ml_101.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning 101"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Basics of a ML Decision Trees\n",
    "2. Basic Data Exploration using Pandas\n",
    "3. Selecting Features -> Independent variables\n",
    "4. Selecting Predictor -> Dependent variable\n",
    "5. Model Building & Fitting it\n",
    "6. Checking Accuracy using the Mean Absolute Error\n",
    "7. Underfitting & Overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Data Exploration using Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Library importations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Postcode</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>Lattitude</th>\n",
       "      <th>Longtitude</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13580.000000</td>\n",
       "      <td>1.358000e+04</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>13518.000000</td>\n",
       "      <td>13580.000000</td>\n",
       "      <td>7130.000000</td>\n",
       "      <td>8205.000000</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>1.075684e+06</td>\n",
       "      <td>10.137776</td>\n",
       "      <td>3105.301915</td>\n",
       "      <td>2.914728</td>\n",
       "      <td>1.534242</td>\n",
       "      <td>1.610075</td>\n",
       "      <td>558.416127</td>\n",
       "      <td>151.967650</td>\n",
       "      <td>1964.684217</td>\n",
       "      <td>-37.809203</td>\n",
       "      <td>144.995216</td>\n",
       "      <td>7454.417378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.955748</td>\n",
       "      <td>6.393107e+05</td>\n",
       "      <td>5.868725</td>\n",
       "      <td>90.676964</td>\n",
       "      <td>0.965921</td>\n",
       "      <td>0.691712</td>\n",
       "      <td>0.962634</td>\n",
       "      <td>3990.669241</td>\n",
       "      <td>541.014538</td>\n",
       "      <td>37.273762</td>\n",
       "      <td>0.079260</td>\n",
       "      <td>0.103916</td>\n",
       "      <td>4378.581772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.500000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1196.000000</td>\n",
       "      <td>-38.182550</td>\n",
       "      <td>144.431810</td>\n",
       "      <td>249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.500000e+05</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>3044.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>1940.000000</td>\n",
       "      <td>-37.856822</td>\n",
       "      <td>144.929600</td>\n",
       "      <td>4380.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.030000e+05</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>3084.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>-37.802355</td>\n",
       "      <td>145.000100</td>\n",
       "      <td>6555.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.330000e+06</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>174.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>-37.756400</td>\n",
       "      <td>145.058305</td>\n",
       "      <td>10331.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000e+06</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>3977.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>433014.000000</td>\n",
       "      <td>44515.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>-37.408530</td>\n",
       "      <td>145.526350</td>\n",
       "      <td>21650.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms         Price      Distance      Postcode      Bedroom2  \\\n",
       "count  13580.000000  1.358000e+04  13580.000000  13580.000000  13580.000000   \n",
       "mean       2.937997  1.075684e+06     10.137776   3105.301915      2.914728   \n",
       "std        0.955748  6.393107e+05      5.868725     90.676964      0.965921   \n",
       "min        1.000000  8.500000e+04      0.000000   3000.000000      0.000000   \n",
       "25%        2.000000  6.500000e+05      6.100000   3044.000000      2.000000   \n",
       "50%        3.000000  9.030000e+05      9.200000   3084.000000      3.000000   \n",
       "75%        3.000000  1.330000e+06     13.000000   3148.000000      3.000000   \n",
       "max       10.000000  9.000000e+06     48.100000   3977.000000     20.000000   \n",
       "\n",
       "           Bathroom           Car       Landsize  BuildingArea    YearBuilt  \\\n",
       "count  13580.000000  13518.000000   13580.000000   7130.000000  8205.000000   \n",
       "mean       1.534242      1.610075     558.416127    151.967650  1964.684217   \n",
       "std        0.691712      0.962634    3990.669241    541.014538    37.273762   \n",
       "min        0.000000      0.000000       0.000000      0.000000  1196.000000   \n",
       "25%        1.000000      1.000000     177.000000     93.000000  1940.000000   \n",
       "50%        1.000000      2.000000     440.000000    126.000000  1970.000000   \n",
       "75%        2.000000      2.000000     651.000000    174.000000  1999.000000   \n",
       "max        8.000000     10.000000  433014.000000  44515.000000  2018.000000   \n",
       "\n",
       "          Lattitude    Longtitude  Propertycount  \n",
       "count  13580.000000  13580.000000   13580.000000  \n",
       "mean     -37.809203    144.995216    7454.417378  \n",
       "std        0.079260      0.103916    4378.581772  \n",
       "min      -38.182550    144.431810     249.000000  \n",
       "25%      -37.856822    144.929600    4380.000000  \n",
       "50%      -37.802355    145.000100    6555.000000  \n",
       "75%      -37.756400    145.058305   10331.000000  \n",
       "max      -37.408530    145.526350   21650.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#file path\n",
    "melbourneCSVPath = \"data/melb_data.csv\"\n",
    "# load data\n",
    "melbourneDF = pd.read_csv(melbourneCSVPath)\n",
    "# describe data\n",
    "melbourneDF.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation Of Data: \n",
    "\n",
