipeline(\n",
    "            PolynomialFeatures(interaction_only=True),\n",
    "            linear_model.LinearRegression(fit_intercept=False, n_jobs=-1),\n",
    "        )\n",
    "    },\n",
    "    \"ridge\": {\"model\": linear_model.RidgeCV()},\n",
    "    \"decision_tree\": {\n",
    "        \"model\": tree.DecisionTreeRegressor(max_depth=7, random_state=random_state)\n",
    "    },\n",
    "    \"knn\": {\"model\": neighbors.KNeighborsRegressor(n_neighbors=7, n_jobs=-1)},\n",
    "    \"random_forest\": {\n",
    "        \"model\": ensemble.RandomForestRegressor(\n",
    "            max_depth=7, random_state=random_state, n_jobs=-1\n",
    "        )\n",
    "    },\n",
    "    \"mlp\": {\n",
    "        \"model\": neural_network.MLPRegressor(\n",
    "            activation=\"tanh\",\n",
    "            hidden_layer_sizes=(3,),\n",
    "            max_iter=500,\n",
    "            early_stopping=True,\n",
    "            random_state=random_state,\n",
    "        )\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Обучение и оценка моделей с помощью различных алгоритмов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: logistic\n",
      "MSE (train): 0.24060150375939848\n",
      "MSE (test): 0.23455933379597502\n",
      "MAE (train): 0.24060150375939848\n",
      "MAE (test): 0.23455933379597502\n",
      "R2 (train): 0.015780807725750634\n",
      "R2 (test): 0.045807954005714024\n",
      "STD (train): 0.48387852043102103\n",
      "STD (test): 0.4780359236045559\n",
      "----------------------------------------\n",
      "Model: ridge\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\MII\\laboratory\\mai\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE (train): 0.11596298438403702\n",
      "MSE (test): 0.11265325005783021\n",
      "MAE (train): 0.11596298438403702\n",
      "MAE (test): 0.11265325005783021\n",
      "R2 (train): 0.5256347402620505\n",
      "R2 (test): 0.541724332939628\n",
      "STD (train): 0.3405113334365492\n",
      "STD (test): 0.3356321137822519\n",
      "----------------------------------------\n",
      "Model: decision_tree\n",
      "MSE (train): 0.0\n",
      "MSE (test): 0.0\n",
      "MAE (train): 0.0\n",
      "MAE (test): 0.0\n",
      "R2 (train): 1.0\n",
      "R2 (test): 1.0\n",
      "STD (train): 0.0\n",
      "STD (test): 0.0\n",
      "----------------------------------------\n",
      "Model: knn\n",
      "MSE (train): 0.1949681897050318\n",
      "MSE (test): 0.27989821882951654\n",
      "MAE (train): 0.1949681897050318\n",
      "MAE (test): 0.27989821882951654\n",
      "R2 (train): 0.20245122664507342\n",
      "R2 (test): -0.13863153417464114\n",
      "STD (train): 0.43948973967967464\n",
      "STD (test): 0.5264647910268833\n",
      "----------------------------------------\n",
      "Model: naive_bayes\n",
      "MSE (train): 0.26928860613071137\n",
      "MSE (test): 0.2690261392551469\n",
      "MAE (train): 0.26928860613071137\n",
      "MAE (test): 0.2690261392551469\n",
      "R2 (train): -0.10156840366079445\n",
      "R2 (test): -0.09440369772322943\n",
      "STD (train): 0.47316941542228536\n",
      "STD (test): 0.47206502931490235\n",
      "----------------------------------------\n",
      "Model: gradient_boosting\n",
      "MSE (train): 0.0\n",
      "MSE (test): 0.0\n",
      "MAE (train): 0.0\n",
      "MAE (test): 0.0\n",
      "R2 (train): 1.0\n",
      "R2 (test): 1.0\n",
      "STD (train): 0.0\n",
      "STD (test): 0.0\n",
      "----------------------------------------\n",
      "Model: random_forest\n",
      "MSE (train): 0.0\n",
      "MSE (test): 0.0\n",
      "MAE (train): 0.0\n",
      "MAE (test): 0.0\n",
      "R2 (train): 1.0\n",
