ds/pandas_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Housing Prices Competition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://storage.googleapis.com/kaggle-competitions/kaggle/5407/media/housesbanner.png)\n",
    "\n",
    "\n",
    "Попросите покупателя жилья описать дом своей мечты, и он, вероятно, не начнет с высоты подвального потолка или близости к железной дороге восток - запад. Но данные этого конкурса игровых площадок доказывают, что на переговоры о цене влияет гораздо больше, чем количество спален или забор из белого штакетника.\n",
    "\n",
    "С 79 объясняющими переменными, описывающими (почти) каждый аспект жилых домов в Эймсе, штат Айова, этот набор данныхставит перед вами задачу предсказать окончательную цену каждого дома\n",
    "\n",
    "Набор данных [тут](https://www.kaggle.com/c/home-data-for-ml-course/overview/evaluation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('./data/train.csv')\n",
    "df_test = pd.read_csv('./data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0  1461          20       RH         80.0    11622   Pave   NaN      Reg   \n",
       "1  1462          20       RL         81.0    14267   Pave   NaN      IR1   \n",
       "2  1463          60       RL         74.0    13830   Pave   NaN      IR1   \n",
       "3  1464          60       RL         78.0     9978   Pave   NaN      IR1   \n",
       "4  1465         120       RL         43.0     5005   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence MiscFeature  \\\n",
       "0         Lvl    AllPub  ...         120        0    NaN  MnPrv         NaN   \n",
       "1         Lvl    AllPub  ...           0        0    NaN    NaN        Gar2   \n",
       "2         Lvl    AllPub  ...           0        0    NaN  MnPrv         NaN   \n",
       "3         Lvl    AllPub  ...           0        0    NaN    NaN         NaN   \n",
       "4         HLS    AllPub  ...         144        0    NaN    NaN         NaN   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  \n",
       "0       0      6    2010        WD         Normal  \n",
       "1   12500      6    2010        WD         Normal  \n",
       "2       0      3    2010        WD         Normal  \n",
       "3       0      6    2010        WD         Normal  \n",
       "4       0      1    2010        WD         Normal  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columnsumns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_train[['LotArea', '2ndFlrSF', 'KitchenAbvGr', 'YearBuilt']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_train['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'SalePrice')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[\"2ndFlrSF\"], y)\n",
    "plt.xlabel(\"2ndFlrSF\")\n",
    "plt.ylabel(\"SalePrice\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>LotArea</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>YearBuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8450</td>\n",
       "      <td>854</td>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9600</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11250</td>\n",
       "      <td>866</td>\n",
       "      <td>1</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9550</td>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>1915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14260</td>\n",
       "      <td>1053</td>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>7917</td>\n",
       "      <td>694</td>\n",
       "      <td>1</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>13175</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>9042</td>\n",
       "      <td>1152</td>\n",
       "      <td>1</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>9717</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>9937</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      LotArea  2ndFlrSF  KitchenAbvGr  YearBuilt\n",
       "0        8450       854             1       2003\n",
       "1        9600         0             1       1976\n",
       "2       11250       866             1       2001\n",
       "3        9550       756             1       1915\n",
       "4       14260      1053             1       2000\n",
       "...       ...       ...           ...        ...\n",
       "1455     7917       694             1       1999\n",
       "1456    13175         0             1       1978\n",
       "1457     9042      1152             1       1941\n",
       "1458     9717         0             1       1950\n",
       "1459     9937         0             1       1965\n",
       "\n",
       "[1460 rows x 4 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3614/3984563265.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X[['LotArea', '2ndFlrSF', 'KitchenAbvGr']] = scaler.fit_transform(X[['LotArea', '2ndFlrSF', 'KitchenAbvGr']])\n"
     ]
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X[['LotArea', '2ndFlrSF', 'KitchenAbvGr']] = scaler.fit_transform(X[['LotArea', '2ndFlrSF', 'KitchenAbvGr']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "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>LotArea</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>YearBuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.033420</td>\n",
