L1_L2_Regulatization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "18309407",
   "metadata": {},
   "source": [
    "Here, we will look into, 1) What is overfitting, underfitting 2) How to address overfitting using L1 and L2 regularization 3) Write code in python and sklearn for housing price prediction where we will see a model overfit when we use simple linear regression. Then we will use Lasso regression (L1 regularization) and ridge regression (L2 regression) to address this overfitting issue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bddd4591",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# suppress warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8844f835",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</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>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>68 Studley St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SS</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>3/09/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8014</td>\n",
       "      <td>144.9958</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>3067.0</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 City Council</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>2</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>79.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</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>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>18/659 Victoria St</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VB</td>\n",
       "      <td>Rounds</td>\n",
       "      <td>4/02/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8114</td>\n",
       "      <td>145.0116</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>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>150.0</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8093</td>\n",
       "      <td>144.9944</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb             Address  Rooms Type      Price Method SellerG  \\\n",
       "0  Abbotsford       68 Studley St      2    h        NaN     SS  Jellis   \n",
       "1  Abbotsford        85 Turner St      2    h  1480000.0      S  Biggin   \n",
       "2  Abbotsford     25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
       "3  Abbotsford  18/659 Victoria St      3    u        NaN     VB  Rounds   \n",
       "4  Abbotsford        5 Charles St      3    h  1465000.0     SP  Biggin   \n",
       "\n",
       "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "0  3/09/2016       2.5    3067.0  ...       1.0  1.0     126.0           NaN   \n",
       "1  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
       "2  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
       "3  4/02/2016       2.5    3067.0  ...       2.0  1.0       0.0           NaN   \n",
       "4  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
       "\n",
       "   YearBuilt         CouncilArea Lattitude  Longtitude             Regionname  \\\n",
       "0        NaN  Yarra City Council  -37.8014    144.9958  Northern Metropolitan   \n",
       "1        NaN  Yarra City Council  -37.7996    144.9984  Northern Metropolitan   \n",
       "2     1900.0  Yarra City Council  -37.8079    144.9934  Northern Metropolitan   \n",
       "3        NaN  Yarra City Council  -37.8114    145.0116  Northern Metropolitan   \n",
       "4     1900.0  Yarra City Council  -37.8093    144.9944  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 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Melbourne_housing_FULL.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88481f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb             351\n",
       "Address          34009\n",
       "Rooms               12\n",
       "Type                 3\n",
       "Price             2871\n",
       "Method               9\n",
       "SellerG            388\n",
       "Date                78\n",
       "Distance           215\n",
       "Postcode           211\n",
       "Bedroom2            15\n",
       "Bathroom            11\n",
       "Car                 15\n",
       "Landsize          1684\n",
       "BuildingArea       740\n",
       "YearBuilt          160\n",
       "CouncilArea         33\n",
       "Lattitude        13402\n",
       "Longtitude       14524\n",
       "Regionname           8\n",
       "Propertycount      342\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7099be7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34857, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0d68c2ef",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0972f437",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new=df[['Suburb', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n",
    "      'Distance', 'Bedroom2', 'Bathroom', 'Car',\n",
    "       'Landsize', 'BuildingArea', 'CouncilArea', 'Regionname', 'Propertycount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eab94e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Suburb</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>CouncilArea</th>\n",
       "      <th>Regionname</th>\n",
       "      <th>Propertycount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SS</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</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>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>3</td>\n",
       "      <td>u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VB</td>\n",
       "      <td>Rounds</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</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>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb  Rooms Type      Price Method SellerG  Distance  Bedroom2  \\\n",
       "0  Abbotsford      2    h        NaN     SS  Jellis       2.5       2.0   \n",
       "1  Abbotsford      2    h  1480000.0      S  Biggin       2.5       2.0   \n",
       "2  Abbotsford      2    h  1035000.0      S  Biggin       2.5       2.0   \n",
       "3  Abbotsford      3    u        NaN     VB  Rounds       2.5       3.0   \n",
       "4  Abbotsford      3    h  1465000.0     SP  Biggin       2.5       3.0   \n",
       "\n",
       "   Bathroom  Car  Landsize  BuildingArea         CouncilArea  \\\n",
       "0       1.0  1.0     126.0           NaN  Yarra City Council   \n",
       "1       1.0  1.0     202.0           NaN  Yarra City Council   \n",
       "2       1.0  0.0     156.0          79.0  Yarra City Council   \n",
       "3       2.0  1.0       0.0           NaN  Yarra City Council   \n",
       "4       2.0  0.0     134.0         150.0  Yarra City Council   \n",
       "\n",
       "              Regionname  Propertycount  \n",
       "0  Northern Metropolitan         4019.0  \n",
       "1  Northern Metropolitan         4019.0  \n",
