NB1_IdentifyProblem+DataCleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Predictive Analysis To Predict Melbourne House Price  \n",
    "\n",
    "By Niladri Ghosh\n",
    "\n",
    "## 1. Identify Problem\n",
    "\n",
    "Real Estate has became one of the main places to put your investment in recent times. With the current instability in the market it has became too risky to invest in stock markets, gold prices constanly going down and goverment not considering crypocurrency as a serious option. That being said, owing a property seems to be a viable option to put your money in. So with this motivation we need to analyze what fectors leads to the price of a property in an area, we'll be predicting house prices in Melbourne, Australia.\n",
    "\n",
    "### 1.1 Expected Outcome\n",
    "\n",
    "Given house price data from Domain.com.au (and other sources) provides various features of a particular property like number of rooms, address, suburb ,etc. Since this is a __regression problem__, our __final output would be the price of the house estimated by our model__.\n",
    "\n",
    "### 1.2 Objective\n",
    "\n",
    "Since many features in our data is categorical value, we need to find appropriate techniques to convert them into integers, as our model only inputs numerical values and finally predict house prices. In machine learning this is a regression problem.\n",
    "\n",
    "> Thus, the goal is to estimate the price of various houses provided the different unique features of the property. To acheive this we have used machine learning regression methods to fit a function that can predict the continuos values (price) for the new inputs.\n",
    "\n",
    "### 1.3 Identify Data Sources\n",
    "\n",
    "The [Melbourne House Price](https://www.kaggle.com/anthonypino/melbourne-housing-market?select=Melbourne_housing_FULL.csv) dataset is available at Kaggle by [Tony Pino](https://www.kaggle.com/anthonypino). The dataset contains 34,857 rows of data and 21 columns (20 features and 1 taget).\n",
    "\n",
    "* The 5th column provides the price of the house.\n",
    "* Columns 1-4 and 6-21 provides various details for the given house."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Getting Started : Load libraries and set options__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "# ignore warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "# display all columns\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Load Dataset__\n",
    "\n",
    "First load the supplied CSV file using Pandas read_csv function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "df_primary = pd.read_csv('data/data_full.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create copy of dataframe\n",
    "df = df_primary.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Inspecting the data__\n",
    "\n",
    "The first step is to visually inspect the new dataset. There are multiple ways to acheive this:\n",
    "* The easiest way is to fetch first 5 rows is using DataFrame.head(), here df.head().\n",
    "* Alternatively we can fetch the last 5 rows using DataFrame.tail(), here df.tail().\n",
    "\n",
    "__NOTE:__ \n",
    "\n",
    "For both the above methods we can add a parameter inside the parenthesis '()' to specify how many rows we want to display, thus we can inspect the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Bedroom2</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>2.0</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>2.0</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>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>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>3.0</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>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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>40 Federation La</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>850000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7969</td>\n",
       "      <td>144.9969</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</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>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.8072</td>\n",
       "      <td>144.9941</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>16 Maugie St</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SN</td>\n",
       "      <td>Nelson</td>\n",
       "      <td>6/08/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7965</td>\n",
       "      <td>144.9965</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>53 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Biggin</td>\n",
       "      <td>6/08/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7995</td>\n",
       "      <td>144.9974</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Abbotsford</td>\n",
       "      <td>99 Turner St</td>\n",
       "      <td>2</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Collins</td>\n",
       "      <td>6/08/2016</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3067.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1900.0</td>\n",
       "      <td>Yarra City Council</td>\n",
       "      <td>-37.7996</td>\n",
