House_Price_Prediction_Project.ipynb

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    "colab": {
      "name": "House_Price_Prediction_Project.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM+O8SDQa7BB4Kix1lo9tHM",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/satishgunjal/Real-Estate-Price-Prediction-Project/blob/master/House_Price_Prediction_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "Eb4sSQMBp04P",
        "colab_type": "text"
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      "source": [
        "# Real Estate Price Prediction Project\n",
        "\n",
        "## Introduction\n",
        "Creating a Machine Learning model to predict the home prices in Bangalore, India. We are going to use the dataset from Kaggle.com.\n",
        "We are also going to create a single page website which will provide the front end to access our model for predictions.\n",
        "\n",
        "Below data science concepts are used in this project\n",
        "* Data loading and cleaning\n",
        "* Outlier detection and removal\n",
        "* Feature engineering\n",
        "* Dimensionality reduction\n",
        "* Gridsearchcv for hyperparameter tunning\n",
        "* K fold cross validation\n",
        "\n",
        "Technology and tools used in this project\n",
        "* Python\n",
        "* Numpy and Pandas for data cleaning\n",
        "* Matplotlib for data visualization\n",
        "* Sklearn for model building\n",
        "* Google Colaboratory Notebook\n",
        "* Python flask for http server\n",
        "* HTML/CSS/Javascript for UI\n",
        "\n",
        "## Steps\n",
        "1. We will first build a model using sklearn and linear regression using banglore home prices dataset from kaggle.com.\n",
        "2. Second step would be to write a python flask server that uses the saved model to serve http requests.\n",
        "3. Third component is the website built in html, css and javascript that allows user to enter home square ft area, bedrooms etc and it will call python flask server to retrieve the predicted price. \n",
        "\n",
        "## Dataset Reference\n",
        "* [Bengaluru House price data](https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data)\n",
        "* I have also uploaed the csv file in this repository [Bengaluru_House_Data.csv](Bengaluru_House_Data.csv)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KiEhp5z_p9dS",
        "colab_type": "text"
      },
      "source": [
        "#Step#1: Import Libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpgiGgOApwMW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import matplotlib\n",
        "matplotlib.rcParams['figure.figsize'] = (20,10) # width, height in inches\n",
        "from google.colab import files\n",
        "import io"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wm09zH0OtRFK",
        "colab_type": "text"
      },
      "source": [
        "#Step#2: Load the data\n",
        "* Load the data in dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0aMK2YLItYrj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# for local notebook\n",
        "#df1 = pd.read_csv('Bengaluru_House_Data.csv')\n",
        "#df1.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CktYqH-Pt52h",
        "colab_type": "code",
        "outputId": "a8b79a0e-e2b5-4b1f-859b-d410b6849068",
        "colab": {
          "resources": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "source": [
        "#For google colab\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e3f2e159-6cda-44c0-b405-cfdaa7d0ab61\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-e3f2e159-6cda-44c0-b405-cfdaa7d0ab61\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Bengaluru_House_Data.csv to Bengaluru_House_Data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-WpbLtUVtgdL",
        "colab_type": "code",
        "outputId": "defaa4c2-e71b-4d25-a32d-19893b5d2ab4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        }
      },
      "source": [
        "df1 = pd.read_csv(io.StringIO(uploaded['Bengaluru_House_Data.csv'].decode('utf-8')))\n",
        "df1.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>area_type</th>\n",
              "      <th>availability</th>\n",
              "      <th>location</th>\n",
              "      <th>size</th>\n",
              "      <th>society</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>balcony</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Super built-up  Area</td>\n",
              "      <td>19-Dec</td>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>Coomee</td>\n",
              "      <td>1056</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>39.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Plot  Area</td>\n",
              "      <td>Ready To Move</td>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>Theanmp</td>\n",
              "      <td>2600</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>120.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Built-up  Area</td>\n",
              "      <td>Ready To Move</td>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1440</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>62.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Super built-up  Area</td>\n",
              "      <td>Ready To Move</td>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Soiewre</td>\n",
              "      <td>1521</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>95.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Super built-up  Area</td>\n",
              "      <td>Ready To Move</td>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1200</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>51.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              area_type   availability  ... balcony   price\n",
              "0  Super built-up  Area         19-Dec  ...     1.0   39.07\n",
              "1            Plot  Area  Ready To Move  ...     3.0  120.00\n",
              "2        Built-up  Area  Ready To Move  ...     3.0   62.00\n",
              "3  Super built-up  Area  Ready To Move  ...     1.0   95.00\n",
              "4  Super built-up  Area  Ready To Move  ...     1.0   51.00\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VW1i_x9IuljP",
        "colab_type": "text"
      },
      "source": [
        "#Step#3: Understand the data\n",
        "* Finalize the columns to work with and drop the rest of them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FnAnCZ75uogp",
        "colab_type": "code",
        "outputId": "bb51c6f0-128e-4491-bbb2-d89b39667f59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Get the no of rows and columns\n",
        "df1.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13320, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6epvumHFu65A",
        "colab_type": "code",
