Model Selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d43fe1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression, SGDRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2885cdf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('log_transformed.csv')\n",
    "test = pd.read_csv('log_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba00e887",
   "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>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>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",
       "      <th>LotFrontage_nan</th>\n",
       "      <th>MasVnrArea_nan</th>\n",
       "      <th>GarageYrBlt_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.693147</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.605392</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>12.247699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.604894</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>12.109016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.234107</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>7.605392</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>12.317171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.609438</td>\n",
       "      <td>4.262680</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.604396</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>11.849405</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.791759</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>7.605392</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>12.429220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  MSSubClass  MSZoning  LotFrontage   LotArea    Street     Alley  \\\n",
       "0  0.693147    4.110874  1.386294     4.189655  9.042040  0.693147  1.098612   \n",
       "1  1.098612    3.044522  1.386294     4.394449  9.169623  0.693147  1.098612   \n",
       "2  1.386294    4.110874  1.386294     4.234107  9.328212  0.693147  1.098612   \n",
       "3  1.609438    4.262680  1.386294     4.110874  9.164401  0.693147  1.098612   \n",
       "4  1.791759    4.110874  1.386294     4.442651  9.565284  0.693147  1.098612   \n",
       "\n",
       "   LotShape  LandContour  Utilities  ...  MiscFeature  MiscVal    MoSold  \\\n",
       "0  0.000000     0.693147   0.693147  ...     1.386294      0.0  1.098612   \n",
       "1  0.000000     0.693147   0.693147  ...     1.386294      0.0  1.791759   \n",
       "2  0.693147     0.693147   0.693147  ...     1.386294      0.0  2.302585   \n",
       "3  0.693147     0.693147   0.693147  ...     1.386294      0.0  1.098612   \n",
       "4  0.693147     0.693147   0.693147  ...     1.386294      0.0  2.564949   \n",
       "\n",
       "     YrSold  SaleType  SaleCondition  SalePrice  LotFrontage_nan  \\\n",
       "0  7.605392  1.609438       1.609438  12.247699              0.0   \n",
       "1  7.604894  1.609438       1.609438  12.109016              0.0   \n",
       "2  7.605392  1.609438       1.609438  12.317171              0.0   \n",
       "3  7.604396  1.609438       0.693147  11.849405              0.0   \n",
       "4  7.605392  1.609438       1.609438  12.429220              0.0   \n",
       "\n",
       "   MasVnrArea_nan  GarageYrBlt_nan  \n",
       "0             0.0              0.0  \n",
       "1             0.0              0.0  \n",
       "2             0.0              0.0  \n",
       "3             0.0              0.0  \n",
       "4             0.0              0.0  \n",
       "\n",
       "[5 rows x 84 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a0da705",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['SalePrice'].copy()\n",
    "x = train.drop('SalePrice', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9fc6a61c",
   "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>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>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>LotFrontage_nan</th>\n",
       "      <th>MasVnrArea_nan</th>\n",
       "      <th>GarageYrBlt_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.693147</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.605392</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.604894</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.234107</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>7.605392</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.609438</td>\n",
       "      <td>4.262680</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.604396</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.791759</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>7.605392</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  MSSubClass  MSZoning  LotFrontage   LotArea    Street     Alley  \\\n",
       "0  0.693147    4.110874  1.386294     4.189655  9.042040  0.693147  1.098612   \n",
       "1  1.098612    3.044522  1.386294     4.394449  9.169623  0.693147  1.098612   \n",
       "2  1.386294    4.110874  1.386294     4.234107  9.328212  0.693147  1.098612   \n",
       "3  1.609438    4.262680  1.386294     4.110874  9.164401  0.693147  1.098612   \n",
       "4  1.791759    4.110874  1.386294     4.442651  9.565284  0.693147  1.098612   \n",
       "\n",
       "   LotShape  LandContour  Utilities  ...     Fence  MiscFeature  MiscVal  \\\n",
       "0  0.000000     0.693147   0.693147  ...  1.609438     1.386294      0.0   \n",
       "1  0.000000     0.693147   0.693147  ...  1.609438     1.386294      0.0   \n",
       "2  0.693147     0.693147   0.693147  ...  1.609438     1.386294      0.0   \n",
       "3  0.693147     0.693147   0.693147  ...  1.609438     1.386294      0.0   \n",
       "4  0.693147     0.693147   0.693147  ...  1.609438     1.386294      0.0   \n",
       "\n",
       "     MoSold    YrSold  SaleType  SaleCondition  LotFrontage_nan  \\\n",
       "0  1.098612  7.605392  1.609438       1.609438              0.0   \n",
       "1  1.791759  7.604894  1.609438       1.609438              0.0   \n",
       "2  2.302585  7.605392  1.609438       1.609438              0.0   \n",
       "3  1.098612  7.604396  1.609438       0.693147              0.0   \n",
       "4  2.564949  7.605392  1.609438       1.609438              0.0   \n",
       "\n",
       "   MasVnrArea_nan  GarageYrBlt_nan  \n",
       "0             0.0              0.0  \n",
       "1             0.0              0.0  \n",
       "2             0.0              0.0  \n",
       "3             0.0              0.0  \n",
       "4             0.0              0.0  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88deeffb",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.drop('Id', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1be7a624",