       "2  Northern Metropolitan         4019.0  \n",
       "3  Northern Metropolitan         4019.0  \n",
       "4  Northern Metropolitan         4019.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c9ebcaa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34857, 15)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a944b485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb               0\n",
       "Rooms                0\n",
       "Type                 0\n",
       "Price             7610\n",
       "Method               0\n",
       "SellerG              0\n",
       "Distance             1\n",
       "Bedroom2          8217\n",
       "Bathroom          8226\n",
       "Car               8728\n",
       "Landsize         11810\n",
       "BuildingArea     21115\n",
       "CouncilArea          3\n",
       "Regionname           3\n",
       "Propertycount        3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d85f6639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb               0\n",
       "Rooms                0\n",
       "Type                 0\n",
       "Price             7610\n",
       "Method               0\n",
       "SellerG              0\n",
       "Distance             0\n",
       "Bedroom2             0\n",
       "Bathroom             0\n",
       "Car                  0\n",
       "Landsize         11810\n",
       "BuildingArea     21115\n",
       "CouncilArea          3\n",
       "Regionname           3\n",
       "Propertycount        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_fill_zero=['Propertycount','Distance','Bedroom2','Bathroom','Car']\n",
    "df_new[cols_to_fill_zero]=df_new[cols_to_fill_zero].fillna(0)\n",
    "df_new.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "31916838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb              0\n",
       "Rooms               0\n",
       "Type                0\n",
       "Price            7610\n",
       "Method              0\n",
       "SellerG             0\n",
       "Distance            0\n",
       "Bedroom2            0\n",
       "Bathroom            0\n",
       "Car                 0\n",
       "Landsize            0\n",
       "BuildingArea        0\n",
       "CouncilArea         3\n",
       "Regionname          3\n",
       "Propertycount       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['Landsize']=df_new['Landsize'].fillna(df_new.Landsize.mean())\n",
    "df_new['BuildingArea']=df_new['BuildingArea'].fillna(df_new.BuildingArea.mean())\n",
    "df_new.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dbac63f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Suburb           0\n",
       "Rooms            0\n",
       "Type             0\n",
       "Price            0\n",
       "Method           0\n",
       "SellerG          0\n",
       "Distance         0\n",
       "Bedroom2         0\n",
       "Bathroom         0\n",
       "Car              0\n",
       "Landsize         0\n",
       "BuildingArea     0\n",
       "CouncilArea      0\n",
       "Regionname       0\n",
       "Propertycount    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.dropna(inplace=True)\n",
    "df_new.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "71536d81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27244, 15)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc2bafcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>Rooms</th>\n",
       "      <th>Type</th>\n",
       "      <th>Price</th>\n",
       "      <th>Method</th>\n",
       "      <th>SellerG</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>CouncilArea</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>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>160.2564</td>\n",
       "      <td>Yarra City Council</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>2</td>\n",
       "      <td>h</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>Yarra City Council</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>3</td>\n",
       "      <td>h</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>160.2564</td>\n",
       "      <td>Yarra City Council</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>4</td>\n",
       "      <td>h</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>VB</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0000</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Suburb  Rooms Type      Price Method SellerG  Distance  Bedroom2  \\\n",
       "1  Abbotsford      2    h  1480000.0      S  Biggin       2.5       2.0   \n",
       "2  Abbotsford      2    h  1035000.0      S  Biggin       2.5       2.0   \n",
       "4  Abbotsford      3    h  1465000.0     SP  Biggin       2.5       3.0   \n",
       "5  Abbotsford      3    h   850000.0     PI  Biggin       2.5       3.0   \n",
       "6  Abbotsford      4    h  1600000.0     VB  Nelson       2.5       3.0   \n",
       "\n",
       "   Bathroom  Car  Landsize  BuildingArea         CouncilArea  \\\n",
       "1       1.0  1.0     202.0      160.2564  Yarra City Council   \n",
       "2       1.0  0.0     156.0       79.0000  Yarra City Council   \n",
       "4       2.0  0.0     134.0      150.0000  Yarra City Council   \n",
       "5       2.0  1.0      94.0      160.2564  Yarra City Council   \n",
       "6       1.0  2.0     120.0      142.0000  Yarra City Council   \n",
       "\n",
       "              Regionname  Propertycount  \n",
       "1  Northern Metropolitan         4019.0  \n",
       "2  Northern Metropolitan         4019.0  \n",
       "4  Northern Metropolitan         4019.0  \n",
       "5  Northern Metropolitan         4019.0  \n",
       "6  Northern Metropolitan         4019.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e815167c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rooms</th>\n",
       "      <th>Price</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Bedroom2</th>\n",
       "      <th>Bathroom</th>\n",
       "      <th>Car</th>\n",
       "      <th>Landsize</th>\n",
       "      <th>BuildingArea</th>\n",
       "      <th>Propertycount</th>\n",
       "      <th>Suburb_Aberfeldie</th>\n",
       "      <th>...</th>\n",
       "      <th>CouncilArea_Wyndham City Council</th>\n",
       "      <th>CouncilArea_Yarra City Council</th>\n",
       "      <th>CouncilArea_Yarra Ranges Shire Council</th>\n",
       "      <th>Regionname_Eastern Victoria</th>\n",
       "      <th>Regionname_Northern Metropolitan</th>\n",
       "      <th>Regionname_Northern Victoria</th>\n",
       "      <th>Regionname_South-Eastern Metropolitan</th>\n",
       "      <th>Regionname_Southern Metropolitan</th>\n",
       "      <th>Regionname_Western Metropolitan</th>\n",
       "      <th>Regionname_Western Victoria</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1480000.0</td>\n",