        "outputId": "e35ab577-7f67-4857-ecda-f856cf5ae749",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "#Get all the column names\n",
        "df1.columns"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['area_type', 'availability', 'location', 'size', 'society',\n",
              "       'total_sqft', 'bath', 'balcony', 'price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXKabzqPw2TX",
        "colab_type": "code",
        "outputId": "3f1b6600-50b9-40f1-ef8d-1a679778a8f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#Lets check the unique values 'area_type' column\n",
        "df1.area_type.unique()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Super built-up  Area', 'Plot  Area', 'Built-up  Area',\n",
              "       'Carpet  Area'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USYtVrQ7xCyk",
        "colab_type": "code",
        "outputId": "be7df7d3-82bd-4a4b-fea8-e6990db487e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "#Let get the count of trianing examples for each area type\n",
        "df1.area_type.value_counts()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Super built-up  Area    8790\n",
              "Built-up  Area          2418\n",
              "Plot  Area              2025\n",
              "Carpet  Area              87\n",
              "Name: area_type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m56z9xeYxZu4",
        "colab_type": "text"
      },
      "source": [
        "## Dropping the columns\n",
        "* As such all the columns are important for price prediction, but for the sake of this project I am going to drop few columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mex2QHoExuSL",
        "colab_type": "code",
        "outputId": "3b0ad164-09d7-4a67-ae74-9e1dd1aa917e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "# Note everytime we make change in dataset we store it in new dataframe\n",
        "df2 = df1.drop(['area_type', 'availability', 'society', 'balcony'],axis='columns')\n",
        "\n",
        "print('Rows and columns are = ', df2.shape)\n",
        "df2.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows and columns are =  (13320, 5)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   location       size total_sqft  bath   price\n",
              "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
              "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
              "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
              "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
              "4                  Kothanur      2 BHK       1200   2.0   51.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ha_o7t4SzOKy",
        "colab_type": "text"
      },
      "source": [
        "#Step#4: Data Cleaning\n",
        "* Check for na values\n",
        "* Verify unique values of each column\n",
        "* Make sure values are correct (eg. 23 BHK home with only 2000 Sqrft size seems wrong)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iaeMca_Q1s0v",
        "colab_type": "text"
      },
      "source": [
        "## Handling null values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuwIGLtXzeb5",
        "colab_type": "code",
        "outputId": "a52fe301-c53f-4b16-d7fa-bb60c82cfc98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "# Get the sum of all na values from dataset\n",
        "df2.isna().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location       1\n",
              "size          16\n",
              "total_sqft     0\n",
              "bath          73\n",
              "price          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKJHdiSB1yvg",
        "colab_type": "text"
      },
      "source": [
        "Since null values as comapre to total training examples(13320) is verry less we can safly drop those examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8IQc05D1_4W",
        "colab_type": "code",
        "outputId": "d01a268d-1e7f-4856-d86b-cb9cfc0e6384",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df3 = df2.dropna()\n",
        "df3.isnull().sum()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location      0\n",
              "size          0\n",
              "total_sqft    0\n",
              "bath          0\n",
              "price         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5H72W9972Nxl",
        "colab_type": "code",
        "outputId": "52092d25-a142-4f4c-9f71-c6761f2b5b4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "# Since all oor training examples containing null values are dropped lets check the shape of the dataset again\n",
        "df3.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13246, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0mr1--H39Y_",
        "colab_type": "text"
      },
      "source": [
        "## Feature Engineering\n",
        "* 'size' column contgaines the size of house in terms of BHK( Bedroom Hall Kitchen)\n",
        "* To simply it we can create new column by the name 'bhk' and add only numeric value of how many BHK's"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22xwyWtn46gZ",
        "colab_type": "code",
        "outputId": "6797a48c-8f9c-4baf-cd20-ebf63a3a49b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df3['size'].unique()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2 BHK', '4 Bedroom', '3 BHK', '4 BHK', '6 Bedroom', '3 Bedroom',\n",
              "       '1 BHK', '1 RK', '1 Bedroom', '8 Bedroom', '2 Bedroom',\n",
              "       '7 Bedroom', '5 BHK', '7 BHK', '6 BHK', '5 Bedroom', '11 BHK',\n",
              "       '9 BHK', '9 Bedroom', '27 BHK', '10 Bedroom', '11 Bedroom',\n",
              "       '10 BHK', '19 BHK', '16 BHK', '43 Bedroom', '14 BHK', '8 BHK',\n",
              "       '12 Bedroom', '13 BHK', '18 Bedroom'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXqO4aFr4pK4",
        "colab_type": "code",
        "outputId": "93f6178b-4618-432e-ecd8-33aef8c7b256",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df4 = df3.copy()\n",
        "\n",
        "# Using lambda function we can get the BHK numeric value\n",
        "df4['bhk'] = df3['size'].apply(lambda x: int(x.split(' ')[0]))\n",
        "#df3['bhk'] = df3['size'].apply(lambda x: int(x.split(' ')[0]))\n",
        "df4.bhk.unique()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  4,  3,  6,  1,  8,  7,  5, 11,  9, 27, 10, 19, 16, 43, 14, 12,\n",
              "       13, 18])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v7H2gM9l7s_p",
        "colab_type": "text"
      },
      "source": [
        "From above data we can see that there are home with upto 43 BHK's in Bangalore.. must be apolitician :)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIf7Uegv74SE",
        "colab_type": "code",
        "outputId": "eeb515b8-b2f3-41fa-d729-c24a4b7d0056",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Get the training examples with home size more than 20 BHK\n",
