diff --git a/!kursach.ipynb b/!kursach.ipynb
new file mode 100644
index 0000000..d6c49fa
--- /dev/null
+++ b/!kursach.ipynb
@@ -0,0 +1,10495 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2c1426ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9085d8a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error\n",
+    "from sklearn.metrics import r2_score\n",
+    "from sklearn.metrics import mean_absolute_error\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import roc_auc_score\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.manifold import TSNE\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.ensemble import RandomForestRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c4697627",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#%matplotlib inline\n",
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "plt.style.use('fivethirtyeight')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "475d292b",
+   "metadata": {},
+   "source": [
+    "# Загрузка данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "84f8f88d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df = pd.read_csv('kursach_dir/train.csv').set_index(['Id','DistrictId'])\n",
+    "test_df = pd.read_csv('kursach_dir/test.csv').set_index(['Id','DistrictId'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe02de91",
+   "metadata": {},
+   "source": [
+    "Вывод информации о данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40da78cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colours = ['#000099', '#ffff00']\n",
+    "sns.heatmap(train_df.isnull(), cmap=sns.color_palette(colours))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d4aff40",
+   "metadata": {},
+   "source": [
+    "Процентная потеря данных и типы данных  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a2ba185e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Пропуск данных\n",
+      "\n",
+      "Rooms           -   0%   float64\n",
+      "Square          -   0%   float64\n",
+      "LifeSquare      -  21%   float64\n",
+      "KitchenSquare   -   0%   float64\n",
+      "Floor           -   0%   int64\n",
+      "HouseFloor      -   0%   float64\n",
+      "HouseYear       -   0%   int64\n",
+      "Ecology_1       -   0%   float64\n",
+      "Ecology_2       -   0%   object\n",
+      "Ecology_3       -   0%   object\n",
+      "Social_1        -   0%   int64\n",
+      "Social_2        -   0%   int64\n",
+      "Social_3        -   0%   int64\n",
+      "Healthcare_1    -  48%   float64\n",
+      "Helthcare_2     -   0%   int64\n",
+      "Shops_1         -   0%   int64\n",
+      "Shops_2         -   0%   object\n",
+      "Price           -   0%   float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Пропуск данных\\n')\n",
+    "for col in train_df.columns:\n",
+    "    pct_missing = np.mean(train_df[col].isnull())\n",
+    "    print('{:15} - {:3}%   {}'.format(col, round(pct_missing*100), train_df[col].dtypes))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "6c8bc095",
+   "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>Rooms</th>\n",
+       "      <th>Square</th>\n",
+       "      <th>LifeSquare</th>\n",
+       "      <th>KitchenSquare</th>\n",
+       "      <th>Floor</th>\n",
+       "      <th>HouseFloor</th>\n",
+       "      <th>HouseYear</th>\n",
+       "      <th>Ecology_1</th>\n",
+       "      <th>Social_1</th>\n",
+       "      <th>Social_2</th>\n",
+       "      <th>Social_3</th>\n",
+       "      <th>Healthcare_1</th>\n",
+       "      <th>Helthcare_2</th>\n",
+       "      <th>Shops_1</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>7887.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>1.000000e+04</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>5202.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "      <td>10000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.890500</td>\n",
+       "      <td>56.315775</td>\n",
+       "      <td>37.199645</td>\n",
+       "      <td>6.273300</td>\n",
+       "      <td>8.526700</td>\n",
+       "      <td>12.609400</td>\n",
+       "      <td>3.990166e+03</td>\n",
+       "      <td>0.118858</td>\n",
+       "      <td>24.687000</td>\n",
+       "      <td>5352.157400</td>\n",
+       "      <td>8.039200</td>\n",
+       "      <td>1142.904460</td>\n",
+       "      <td>1.319500</td>\n",
+       "      <td>4.231300</td>\n",
+       "      <td>214138.857399</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.839512</td>\n",
+       "      <td>21.058732</td>\n",
+       "      <td>86.241209</td>\n",
+       "      <td>28.560917</td>\n",
+       "      <td>5.241148</td>\n",
+       "      <td>6.775974</td>\n",
+       "      <td>2.005003e+05</td>\n",
+       "      <td>0.119025</td>\n",
+       "      <td>17.532614</td>\n",
+       "      <td>4006.799803</td>\n",
+       "      <td>23.831875</td>\n",
+       "      <td>1021.517264</td>\n",
