XGboost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "506bc035",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "749846ec",
   "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>Unnamed: 0</th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Date</th>\n",
       "      <th>Distance</th>\n",
       "      <th>...</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>85 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>3/12/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>4/06/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      Suburb           Address  Rooms Type      Price Method  \\\n",
       "0           1  Abbotsford      85 Turner St      2    h  1480000.0      S   \n",
       "1           2  Abbotsford   25 Bloomburg St      2    h  1035000.0      S   \n",
       "2           4  Abbotsford      5 Charles St      3    h  1465000.0     SP   \n",
       "3           5  Abbotsford  40 Federation La      3    h   850000.0     PI   \n",
       "4           6  Abbotsford       55a Park St      4    h  1600000.0     VB   \n",
       "\n",
       "  SellerG       Date  Distance  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0  Biggin  3/12/2016       2.5  ...       1.0  1.0     202.0           NaN   \n",
       "1  Biggin  4/02/2016       2.5  ...       1.0  0.0     156.0          79.0   \n",
       "2  Biggin  4/03/2017       2.5  ...       2.0  0.0     134.0         150.0   \n",
       "3  Biggin  4/03/2017       2.5  ...       2.0  1.0      94.0           NaN   \n",
       "4  Nelson  4/06/2016       2.5  ...       1.0  2.0     120.0         142.0   \n",
       "\n",
       "   YearBuilt  CouncilArea  Lattitude Longtitude             Regionname  \\\n",
       "0        NaN        Yarra   -37.7996   144.9984  Northern Metropolitan   \n",
       "1     1900.0        Yarra   -37.8079   144.9934  Northern Metropolitan   \n",
       "2     1900.0        Yarra   -37.8093   144.9944  Northern Metropolitan   \n",
       "3        NaN        Yarra   -37.7969   144.9969  Northern Metropolitan   \n",
       "4     2014.0        Yarra   -37.8072   144.9941  Northern Metropolitan   \n",
       "\n",
       "   Propertycount  \n",
       "0         4019.0  \n",
       "1         4019.0  \n",
       "2         4019.0  \n",
       "3         4019.0  \n",
       "4         4019.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('me_data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56e75024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18396, 22)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b684b376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0           0\n",
       "Suburb               0\n",
       "Address              0\n",
       "Rooms                0\n",
       "Type                 0\n",
       "Price                0\n",
       "Method               0\n",
       "SellerG              0\n",
       "Date                 0\n",
       "Distance             1\n",
       "Postcode             1\n",
       "Bedroom2          3469\n",
       "Bathroom          3471\n",
       "Car               3576\n",
       "Landsize          4793\n",
       "BuildingArea     10634\n",
       "YearBuilt         9438\n",
       "CouncilArea       6163\n",
       "Lattitude         3332\n",
       "Longtitude        3332\n",
       "Regionname           1\n",
       "Propertycount        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "96da7fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Suburb</th>\n",
       "      <th>Address</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>25 Bloomburg St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.00</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
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       "      <td>144.99340</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>5 Charles St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.00</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.80930</td>\n",
       "      <td>144.99440</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>55a Park St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>4/06/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.00</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.80720</td>\n",
       "      <td>144.99410</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>124 Yarra St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1876000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>7/05/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>210.00</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.80240</td>\n",
       "      <td>144.99930</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>98 Charles St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1636000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>8/10/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>107.00</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>Yarra</td>\n",
       "      <td>-37.80600</td>\n",
       "      <td>144.99540</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>15388</th>\n",
       "      <td>19732</td>\n",
       "      <td>Whittlesea</td>\n",
       "      <td>30 Sherwin St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>601000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Ray</td>\n",
       "      <td>29/07/2017</td>\n",
       "      <td>35.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>149.00</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Whittlesea</td>\n",
       "      <td>-37.51232</td>\n",
       "      <td>145.13282</td>\n",
       "      <td>Northern Victoria</td>\n",
       "      <td>2170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15389</th>\n",