   "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>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>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>LotFrontage_nan</th>\n",
       "      <th>MasVnrArea_nan</th>\n",
       "      <th>GarageYrBlt_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.287561</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>9.360741</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.288244</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.406719</td>\n",
       "      <td>9.565775</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>9.433564</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.288928</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>9.534668</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.289611</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.369448</td>\n",
       "      <td>9.208238</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.290293</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>3.784190</td>\n",
       "      <td>8.518392</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  MSSubClass  MSZoning  LotFrontage   LotArea    Street     Alley  \\\n",
       "0  7.287561    3.044522  1.098612     4.394449  9.360741  0.693147  1.098612   \n",
       "1  7.288244    3.044522  1.386294     4.406719  9.565775  0.693147  1.098612   \n",
       "2  7.288928    4.110874  1.386294     4.317488  9.534668  0.693147  1.098612   \n",
       "3  7.289611    4.110874  1.386294     4.369448  9.208238  0.693147  1.098612   \n",
       "4  7.290293    4.795791  1.386294     3.784190  8.518392  0.693147  1.098612   \n",
       "\n",
       "   LotShape  LandContour  Utilities  ...     Fence  MiscFeature   MiscVal  \\\n",
       "0  0.000000     0.693147   0.693147  ...  1.098612     1.386294  0.000000   \n",
       "1  0.693147     0.693147   0.693147  ...  1.609438     1.098612  9.433564   \n",
       "2  0.693147     0.693147   0.693147  ...  1.098612     1.386294  0.000000   \n",
       "3  0.693147     0.693147   0.693147  ...  1.609438     1.386294  0.000000   \n",
       "4  0.693147     1.386294   0.693147  ...  1.609438     1.386294  0.000000   \n",
       "\n",
       "     MoSold    YrSold  SaleType  SaleCondition  LotFrontage_nan  \\\n",
       "0  1.945910  7.606387  1.609438       1.609438              0.0   \n",
       "1  1.945910  7.606387  1.609438       1.609438              0.0   \n",
       "2  1.386294  7.606387  1.609438       1.609438              0.0   \n",
       "3  1.945910  7.606387  1.609438       1.609438              0.0   \n",
       "4  0.693147  7.606387  1.609438       1.609438              0.0   \n",
       "\n",
       "   MasVnrArea_nan  GarageYrBlt_nan  \n",
       "0             0.0              0.0  \n",
       "1             0.0              0.0  \n",
       "2             0.0              0.0  \n",
       "3             0.0              0.0  \n",
       "4             0.0              0.0  \n",
       "\n",
       "[5 rows x 83 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "30ab5db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop('Id', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd21df82",
   "metadata": {},
   "outputs": [],
   "source": [
    "linearregression = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "318402b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearregression.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff1547bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9062157462070137"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearregression.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69a3f624",
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd = SGDRegressor(eta0=0.004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "15d215eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(eta0=0.004)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "15ddb4a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8492978632487087"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "90025e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "svr = SVR(kernel='poly', degree=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57dffee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(degree=4, kernel='poly')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d58dd207",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9267769792238132"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "311bdd1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 82)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c09660b",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_score = linearregression.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1e0310f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.66810662, 11.97161446, 12.09116565, ..., 12.0346795 ,\n",
       "       11.75761748, 12.36675554])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54745344",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inverse of numpy\n",
    "import numpy as np\n",
    "reg_score = np.e**reg_score - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76af4507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([116785.95047615, 158198.86138574, 178288.80926354, ...,\n",
       "       168497.05682805, 127721.78833794, 234861.50559184])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0aa2f4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "svr_score = svr.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "921ab73f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11.68843115, 12.07818993, 12.11661595, ..., 12.02470375,\n",
       "       11.60904606, 12.33426453])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "be90b73d",
   "metadata": {},
   "outputs": [],
   "source": [
    "svr_score = np.e**svr_score - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "494623c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([119183.87630894, 175990.31585782, 182884.57176662, ...,\n",
       "       166824.51864118, 110088.18173211, 227353.22167149])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6dfaa4ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "128493.88923034928\n"
     ]
    }
   ],
   "source": [
    "print(np.e**11.76364441 - 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "55d7174f",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27dd15f0",