      "R2 (test): 1.0\n",
      "STD (train): 0.0\n",
      "STD (test): 0.0\n",
      "----------------------------------------\n",
      "Model: mlp\n",
      "MSE (train): 0.4253903990746096\n",
      "MSE (test): 0.4353458246588018\n",
      "MAE (train): 0.4253903990746096\n",
      "MAE (test): 0.4353458246588018\n",
      "R2 (train): -0.7401279228791116\n",
      "R2 (test): -0.7709954936501442\n",
      "STD (train): 0.4959884986820156\n",
      "STD (test): 0.49782384226978177\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Проверка наличия необходимых переменных\n",
    "if 'class_models' not in locals():\n",
    "    raise ValueError(\"class_models is not defined\")\n",
    "if 'X_train' not in locals() or 'X_test' not in locals() or 'y_train' not in locals() or 'y_test' not in locals():\n",
    "    raise ValueError(\"Train/test data is not defined\")\n",
    "\n",
    "\n",
    "y_train = np.ravel(y_train)  \n",
    "y_test = np.ravel(y_test)      \n",
    "\n",
    "# Инициализация списка для хранения результатов\n",
    "results = []\n",
    "\n",
    "# Проход по моделям и оценка их качества\n",
    "for model_name in class_models.keys():\n",
    "    print(f\"Model: {model_name}\")\n",
    "    \n",
    "    # Извлечение модели из словаря\n",
    "    model = class_models[model_name][\"model\"]\n",
    "    \n",
    "    # Создание пайплайна\n",
    "    model_pipeline = Pipeline([(\"pipeline\", pipeline_end), (\"model\", model)])\n",
    "    \n",
    "    # Обучение модели\n",
    "    model_pipeline.fit(X_train, y_train)\n",
    "\n",
    "    # Предсказание для обучающей и тестовой выборки\n",
    "    y_train_predict = model_pipeline.predict(X_train)\n",
    "    y_test_predict = model_pipeline.predict(X_test)\n",
    "\n",
    "    # Сохранение пайплайна и предсказаний\n",
    "    class_models[model_name][\"pipeline\"] = model_pipeline\n",
    "    class_models[model_name][\"preds\"] = y_test_predict\n",
    "\n",
    "    # Вычисление метрик для регрессии\n",
    "    class_models[model_name][\"MSE_train\"] = metrics.mean_squared_error(y_train, y_train_predict)\n",
    "    class_models[model_name][\"MSE_test\"] = metrics.mean_squared_error(y_test, y_test_predict)\n",
    "    class_models[model_name][\"MAE_train\"] = metrics.mean_absolute_error(y_train, y_train_predict)\n",
    "    class_models[model_name][\"MAE_test\"] = metrics.mean_absolute_error(y_test, y_test_predict)\n",
    "    class_models[model_name][\"R2_train\"] = metrics.r2_score(y_train, y_train_predict)\n",
    "    class_models[model_name][\"R2_test\"] = metrics.r2_score(y_test, y_test_predict)\n",
    "\n",
    "    # Дополнительные метрики\n",
    "    class_models[model_name][\"STD_train\"] = np.std(y_train - y_train_predict)\n",
    "    class_models[model_name][\"STD_test\"] = np.std(y_test - y_test_predict)\n",
    "\n",
    "    # Вывод результатов для текущей модели\n",
    "    print(f\"MSE (train): {class_models[model_name]['MSE_train']}\")\n",
    "    print(f\"MSE (test): {class_models[model_name]['MSE_test']}\")\n",
    "    print(f\"MAE (train): {class_models[model_name]['MAE_train']}\")\n",
    "    print(f\"MAE (test): {class_models[model_name]['MAE_test']}\")\n",
    "    print(f\"R2 (train): {class_models[model_name]['R2_train']}\")\n",
    "    print(f\"R2 (test): {class_models[model_name]['R2_test']}\")\n",
    "    print(f\"STD (train): {class_models[model_name]['STD_train']}\")\n",
    "    print(f\"STD (test): {class_models[model_name]['STD_test']}\")\n",
    "    print(\"-\" * 40)  # Разделитель для разных моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Пример использования обученной модели (конвейера регрессии) для предсказания**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Подбор гиперпараметров методом поиска по сетке**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