       "      <td>19733</td>\n",
       "      <td>Williamstown</td>\n",
       "      <td>75 Cecil St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1050000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Williams</td>\n",
       "      <td>29/07/2017</td>\n",
       "      <td>6.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>115.00</td>\n",
       "      <td>1890.0</td>\n",
       "      <td>Hobsons Bay</td>\n",
       "      <td>-37.86558</td>\n",
       "      <td>144.90474</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15390</th>\n",
       "      <td>19734</td>\n",
       "      <td>Williamstown</td>\n",
       "      <td>2/29 Dover Rd</td>\n",
       "      <td>1</td>\n",
       "      <td>u</td>\n",
       "      <td>385000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Williams</td>\n",
       "      <td>29/07/2017</td>\n",
       "      <td>6.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.64</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>Hobsons Bay</td>\n",
       "      <td>-37.85588</td>\n",
       "      <td>144.89936</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15392</th>\n",
       "      <td>19736</td>\n",
       "      <td>Windsor</td>\n",
       "      <td>201/152 Peel St</td>\n",
       "      <td>2</td>\n",
       "      <td>u</td>\n",
       "      <td>560000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>hockingstuart</td>\n",
       "      <td>29/07/2017</td>\n",
       "      <td>4.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.60</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Stonnington</td>\n",
       "      <td>-37.85581</td>\n",
       "      <td>144.99025</td>\n",
       "      <td>Southern Metropolitan</td>\n",
       "      <td>4380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15395</th>\n",
       "      <td>19739</td>\n",
       "      <td>Yarraville</td>\n",
       "      <td>54 Pentland Pde</td>\n",
       "      <td>6</td>\n",
       "      <td>h</td>\n",
       "      <td>2450000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Village</td>\n",
       "      <td>29/07/2017</td>\n",
       "      <td>6.3</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1087.0</td>\n",
       "      <td>388.50</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>Maribyrnong</td>\n",
       "      <td>-37.81038</td>\n",
       "      <td>144.89389</td>\n",
       "      <td>Western Metropolitan</td>\n",
       "      <td>6543.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6196 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0        Suburb          Address  Rooms Type      Price  \\\n",
       "1               2    Abbotsford  25 Bloomburg St      2    h  1035000.0   \n",
       "2               4    Abbotsford     5 Charles St      3    h  1465000.0   \n",
       "4               6    Abbotsford      55a Park St      4    h  1600000.0   \n",
       "6              11    Abbotsford     124 Yarra St      3    h  1876000.0   \n",
       "7              14    Abbotsford    98 Charles St      2    h  1636000.0   \n",
       "...           ...           ...              ...    ...  ...        ...   \n",
       "15388       19732    Whittlesea    30 Sherwin St      3    h   601000.0   \n",
       "15389       19733  Williamstown      75 Cecil St      3    h  1050000.0   \n",
       "15390       19734  Williamstown    2/29 Dover Rd      1    u   385000.0   \n",
       "15392       19736       Windsor  201/152 Peel St      2    u   560000.0   \n",
       "15395       19739    Yarraville  54 Pentland Pde      6    h  2450000.0   \n",
       "\n",
       "      Method        SellerG        Date  Distance  ...  Bathroom  Car  \\\n",
       "1          S         Biggin   4/02/2016       2.5  ...       1.0  0.0   \n",
       "2         SP         Biggin   4/03/2017       2.5  ...       2.0  0.0   \n",
       "4         VB         Nelson   4/06/2016       2.5  ...       1.0  2.0   \n",
       "6          S         Nelson   7/05/2016       2.5  ...       2.0  0.0   \n",
       "7          S         Nelson   8/10/2016       2.5  ...       1.0  2.0   \n",
       "...      ...            ...         ...       ...  ...       ...  ...   \n",
       "15388      S            Ray  29/07/2017      35.5  ...       2.0  1.0   \n",
       "15389     VB       Williams  29/07/2017       6.8  ...       1.0  0.0   \n",
       "15390     SP       Williams  29/07/2017       6.8  ...       1.0  1.0   \n",
       "15392     PI  hockingstuart  29/07/2017       4.6  ...       1.0  1.0   \n",
       "15395     VB        Village  29/07/2017       6.3  ...       3.0  2.0   \n",
       "\n",
       "       Landsize  BuildingArea  YearBuilt  CouncilArea  Lattitude Longtitude  \\\n",
       "1         156.0         79.00     1900.0        Yarra  -37.80790  144.99340   \n",
       "2         134.0        150.00     1900.0        Yarra  -37.80930  144.99440   \n",
       "4         120.0        142.00     2014.0        Yarra  -37.80720  144.99410   \n",
       "6         245.0        210.00     1910.0        Yarra  -37.80240  144.99930   \n",
       "7         256.0        107.00     1890.0        Yarra  -37.80600  144.99540   \n",
       "...         ...           ...        ...          ...        ...        ...   \n",
       "15388     972.0        149.00     1996.0   Whittlesea  -37.51232  145.13282   \n",
       "15389     179.0        115.00     1890.0  Hobsons Bay  -37.86558  144.90474   \n",
       "15390       0.0         35.64     1967.0  Hobsons Bay  -37.85588  144.89936   \n",
       "15392       0.0         61.60     2012.0  Stonnington  -37.85581  144.99025   \n",