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>169277.052498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>187758.393989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>183583.683570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>179317.477511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>150730.079977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  169277.052498\n",
       "1  1462  187758.393989\n",
       "2  1463  183583.683570\n",
       "3  1464  179317.477511\n",
       "4  1465  150730.079977"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a924b77e",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_reg = submission.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "69ec81d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_reg.SalePrice = reg_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "43536d86",
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>116785.950476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>158198.861386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>178288.809264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>198011.398659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>197499.427460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  116785.950476\n",
       "1  1462  158198.861386\n",
       "2  1463  178288.809264\n",
       "3  1464  198011.398659\n",
       "4  1465  197499.427460"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission_reg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ceca7331",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_svr = submission.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "28e394c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission_svr.SalePrice = svr_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f4e69279",
   "metadata": {},
   "outputs": [],
   "source": [
    "#submission_reg.to_csv('Regression.csv', index = False)\n",
    "submission_svr.to_csv('SVR.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "15431316",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e5c9c8b7",
   "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>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>LotConfig</th>\n",
       "      <th>...</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>LotFrontage_nan</th>\n",
       "      <th>MasVnrArea_nan</th>\n",
       "      <th>GarageYrBlt_nan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>9.042040</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.605392</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.044522</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.394449</td>\n",
       "      <td>9.169623</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>7.604894</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.234107</td>\n",
       "      <td>9.328212</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>7.605392</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.262680</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>9.164401</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.604396</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.110874</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.565284</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>...</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>7.605392</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  MSZoning  LotFrontage   LotArea    Street     Alley  LotShape  \\\n",
       "0    4.110874  1.386294     4.189655  9.042040  0.693147  1.098612  0.000000   \n",
       "1    3.044522  1.386294     4.394449  9.169623  0.693147  1.098612  0.000000   \n",
       "2    4.110874  1.386294     4.234107  9.328212  0.693147  1.098612  0.693147   \n",
       "3    4.262680  1.386294     4.110874  9.164401  0.693147  1.098612  0.693147   \n",
       "4    4.110874  1.386294     4.442651  9.565284  0.693147  1.098612  0.693147   \n",
       "\n",
       "   LandContour  Utilities  LotConfig  ...     Fence  MiscFeature  MiscVal  \\\n",
       "0     0.693147   0.693147   0.000000  ...  1.609438     1.386294      0.0   \n",
       "1     0.693147   0.693147   0.693147  ...  1.609438     1.386294      0.0   \n",
       "2     0.693147   0.693147   0.000000  ...  1.609438     1.386294      0.0   \n",
       "3     0.693147   0.693147   1.098612  ...  1.609438     1.386294      0.0   \n",
       "4     0.693147   0.693147   0.693147  ...  1.609438     1.386294      0.0   \n",
       "\n",
       "     MoSold    YrSold  SaleType  SaleCondition  LotFrontage_nan  \\\n",
       "0  1.098612  7.605392  1.609438       1.609438              0.0   \n",
       "1  1.791759  7.604894  1.609438       1.609438              0.0   \n",
       "2  2.302585  7.605392  1.609438       1.609438              0.0   \n",
       "3  1.098612  7.604396  1.609438       0.693147              0.0   \n",
       "4  2.564949  7.605392  1.609438       1.609438              0.0   \n",
       "\n",
       "   MasVnrArea_nan  GarageYrBlt_nan  \n",
       "0             0.0              0.0  \n",
       "1             0.0              0.0  \n",
       "2             0.0              0.0  \n",
       "3             0.0              0.0  \n",
       "4             0.0              0.0  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb1081a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "13225a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "18c88e28",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree = DecisionTreeRegressor(max_depth=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3468ca7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=4)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "75c3bbba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7903547930314009"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f4cc0be8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7706310136669002"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2cbc5521",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "204c972c",
   "metadata": {},
   "outputs": [],
   "source": [
    "xg = XGBRegressor(max_depth=2, learning_rate=0.330000012)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a2d3811f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "             gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.330000012,\n",