+       "      <td>1.493601</td>\n",
+       "      <td>4.806341</td>\n",
+       "      <td>92872.293865</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.136859</td>\n",
+       "      <td>0.370619</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.910000e+03</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>168.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>59174.778028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>41.774881</td>\n",
+       "      <td>22.769832</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>1.974000e+03</td>\n",
+       "      <td>0.017647</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>1564.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>350.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>153872.633942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>52.513310</td>\n",
+       "      <td>32.781260</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>1.977000e+03</td>\n",
+       "      <td>0.075424</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>5285.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>900.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>192269.644879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>65.900625</td>\n",
+       "      <td>45.128803</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>2.001000e+03</td>\n",
+       "      <td>0.195781</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>7227.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1548.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>249135.462171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>19.000000</td>\n",
+       "      <td>641.065193</td>\n",
+       "      <td>7480.592129</td>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>117.000000</td>\n",
+       "      <td>2.005201e+07</td>\n",
+       "      <td>0.521867</td>\n",
+       "      <td>74.000000</td>\n",
+       "      <td>19083.000000</td>\n",
+       "      <td>141.000000</td>\n",
+       "      <td>4849.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>633233.466570</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Rooms        Square   LifeSquare  KitchenSquare         Floor  \\\n",
+       "count  10000.000000  10000.000000  7887.000000   10000.000000  10000.000000   \n",
+       "mean       1.890500     56.315775    37.199645       6.273300      8.526700   \n",
+       "std        0.839512     21.058732    86.241209      28.560917      5.241148   \n",
+       "min        0.000000      1.136859     0.370619       0.000000      1.000000   \n",
+       "25%        1.000000     41.774881    22.769832       1.000000      4.000000   \n",
+       "50%        2.000000     52.513310    32.781260       6.000000      7.000000   \n",
+       "75%        2.000000     65.900625    45.128803       9.000000     12.000000   \n",
+       "max       19.000000    641.065193  7480.592129    2014.000000     42.000000   \n",
+       "\n",
+       "         HouseFloor     HouseYear     Ecology_1      Social_1      Social_2  \\\n",
+       "count  10000.000000  1.000000e+04  10000.000000  10000.000000  10000.000000   \n",
+       "mean      12.609400  3.990166e+03      0.118858     24.687000   5352.157400   \n",
+       "std        6.775974  2.005003e+05      0.119025     17.532614   4006.799803   \n",
+       "min        0.000000  1.910000e+03      0.000000      0.000000    168.000000   \n",
+       "25%        9.000000  1.974000e+03      0.017647      6.000000   1564.000000   \n",
+       "50%       13.000000  1.977000e+03      0.075424     25.000000   5285.000000   \n",
+       "75%       17.000000  2.001000e+03      0.195781     36.000000   7227.000000   \n",
+       "max      117.000000  2.005201e+07      0.521867     74.000000  19083.000000   \n",
+       "\n",
+       "           Social_3  Healthcare_1   Helthcare_2       Shops_1          Price  \n",
+       "count  10000.000000   5202.000000  10000.000000  10000.000000   10000.000000  \n",
+       "mean       8.039200   1142.904460      1.319500      4.231300  214138.857399  \n",
+       "std       23.831875   1021.517264      1.493601      4.806341   92872.293865  \n",
+       "min        0.000000      0.000000      0.000000      0.000000   59174.778028  \n",
+       "25%        0.000000    350.000000      0.000000      1.000000  153872.633942  \n",
+       "50%        2.000000    900.000000      1.000000      3.000000  192269.644879  \n",
+       "75%        5.000000   1548.000000      2.000000      6.000000  249135.462171  \n",
+       "max      141.000000   4849.000000      6.000000     23.000000  633233.466570  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf2cefc6",
+   "metadata": {},
+   "source": [
+    "Номинативные переменные"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "344c8435",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "object_titel = train_df.select_dtypes(include='object').columns.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6b9ab32f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "B    0.9903\n",