       "      <td>144.9989</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>4019.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "5  Abbotsford    40 Federation La      3    h   850000.0     PI   Biggin   \n",
       "6  Abbotsford         55a Park St      4    h  1600000.0     VB   Nelson   \n",
       "7  Abbotsford        16 Maugie St      4    h        NaN     SN   Nelson   \n",
       "8  Abbotsford        53 Turner St      2    h        NaN      S   Biggin   \n",
       "9  Abbotsford        99 Turner St      2    h        NaN      S  Collins   \n",
       "\n",
       "        Date  Distance  Postcode  Bedroom2  Bathroom  Car  Landsize  \\\n",
       "0  3/09/2016       2.5    3067.0       2.0       1.0  1.0     126.0   \n",
       "1  3/12/2016       2.5    3067.0       2.0       1.0  1.0     202.0   \n",
       "2  4/02/2016       2.5    3067.0       2.0       1.0  0.0     156.0   \n",
       "3  4/02/2016       2.5    3067.0       3.0       2.0  1.0       0.0   \n",
       "4  4/03/2017       2.5    3067.0       3.0       2.0  0.0     134.0   \n",
       "5  4/03/2017       2.5    3067.0       3.0       2.0  1.0      94.0   \n",
       "6  4/06/2016       2.5    3067.0       3.0       1.0  2.0     120.0   \n",
       "7  6/08/2016       2.5    3067.0       3.0       2.0  2.0     400.0   \n",
       "8  6/08/2016       2.5    3067.0       4.0       1.0  2.0     201.0   \n",
       "9  6/08/2016       2.5    3067.0       3.0       2.0  1.0     202.0   \n",
       "\n",
       "   BuildingArea  YearBuilt         CouncilArea  Lattitude  Longtitude  \\\n",
       "0           NaN        NaN  Yarra City Council   -37.8014    144.9958   \n",
       "1           NaN        NaN  Yarra City Council   -37.7996    144.9984   \n",
       "2          79.0     1900.0  Yarra City Council   -37.8079    144.9934   \n",
       "3           NaN        NaN  Yarra City Council   -37.8114    145.0116   \n",
       "4         150.0     1900.0  Yarra City Council   -37.8093    144.9944   \n",
       "5           NaN        NaN  Yarra City Council   -37.7969    144.9969   \n",
       "6         142.0     2014.0  Yarra City Council   -37.8072    144.9941   \n",
       "7         220.0     2006.0  Yarra City Council   -37.7965    144.9965   \n",
       "8           NaN     1900.0  Yarra City Council   -37.7995    144.9974   \n",
       "9           NaN     1900.0  Yarra City Council   -37.7996    144.9989   \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  \n",
       "5  Northern Metropolitan         4019.0  \n",
       "6  Northern Metropolitan         4019.0  \n",
       "7  Northern Metropolitan         4019.0  \n",
       "8  Northern Metropolitan         4019.0  \n",
       "9  Northern Metropolitan         4019.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34857, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check shape of the given data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe here the number of 34,857 rows, each with 21 columns.\n",
    "\n",
    "On contrary we can use use info() method provided by pandas to generate a consise summary of the data. It provides the detail about each column, number of rows, null values, the data type and the memory usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34857 entries, 0 to 34856\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Suburb         34857 non-null  object \n",
      " 1   Address        34857 non-null  object \n",
      " 2   Rooms          34857 non-null  int64  \n",
      " 3   Type           34857 non-null  object \n",
      " 4   Price          27247 non-null  float64\n",
      " 5   Method         34857 non-null  object \n",
      " 6   SellerG        34857 non-null  object \n",
      " 7   Date           34857 non-null  object \n",
      " 8   Distance       34856 non-null  float64\n",
      " 9   Postcode       34856 non-null  float64\n",
      " 10  Bedroom2       26640 non-null  float64\n",
      " 11  Bathroom       26631 non-null  float64\n",
      " 12  Car            26129 non-null  float64\n",
      " 13  Landsize       23047 non-null  float64\n",
      " 14  BuildingArea   13742 non-null  float64\n",
      " 15  YearBuilt      15551 non-null  float64\n",
      " 16  CouncilArea    34854 non-null  object \n",
      " 17  Lattitude      26881 non-null  float64\n",
      " 18  Longtitude     26881 non-null  float64\n",
      " 19  Regionname     34854 non-null  object \n",
      " 20  Propertycount  34854 non-null  float64\n",
      "dtypes: float64(12), int64(1), object(8)\n",
      "memory usage: 5.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some wrong data types. The postcode columns is float, it's a categorical value. And the following columns :\n",
    "\n",
    "* Date column needs to be set as datetime.\n",
    "* Bathroom, Car and Yearbuilt to be set as integer.\n",
    "* Postcode and Propertycount to be set as categorical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change Postcode and Propertycount as categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#change Postcode\n",
    "df['Postcode']= df['Postcode'].astype('object')\n",
    "\n",
    "#change Propertycount\n",
