fitting-prediction-melb.py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../../data/melb_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n",
       "       'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',\n",
       "       'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude',\n",
       "       'Longtitude', 'Regionname', 'Propertycount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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>Postcode</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>1</th>\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>3067.0</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",
       "      <td>-37.80790</td>\n",
       "      <td>144.99340</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>3067.0</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>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>3067.0</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>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>3067.0</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>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>3067.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12205</th>\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>3757.0</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>12206</th>\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>3016.0</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>12207</th>\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>3016.0</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>12209</th>\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>3181.0</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>12212</th>\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>3013.0</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 × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Suburb          Address  Rooms Type      Price Method  \\\n",
       "1        Abbotsford  25 Bloomburg St      2    h  1035000.0      S   \n",
       "2        Abbotsford     5 Charles St      3    h  1465000.0     SP   \n",
       "4        Abbotsford      55a Park St      4    h  1600000.0     VB   \n",
       "6        Abbotsford     124 Yarra St      3    h  1876000.0      S   \n",
       "7        Abbotsford    98 Charles St      2    h  1636000.0      S   \n",
       "...             ...              ...    ...  ...        ...    ...   \n",
       "12205    Whittlesea    30 Sherwin St      3    h   601000.0      S   \n",
       "12206  Williamstown      75 Cecil St      3    h  1050000.0     VB   \n",
       "12207  Williamstown    2/29 Dover Rd      1    u   385000.0     SP   \n",
       "12209       Windsor  201/152 Peel St      2    u   560000.0     PI   \n",
       "12212    Yarraville  54 Pentland Pde      6    h  2450000.0     VB   \n",
       "\n",
       "             SellerG        Date  Distance  Postcode  ...  Bathroom  Car  \\\n",
       "1             Biggin   4/02/2016       2.5    3067.0  ...       1.0  0.0   \n",
       "2             Biggin   4/03/2017       2.5    3067.0  ...       2.0  0.0   \n",
       "4             Nelson   4/06/2016       2.5    3067.0  ...       1.0  2.0   \n",
       "6             Nelson   7/05/2016       2.5    3067.0  ...       2.0  0.0   \n",
       "7             Nelson   8/10/2016       2.5    3067.0  ...       1.0  2.0   \n",
       "...              ...         ...       ...       ...  ...       ...  ...   \n",
       "12205            Ray  29/07/2017      35.5    3757.0  ...       2.0  1.0   \n",
       "12206       Williams  29/07/2017       6.8    3016.0  ...       1.0  0.0   \n",
       "12207       Williams  29/07/2017       6.8    3016.0  ...       1.0  1.0   \n",
       "12209  hockingstuart  29/07/2017       4.6    3181.0  ...       1.0  1.0   \n",
       "12212        Village  29/07/2017       6.3    3013.0  ...       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",
       "12205     972.0        149.00     1996.0   Whittlesea -37.51232   145.13282   \n",
       "12206     179.0        115.00     1890.0  Hobsons Bay -37.86558   144.90474   \n",
       "12207       0.0         35.64     1967.0  Hobsons Bay -37.85588   144.89936   \n",
       "12209       0.0         61.60     2012.0  Stonnington -37.85581   144.99025   \n",
       "12212    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",
       "12205      Northern Victoria        2170.0  \n",
       "12206   Western Metropolitan        6380.0  \n",
       "12207   Western Metropolitan        6380.0  \n",
       "12209  Southern Metropolitan        4380.0  \n",
       "12212   Western Metropolitan        6543.0  \n",
       "\n",
       "[6196 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=0) # drop the rows which is not available (axis=0 selects rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select prediction target, this time is is 'Price'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Price'] # in convention \"target prediction\" is called \"y\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "melbe_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13580, 21)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[melbe_features] # Feature selection is called X in convention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Bathroom</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.937997</td>\n",
       "      <td>1.534242</td>\n",
       "      <td>558.416127</td>\n",
       "      <td>-37.809203</td>\n",
       "      <td>144.995216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.955748</td>\n",
       "      <td>0.691712</td>\n",
       "      <td>3990.669241</td>\n",
       "      <td>0.079260</td>\n",
       "      <td>0.103916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-38.182550</td>\n",
       "      <td>144.431810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>-37.856822</td>\n",
       "      <td>144.929600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>-37.802355</td>\n",
       "      <td>145.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>-37.756400</td>\n",
       "      <td>145.058305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>433014.000000</td>\n",
       "      <td>-37.408530</td>\n",
       "      <td>145.526350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms      Bathroom       Landsize     Lattitude    Longtitude\n",
       "count  13580.000000  13580.000000   13580.000000  13580.000000  13580.000000\n",
       "mean       2.937997      1.534242     558.416127    -37.809203    144.995216\n",
       "std        0.955748      0.691712    3990.669241      0.079260      0.103916\n",
       "min        1.000000      0.000000       0.000000    -38.182550    144.431810\n",
       "25%        2.000000      1.000000     177.000000    -37.856822    144.929600\n",
       "50%        3.000000      1.000000     440.000000    -37.802355    145.000100\n",
       "75%        3.000000      2.000000     651.000000    -37.756400    145.058305\n",
       "max       10.000000      8.000000  433014.000000    -37.408530    145.526350"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>Bathroom</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>-37.8079</td>\n",
       "      <td>144.9934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms  Bathroom  Landsize  Lattitude  Longtitude\n",
       "0      2       1.0     202.0   -37.7996    144.9984\n",
       "1      2       1.0     156.0   -37.8079    144.9934\n",
       "2      3       2.0     134.0   -37.8093    144.9944\n",
       "3      3       2.0      94.0   -37.7969    144.9969\n",
       "4      4       1.0     120.0   -37.8072    144.9941"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building MODEL using scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define model. Specify a number for random_state to ensure same results each run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "melb_model = DecisionTreeRegressor(random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=1, splitter='best')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melb_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In practice, we want to make predictions for new houses coming on the market rather than \n",
    "# the houses we already have prices for. But we'll make predictions for the first few rows of the \n",
    "# training data to see how the predict function works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making predictions for the following 5 houses:\n",
      "   Rooms  Bathroom  Landsize  Lattitude  Longtitude\n",
      "0      2       1.0     202.0   -37.7996    144.9984\n",
      "1      2       1.0     156.0   -37.8079    144.9934\n",
      "2      3       2.0     134.0   -37.8093    144.9944\n",
      "3      3       2.0      94.0   -37.7969    144.9969\n",
      "4      4       1.0     120.0   -37.8072    144.9941\n",
      "The predictions are\n",
      "[1480000. 1035000. 1465000.  850000. 1600000.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Making predictions for the following 5 houses:\")\n",
    "print(X.head())\n",
    "print(\"The predictions are\")\n",
    "print(melb_model.predict(X.head()))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}