+      "A    0.0097\n",
+      "Name: Ecology_2, dtype: float64\n",
+      "\n",
+      "B    0.9725\n",
+      "A    0.0275\n",
+      "Name: Ecology_3, dtype: float64\n",
+      "\n",
+      "B    0.9175\n",
+      "A    0.0825\n",
+      "Name: Shops_2, dtype: float64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for obj in object_titel:\n",
+    "    print(train_df[obj].value_counts(normalize=True), end = '\\n\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bc37e56",
+   "metadata": {},
+   "source": [
+    "# Работа с данными"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51d90268",
+   "metadata": {},
+   "source": [
+    "По параметру Healthcare_1 слишком большой процен утери данных, поэтому убираю его из выборки"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "be39737c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.drop (columns='Healthcare_1', axis= 1 , inplace= True )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c202d79b",
+   "metadata": {},
+   "source": [
+    "заполнение LifeSquare средним"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5b757f89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.loc[train_df['LifeSquare'].isnull(), 'LifeSquare'] = train_df['LifeSquare'].median()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3539be9",
+   "metadata": {},
+   "source": [
+    "Устранение выбросов по LifeSquare, Rooms, Square, KitchenSquare"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22b27ea8",
+   "metadata": {},
+   "source": [
+    "До устранения выбросов"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "90a9f85d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "<Figure size 1000x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (10, 8))\n",
+    "\n",
+    "plt.subplot(221)\n",
+    "#train_df['LifeSquare'].hist()\n",
+    "sns.kdeplot(train_df['LifeSquare'], fill=True, legend=False)\n",
+    "plt.ylabel('count')\n",
+    "plt.xlabel('LifeSquare')\n",
+    "plt.title('среднее {:.4}'.format(train_df['LifeSquare'].median()), loc='left')\n",
+    "\n",
+    "plt.subplot(222)\n",
+    "#train_df['Rooms'].hist()\n",
+    "sns.kdeplot(train_df['Rooms'], fill=True, legend=False)\n",
+    "plt.ylabel('count')\n",
+    "plt.xlabel('Rooms')\n",
+    "plt.title('среднее {:.4}'.format(train_df['Rooms'].median()), loc='left')\n",
+    "\n",
+    "plt.subplot(223)\n",
+    "#train_df['Square'].hist()\n",
+    "sns.kdeplot(train_df['Square'], fill=True, legend=False)\n",
+    "plt.ylabel('count')\n",
+    "plt.xlabel('Square')\n",
+    "plt.title('среднее {:.4}'.format(train_df['Square'].median()), loc='left')\n",
+    "\n",
+    "plt.subplot(224)\n",
+    "#train_df['KitchenSquare'].hist()\n",
+    "sns.kdeplot(train_df['KitchenSquare'], fill=True, legend=False)\n",
+    "plt.ylabel('count')\n",
+    "plt.xlabel('KitchenSquare')\n",
+    "plt.title('среднее {:.4}'.format(train_df['KitchenSquare'].median()), loc='left')\n",
+    "\n",
+    "plt.subplots_adjust(wspace = 0.3, hspace = 0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "6db869c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.loc[train_df['LifeSquare'] > 300, 'LifeSquare'] = train_df['LifeSquare'].median() \n",
+    "train_df.loc[~train_df['Rooms'].isin(range(1,6)), 'Rooms'] = train_df['Rooms'].median()\n",
+    "train_df.loc[train_df['Square'] > 500, 'Square'] = train_df['Square'].median()\n",
+    "train_df.loc[train_df['KitchenSquare'] > 60, 'KitchenSquare'] = train_df['KitchenSquare'].median()\n",
+    "train_df.loc[train_df['KitchenSquare'] < 2, 'KitchenSquare'] = train_df['KitchenSquare'].median()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "528a8ebb",
+   "metadata": {},
+   "source": [
+    "После устранения"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8d4958cf",
+   "metadata": {},
+   "outputs": [
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+       "<Figure size 1000x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (10, 8))\n",
+    "\n",
+    "plt.subplot(221)\n",
+    "#train_df['LifeSquare'].hist()\n",
+    "sns.kdeplot(train_df['LifeSquare'], fill=True, legend=False)\n",
+    "plt.ylabel('count')\n",
+    "plt.xlabel('LifeSquare')\n",
+    "plt.title('среднее {:.4}'.format(train_df['LifeSquare'].median()), loc='left')\n",
+    "\n",
+    "plt.subplot(222)\n",
+    "#train_df['Rooms'].hist()\n",
+    "sns.kdeplot(train_df['Rooms'], fill=True, legend=False)\n",
+    "plt.ylabel('count')\n",
+    "plt.xlabel('Rooms')\n",
+    "plt.title('среднее {:.4}'.format(train_df['Rooms'].median()), loc='left')\n",
+    "\n",
+    "plt.subplot(223)\n",
+    "#train_df['Square'].hist()\n",
+    "sns.kdeplot(train_df['Square'], fill=True, legend=False)\n",
+    "plt.ylabel('count')\n",
+    "plt.xlabel('Square')\n",