    "df['Propertycount']= df['Propertycount'].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we analyse Bedroom2 column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data= df, x='Bedroom2', y='Rooms');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We __remove__ the column __Bedroom2__, mainly for two reasons:\n",
    "\n",
    "* The column has been scraped for number of Bedrooms, from a different source which is not reliable.\n",
    "* From the above plot we can clearly see that there is a linear relation, which mean they are almost same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Bedroom2'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use the describe method provided by Pandas to check the in-depth statistics of our numerical values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Price</th>\n",
       "      <th>Distance</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34857.000000</td>\n",
       "      <td>2.724700e+04</td>\n",
       "      <td>34856.000000</td>\n",
       "      <td>26631.000000</td>\n",
       "      <td>26129.000000</td>\n",
       "      <td>23047.000000</td>\n",
       "      <td>13742.00000</td>\n",
       "      <td>15551.000000</td>\n",
       "      <td>26881.000000</td>\n",
       "      <td>26881.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.031012</td>\n",
       "      <td>1.050173e+06</td>\n",
       "      <td>11.184929</td>\n",
       "      <td>1.624798</td>\n",
       "      <td>1.728845</td>\n",
       "      <td>593.598993</td>\n",
       "      <td>160.25640</td>\n",
       "      <td>1965.289885</td>\n",
       "      <td>-37.810634</td>\n",
       "      <td>145.001851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.969933</td>\n",
       "      <td>6.414671e+05</td>\n",
       "      <td>6.788892</td>\n",
       "      <td>0.724212</td>\n",
       "      <td>1.010771</td>\n",
       "      <td>3398.841946</td>\n",
       "      <td>401.26706</td>\n",
       "      <td>37.328178</td>\n",
       "      <td>0.090279</td>\n",
       "      <td>0.120169</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1196.000000</td>\n",
       "      <td>-38.190430</td>\n",
       "      <td>144.423790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.350000e+05</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>102.00000</td>\n",
       "      <td>1940.000000</td>\n",
       "      <td>-37.862950</td>\n",
       "      <td>144.933500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.700000e+05</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>521.000000</td>\n",
       "      <td>136.00000</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>-37.807600</td>\n",
       "      <td>145.007800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.295000e+06</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>188.00000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>-37.754100</td>\n",
       "      <td>145.071900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.120000e+07</td>\n",
       "      <td>48.100000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>433014.000000</td>\n",
       "      <td>44515.00000</td>\n",
       "      <td>2106.000000</td>\n",
       "      <td>-37.390200</td>\n",
       "      <td>145.526350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Rooms         Price      Distance      Bathroom           Car  \\\n",
       "count  34857.000000  2.724700e+04  34856.000000  26631.000000  26129.000000   \n",
       "mean       3.031012  1.050173e+06     11.184929      1.624798      1.728845   \n",
       "std        0.969933  6.414671e+05      6.788892      0.724212      1.010771   \n",
       "min        1.000000  8.500000e+04      0.000000      0.000000      0.000000   \n",
       "25%        2.000000  6.350000e+05      6.400000      1.000000      1.000000   \n",
       "50%        3.000000  8.700000e+05     10.300000      2.000000      2.000000   \n",
       "75%        4.000000  1.295000e+06     14.000000      2.000000      2.000000   \n",
       "max       16.000000  1.120000e+07     48.100000     12.000000     26.000000   \n",
       "\n",
       "            Landsize  BuildingArea     YearBuilt     Lattitude    Longtitude  \n",
       "count   23047.000000   13742.00000  15551.000000  26881.000000  26881.000000  \n",
       "mean      593.598993     160.25640   1965.289885    -37.810634    145.001851  \n",
       "std      3398.841946     401.26706     37.328178      0.090279      0.120169  \n",
       "min         0.000000       0.00000   1196.000000    -38.190430    144.423790  \n",
       "25%       224.000000     102.00000   1940.000000    -37.862950    144.933500  \n",
       "50%       521.000000     136.00000   1970.000000    -37.807600    145.007800  \n",
       "75%       670.000000     188.00000   2000.000000    -37.754100    145.071900  \n",
       "max    433014.000000   44515.00000   2106.000000    -37.390200    145.526350  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We encounter some strange values from above result, so  we carefully analyse these values:\n",
    "\n",
    "* Bathroom contains values 0 and 12. Logically number of bathrooms would be between 1 and 7.\n",
    "* BuildingArea contains 0 and miximum area of 44515 square meters which is next to impossible. \n",