       "      <td>2.5</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>160.2564</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1035000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>79.0000</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1465000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>150.0000</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>160.2564</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>1600000.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0000</td>\n",
       "      <td>4019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 745 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rooms      Price  Distance  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "1      2  1480000.0       2.5       2.0       1.0  1.0     202.0   \n",
       "2      2  1035000.0       2.5       2.0       1.0  0.0     156.0   \n",
       "4      3  1465000.0       2.5       3.0       2.0  0.0     134.0   \n",
       "5      3   850000.0       2.5       3.0       2.0  1.0      94.0   \n",
       "6      4  1600000.0       2.5       3.0       1.0  2.0     120.0   \n",
       "\n",
       "   BuildingArea  Propertycount  Suburb_Aberfeldie  ...  \\\n",
       "1      160.2564         4019.0                  0  ...   \n",
       "2       79.0000         4019.0                  0  ...   \n",
       "4      150.0000         4019.0                  0  ...   \n",
       "5      160.2564         4019.0                  0  ...   \n",
       "6      142.0000         4019.0                  0  ...   \n",
       "\n",
       "   CouncilArea_Wyndham City Council  CouncilArea_Yarra City Council  \\\n",
       "1                                 0                               1   \n",
       "2                                 0                               1   \n",
       "4                                 0                               1   \n",
       "5                                 0                               1   \n",
       "6                                 0                               1   \n",
       "\n",
       "   CouncilArea_Yarra Ranges Shire Council  Regionname_Eastern Victoria  \\\n",
       "1                                       0                            0   \n",
       "2                                       0                            0   \n",
       "4                                       0                            0   \n",
       "5                                       0                            0   \n",
       "6                                       0                            0   \n",
       "\n",
       "   Regionname_Northern Metropolitan  Regionname_Northern Victoria  \\\n",
       "1                                 1                             0   \n",
       "2                                 1                             0   \n",
       "4                                 1                             0   \n",
       "5                                 1                             0   \n",
       "6                                 1                             0   \n",
       "\n",
       "   Regionname_South-Eastern Metropolitan  Regionname_Southern Metropolitan  \\\n",
       "1                                      0                                 0   \n",
       "2                                      0                                 0   \n",
       "4                                      0                                 0   \n",
       "5                                      0                                 0   \n",
       "6                                      0                                 0   \n",
       "\n",
       "   Regionname_Western Metropolitan  Regionname_Western Victoria  \n",
       "1                                0                            0  \n",
       "2                                0                            0  \n",
       "4                                0                            0  \n",
       "5                                0                            0  \n",
       "6                                0                            0  \n",
       "\n",
       "[5 rows x 745 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# changing the string columns into dummies\n",
    "df_new=pd.get_dummies(df_new,drop_first=True)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf4017d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df_new.drop('Price',axis=1)\n",
    "y=df_new['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b52ae1df",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b65dc865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13853683161538477"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg=LinearRegression().fit(X_train,y_train)\n",
    "reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "308c65fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6827792395792723"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78ad3227",
   "metadata": {},
   "source": [
    "The scores on test sets are very low as comparison to the traing set score,\n",
    "this clearly shows that the model is overfitting for training sets.with overfitting the model works best with the training data but,poorly perform on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9860cce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=50, max_iter=100, tol=0.1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lasso is L1 regularization\n",
    "from sklearn import linear_model\n",
    "lasso_reg=linear_model.Lasso(alpha=50,max_iter=100,tol=0.1)\n",
    "lasso_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3c2c7452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6637669697137103"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2518fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6767356948457685"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0b838fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=50, max_iter=100, tol=0.1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ridge is L2 regularization\n",
    "from sklearn.linear_model import Ridge\n",
    "ridge_reg=Ridge(alpha=50,max_iter=100,tol=0.1)\n",
    "ridge_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bdeec2b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6670848945194957"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c10ea579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6622376739684328"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg.score(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fe1a04",
   "metadata": {},
   "source": [
    "With Regularization the model works almost same with both train as well as test sets,hence reduces the overfitting and increase the stability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10e3dde0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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