       "      <td>0.413559</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.038795</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.046507</td>\n",
       "      <td>0.419370</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.038561</td>\n",
       "      <td>0.366102</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.060576</td>\n",
       "      <td>0.509927</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>0.030929</td>\n",
       "      <td>0.336077</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>0.055505</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>0.036187</td>\n",
       "      <td>0.557869</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>0.039342</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>0.040370</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LotArea  2ndFlrSF  KitchenAbvGr  YearBuilt\n",
       "0     0.033420  0.413559      0.333333       2003\n",
       "1     0.038795  0.000000      0.333333       1976\n",
       "2     0.046507  0.419370      0.333333       2001\n",
       "3     0.038561  0.366102      0.333333       1915\n",
       "4     0.060576  0.509927      0.333333       2000\n",
       "...        ...       ...           ...        ...\n",
       "1455  0.030929  0.336077      0.333333       1999\n",
       "1456  0.055505  0.000000      0.333333       1978\n",
       "1457  0.036187  0.557869      0.333333       1941\n",
       "1458  0.039342  0.000000      0.333333       1950\n",
       "1459  0.040370  0.000000      0.333333       1965\n",
       "\n",
       "[1460 rows x 4 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 9/9 [00:06<00:00,  1.47it/s]\n"
     ]
    }
   ],
   "source": [
    "errors = []\n",
    "for estm in tqdm(range(100, 1000, 100)):\n",
    "    regr = RandomForestRegressor(max_depth=7,n_estimators=estm, random_state=0)\n",
    "    regr.fit(X_train, y_train)\n",
    "    errors.append(mean_absolute_error(y_test,regr.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "regr = RandomForestRegressor(max_depth=20,  n_estimators=1000, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=20, n_estimators=1000, random_state=0)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32617.359258789253"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_test,regr.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm, datasets\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters = {'max_depth':[1, 20], 'n_estimators':[100, 1000]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Метрики\n",
      "==============================\n",
      "accuracy\n",
      "adjusted_mutual_info_score\n",
      "adjusted_rand_score\n",
      "average_precision\n",
      "balanced_accuracy\n",
      "completeness_score\n",
      "explained_variance\n",
      "f1\n",
      "f1_macro\n",
      "f1_micro\n",
      "f1_samples\n",
      "f1_weighted\n",
      "fowlkes_mallows_score\n",
      "homogeneity_score\n",
      "jaccard\n",
      "jaccard_macro\n",
      "jaccard_micro\n",
      "jaccard_samples\n",
      "jaccard_weighted\n",
      "max_error\n",
      "mutual_info_score\n",
      "neg_brier_score\n",
      "neg_log_loss\n",
      "neg_mean_absolute_error\n",
      "neg_mean_absolute_percentage_error\n",
      "neg_mean_gamma_deviance\n",
      "neg_mean_poisson_deviance\n",
      "neg_mean_squared_error\n",
      "neg_mean_squared_log_error\n",
      "neg_median_absolute_error\n",
      "neg_root_mean_squared_error\n",
      "normalized_mutual_info_score\n",
      "precision\n",
      "precision_macro\n",
      "precision_micro\n",
      "precision_samples\n",
      "precision_weighted\n",
      "r2\n",
      "rand_score\n",
      "recall\n",
      "recall_macro\n",
      "recall_micro\n",
      "recall_samples\n",
      "recall_weighted\n",
      "roc_auc\n",
      "roc_auc_ovo\n",
      "roc_auc_ovo_weighted\n",
      "roc_auc_ovr\n",
      "roc_auc_ovr_weighted\n",
      "top_k_accuracy\n",
      "v_measure_score\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "print(\"Метрики\")\n",
    "print(\"=\"*30)\n",
    "for x in sorted(sklearn.metrics.SCORERS.keys()):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': [1, 20], 'n_estimators': [100, 1000]},\n",
       "             scoring='neg_mean_absolute_error')"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GridSearchCV(RandomForestRegressor(), parameters, scoring='neg_mean_absolute_error')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 20, 'n_estimators': 1000}"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mean_absolute_error')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(list(range(100,1000,100)),errors )\n",
    "plt.xlabel(\"Depth of RF\")\n",
    "plt.ylabel(\"mean_absolute_error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31598.08208076311"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(errors)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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