      "Best parameters: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "Best MSE: 0.14752641202600872\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Convert the date column to a datetime object and extract numeric features\n",
    "df['date'] = pd.to_datetime(df['date'], errors='coerce')  # Coerce invalid dates to NaT\n",
    "df.dropna(subset=['date'], inplace=True)  # Drop rows with invalid dates\n",
    "df['year'] = df['date'].dt.year\n",
    "df['month'] = df['date'].dt.month\n",
    "df['day'] = df['date'].dt.day\n",
    "\n",
    "# Prepare predictors and target\n",
    "X = df[['yr_built', 'year', 'month', 'day', 'price', 'price_category']]\n",
    "y = df['average_price']\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Define model and parameter grid\n",
    "model = RandomForestRegressor()\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10]\n",
    "}\n",
    "\n",
    "# Hyperparameter tuning with GridSearchCV\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid,\n",
    "                           scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Fit the model\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Output the best parameters and score\n",
    "print(\"Best parameters:\", grid_search.best_params_)\n",
    "print(\"Best MSE:\", -grid_search.best_score_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Обучение модели с новыми гиперпараметрами и сравнение новых и старых данных**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n",
      "Старые параметры: {'max_depth': 10, 'min_samples_split': 15, 'n_estimators': 200}\n",
      "Лучший результат (MSE) на старых параметрах: 0.14727400921908354\n",
      "\n",
      "Новые параметры: {'max_depth': 10, 'min_samples_split': 10, 'n_estimators': 200}\n",
      "Лучший результат (MSE) на новых параметрах: 0.148833681322309\n",
      "Среднеквадратическая ошибка (MSE) на тестовых данных: 0.14451630134635543\n",
      "Корень среднеквадратичной ошибки (RMSE) на тестовых данных: 0.3801529972870863\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# 1. Настройка параметров для старых значений\n",
    "old_param_grid = {\n",
    "    'n_estimators': [50, 100, 200],  # Количество деревьев\n",
    "    'max_depth': [None, 10, 20, 30],  # Максимальная глубина дерева\n",
    "    'min_samples_split': [2, 10, 15]   # Минимальное количество образцов для разбиения узла\n",
    "}\n",
    "\n",
    "# Подбор гиперпараметров с помощью Grid Search для старых параметров\n",
    "old_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
    "        param_grid=old_param_grid, scoring='neg_mean_squared_error', cv=5, n_jobs=-1, verbose=2)\n",
    "\n",
    "# Обучение модели на тренировочных данных\n",
    "old_grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 2. Результаты подбора для старых параметров\n",
    "old_best_params = old_grid_search.best_params_\n",
    "old_best_mse = -old_grid_search.best_score_  # Меняем знак, так как берем отрицательное значение MSE\n",
    "\n",
    "# 3. Настройка параметров для новых значений\n",
    "new_param_grid = {\n",
    "    'n_estimators': [200],\n",
    "    'max_depth': [10],\n",
    "    'min_samples_split': [10]\n",
    "}\n",
    "\n",
    "# Подбор гиперпараметров с помощью Grid Search для новых параметров\n",
    "new_grid_search = GridSearchCV(estimator=RandomForestRegressor(), \n",
    "        param_grid=new_param_grid, scoring='neg_mean_squared_error', cv=2)\n",
    "\n",
    "# Обучение модели на тренировочных данных\n",
    "new_grid_search.fit(X_train, y_train)\n",
    "\n",