       "             max_delta_step=0, max_depth=2, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1d34bb33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9600817418328546"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "32803bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9071835310417843"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e81adffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import RadiusNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "0b17faf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "rn = RadiusNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "3508b194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RadiusNeighborsRegressor()"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rn.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "83e04e88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999306771235256"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rn.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "2ee8ba78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1168, 82)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "602f62f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "58ce0734",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestRegressor(n_estimators=65, max_depth=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "51e8df6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=8, n_estimators=65)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ae0191b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.963540896769724"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8876574e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.880814036270371"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c17b688e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import PassiveAggressiveRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b28b8f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "passive = PassiveAggressiveRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "50809e47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassiveAggressiveRegressor()"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passive.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "d231e07f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8464030461123411"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passive.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "a59f6919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8465723063608847"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "passive.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f08792ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import ARDRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ee409d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "ard = ARDRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "aecf35cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARDRegression()"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ard.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "027517f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8986377064392037"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ard.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "503f0a6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8944849820074079"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ard.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "7c895328",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "9a07aa74",
   "metadata": {},
   "outputs": [],
   "source": [
    "svr = SVR(kernel='poly', degree=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "6916eefd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(degree=4, kernel='poly')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "4d8720ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93192299575323"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "222b336d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.893812988190747"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "0cd31dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "ard_score = ard.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "676a7f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "48707498",
   "metadata": {},
   "outputs": [],
   "source": [
    "ard_score = np.e**ard_score - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "4199d98d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ard_submission = pd.read_csv('sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "5623ab6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ard_submission.SalePrice = ard_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "38bbaf9b",
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>110204.155276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>154478.622389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>174182.940901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>192172.630468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>195739.909933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  110204.155276\n",
       "1  1462  154478.622389\n",
       "2  1463  174182.940901\n",
       "3  1464  192172.630468\n",
       "4  1465  195739.909933"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ard_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "da1ae1b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ard_submission.to_csv('ard_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "467513a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_score = forest.predict(test)\n",
    "forest_score = np.e**forest_score - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "id": "b5a2a2a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_submission = ard_submission.copy()\n",
    "forest_submission.SalePrice = forest_score.copy()\n",
    "forest_submission.to_csv('forest_submission.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "6c5aa572",
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_score = xg.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "22888856",
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_score = np.e**xg_score - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "38e224b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "ard_submission.SalePrice = xg_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "cc6c7122",
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>133744.296875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>155819.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>186155.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>188598.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>195384.218750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>80708.093750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>81587.695312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>150500.843750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>118347.671875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>201667.953125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id      SalePrice\n",
       "0     1461  133744.296875\n",