+    "plt.title('среднее {:.4}'.format(train_df['Square'].median()), loc='left')\n",
+    "\n",
+    "plt.subplot(224)\n",
+    "#train_df['KitchenSquare'].hist()\n",
+    "sns.kdeplot(train_df['KitchenSquare'], fill=True, legend=False)\n",
+    "plt.ylabel('count')\n",
+    "plt.xlabel('KitchenSquare')\n",
+    "plt.title('среднее {:.4}'.format(train_df['KitchenSquare'].median()), loc='left')\n",
+    "\n",
+    "plt.subplots_adjust(wspace = 0.3, hspace = 0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a71736bd",
+   "metadata": {},
+   "source": [
+    "Избавляюсь от нольэтажных домов, и домов из будущего"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0ebe3090",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.loc[train_df['HouseFloor'] == 0, 'HouseFloor'] = train_df['HouseFloor'].median()\n",
+    "train_df.loc[train_df['HouseYear'] >2023, 'HouseYear'] = train_df['HouseYear'].median()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7649bd26",
+   "metadata": {},
+   "source": [
+    "Преобразую в бинарные призника"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bc444cc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for obj in object_titel:\n",
+    "    train_df[obj] = train_df[obj].replace({'A':0, 'B':1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "750b1f05",
+   "metadata": {},
+   "source": [
+    "Избавляемя от случаев неправильного указания этажей и площадей"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a8851004",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.loc[train_df['HouseFloor'] < train_df['Floor'], 'Floor'] = \\\n",
+    "train_df.loc[train_df['HouseFloor'] < train_df['Floor'], 'HouseFloor']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "609b46c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df.loc[train_df['Square'] < (train_df['KitchenSquare'] + train_df['LifeSquare']), 'Square'] = \\\n",
+    "train_df.loc[train_df['Square'] < (train_df['KitchenSquare'] + train_df['LifeSquare']), 'KitchenSquare'] + \\\n",
+    "train_df.loc[train_df['Square'] < (train_df['KitchenSquare'] + train_df['LifeSquare']), 'LifeSquare']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d85c604b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y_train = pd.DataFrame(train_df['Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b43159db",
+   "metadata": {},
+   "outputs": [
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+   "id": "ff3e49c6",
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+    "x_train_df = pd.DataFrame(train_df.loc[:, train_df.columns != 'Price'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "5d62cca8",
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+       "      <td>0.049637</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>34</td>\n",
+       "      <td>7759</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5809</th>\n",
+       "      <th>58</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>61.731512</td>\n",
+       "      <td>52.731512</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>0.437885</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>23</td>\n",
+       "      <td>5735</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10783</th>\n",
+       "      <th>99</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>39.649192</td>\n",
+       "      <td>23.776169</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1976</td>\n",
+       "      <td>0.012339</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35</td>\n",
+       "      <td>5776</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Rooms     Square  LifeSquare  KitchenSquare  Floor  \\\n",
+       "Id    DistrictId                                                       \n",
+       "14038 35            2.0  47.981561   29.442751            6.0      7   \n",
+       "15053 41            3.0  65.683640   40.049543            8.0      7   \n",
+       "4765  53            2.0  44.947953   29.197612            6.0      8   \n",
+       "5809  58            2.0  61.731512   52.731512            9.0      8   \n",
+       "10783 99            1.0  39.649192   23.776169            7.0     11   \n",
+       "\n",
+       "                  HouseFloor  HouseYear  Ecology_1  Ecology_2  Ecology_3  \\\n",
+       "Id    DistrictId                                                           \n",
+       "14038 35                 9.0       1969   0.089040          1          1   \n",
+       "15053 41                 9.0       1978   0.000070          1          1   \n",
+       "4765  53                12.0       1968   0.049637          1          1   \n",
+       "5809  58                17.0       1977   0.437885          1          1   \n",
+       "10783 99                12.0       1976   0.012339          1          1   \n",
+       "\n",
+       "                  Social_1  Social_2  Social_3  Helthcare_2  Shops_1  Shops_2  \n",