    "* YearBuilt have values more than 2018, sice time of scrape is 2018 and needs to be rectified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0     12969\n",
       "2.0     11064\n",
       "3.0      2181\n",
       "4.0       269\n",
       "5.0        77\n",
       "0.0        46\n",
       "6.0        16\n",
       "7.0         4\n",
       "8.0         3\n",
       "9.0         1\n",
       "12.0        1\n",
       "Name: Bathroom, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check number of bathrooms\n",
    "df['Bathroom'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 46 houses that do not have bathroom! One possible idea, they should be old houses with a common bathroom outside, so we will consider these as accurate values. On the other hand there is a property with 12 bathrooms, which is least likely. \n",
    "\n",
    "We analyse houses with more than 7 bathrooms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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>2536</th>\n",
       "      <td>Burwood</td>\n",
       "      <td>36 Cumming St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PI</td>\n",
       "      <td>Ross</td>\n",
       "      <td>8/10/2016</td>\n",
       "      <td>11.7</td>\n",
       "      <td>3125</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Monash City Council</td>\n",
       "      <td>-37.85300</td>\n",
       "      <td>145.11290</td>\n",
       "      <td>Southern Metropolitan</td>\n",
       "      <td>5678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8251</th>\n",
       "      <td>Preston</td>\n",
       "      <td>421 Murray Rd</td>\n",
       "      <td>4</td>\n",
       "      <td>h</td>\n",
       "      <td>760000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Barry</td>\n",
       "      <td>22/05/2016</td>\n",
       "      <td>8.8</td>\n",
       "      <td>3072</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1928.0</td>\n",
       "      <td>Darebin City Council</td>\n",
       "      <td>-37.73670</td>\n",
       "      <td>144.98950</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>14577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17472</th>\n",
       "      <td>Camberwell</td>\n",
       "      <td>1088 Toorak Rd</td>\n",
       "      <td>8</td>\n",
       "      <td>h</td>\n",
       "      <td>2200000.0</td>\n",
       "      <td>PI</td>\n",
       "      <td>Ross</td>\n",
       "      <td>8/07/2017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3124</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Boroondara City Council</td>\n",
       "      <td>-37.84812</td>\n",
       "      <td>145.07414</td>\n",
       "      <td>Southern Metropolitan</td>\n",
       "      <td>8920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25717</th>\n",
       "      <td>Burwood</td>\n",
       "      <td>79 Elgar Rd</td>\n",
       "      <td>10</td>\n",
       "      <td>h</td>\n",
       "      <td>2315000.0</td>\n",
       "      <td>SP</td>\n",
       "      <td>Buxton</td>\n",
       "      <td>28/10/2017</td>\n",
       "      <td>10.4</td>\n",
       "      <td>3125</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>Monash City Council</td>\n",
       "      <td>-37.84461</td>\n",
       "      <td>145.10968</td>\n",
       "      <td>Southern Metropolitan</td>\n",
       "      <td>5678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26210</th>\n",
       "      <td>Mitcham</td>\n",
       "      <td>20 Harrison St</td>\n",
       "      <td>16</td>\n",
       "      <td>h</td>\n",
       "      <td>5000000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Buxton</td>\n",
       "      <td>28/10/2017</td>\n",
       "      <td>17.2</td>\n",
       "      <td>3132</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Whitehorse City Council</td>\n",
       "      <td>-37.81405</td>\n",
       "      <td>145.19891</td>\n",
       "      <td>Eastern Metropolitan</td>\n",
       "      <td>6871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Suburb         Address  Rooms Type      Price Method SellerG  \\\n",
       "2536      Burwood   36 Cumming St      3    h        NaN     PI    Ross   \n",
       "8251      Preston   421 Murray Rd      4    h   760000.0     PI   Barry   \n",
       "17472  Camberwell  1088 Toorak Rd      8    h  2200000.0     PI    Ross   \n",
       "25717     Burwood     79 Elgar Rd     10    h  2315000.0     SP  Buxton   \n",
       "26210     Mitcham  20 Harrison St     16    h  5000000.0      S  Buxton   \n",
       "\n",
       "             Date  Distance Postcode  Bathroom  Car  Landsize  BuildingArea  \\\n",
       "2536    8/10/2016      11.7     3125      12.0  0.0     946.0           NaN   \n",
       "8251   22/05/2016       8.8     3072       8.0  7.0    1254.0         280.0   \n",
       "17472   8/07/2017       7.7     3124       8.0  4.0     650.0           NaN   \n",
       "25717  28/10/2017      10.4     3125       9.0  2.0     612.0         466.0   \n",
       "26210  28/10/2017      17.2     3132       8.0  8.0       NaN           NaN   \n",
       "\n",
       "       YearBuilt              CouncilArea  Lattitude  Longtitude  \\\n",
       "2536      1950.0      Monash City Council  -37.85300   145.11290   \n",