    "- mean number of rooms is at 3 (rounded to nearest integer)\n",
    "- highest number of rooms is 10\n",
    "- average time of building is in 1964\n",
    "- latest building was built in 2018\n",
    "- as of the whole period between 1196 to 2018 is 21550\n",
    "- there exists a probable outlier in smallest year of construction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Data for modelling \n",
    "\n",
    "There exists two approaches: \n",
    "- Use of intuition\n",
    "- Use of Statistical techniques\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the columns or features present\n",
    "melbourneDF.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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            0\n",
       "Postcode            0\n",
       "Bedroom2            0\n",
       "Bathroom            0\n",
       "Car                62\n",
       "Landsize            0\n",
       "BuildingArea     6450\n",
       "YearBuilt        5375\n",
       "CouncilArea      1369\n",
       "Lattitude           0\n",
       "Longtitude          0\n",
       "Regionname          0\n",
       "Propertycount       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values\n",
    "melbourneDF.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above we note that Features BuildingArea, YearBuilt, CouncilArea have missing values. This can be solved through dropping them or replacing with Measures of Central Tendency depending on the distribution. In this particular case we will drop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop rows with null values then override intial melbourneDF data frame\n",
    "melbourneDF = melbourneDF.dropna(axis=0)\n",
    "melbourneDF "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Prediction target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        1035000.0\n",
       "2        1465000.0\n",
       "4        1600000.0\n",
       "6        1876000.0\n",
       "7        1636000.0\n",
       "           ...    \n",
       "12205     601000.0\n",
       "12206    1050000.0\n",
       "12207     385000.0\n",
       "12209     560000.0\n",
       "12212    2450000.0\n",
       "Name: Price, Length: 6196, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select prediction variable using dot-notation\n",
    "y = melbourneDF.Price \n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting Features -> Independent variables\n",
    "Note: in this first section the selection of the independent features was not done using statistical techniques but intuition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array containging our target features (independent variables)\n",
    "melbourneFeatures=['Rooms', 'Bathroom','Landsize', 'Lattitude', 'Longtitude']\n",
    "x = melbourneDF[melbourneFeatures]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking the data using the describe and also methods like .head() enable furthur understanding of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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>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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>-37.8024</td>\n",
       "      <td>144.9993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>-37.8060</td>\n",
       "      <td>144.9954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms  Bathroom  Landsize  Lattitude  Longtitude\n",
       "1      2       1.0     156.0   -37.8079    144.9934\n",
       "2      3       2.0     134.0   -37.8093    144.9944\n",
       "4      4       1.0     120.0   -37.8072    144.9941\n",
       "6      3       2.0     245.0   -37.8024    144.9993\n",
       "7      2       1.0     256.0   -37.8060    144.9954"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary of the independent variables\n",
    "x.describe()\n",
    "# view first 5 records\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Model\n",
    "library used: [scikit-learn](https://scikit-learn.org/stable/)\n",
    "Steps: \n",
    "- Define the type of model & parameters specifies to it\n",
    "- Fit the data\n",
    "- Predict\n",
    "- Evaluate by checking the accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining a Decision Tree model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importations\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting the model \n",
    "Many machine learning models allow some randomness in model training. Specifying a number for random_state ensures you get the same results in each run. This is considered a good practice. You use any number, and model quality won't depend meaningfully on exactly what value you choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "DecisionTreeRegressor(random_state=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#defining the model, specify the random_state to ensure same results in each run\n",
    "melbourneModel = DecisionTreeRegressor(random_state=1)\n",
    "#fit model\n",
    "melbourneModel.fit(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objective is to predict prices of new houses coming into the market, rather than prices we already have from our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making predictions for the following 5 houses:\n",
      "   Rooms  Bathroom  Landsize  Lattitude  Longtitude\n",
      "1      2       1.0     156.0   -37.8079    144.9934\n",
      "2      3       2.0     134.0   -37.8093    144.9944\n",
      "4      4       1.0     120.0   -37.8072    144.9941\n",
      "6      3       2.0     245.0   -37.8024    144.9993\n",
      "7      2       1.0     256.0   -37.8060    144.9954\n",
      "Predictions are: \n",
      "[1035000. 1465000. 1600000. 1876000. 1636000.]\n",
      "1    1035000.0\n",
      "2    1465000.0\n",
      "4    1600000.0\n",
      "6    1876000.0\n",
      "7    1636000.0\n",
      "Name: Price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Making predictions for the following 5 houses:\")\n",