+       "Id    DistrictId                                                               \n",
+       "14038 35                33      7976         5            0       11        1  \n",
+       "15053 41                46     10309         1            1       16        1  \n",
+       "4765  53                34      7759         0            1        3        1  \n",
+       "5809  58                23      5735         3            0        5        1  \n",
+       "10783 99                35      5776         1            2        4        1  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "f0b07d39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "93e5547e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_scaled = pd.DataFrame(scaler.fit_transform(x_train_df), columns=x_train_df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "c82473ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_valid, y_train, y_valid = train_test_split(X_train_scaled, Y_train, train_size=0.70, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "f8708c7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RandomForestRegressor(n_estimators=500, max_depth=12, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "b717feb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_depth=12, n_estimators=500, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=12, n_estimators=500, random_state=42)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor(max_depth=12, n_estimators=500, random_state=42)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X_train, y_train.values[:, 0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "f6b7d304",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train_preds = model.predict(X_train)\n",
+    "y_valid_preds = model.predict(X_valid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "8e10fdde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "check_valid = pd.DataFrame({\n",
+    "    'Тестовые данные valid': y_valid.values[:, 0], \n",
+    "    'Предсказание valid': y_valid_preds})\n",
+    "\n",
+    "check_train = pd.DataFrame({\n",
+    "    'Тестовые данные train': y_train.values[:, 0], \n",
+    "    'Предсказание train': y_train_preds})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "aca3138f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7032729770876911"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(check_valid['Тестовые данные valid'], check_valid['Предсказание valid'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "f23ebaf1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8985500168372904"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(check_train['Тестовые данные train'], check_train['Предсказание train'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ae392d3",
+   "metadata": {},
+   "source": [
+    "# Работа с тестовыми данными"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "212b516a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_df.drop (columns='Healthcare_1', axis= 1 , inplace= True )\n",
+    "test_df.loc[test_df['LifeSquare'].isnull(), 'LifeSquare'] = test_df['LifeSquare'].median()\n",
+    "test_df.loc[test_df['LifeSquare'] > 300, 'LifeSquare'] = test_df['LifeSquare'].median() \n",
+    "test_df.loc[~test_df['Rooms'].isin(range(1,6)), 'Rooms'] = test_df['Rooms'].median()\n",
+    "test_df.loc[test_df['Square'] > 500, 'Square'] = test_df['Square'].median()\n",
+    "test_df.loc[test_df['KitchenSquare'] > 60, 'KitchenSquare'] = test_df['KitchenSquare'].median()\n",
+    "test_df.loc[test_df['HouseFloor'] == 0, 'HouseFloor'] = test_df['HouseFloor'].median()\n",
+    "test_df.loc[test_df['HouseYear'] >2023, 'HouseYear'] = test_df['HouseYear'].median()\n",
+    "for obj in object_titel:\n",
+    "    test_df[obj] = test_df[obj].replace({'A':0, 'B':1})\n",
+    "test_df.loc[test_df['HouseFloor'] < test_df['Floor'], 'Floor'] = \\\n",
+    "test_df.loc[test_df['HouseFloor'] < test_df['Floor'], 'HouseFloor']\n",
+    "test_df.loc[test_df['Square'] < (test_df['KitchenSquare'] + test_df['LifeSquare']), 'Square'] = \\\n",
+    "test_df.loc[test_df['Square'] < (test_df['KitchenSquare'] + test_df['LifeSquare']), 'KitchenSquare'] + \\\n",
+    "test_df.loc[test_df['Square'] < (test_df['KitchenSquare'] + test_df['LifeSquare']), 'LifeSquare']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "ba5724aa",
+   "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></th>\n",
+       "      <th>Rooms</th>\n",
+       "      <th>Square</th>\n",
+       "      <th>LifeSquare</th>\n",
+       "      <th>KitchenSquare</th>\n",
+       "      <th>Floor</th>\n",
+       "      <th>HouseFloor</th>\n",
+       "      <th>HouseYear</th>\n",
+       "      <th>Ecology_1</th>\n",
+       "      <th>Ecology_2</th>\n",
+       "      <th>Ecology_3</th>\n",
+       "      <th>Social_1</th>\n",
+       "      <th>Social_2</th>\n",
+       "      <th>Social_3</th>\n",
+       "      <th>Helthcare_2</th>\n",