       "8251      1928.0     Darebin City Council  -37.73670   144.98950   \n",
       "17472        NaN  Boroondara City Council  -37.84812   145.07414   \n",
       "25717     2015.0      Monash City Council  -37.84461   145.10968   \n",
       "26210        NaN  Whitehorse City Council  -37.81405   145.19891   \n",
       "\n",
       "                  Regionname Propertycount  \n",
       "2536   Southern Metropolitan          5678  \n",
       "8251   Northern Metropolitan         14577  \n",
       "17472  Southern Metropolitan          8920  \n",
       "25717  Southern Metropolitan          5678  \n",
       "26210   Eastern Metropolitan          6871  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check bathroom\n",
    "df.loc[df['Bathroom']>7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We analyse houses with less than 1 BuildingArea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>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>7211</th>\n",
       "      <td>North Melbourne</td>\n",
       "      <td>19 Shands La</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>841000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Jellis</td>\n",
       "      <td>4/03/2017</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3051</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>Melbourne City Council</td>\n",
       "      <td>-37.79530</td>\n",
       "      <td>144.94370</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>6821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16540</th>\n",
       "      <td>South Melbourne</td>\n",
       "      <td>148 Cobden St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>Marshall</td>\n",
       "      <td>24/06/2017</td>\n",
       "      <td>1.9</td>\n",
       "      <td>3205</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>Port Phillip City Council</td>\n",
       "      <td>-37.83658</td>\n",
       "      <td>144.96292</td>\n",
       "      <td>Southern Metropolitan</td>\n",
       "      <td>5943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19775</th>\n",
       "      <td>Balwyn North</td>\n",
       "      <td>14 Wanbrow Av</td>\n",
       "      <td>5</td>\n",
       "      <td>h</td>\n",
       "      <td>1950000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>RT</td>\n",
       "      <td>3/09/2017</td>\n",
       "      <td>9.7</td>\n",
       "      <td>3104</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>Boroondara City Council</td>\n",
       "      <td>-37.80235</td>\n",
       "      <td>145.09311</td>\n",
       "      <td>Southern Metropolitan</td>\n",
       "      <td>7809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19840</th>\n",
       "      <td>Bundoora</td>\n",
       "      <td>22 Moreton Cr</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>814000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Barry</td>\n",
       "      <td>3/09/2017</td>\n",
       "      <td>12.1</td>\n",
       "      <td>3083</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>542.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>Banyule City Council</td>\n",
       "      <td>-37.70861</td>\n",
       "      <td>145.05691</td>\n",
       "      <td>Northern Metropolitan</td>\n",
       "      <td>10175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20038</th>\n",
       "      <td>Heidelberg Heights</td>\n",
       "      <td>8 Thames St</td>\n",
       "      <td>3</td>\n",
       "      <td>h</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SN</td>\n",
       "      <td>Miles</td>\n",
       "      <td>3/09/2017</td>\n",
       "      <td>8.8</td>\n",
       "      <td>3081</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>658.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>Banyule City Council</td>\n",
       "      <td>-37.75287</td>\n",
       "      <td>145.05547</td>\n",
       "      <td>Eastern Metropolitan</td>\n",
       "      <td>2947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Suburb        Address  Rooms Type      Price Method  \\\n",
       "7211      North Melbourne   19 Shands La      2    t   841000.0      S   \n",
       "16540     South Melbourne  148 Cobden St      3    h        NaN      S   \n",
       "19775        Balwyn North  14 Wanbrow Av      5    h  1950000.0      S   \n",
       "19840            Bundoora  22 Moreton Cr      3    h   814000.0      S   \n",
       "20038  Heidelberg Heights    8 Thames St      3    h        NaN     SN   \n",
       "\n",
       "        SellerG        Date  Distance Postcode  Bathroom  Car  Landsize  \\\n",
       "7211     Jellis   4/03/2017       2.3     3051       1.0  1.0     215.0   \n",
       "16540  Marshall  24/06/2017       1.9     3205       2.0  0.0     282.0   \n",
       "19775        RT   3/09/2017       9.7     3104       3.0  2.0     743.0   \n",
       "19840     Barry   3/09/2017      12.1     3083       1.0  2.0     542.0   \n",
       "20038     Miles   3/09/2017       8.8     3081       1.0  2.0     658.0   \n",
       "\n",
       "       BuildingArea  YearBuilt                CouncilArea  Lattitude  \\\n",
       "7211           0.00     2000.0     Melbourne City Council  -37.79530   \n",