        "df4[df4.bhk >20]"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1718</th>\n",
              "      <td>2Electronic City Phase II</td>\n",
              "      <td>27 BHK</td>\n",
              "      <td>8000</td>\n",
              "      <td>27.0</td>\n",
              "      <td>230.0</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4684</th>\n",
              "      <td>Munnekollal</td>\n",
              "      <td>43 Bedroom</td>\n",
              "      <td>2400</td>\n",
              "      <td>40.0</td>\n",
              "      <td>660.0</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       location        size total_sqft  bath  price  bhk\n",
              "1718  2Electronic City Phase II      27 BHK       8000  27.0  230.0   27\n",
              "4684                Munnekollal  43 Bedroom       2400  40.0  660.0   43"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DRDeD7zA8HGN",
        "colab_type": "text"
      },
      "source": [
        "Note above 43 BHK home area is only 2400 sqrft only. We will remove this data error later. First lets clean the 'total_sqft' column"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UyEupWM462u0",
        "colab_type": "text"
      },
      "source": [
        "Now lets check the unique values in 'total_sqft' column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fTs8ovHr66kR",
        "colab_type": "code",
        "outputId": "a0c42257-194f-4b84-de78-e385ed8b0044",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df4.total_sqft.unique()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1EAC-zLo8dd2",
        "colab_type": "text"
      },
      "source": [
        "Note above, there are few records with range of the area like '1133 - 1384'.\n",
        "Lets write a function to identify such values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fFCT1t558pR4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def is_float(x):\n",
        "  try:\n",
        "    float(x)\n",
        "  except:\n",
        "    return False\n",
        "\n",
        "  return True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGFdPmGY9UP3",
        "colab_type": "code",
        "outputId": "967a9fa3-9784-4827-dae3-eda399cffb79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "# Test the function\n",
        "print('is this (123) float value = %s' % (is_float(123)))\n",
        "print('is this (1133 - 1384) float value = %s' % (is_float('1133 - 1384')))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "is this (123) float value = True\n",
            "is this (1133 - 1384) float value = False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtHbBFx7-s9N",
        "colab_type": "code",
        "outputId": "53c7c80e-4404-456d-b608-306f5098b313",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Lets apply this function to 'total_sqft' column\n",
        "\n",
        "#Showing training examples where 'total_sqft' vale is not float\n",
        "df4[~df4['total_sqft'].apply(is_float)].head(10) "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Yelahanka</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>2100 - 2850</td>\n",
              "      <td>4.0</td>\n",
              "      <td>186.000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Hebbal</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>3067 - 8156</td>\n",
              "      <td>4.0</td>\n",
              "      <td>477.000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>8th Phase JP Nagar</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1042 - 1105</td>\n",
              "      <td>2.0</td>\n",
              "      <td>54.005</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Sarjapur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1145 - 1340</td>\n",
              "      <td>2.0</td>\n",
              "      <td>43.490</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>KR Puram</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1015 - 1540</td>\n",
              "      <td>2.0</td>\n",
              "      <td>56.800</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>Kengeri</td>\n",
              "      <td>1 BHK</td>\n",
              "      <td>34.46Sq. Meter</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18.500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>Hennur Road</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1195 - 1440</td>\n",
              "      <td>2.0</td>\n",
              "      <td>63.770</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>648</th>\n",
              "      <td>Arekere</td>\n",
              "      <td>9 Bedroom</td>\n",
              "      <td>4125Perch</td>\n",
              "      <td>9.0</td>\n",
              "      <td>265.000</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>661</th>\n",
              "      <td>Yelahanka</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1120 - 1145</td>\n",
              "      <td>2.0</td>\n",
              "      <td>48.130</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>Bettahalsoor</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>3090 - 5002</td>\n",
              "      <td>4.0</td>\n",
              "      <td>445.000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               location       size      total_sqft  bath    price  bhk\n",
              "30            Yelahanka      4 BHK     2100 - 2850   4.0  186.000    4\n",
              "122              Hebbal      4 BHK     3067 - 8156   4.0  477.000    4\n",
              "137  8th Phase JP Nagar      2 BHK     1042 - 1105   2.0   54.005    2\n",
              "165            Sarjapur      2 BHK     1145 - 1340   2.0   43.490    2\n",
              "188            KR Puram      2 BHK     1015 - 1540   2.0   56.800    2\n",
              "410             Kengeri      1 BHK  34.46Sq. Meter   1.0   18.500    1\n",
              "549         Hennur Road      2 BHK     1195 - 1440   2.0   63.770    2\n",
              "648             Arekere  9 Bedroom       4125Perch   9.0  265.000    9\n",
              "661           Yelahanka      2 BHK     1120 - 1145   2.0   48.130    2\n",
              "672        Bettahalsoor  4 Bedroom     3090 - 5002   4.0  445.000    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqQCgzAQ-ryl",
        "colab_type": "text"
      },
      "source": [
        "* Since most the value are range of sqft, we can write afunction to get the average value from a range. \n",
        "* There are few values like '34.46Sq. Meter' and '4125Perch' we can also try and convert those values into sqft but for now I amgoing to ignore them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GM0aabX-_54p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convert_range_to_sqft(x):\n",
        "  try:\n",
        "    tokens = x.split('-')\n",
        "\n",