+       "      <th>Shops_1</th>\n",
+       "      <th>Shops_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Id</th>\n",
+       "      <th>DistrictId</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <th>58</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>49.882643</td>\n",
+       "      <td>33.432782</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>0.310199</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2748</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15856</th>\n",
+       "      <th>74</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>69.263183</td>\n",
+       "      <td>32.925087</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>0.075779</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1437</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5480</th>\n",
+       "      <th>190</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>27.948246</td>\n",
+       "      <td>15.948246</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1909</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>7538</td>\n",
+       "      <td>87</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15664</th>\n",
+       "      <th>47</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>73.046609</td>\n",
+       "      <td>51.940842</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>0.101872</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>23</td>\n",
+       "      <td>4583</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14275</th>\n",
+       "      <th>27</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>47.527111</td>\n",
+       "      <td>43.387569</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>0.072158</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>629</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8180</th>\n",
+       "      <th>11</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>67.133911</td>\n",
+       "      <td>50.809797</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1973</td>\n",
+       "      <td>0.000170</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>36</td>\n",
+       "      <td>5992</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4695</th>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>40.198472</td>\n",
+       "      <td>21.807061</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>0.007122</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>264</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5783</th>\n",
+       "      <th>12</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>77.842178</td>\n",
+       "      <td>48.282625</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1989</td>\n",
+       "      <td>0.090799</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>74</td>\n",
+       "      <td>19083</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4780</th>\n",
+       "      <th>62</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>81.305222</td>\n",
+       "      <td>32.925087</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>0.072158</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>629</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12504</th>\n",
+       "      <th>30</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>60.555693</td>\n",
+       "      <td>32.925087</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>0.000078</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>22</td>\n",
+       "      <td>6398</td>\n",
+       "      <td>141</td>\n",
+       "      <td>3</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5000 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Rooms     Square  LifeSquare  KitchenSquare  Floor  \\\n",
+       "Id    DistrictId                                                       \n",
+       "725   58            2.0  49.882643   33.432782            6.0      6   \n",
+       "15856 74            2.0  69.263183   32.925087            1.0      1   \n",
+       "5480  190           1.0  27.948246   15.948246           12.0      2   \n",
+       "15664 47            2.0  73.046609   51.940842            9.0     22   \n",
+       "14275 27            1.0  47.527111   43.387569            1.0     17   \n",
+       "...                 ...        ...         ...            ...    ...   \n",
+       "8180  11            3.0  67.133911   50.809797            6.0      5   \n",
+       "4695  1             1.0  40.198472   21.807061           10.0     12   \n",
+       "5783  12            3.0  77.842178   48.282625            9.0     22   \n",
+       "4780  62            2.0  81.305222   32.925087            0.0      4   \n",
+       "12504 30            2.0  60.555693   32.925087            1.0     10   \n",
+       "\n",
+       "                  HouseFloor  HouseYear  Ecology_1  Ecology_2  Ecology_3  \\\n",
+       "Id    DistrictId                                                           \n",