       "16540          0.01     1920.0  Port Phillip City Council  -37.83658   \n",
       "19775          0.00     1949.0    Boroondara City Council  -37.80235   \n",
       "19840          0.00     1970.0       Banyule City Council  -37.70861   \n",
       "20038          0.00     1920.0       Banyule City Council  -37.75287   \n",
       "\n",
       "       Longtitude             Regionname Propertycount  \n",
       "7211    144.94370  Northern Metropolitan          6821  \n",
       "16540   144.96292  Southern Metropolitan          5943  \n",
       "19775   145.09311  Southern Metropolitan          7809  \n",
       "19840   145.05691  Northern Metropolitan         10175  \n",
       "20038   145.05547   Eastern Metropolitan          2947  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check buildingarea\n",
    "df[df['BuildingArea']<1].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count number of houses with less than 1 Building area\n",
    "len(df[df['BuildingArea']<1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In simple words these are 77 points are the results of data entry mistakes. We drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows for umber of houses with less than 1 Building area (uninary operator for removing data) \n",
    "df = df[~(df['BuildingArea'] < 1)]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since there are missing values in the data we reset the index for dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since BuildingArea coulumn has a maximum value of 4, clearly a wrong entry. Analze with Boxplot for outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data= df,y='BuildingArea');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Absolutely right ,assumption was right. Further we analyse the row and rectify the data point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>index</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>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>22614</th>\n",
       "      <td>22632</td>\n",
       "      <td>New Gisborne</td>\n",
       "      <td>71 Hamilton Rd</td>\n",
       "      <td>5</td>\n",
       "      <td>h</td>\n",
       "      <td>1355000.0</td>\n",
       "      <td>S</td>\n",
       "      <td>Raine</td>\n",
       "      <td>23/09/2017</td>\n",
       "      <td>48.1</td>\n",
       "      <td>3438</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>44500.0</td>\n",
       "      <td>44515.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Macedon Ranges Shire Council</td>\n",
       "      <td>-37.45392</td>\n",
       "      <td>144.58864</td>\n",
       "      <td>Northern Victoria</td>\n",
       "      <td>849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index        Suburb         Address  Rooms Type      Price Method  \\\n",
       "22614  22632  New Gisborne  71 Hamilton Rd      5    h  1355000.0      S   \n",
       "\n",
       "      SellerG        Date  Distance Postcode  Bathroom  Car  Landsize  \\\n",
       "22614   Raine  23/09/2017      48.1     3438       3.0  5.0   44500.0   \n",
       "\n",
       "       BuildingArea  YearBuilt                   CouncilArea  Lattitude  \\\n",
       "22614       44515.0        NaN  Macedon Ranges Shire Council  -37.45392   \n",
       "\n",
       "       Longtitude         Regionname Propertycount  \n",
       "22614   144.58864  Northern Victoria           849  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the row\n",
    "df[df['BuildingArea']>40000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The value is clearly absurd and should be 455, the data entry person must have moved two decimal points further!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replace 44500.0 with 445\n",
    "df['BuildingArea'].replace(44515.0, 445.15, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check for duplicate values using pandas dataframe duplicated method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check total duplicated values\n",
    "df.duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have no duplicate data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pandas method __dtypes.value_counts()__ provides the number of columns of each type in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object     10\n",
       "float64     9\n",
       "int64       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above results, out of 20 columns 9 are float values, 8 are object (character values) and 1 is integer value, one integer column is index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6791.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BuildingArea'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Save the cleaner version of dataframe for future analysis.__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save data\n",
    "df.drop(columns=['index'],inplace=True)\n",
    "df.to_csv('data/data_v1.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Next notebook involves removal of outliers and taking closer look at attributes. In notebook title : NB2_FeatureEngineering."
   ]
  }
 ],
 "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": "3.6.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}