        "    if len(tokens) == 2:\n",
        "      return (float(tokens[0]) + float(tokens[1]))/2\n",
        "    else:\n",
        "      return float(x)\n",
        "  except:\n",
        "    return None"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LVZTUL81A7sB",
        "colab_type": "code",
        "outputId": "6bb89ff0-673f-481b-9d38-0085cb5d21da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Lets test the convert_range_to_sqft()\n",
        "print('Return value for i/p 12345 = %s' % (convert_range_to_sqft('12345')))\n",
        "print('Return value for i/p 1133 - 1384 = %s' % (convert_range_to_sqft('1133 - 1384')))\n",
        "print('Return value for i/p 34.46Sq. Meter = %s' % (convert_range_to_sqft('34.46Sq. Meter')))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Return value for i/p 12345 = 12345.0\n",
            "Return value for i/p 1133 - 1384 = 1258.5\n",
            "Return value for i/p 34.46Sq. Meter = None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBANFxdlBX0o",
        "colab_type": "code",
        "outputId": "5871715b-aacf-4bf5-93ce-b7cf75060aa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "# Lets apply this function for total_sqft column\n",
        "df5 = df4.copy()\n",
        "\n",
        "df5.total_sqft = df4.total_sqft.apply(convert_range_to_sqft)\n",
        "df5.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   location       size  total_sqft  bath   price  bhk\n",
              "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2\n",
              "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
              "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
              "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
              "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpRkrMrQPk3o",
        "colab_type": "code",
        "outputId": "8a450d04-0522-4717-de30-234c8958803c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "# Since our converion function will return null for values like 34.46Sq. Meter. Lets check for any null values in it\n",
        "df5.total_sqft.isnull().sum()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "46"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMrQ3fqaQ3ND",
        "colab_type": "code",
        "outputId": "b74e2b6d-ba07-4a1f-fedd-5b682e742216",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "# Lets dro the null training sets from total_sqft\n",
        "df6 = df5.dropna()\n",
        "df6.total_sqft.isnull().sum()\n",
        "\n",
        "# OR\n",
        "#We can also select the not null training set using below filter\n",
        "#df6 = df5[df5.total_sqft.notnull()]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5v-jRNLgSX_Z",
        "colab_type": "code",
        "outputId": "f12a0f90-3878-4b52-f66e-e0a83e73d131",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "# Lets cross check the values of 'total_sqft'\n",
        "print('total_sqft value for 30th training set in df4 = %s' % (df4.total_sqft[30]))\n",
        "print('total_sqft value for 30th training set in df6 = %s' % (df6.total_sqft[30]))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total_sqft value for 30th training set in df4 = 2100 - 2850\n",
            "total_sqft value for 30th training set in df6 = 2475.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zCUTiJ4lA7Ak",
        "colab_type": "text"
      },
      "source": [
        "## Feature Engineering\n",
        "* 'price' column containes the price of house in lacka ( 1 lakh = 100000)\n",
        "* Price per square fit is important parameter in house prices.\n",
        "* So we can create new column by the name 'price_per_sqft' and add price per sqft in it. formula = (price * 100000)/total_sqft"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXYoe6x7r8AT",
        "colab_type": "code",
        "outputId": "51260d57-71c1-4044-d550-5de61bed8462",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df7 = df6.copy()\n",
        "\n",
        "df7['price_per_sqft'] = (df6['price'] * 100000)/df6['total_sqft']\n",
        "df7.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "      <td>2</td>\n",
              "      <td>3699.810606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>4</td>\n",
              "      <td>4615.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "      <td>3</td>\n",
              "      <td>4305.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "      <td>3</td>\n",
              "      <td>6245.890861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "      <td>2</td>\n",
              "      <td>4250.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   location       size  total_sqft  ...   price  bhk  price_per_sqft\n",
              "0  Electronic City Phase II      2 BHK      1056.0  ...   39.07    2     3699.810606\n",
              "1          Chikka Tirupathi  4 Bedroom      2600.0  ...  120.00    4     4615.384615\n",
              "2               Uttarahalli      3 BHK      1440.0  ...   62.00    3     4305.555556\n",
              "3        Lingadheeranahalli      3 BHK      1521.0  ...   95.00    3     6245.890861\n",
              "4                  Kothanur      2 BHK      1200.0  ...   51.00    2     4250.000000\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOZpp4wZt9TR",
        "colab_type": "code",
        "outputId": "ef89bbb8-8448-4533-91cc-705af886113a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df7_stats = df7['price_per_sqft'].describe()\n",
        "df7_stats"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.320000e+04\n",
              "mean     7.920759e+03\n",
              "std      1.067272e+05\n",
              "min      2.678298e+02\n",
              "25%      4.267701e+03\n",
              "50%      5.438331e+03\n",
              "75%      7.317073e+03\n",
              "max      1.200000e+07\n",
              "Name: price_per_sqft, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWjFhsUQvMiJ",
        "colab_type": "text"
      },
      "source": [
        "## Dimesionality Reduction\n",
        "* Dimensionality reduction is simply a process of reducing the dimension( or number of random variables) of your feature set\n",
        "* In our dataset 'location' is categorical variable with 1287 categories.\n",
        "* Before using One Hot Encoding to create dummy variables we must reduce the number of categories by using dimensionality reduction so that we will get less number of dummy variables.\n",
        "* Our criteria for dimesionality reduction for 'location' is to use 'other' location for any location having less than 10 data points.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hsh77cctzGbM",
        "colab_type": "code",
        "outputId": "494c1a94-1a0f-4bc2-95f5-795b2efe7450",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Trim the location values\n",
        "df7.location = df7.location.apply(lambda x: x.strip())\n",