+       "725   58                14.0       1972   0.310199          1          1   \n",
+       "15856 74                 1.0       1977   0.075779          1          1   \n",
+       "5480  190                5.0       1909   0.000000          1          1   \n",
+       "15664 47                22.0       2007   0.101872          1          1   \n",
+       "14275 27                17.0       2017   0.072158          1          1   \n",
+       "...                      ...        ...        ...        ...        ...   \n",
+       "8180  11                 9.0       1973   0.000170          1          1   \n",
+       "4695  1                 17.0       2017   0.007122          1          1   \n",
+       "5783  12                22.0       1989   0.090799          1          1   \n",
+       "4780  62                12.0       1977   0.072158          1          1   \n",
+       "12504 30                17.0       1977   0.000078          1          1   \n",
+       "\n",
+       "                  Social_1  Social_2  Social_3  Helthcare_2  Shops_1  Shops_2  \n",
+       "Id    DistrictId                                                               \n",
+       "725   58                11      2748         1            0        0        1  \n",
+       "15856 74                 6      1437         3            0        2        1  \n",
+       "5480  190               30      7538        87            5        5        1  \n",
+       "15664 47                23      4583         3            3        3        1  \n",
+       "14275 27                 2       629         1            0        0        0  \n",
+       "...                    ...       ...       ...          ...      ...      ...  \n",
+       "8180  11                36      5992         0            1        1        1  \n",
+       "4695  1                  1       264         0            0        1        1  \n",
+       "5783  12                74     19083         2            5       15        1  \n",
+       "4780  62                 2       629         1            0        0        0  \n",
+       "12504 30                22      6398       141            3       23        1  \n",
+       "\n",
+       "[5000 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "3d36776b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test = test_df\n",
+    "X_test_scaled = pd.DataFrame(scaler.transform(X_test), columns=X_test.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "aeff24bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test_preds = pd.DataFrame({\n",
+    "    'Price': model.predict(X_test_scaled)})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "18e11f42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>161932.138440</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>200353.954987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>234677.142834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>313861.119272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>132044.356213</td>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
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+       "      <th>4995</th>\n",
+       "      <td>246670.219320</td>\n",
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+       "    <tr>\n",
+       "      <th>4996</th>\n",
+       "      <td>124478.913625</td>\n",
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+       "    <tr>\n",
+       "      <th>4997</th>\n",
+       "      <td>322968.655279</td>\n",
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+       "    <tr>\n",
+       "      <th>4998</th>\n",
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+       "      <th>4999</th>\n",
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+       "<p>5000 rows × 1 columns</p>\n",
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+       "0     161932.138440\n",
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+       "3     313861.119272\n",
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+       "4997  322968.655279\n",
+       "4998  218297.686478\n",
+       "4999  262240.260899\n",
+       "\n",
+       "[5000 rows x 1 columns]"
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+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize = (10, 8))\n",
+    "\n",
+    "sns.kdeplot(train_df['Price'], fill=True)\n",
+    "sns.kdeplot(y_test_preds['Price'], fill=True)\n",
+    "plt.xlabel('price')\n",
+    "\n",
+    "plt.suptitle('Распределение цены')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "b7790d5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_test_preds.to_csv (r'!price_predict.csv', index= False )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/!price_predict.csv b/!price_predict.csv
new file mode 100644
index 0000000..0ea94ff
--- /dev/null
+++ b/!price_predict.csv
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