       "15395    1087.0        388.50     1920.0  Maribyrnong  -37.81038  144.89389   \n",
       "\n",
       "                  Regionname  Propertycount  \n",
       "1      Northern Metropolitan         4019.0  \n",
       "2      Northern Metropolitan         4019.0  \n",
       "4      Northern Metropolitan         4019.0  \n",
       "6      Northern Metropolitan         4019.0  \n",
       "7      Northern Metropolitan         4019.0  \n",
       "...                      ...            ...  \n",
       "15388      Northern Victoria         2170.0  \n",
       "15389   Western Metropolitan         6380.0  \n",
       "15390   Western Metropolitan         6380.0  \n",
       "15392  Southern Metropolitan         4380.0  \n",
       "15395   Western Metropolitan         6543.0  \n",
       "\n",
       "[6196 rows x 22 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5271f0dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6196, 22)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8d73bd3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6196 entries, 1 to 15395\n",
      "Data columns (total 22 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Unnamed: 0     6196 non-null   int64  \n",
      " 1   Suburb         6196 non-null   object \n",
      " 2   Address        6196 non-null   object \n",
      " 3   Rooms          6196 non-null   int64  \n",
      " 4   Type           6196 non-null   object \n",
      " 5   Price          6196 non-null   float64\n",
      " 6   Method         6196 non-null   object \n",
      " 7   SellerG        6196 non-null   object \n",
      " 8   Date           6196 non-null   object \n",
      " 9   Distance       6196 non-null   float64\n",
      " 10  Postcode       6196 non-null   float64\n",
      " 11  Bedroom2       6196 non-null   float64\n",
      " 12  Bathroom       6196 non-null   float64\n",
      " 13  Car            6196 non-null   float64\n",
      " 14  Landsize       6196 non-null   float64\n",
      " 15  BuildingArea   6196 non-null   float64\n",
      " 16  YearBuilt      6196 non-null   float64\n",
      " 17  CouncilArea    6196 non-null   object \n",
      " 18  Lattitude      6196 non-null   float64\n",
      " 19  Longtitude     6196 non-null   float64\n",
      " 20  Regionname     6196 non-null   object \n",
      " 21  Propertycount  6196 non-null   float64\n",
      "dtypes: float64(12), int64(2), object(8)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f53254",
   "metadata": {},
   "source": [
    "Selectiong only the columns we are about to use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b231a43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Rooms', 'Distance', 'Landsize', 'BuildingArea', 'YearBuilt']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "useful_cols = ['Rooms', 'Distance', 'Landsize', 'BuildingArea', 'YearBuilt']\n",
    "useful_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cde906e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "x= data[useful_cols]\n",
    "y= data.Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4569973e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "84e7e4e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>3</td>\n",
       "      <td>11.1</td>\n",
       "      <td>276.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12741</th>\n",
       "      <td>2</td>\n",
       "      <td>17.9</td>\n",
       "      <td>257.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>1985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4846</th>\n",
       "      <td>2</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1967.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5483</th>\n",
       "      <td>4</td>\n",
       "      <td>11.2</td>\n",
       "      <td>650.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13979</th>\n",
       "      <td>4</td>\n",
       "      <td>7.8</td>\n",
       "      <td>494.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>1900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9554</th>\n",
       "      <td>3</td>\n",
       "      <td>13.9</td>\n",
       "      <td>294.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6885</th>\n",
       "      <td>3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>217.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081</th>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3592</th>\n",
       "      <td>3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>609.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6460</th>\n",
       "      <td>2</td>\n",
       "      <td>11.2</td>\n",
       "      <td>180.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2004.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4337 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Rooms  Distance  Landsize  BuildingArea  YearBuilt\n",
       "290        3      11.1     276.0         100.0     2010.0\n",
       "12741      2      17.9     257.0          83.0     1985.0\n",
       "4846       2       7.4       0.0          91.0     1967.0\n",
       "5483       4      11.2     650.0         150.0     1960.0\n",
       "13979      4       7.8     494.0         176.0     1900.0\n",
       "...      ...       ...       ...           ...        ...\n",
       "9554       3      13.9     294.0         105.0     1950.0\n",
       "6885       3       2.6     217.0         180.0     1910.0\n",
       "3081       2       8.0       0.0          73.0     1975.0\n",
       "3592       3       9.2     609.0         140.0     1930.0\n",
       "6460       2      11.2     180.0          61.0     2004.0\n",
       "\n",
       "[4337 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain, xtest, ytrain, ytest =  train_test_split(x,y,test_size = 0.3, random_state = 32)\n",