    "# 4. Результаты подбора для новых параметров\n",
    "new_best_params = new_grid_search.best_params_\n",
    "new_best_mse = -new_grid_search.best_score_  # Меняем знак, так как берем отрицательное значение MSE\n",
    "\n",
    "# 5. Обучение модели с лучшими параметрами для новых значений\n",
    "model_best = RandomForestRegressor(**new_best_params)\n",
    "model_best.fit(X_train, y_train)\n",
    "\n",
    "# Прогнозирование на тестовой выборке\n",
    "y_pred = model_best.predict(X_test)\n",
    "\n",
    "# Оценка производительности модели\n",
    "mse = metrics.mean_squared_error(y_test, y_pred)\n",
    "rmse = np.sqrt(mse)\n",
    "\n",
    "# Вывод результатов\n",
    "print(\"Старые параметры:\", old_best_params)\n",
    "print(\"Лучший результат (MSE) на старых параметрах:\", old_best_mse)\n",
    "print(\"\\nНовые параметры:\", new_best_params)\n",
    "print(\"Лучший результат (MSE) на новых параметрах:\", new_best_mse)\n",
    "print(\"Среднеквадратическая ошибка (MSE) на тестовых данных:\", mse)\n",
    "print(\"Корень среднеквадратичной ошибки (RMSE) на тестовых данных:\", rmse)\n",
    "\n",
    "# Визуализация ошибок\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(['Старые параметры', 'Новые параметры'], [old_best_mse, new_best_mse], color=['blue', 'orange'])\n",
    "plt.xlabel('Подбор параметров')\n",
    "plt.ylabel('Среднеквадратическая ошибка (MSE)')\n",
    "plt.title('Сравнение MSE для старых и новых параметров')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сравнивая результаты старых и новых параметров, можно сказать, что старые параметры модели позволили добиться меньшей среднеквадратической ошибки, что указывает на более эффективное предсказание по сравнению с новыми настройками. Скорее всего модель обучена достаточно хорошо, учитывая следующие факторы:\n",
    "1. Показатели MSE на тренировочных (0.159) и тестовых данных (0.1589) очень близки. Это говорит о том, что модель не переобучена и не недообучена — она хорошо обобщает на тестовой выборке, что является желаемым результатом.  \n",
    "2. Старые параметры дали наилучший результат, так что модель способна выдать высокую точность при настройке гиперпараметров. Попытка с новыми параметрами позволила оценить, как модель реагирует на изменения параметров, и выяснить, что увеличение max_depth и снижение min_samples_split улучшили результат. Этот процесс настройки параметров — часть процесса улучшения модели.  \n",
    "3. Старые параметры дали наилучший результат, так что модель способна выдать высокую точность при настройке гиперпараметров. Попытка с новыми параметрами позволила оценить, как модель реагирует на изменения параметров, и выяснить, что увеличение max_depth и снижение min_samples_split улучшили результат. Этот процесс настройки параметров — часть процесса улучшения модели.  \n",
    "\n",
    "Таким образом, можно сказать, что модель обучена хорошо, но возможны дальнейшие небольшие улучшения за счет оптимизации гиперпараметров."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.scatter(range(len(y_test)), y_test, label=\"Актуалочка\", color=\"black\", alpha=0.5)\n",
    "plt.scatter(range(len(y_test)), y_pred, label=\"Предсказанные(новые параметры)\", color=\"blue\", alpha=0.5)\n",
    "plt.scatter(range(len(y_test)), y_test_predict, label=\"Предсказанные(старые параметры)\", color=\"red\", alpha=0.5)\n",
    "plt.xlabel(\"Выборка\")\n",
    "plt.ylabel(\"Значения\")\n",
    "plt.legend()\n",
    "plt.title(\"Актуалочка vs Предсказанных значений (Новые and Старые Параметры)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ураааа! Усёёёё, вроде бы всё ^_^"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mai",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}