        "df7.head()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "      <td>2</td>\n",
              "      <td>3699.810606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>4</td>\n",
              "      <td>4615.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "      <td>3</td>\n",
              "      <td>4305.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "      <td>3</td>\n",
              "      <td>6245.890861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "      <td>2</td>\n",
              "      <td>4250.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   location       size  total_sqft  ...   price  bhk  price_per_sqft\n",
              "0  Electronic City Phase II      2 BHK      1056.0  ...   39.07    2     3699.810606\n",
              "1          Chikka Tirupathi  4 Bedroom      2600.0  ...  120.00    4     4615.384615\n",
              "2               Uttarahalli      3 BHK      1440.0  ...   62.00    3     4305.555556\n",
              "3        Lingadheeranahalli      3 BHK      1521.0  ...   95.00    3     6245.890861\n",
              "4                  Kothanur      2 BHK      1200.0  ...   51.00    2     4250.000000\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xpUxd8KI0E7u",
        "colab_type": "code",
        "outputId": "b2ddb176-6725-4a14-d3b3-8e384424897a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Lets get the count of each location\n",
        "location_stats = df7.location.value_counts(ascending=False)\n",
        "location_stats"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Whitefield                                     533\n",
              "Sarjapur  Road                                 392\n",
              "Electronic City                                304\n",
              "Kanakpura Road                                 264\n",
              "Thanisandra                                    235\n",
              "                                              ... \n",
              "Somanna Garden                                   1\n",
              "Howthinarayanappa Garden                         1\n",
              "Bharat Nagar                                     1\n",
              "Annapoorneshwari Layout, JP nagar 7th phase      1\n",
              "Kalkere Channasandra                             1\n",
              "Name: location, Length: 1287, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InVa3_4O1MrE",
        "colab_type": "code",
        "outputId": "b39d54fa-c2a4-44f0-f2d9-1329a945210e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Total number unique location categories are\n",
        "len(location_stats)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1287"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MW8tJ_G612Ms",
        "colab_type": "text"
      },
      "source": [
        "We are going assign a category 'other' for every location where total datapoints are less than 10"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kb-tpnki1XTG",
        "colab_type": "code",
        "outputId": "a0a3a253-89d7-4f03-d11e-e6c4041dcac9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Get total number of categories where data points are less than 10\n",
        "print('Total no of locations where data points are more than 10 = %s' % (len(location_stats[location_stats > 10])))\n",
        "print('Total no of locations where data points are less than 10 = %s' % (len(location_stats[location_stats <= 10])))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total no of locations where data points are more than 10 = 240\n",
            "Total no of locations where data points are less than 10 = 1047\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjKVy2Cf3Ys-",
        "colab_type": "text"
      },
      "source": [
        "Any location having less than 10 data points should be tagged as \"other\" location. This way number of categories can be reduced by huge amount. Later on when we do one hot encoding, it will help us with having fewer dummy columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBNGhv0j3bOn",
        "colab_type": "code",
        "outputId": "30a98cd6-70a2-4e2d-eb31-c57eea44efe0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "location_stats_less_than_10 = location_stats[location_stats <= 10]\n",
        "location_stats_less_than_10"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gunjur Palya                                   10\n",
              "Nagappa Reddy Layout                           10\n",
              "Sector 1 HSR Layout                            10\n",
              "Ganga Nagar                                    10\n",
              "Naganathapura                                  10\n",
              "                                               ..\n",
              "Somanna Garden                                  1\n",
              "Howthinarayanappa Garden                        1\n",
              "Bharat Nagar                                    1\n",
              "Annapoorneshwari Layout, JP nagar 7th phase     1\n",
              "Kalkere Channasandra                            1\n",
              "Name: location, Length: 1047, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4GOHwLzp4Ycl",
        "colab_type": "code",
        "outputId": "3f768eee-b3d1-4fc7-93bf-5f106692b8ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "#Using lambda function assign the 'other' type to every element in 'location_stats_less_than_10'\n",
        "df8 = df7.copy()\n",
        "\n",
        "df8.location = df7.location.apply(lambda x: 'other' if x in location_stats_less_than_10 else x )\n",
        "len(df8.location.unique())"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "241"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IwcrUAiC5ox2",
        "colab_type": "text"
      },
      "source": [
        "Since 1047 location with less than 10 data points are converted to one category 'other'\n",
        "Total no of unique location categories are = 240 +1  = 241"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBv197O_8Oja",
        "colab_type": "code",
        "outputId": "ddb3012f-4bc2-4d04-adf8-b0e4457a30df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "source": [
        "df8.head(10)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "      <td>2</td>\n",
              "      <td>3699.810606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>4</td>\n",
              "      <td>4615.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "      <td>3</td>\n",
              "      <td>4305.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "      <td>3</td>\n",
              "      <td>6245.890861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "      <td>2</td>\n",
              "      <td>4250.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Whitefield</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1170.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>38.00</td>\n",