    "xtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9bf5081e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, gamma=None,\n",
       "             gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
       "             monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, predictor=None, random_state=None,\n",
       "             reg_alpha=None, reg_lambda=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, gamma=None,\n",
       "             gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
       "             monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, predictor=None, random_state=None,\n",
       "             reg_alpha=None, reg_lambda=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, gamma=None,\n",
       "             gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
       "             monotone_constraints=None, n_estimators=100, n_jobs=None,\n",
       "             num_parallel_tree=None, predictor=None, random_state=None,\n",
       "             reg_alpha=None, reg_lambda=None, ...)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor()\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e1b11669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "             importance_type=None, interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints='()', n_estimators=100, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fcd2abd",
   "metadata": {},
   "source": [
    "Now predicting and evalauating the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "097892b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "predict = model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "42e0d8d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 224540.67600188273\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Absolute Error: \" + str(mean_absolute_error(predict, ytest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1f8f8736",
   "metadata": {},
   "outputs": [
    {
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       " (1385000.0, 1403513.1),\n",
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       " (1774500.0, 1730166.0),\n",
       " (855000.0, 518584.94),\n",
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       " (460000.0, 542617.5),\n",
       " ...]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(ytest, predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6c2fae49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "03e44de4",
   "metadata": {},
   "outputs": [
    {
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       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=600, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=600, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "             importance_type=None, interaction_constraints='',\n",
       "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints='()', n_estimators=600, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor(n_estimators = 600)\n",
    "model.fit(xtrain, ytrain,\n",
    "         early_stopping_rounds = 5,\n",
    "         eval_set = [(xtest, ytest)],\n",
    "         verbose= False)\n",
    "# early_stopping_rounds provides a way to automatically find the ideal value for n_estimators.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83aaad06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.06, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=600, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.06, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=600, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "             importance_type=None, interaction_constraints='',\n",
       "             learning_rate=0.06, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints='()', n_estimators=600, n_jobs=0,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor(n_estimators = 600, learning_rate = 0.06)\n",
    "model.fit(xtrain, ytrain,\n",
    "         early_stopping_rounds = 5,\n",
    "         eval_set = [(xtest, ytest)],\n",
    "         verbose= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "25570b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.06, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=600, n_jobs=5,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "             importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "             learning_rate=0.06, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=600, n_jobs=5,\n",
       "             num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "             early_stopping_rounds=None, enable_categorical=False,\n",
       "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "             importance_type=None, interaction_constraints='',\n",
       "             learning_rate=0.06, max_bin=256, max_cat_to_onehot=4,\n",
       "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "             missing=nan, monotone_constraints='()', n_estimators=600, n_jobs=5,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, ...)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = XGBRegressor(n_estimators = 600, learning_rate = 0.06, n_jobs = 5)\n",
    "model.fit(xtrain, ytrain,\n",
    "         early_stopping_rounds = 5,\n",
    "         eval_set = [(xtest, ytest)],\n",
    "         verbose= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "055e5b2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "472d9498",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5b12c70",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5a9ea79",
   "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": "3.9.13"
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