    "print(x.head())\n",
    "print(\"Predictions are: \")\n",
    "print(melbourneModel.predict(x.head())) \n",
    "# checking the predictor values\n",
    "print(y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking accuracy of the model\n",
    "This is achieved by calculation of the Mean Absolute Error(There exists multiple metrics to summarize model quality we just start of with Mean Absolute Error metric ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1115.7467183128902"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# library imporation\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# prediction\n",
    "melbournePrediction = melbourneModel.predict(x)\n",
    "\n",
    "#calculation of Mean Absolute Error -> model accuracy test metric\n",
    "mean_absolute_error(y, melbournePrediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Challenge with the above approach: \"In-Sample\" approach: \n",
    "- Use of one data set to model and evaluate our data\n",
    "a good instance would be if we train the data based on housing prices and  and the model finds out that all houses with green doors do are more expensive then tracking this pattern base don the training data, if given new data and in the new data the above does not hold the predictins will be inacurate. \n",
    "- The above is solved by splitting the data into two, *training* and *validation data*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "254843.23821820528\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# we split the data into training and validation for both X (independent variables) and Y (dependent variable )\n",
    "train_x, validation_x, train_y, validation_y = train_test_split(x,y, random_state=1)\n",
    "\n",
    "# defining the model \n",
    "melbourneModel = DecisionTreeRegressor()\n",
    "\n",
    "# fit model\n",
    "melbourneModel.fit(train_x, train_y)\n",
    "\n",
    "#get predicted values\n",
    "val_predictions = melbourneModel.predict(validation_x)\n",
    "print(mean_absolute_error(validation_y,val_predictions))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: \n",
    "- Mean Absolute Error for first \"in-sample\" predition was at 1115.7467183128902\n",
    "- Mean Absolute Error for second \"Out-of-Sample\" prediction was 278880.3154723477\n",
    "- Assigning a *a random state* argument guarantees we get the same split everytime we split\n",
    "\n",
    "Hence the second model is more accurate as compared to the intial which was close to 1/4 of the houses price\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Underfitting and Overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![AXSEOfI](https://github.com/JosephRidge/Machine-Learning101/assets/42699812/00f6e67a-6da3-4e27-8608-d2d1288323cf)\n",
    "src: https://www.kaggle.com/code/dansbecker/underfitting-and-overfitting/tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Leaf node: 5 Mean Absolute Error: 369673.0400167675\n",
      "Max Leaf node: 50 Mean Absolute Error: 266644.21831092256\n",
      "Max Leaf node: 500 Mean Absolute Error: 243613.31456921576\n",
      "Max Leaf node: 5000 Mean Absolute Error: 256147.37895416399\n"
     ]
    }
   ],
   "source": [
    "# utility function to compare scores from different values for max_leaf_nodes , important importations -> mean_absolute_error $ DecisionTreeRegressor\n",
    "def getMAE(max_leaf_nodes, train_X, val_X, train_y, val_y):\n",
    "    model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=0)\n",
    "    model.fit(train_X, train_y)\n",
    "    preds_val = model.predict(val_X)\n",
    "    mae = mean_absolute_error(val_y, preds_val)\n",
    "    return(mae)\n",
    "\n",
    "\n",
    "# error(MeanAbsoluteError) comparison with differering values of max_leaf_nodes( artbitrarily set)\n",
    "for max_leaf_nodes in [5,50,500,5000]:\n",
    "    myMAE= getMAE(max_leaf_nodes, train_x,validation_x,train_y,validation_y)\n",
    "    print(f\"Max Leaf node: {max_leaf_nodes} Mean Absolute Error: {myMAE}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Deduction\n",
    "- The max_leaf_nodes giving the smallest Mean Absolute Error is the most optimal in the above case it is mean_leaf_node = 500\n",
    "- Overfitting > capturing spurios(not trustworthy) patterns that woint recur in future\n",
    "- underfitting > not capturing revelevant patterns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Uses Many Trees\n",
    "- Makes predictions by averaging the prediction of each component\n",
    "- Better accuracy than single decision tree\n",
    "- Works well with default values\n",
    "- Generally perform reasonably well even without tuning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importations \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190414.59149025998\n"
     ]
    }
   ],
   "source": [
    "forestModel = RandomForestRegressor(random_state=1)\n",
    "#fitting/ training  the model\n",
    "forestModel.fit(train_x, train_y)\n",
    "melbournePredictions = forestModel.predict(validation_x)\n",
    "print(mean_absolute_error(validation_y, melbournePredictions))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deduction\n",
    "- Mean Abosolute Error after using DecisionTreeRegressor => 254843.23821820528\n",
    "- Mean Absolute Error after using RandomForest => 190414.59149025998\n",
    "\n",
    "from the above we witness a significant reduction in error implying better performance of the RandomForest as compared to the intial Decision Tree implementation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Small Win thanks to Team Kaggle\n",
    "\n",
    "![JosephRidge - Intro to Machine Learning](https://github.com/JosephRidge/Machine-Learning101/assets/42699812/b18d3d4c-1961-44ce-a97e-ebc7baae7377)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Sources:\n",
    "https://www.kaggle.com/datasets/dansbecker/melbourne-housing-snapshot"
   ]
  }
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