              "      <td>2</td>\n",
              "      <td>3247.863248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Old Airport Road</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>2732.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>204.00</td>\n",
              "      <td>4</td>\n",
              "      <td>7467.057101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Rajaji Nagar</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>3300.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>600.00</td>\n",
              "      <td>4</td>\n",
              "      <td>18181.818182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Marathahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1310.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>63.25</td>\n",
              "      <td>3</td>\n",
              "      <td>4828.244275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>other</td>\n",
              "      <td>6 Bedroom</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>370.00</td>\n",
              "      <td>6</td>\n",
              "      <td>36274.509804</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   location       size  total_sqft  ...   price  bhk  price_per_sqft\n",
              "0  Electronic City Phase II      2 BHK      1056.0  ...   39.07    2     3699.810606\n",
              "1          Chikka Tirupathi  4 Bedroom      2600.0  ...  120.00    4     4615.384615\n",
              "2               Uttarahalli      3 BHK      1440.0  ...   62.00    3     4305.555556\n",
              "3        Lingadheeranahalli      3 BHK      1521.0  ...   95.00    3     6245.890861\n",
              "4                  Kothanur      2 BHK      1200.0  ...   51.00    2     4250.000000\n",
              "5                Whitefield      2 BHK      1170.0  ...   38.00    2     3247.863248\n",
              "6          Old Airport Road      4 BHK      2732.0  ...  204.00    4     7467.057101\n",
              "7              Rajaji Nagar      4 BHK      3300.0  ...  600.00    4    18181.818182\n",
              "8              Marathahalli      3 BHK      1310.0  ...   63.25    3     4828.244275\n",
              "9                     other  6 Bedroom      1020.0  ...  370.00    6    36274.509804\n",
              "\n",
              "[10 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r_lbCSRd80hj",
        "colab_type": "text"
      },
      "source": [
        "## Outlier Removal\n",
        "* An outlier is an observation that is unlike the other observations. It is rare, or distinct, or does not fit in some way.\n",
        "* Outliers are the data points that represent the extreame variation of dataset\n",
        "* Outliers can be valid data points but since our model is generalization of the data, outliers can affect the performanace of the model. We are going to remove the otliers, but please note its not always a good practice to remove the outliers.\n",
        "* To remove the outliers we can use domain knwoledge and standard deviation\n",
        "\n",
        "### Standard Deviation\n",
        "* Standard deviation is measure of spread that is to khow how much does the data vary from the average\n",
        "* A low standard deviation tells us that the data is closely clustered around the mean (or average), while a high standard deviation indicates that the data is dispersed over a wider range of values. \n",
        "* It is used when the distribution of data is approximately normal, resembling a bell curve.\n",
        "* One standard deviation(1 Sigma) of the mean will cover 68% of the data. i.e. Data between (mean - std deviation) & (mean + std deviation) is 1 Sigma and which is equal to 68%\n",
        "* Here we are going to consider 1 Sigma as our threshold adn any data outside 1 Sigma will be considered as outlier\n",
        "* [How to Use Statistics to Identify Outliers in Data](https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/)\n",
        "* [Reference](https://youtu.be/MRqtXL2WX2M)\n",
        "\n",
        "### Using domain knowledge for outlier removal\n",
        "* Normally square fit per bedroom is 300 (i.e. 2 bhk apartment is minimum 600 sqft)\n",
        "* If you have for example 400 sqft apartment with 2 bhk than that seems suspicious and can be removed as an outlier. \n",
        "* We will remove such outliers by keeping our minimum threshold per bhk to be 300 sqft"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P1MM5m0_x2uq",
        "colab_type": "text"
      },
      "source": [
        "### Using domain knowledge for outlier removal\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4AQivpfg3GQ",
        "colab_type": "code",
        "outputId": "cbe39755-3a39-4566-e812-6d4932d966f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        }
      },
      "source": [
        "# Lets visualize the data where square fit per bedroom is less than 300\n",
        "df8[(df8.total_sqft / df8.bhk) < 300]"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>other</td>\n",
              "      <td>6 Bedroom</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>6</td>\n",
              "      <td>36274.509804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>HSR Layout</td>\n",
              "      <td>8 Bedroom</td>\n",
              "      <td>600.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>8</td>\n",
              "      <td>33333.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Murugeshpalya</td>\n",
              "      <td>6 Bedroom</td>\n",
              "      <td>1407.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>6</td>\n",
              "      <td>10660.980810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Devarachikkanahalli</td>\n",
              "      <td>8 Bedroom</td>\n",
              "      <td>1350.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>8</td>\n",
              "      <td>6296.296296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>other</td>\n",
              "      <td>3 Bedroom</td>\n",
              "      <td>500.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>3</td>\n",
              "      <td>20000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13277</th>\n",
              "      <td>other</td>\n",
              "      <td>7 Bedroom</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>7</td>\n",
              "      <td>15571.428571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13279</th>\n",
              "      <td>other</td>\n",
              "      <td>6 Bedroom</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>6</td>\n",
              "      <td>10833.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13281</th>\n",
              "      <td>Margondanahalli</td>\n",
              "      <td>5 Bedroom</td>\n",