       "1     1462  155819.750000\n",
       "2     1463  186155.812500\n",
       "3     1464  188598.937500\n",
       "4     1465  195384.218750\n",
       "...    ...            ...\n",
       "1454  2915   80708.093750\n",
       "1455  2916   81587.695312\n",
       "1456  2917  150500.843750\n",
       "1457  2918  118347.671875\n",
       "1458  2919  201667.953125\n",
       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ard_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "1ada193e",
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>119183.876309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>175990.315858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>182884.571767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>197267.423387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>195848.968009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>2915</td>\n",
       "      <td>93118.460887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>85237.859101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>166824.518641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>2918</td>\n",
       "      <td>110088.181732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>227353.221671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id      SalePrice\n",
       "0     1461  119183.876309\n",
       "1     1462  175990.315858\n",
       "2     1463  182884.571767\n",
       "3     1464  197267.423387\n",
       "4     1465  195848.968009\n",
       "...    ...            ...\n",
       "1454  2915   93118.460887\n",
       "1455  2916   85237.859101\n",
       "1456  2917  166824.518641\n",
       "1457  2918  110088.181732\n",
       "1458  2919  227353.221671\n",
       "\n",
       "[1459 rows x 2 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp = pd.read_csv('SVR.csv')\n",
    "exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "30199880",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "39e72b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x19d98e09310>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ard_submission.SalePrice, exp.SalePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "05a057d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "308919b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "estimator = [('lr', linearregression),('SVR',svr),('XGB', xg)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "0e85483d",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting = VotingRegressor(estimators=estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "0d12b912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingRegressor(estimators=[('lr', LinearRegression()),\n",
       "                            ('SVR', SVR(degree=4, kernel='poly')),\n",
       "                            ('XGB',\n",
       "                             XGBRegressor(base_score=0.5, booster='gbtree',\n",
       "                                          colsample_bylevel=1,\n",
       "                                          colsample_bynode=1,\n",
       "                                          colsample_bytree=1,\n",
       "                                          enable_categorical=False, gamma=0,\n",
       "                                          gpu_id=-1, importance_type=None,\n",
       "                                          interaction_constraints='',\n",
       "                                          learning_rate=0.330000012,\n",
       "                                          max_delta_step=0, max_depth=2,\n",
       "                                          min_child_weight=1, missing=nan,\n",
       "                                          monotone_constraints='()',\n",
       "                                          n_estimators=100, n_jobs=8,\n",
       "                                          num_parallel_tree=1, predictor='auto',\n",
       "                                          random_state=0, reg_alpha=0,\n",
       "                                          reg_lambda=1, scale_pos_weight=1,\n",
       "                                          subsample=1, tree_method='exact',\n",
       "                                          validate_parameters=1,\n",
       "                                          verbosity=None))])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "1756fcc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9453746718214132"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "43d86f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9115338678954271"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "cac0bf69",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_score = voting.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "8d845af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_score = np.e**voting_score -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c4e65fe9",
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>169277.052498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>187758.393989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>183583.683570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>179317.477511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>150730.079977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  169277.052498\n",
       "1  1462  187758.393989\n",
       "2  1463  183583.683570\n",
       "3  1464  179317.477511\n",
       "4  1465  150730.079977"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "e80829cc",
   "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>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>123754.394623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>159352.040560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>181603.606754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>192486.222746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>199343.929234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id      SalePrice\n",
       "0  1461  123754.394623\n",
       "1  1462  159352.040560\n",
       "2  1463  181603.606754\n",
       "3  1464  192486.222746\n",
       "4  1465  199343.929234"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.SalePrice = voting_score\n",
    "submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "7eac70ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('voting.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59199da2",
   "metadata": {},
   "source": [
    "#### Voting Has given the Best score on Kaggle current ranking 705"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3197dcf3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}