              "      <td>1375.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>5</td>\n",
              "      <td>9090.909091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13303</th>\n",
              "      <td>Vidyaranyapura</td>\n",
              "      <td>5 Bedroom</td>\n",
              "      <td>774.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>5</td>\n",
              "      <td>9043.927649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13311</th>\n",
              "      <td>Ramamurthy Nagar</td>\n",
              "      <td>7 Bedroom</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>7</td>\n",
              "      <td>16666.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>744 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  location       size  total_sqft  ...  price  bhk  price_per_sqft\n",
              "9                    other  6 Bedroom      1020.0  ...  370.0    6    36274.509804\n",
              "45              HSR Layout  8 Bedroom       600.0  ...  200.0    8    33333.333333\n",
              "58           Murugeshpalya  6 Bedroom      1407.0  ...  150.0    6    10660.980810\n",
              "68     Devarachikkanahalli  8 Bedroom      1350.0  ...   85.0    8     6296.296296\n",
              "70                   other  3 Bedroom       500.0  ...  100.0    3    20000.000000\n",
              "...                    ...        ...         ...  ...    ...  ...             ...\n",
              "13277                other  7 Bedroom      1400.0  ...  218.0    7    15571.428571\n",
              "13279                other  6 Bedroom      1200.0  ...  130.0    6    10833.333333\n",
              "13281      Margondanahalli  5 Bedroom      1375.0  ...  125.0    5     9090.909091\n",
              "13303       Vidyaranyapura  5 Bedroom       774.0  ...   70.0    5     9043.927649\n",
              "13311     Ramamurthy Nagar  7 Bedroom      1500.0  ...  250.0    7    16666.666667\n",
              "\n",
              "[744 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fmZBg2-qh1mn",
        "colab_type": "text"
      },
      "source": [
        "Note abobe we have 744 training examples where square fit per bedroom is less than 300. These are outliers, so we can remove them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxjK6p2kiyQP",
        "colab_type": "code",
        "outputId": "c7e2e472-2ba6-448f-c200-d129de65869a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Lets check current dataset shape before removing outliers\n",
        "df8.shape"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13200, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvrtHe2dim4d",
        "colab_type": "code",
        "outputId": "7ba89a73-5e28-4160-a2f8-3cd31e4f89e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df9 = df8[~((df8.total_sqft / df8.bhk) < 300)]\n",
        "df9.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12456, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHsG44TXjWog",
        "colab_type": "text"
      },
      "source": [
        "### Outlier Removal - Using Standard Deviation and Mean\n",
        "* One standard deviation(1 Sigma) of the mean will cover 68% of the data. i.\n",
        "e. Data between (mean - std deviation) & (mean + std deviation) is 1 Sigma and which is equal to 68%\n",
        "* Here any datapin t outside the 1 Sigma deviation (68%) is outlier for us"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDAEesN9jhws",
        "colab_type": "code",
        "outputId": "5234c948-a1f8-4376-f801-efd289713531",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "source": [
        "# Get basic stats of column 'price_per_sqft'\n",
        "df9.price_per_sqft.describe()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     12456.000000\n",
              "mean       6308.502826\n",
              "std        4168.127339\n",
              "min         267.829813\n",
              "25%        4210.526316\n",
              "50%        5294.117647\n",
              "75%        6916.666667\n",
              "max      176470.588235\n",
              "Name: price_per_sqft, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LdwkbY5urxMV",
        "colab_type": "text"
      },
      "source": [
        "Note: Its important to understand that price of every house is location specific. We are going to remove outliers using 'price_per_sqft' for each location"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oXfoxd5phL0",
        "colab_type": "code",
        "outputId": "9a51b9fb-e610-472d-e067-ff1eb4e0b3ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "# Data visualization for 'price_per_sqft' for location 'Rajaji Nagar'\n",
        "# Note here its normal distribuation of data so outlier removal using stad deviation and mean works perfectly here\n",
        "plt.hist(df9[df9.location == \"Rajaji Nagar\"].price_per_sqft,rwidth=0.8)\n",
        "plt.xlabel(\"Price Per Square Feet\")\n",
        "plt.ylabel(\"Count\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irbzERBqtMGo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2482b836-151f-48f2-b2f5-c4a5817fc275"
      },
      "source": [
        "#Lets check current dataset shape before removing outliers\n",
        "df9.shape"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12456, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "safeD73Ys-CW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "75bfdbd6-0da2-4483-f458-cf546a14aca5"
      },
      "source": [
        "# Function to remove outliers using pps(price per sqft)\n",
        "def remove_pps_outliers(df):\n",
        "    df_out = pd.DataFrame()\n",
        "    for key, subdf in df.groupby('location'):\n",
        "        mean = np.mean(subdf.price_per_sqft)\n",
        "        std = np.std(subdf.price_per_sqft)\n",
        "        reduced_df = subdf[(subdf.price_per_sqft>(mean-std)) & (subdf.price_per_sqft<=(mean + std))] # 1 Sigma value i.e 68% of data\n",
        "        df_out = pd.concat([df_out,reduced_df],ignore_index=True) # Storing data in 'df_out' dataframe\n",
        "    return df_out\n",
        "\n",
        "df10 = remove_pps_outliers(df9)\n",
        "df10.shape"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10242, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymSElfeVvenf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "f1df1ae5-5b54-4681-f871-58f8d7b27338"
      },
      "source": [
        "# Data visualization for 'price_per_sqft' for location 'Rajaji Nagar' after outlier removal\n",
        "plt.hist(df10[df10.location == \"Rajaji Nagar\"].price_per_sqft,rwidth=0.8)\n",
        "plt.xlabel(\"Price Per Square Feet\")\n",
        "plt.ylabel(\"Count\")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABIwAAAJNCAYAAABTMu6EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3debBmd13n8c+XtGGXZdKTQUJsBMTBqAgtILhAYDAIY3SMCoVKBMmoA+OuyTg1lDqjcalxXyoFMVCDgDAwRoJg2B1lscMeFkEJEBYTjYoDjpngd/64p4v7vdzuvmn63ud29+tV1XWf5zznOb/f06mT0/3uc85T3R0AAAAAOOgWq54AAAAAALuLYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAsGfVE9iK0047rfft27fqaQAAAACcMK666qq/7u69m712XASjffv25cCBA6ueBgAAAMAJo6o+cKjXXJIGAAAAwCAYAQAAADAIRgAAAAAMghEAAAAAg2AEAAAAwCAYAQAAADAIRgAAAAAMghEAAAAAg2AEAAAAwCAYAQAAADAIRgAAAAAMghEAAAAAg2AEAAAAwCAYAQAAADAIRgAAAAAMghEAAAAAg2AEAAAAwCAYAQAAADAIRgAAAAAMghEAAAAAw7YFo6q6tKquq6p3rFv2C1X17qp6W1W9qKruuF3jAwAAAHB0tvMMo8uSnLNh2ZVJzuruL03y50ku2sbxAQAAADgK2xaMuvu1SW7YsOyPuvum5enrk5yxXeMDAAAAcHRWeQ+jJyb5wxWODwAAAMAm9qxi0Kr6iSQ3JXn2Yda5IMkFSXLmmWfu0MwAAAA4WvsuvGLVUzhhXHPxo1c9BU5yO36GUVWdn+QxSR7f3X2o9br7ku7e39379+7du2PzAwAAADjZ7egZRlV1TpIfS/K13f3JnRwbAAAAgK3ZtjOMquo5SV6X5N5VdW1VPSnJrye5fZIrq+otVfXb2zU+AAAAAEdn284w6u7HbbL4Gds1HgAAAADHxiq/JQ0AAACAXUgwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGDYs+oJAADbZ9+FV6x6CieMay5+9DHdnv82x86x/m8DADjDCAAAAIANBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAAhm0LRlV1aVVdV1XvWLfszlV1ZVW9d/l5p+0aHwAAAICjs51nGF2W5JwNyy5M8oruvleSVyzPAQAAANhFti0Ydfdrk9ywYfG5SZ65PH5mkm/crvEBAAAAODo7fQ+j07v7o8vjjyU5fYfHBwAAAOAIVnbT6+7uJH2o16vqgqo6UFUHrr/++h2cGQAAAMDJbaeD0V9V1V2SZPl53aFW7O5Lunt/d+/fu3fvjk0QAAAA4GS308Ho8iRPWB4/Icnv7/D4AAAAABzBtgWjqnpOktcluXdVXVtVT0pycZJ/U1XvTfKI5TkAAAAAu8ie7dpwdz/uEC89fLvGBAAAAOCzt7KbXgMAAACwOwlGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAwrCUZV9YNVdXVVvaOqnlNVt1rFPAAAAAD4TDsejKrqrkn+Y5L93X1WklOSPHan5wEAAADA5lZ1SdqeJLeuqj1JbpPkIyuaBwAAAAAb7Hgw6u4PJ/nFJB9M8tEkf9/df7TT8wAAAABgc6u4JO1OSc5Ncvckn5fktlX17Zusd0FVHaiqA9dff/1OTxMAAADgpLWKS9IekeT93X19d/+/JC9M8uCNK3X3Jd29v7v37927d8cnCQAAAHCyWkUw+mCSB1XVbaqqkjw8ybtWMA8AAAAANrGKexi9IckLkrwpyduXOVyy0/MAAAAAYHN7VjFodz8tydNWMTYAAAAAh7eKS9IAAAAA2MUEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAACGPauewMlm34VXrHoKJ4RrLn70qqcAAJzA/Jnt2DnWf27z3+bY8Wdq4HCcYQQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAADDloJRVT1kK8sAAAAAOP5t9QyjX9viMgAAAACOc3sO92JVfWWSByfZW1U/tO6lz01yynZODAAAAIDVOGwwSnJqktst691+3fKPJzlvuyYFAAAAwOocNhh192uSvKaqLuvuD+zQnAAAAABYoSOdYXTQLavqkiT71r+nu8/ejkkBAAAAsDpbDUbPT/LbSZ6e5FPbNx0AAAAAVm2rweim7v6tbZ0JAAAAALvCLba43h9U1fdV1V2q6s4Hf23rzAAAAABYia2eYfSE5eePrlvWSb7g2E4HAAAAgFXbUjDq7rtv90QAAAAA2B22FIyq6js3W97dzzq20wEAAABg1bZ6SdpXrHt8qyQPT/KmJIIRAAAAwAlmq5ekPXX986q6Y5LnbsuMAAAAAFiprX5L2kafSOK+RgAAAAAnoK3ew+gPsvataElySpJ/neT3tmtSAAAAAKzOVu9h9IvrHt+U5APdfe02zAcAAACAFdvSJWnd/Zok705y+yR3SnLjdk4KAAAAgNXZUjCqqm9N8sYk35LkW5O8oarO286JAQAAALAaW70k7SeSfEV3X5ckVbU3ycuTvGC7JgYAAADAamz1W9JucTAWLf7mZrwXAAAAgOPIVs8wemlVvSzJc5bn35bkJdszJQAAAABW6bDBqKrumeT07v7Rqvp3Sb5qeel1SZ59tINW1R2TPD3JWUk6yRO7+3VHuz0AAAAAjp0jnWH0y0kuSpLufmGSFyZJVX3J8tq/PcpxfyXJS7v7vKo6NcltjnI7AAAAABxjRwpGp3f32zcu7O63V9W+oxmwqu6Q5GuSnL9s68YkNx7NtgAAAAA49o504+o7Hua1Wx/lmHdPcn2S36mqN1fV06vqtke5LQAAAACOsSMFowNV9eSNC6vqu5NcdZRj7klyvyS/1d1fnuQTSS7cZIwLqupAVR24/vrrj3IoAAAAAG6uI12S9gNJXlRVj8+nA9H+JKcm+aajHPPaJNd29xuW5y/IJsGouy9JckmS7N+/v49yLAAAAABupsMGo+7+qyQPrqqHZe0bzZLkiu5+5dEO2N0fq6oPVdW9u/s9SR6e5J1Huz0AAAAAjq0jnWGUJOnuVyV51TEc96lJnr18Q9pfJvmuY7htAAAAAD4LWwpGx1p3vyVrl7YBAAAAsMsc6abXAAAAAJxkBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAABsEIAAAAgEEwAgAAAGAQjAAAAAAYBCMAAAAAhj2rngDAVuy78IpVT+GEcc3Fj171FAAAgF3OGUYAAAAADIIRAAAAAINgBAAAAMAgGAEAAAAwCEYAAAAADIIRAAAAAINgBAAAAMAgGAEAAAAwCEYAAAAADIIRAAAAAINgBAAAAMAgGAEAAAAwCEYAAAAADIIRAAAAAINgBAAAAMAgGAEAAAAwCEYAAAAADIIRAAAAAINgBAAAAMAgGAEAAAAwCEYAAAAADIIRAAAAAINgBAAAAMAgGAEAAAAwCEYAAAAADIIRAAAAAINgBAAAAMAgGAEAAAAwCEYAAAAADIIRAAAAAINgBAAAAMAgGAEAAAAwCEYAAAAADIIRAAAAAINgBAAAAMAgGAEAAAAwCEYAAAAADIIRAAAAAMPKglFVnVJVb66qF69qDgAAAAB8plWeYfT9Sd61wvEBAAAA2MRKglFVnZHk0UmevorxAQAAADi0VZ1h9MtJfizJP69ofAAAAAAOYc9OD1hVj0lyXXdfVVUPPcx6FyS5IEnOPPPMHZodJ7N9F16x6imcMK65+NGrngIAALCOv+8cOyfL33dWcYbRQ5J8Q1Vdk+S5Sc6uqv+xcaXuvqS793f3/r179+70HAEAAABOWjsejLr7ou4+o7v3JXlskld297fv9DwAAAAA2NwqvyUNAAAAgF1ox+9htF53vzrJq1c5BwAAAAAmZxgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAw7Hoyq6m5V9aqqemdVXV1V37/TcwAAAADg0PasYMybkvxwd7+pqm6f5KqqurK737mCuQAAAACwwY6fYdTdH+3uNy2P/yHJu5LcdafnAQAAAMDmVnoPo6ral+TLk7xhlfMAAAAA4NNWcUlakqSqbpfkfyb5ge7++CavX5DkgiQ588wzd3h2AGzVvguvWPUUThjXXPzoVU8BAACSrOgMo6r6nKzFomd39ws3W6e7L+nu/d29f+/evTs7QQAAAICT2Cq+Ja2SPCPJu7r7v+/0+AAAAAAc3irOMHpIku9IcnZVvWX59fUrmAcAAAAAm9jxexh19/9OUjs9LgAAAABbs9JvSQMAAABg9xGMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYBCMAAAAABgEIwAAAAAGwQgAAACAQTACAAAAYFhJMKqqc6rqPVX1vqq6cBVzAAAAAGBzOx6MquqUJL+R5FFJ7pPkcVV1n52eBwAAAACbW8UZRg9I8r7u/svuvjHJc5Ocu4J5AAAAALCJVQSjuyb50Lrn1y7LAAAAANgFqrt3dsCq85Kc093fvTz/jiQP7O6nbFjvgiQXLE/vneQ9OzpRVum0JH+96kkAN4v9Fo4/9ls4/thv4fiz2/fbz+/uvZu9sGenZ5Lkw0nutu75GcuyobsvSXLJTk2K3aOqDnT3/lXPA9g6+y0cf+y3cPyx38Lx53jeb1dxSdqfJblXVd29qk5N8tgkl69gHgAAAABsYsfPMOrum6rqKUleluSUJJd299U7PQ8AAAAANreKS9LS3S9J8pJVjM1xwaWIcPyx38Lxx34Lxx/7LRx/jtv9dsdveg0AAADA7raKexgBAAAAsIsJRmyLqrq0qq6rqnesW3bnqrqyqt67/LzTsryq6ler6n1V9baqut+69zxhWf+9VfWEdcvvX1VvX97zq1VVO/sJ4cRziP32F6rq3cu++aKquuO61y5a9sH3VNXXrVt+zrLsfVV14brld6+qNyzLn7d88QHwWdhsv1332g9XVVfVactzx1vYBQ6131bVU5dj7tVV9fPrljvewood4s/J962q11fVW6rqQFU9YFl+whxvBSO2y2VJztmw7MIkr+jueyV5xfI8SR6V5F7LrwuS/FayFpiSPC3JA5M8IMnTDkamZZ0nr3vfxrGAm++yfOa+dGWSs7r7S5P8eZKLkqSq7pO1b7n84uU9v1lVp1TVKUl+I2v79X2SPG5ZN0l+Lskvdfc9k/xtkidt78eBk8Jl2eQYWFV3S/LIJB9ct9jxFnaHy7JhX6qqhyU5N8mXdfcXJ/nFZbnjLewOl+Uzj4E/n+Qnu/u+Sf7L8jw5gY63ghHbortfm+SGDYvPTfLM5fEzk3zjuuXP6jWvT3LHqrpLkq9LcmV339Ddf5u1v7ies7z2ud39+l67Cdez1m0LOEqb7bfd/UfdfdPy9PVJzlgen5vkud39T939/iTvy9qB7wFJ3tfdf9ndNyZ5bpJzl38lOTvJC5b3r/9/AHCUDnG8TZJfSvJjSdbfrNLxFnaBQ+y335vk4u7+p2Wd65bljrewCxxiv+0kn7s8vkOSjyyPT5jjrWDETjq9uz+6PP5YktOXx3dN8qF16127LDvc8ms3WQ5srycm+cPl8c3db/9Fkr9bF5/st7BNqurcJB/u7rdueMnxFnavL0zy1culZK+pqq9Yljvewu71A0l+oao+lLWzAi9alp8wx1vBiJVYyqmv6IPjRFX9RJKbkjx71XMBDq2qbpPkP2Xt1Hjg+LEnyZ2TPCjJjyb5vd1yDxPgkL43yQ92992S/GCSZ6x4PsecYMRO+qvldLssPw+eavvhJHdbt94Zy7LDLT9jk+XANqiq85M8Jsnjl9ib3Pz99m+ydjrung3LgWPrHknunuStVXVN1va1N1XVv4rjLexm1yZ54XIJyxuT/HOS0+J4C7vZE5K8cHn8/KxdKpqcQMdbwYiddHnWdqosP39/3fLvXO4m/6Akf79cuvayJI+sqjstNwN7ZJKXLa99vKoetPzLy3eu2xZwDFXVOVm7D8o3dPcn1710eZLHVtUtq+ruWbs53xuT/FmSey3f0HJq1m7UefkSml6V5Lzl/ev/HwAcI9399u7+l929r7v3Ze0voffr7o/F8RZ2s/+V5GFJUlVfmOTUJH8dx1vYzT6S5GuXx2cnee/y+IQ53u458ipw81XVc5I8NMlpVXVt1u4Gf3HWTq99UpIPJPnWZfWXJPn6rN3E75NJvitJuvuGqvrprB0Qk+Snuvvgjca+L2t3qr911u6pcvC+KsBROsR+e1GSWya5cjkz/vXd/T3dfXVV/V6Sd2btUrX/0N2fWrbzlKwdEE9Jcml3X70M8eNJnltV/zXJm3MCnrYLO22z/ba7D7VvOd7CLnCI4+2lSS5dvrL7xiRPWOKP4y3sAofYb5+c5FeWM/r+b9a+ES05gY639emrCwAAAADAJWkAAAAAbCAYAQAAADAIRgAAAAAMghEAAAAAg2AEAAAAwCAYAQArVVWfqqq3VNU7qur5VXWbQ6z3p8dgrPOr6vplvHdW1ZM/i22dXlUvrqq3Ltt6yWc7v2OhqvZV1T8un/Hgr1OPYjvnV9XnbcccAYDdTzACAFbtH7v7vt19VpIbk3zP+herak+SdPeDj9F4z+vu+yZ5aJKfqarTt/Kmg/NY56eSXNndX9bd90ly4TGa31bHP5y/WH5PD/668SiGPD+JYAQAJynBCADYTf44yT2r6qFV9cdVdXmSdyZJVf2fgytV1Y9X1duXs3suXpbdo6peWlVXLe/9osMN1N3XJfmLJJ9fVfevqtcs731ZVd1l2earq+qXq+pAku/fsIm7JLl23fbetrynqurXq+o9VfXyqnpJVZ23vHZNVZ22PN5fVa9eHj+gql5XVW+uqj+tqnsvy8+vqsur6pVJXlFVt62qS6vqjcu65271N7aqHrmM8ablTK7bLcs/47Mv892f5NnLGUq33uo4AMCJ4eb8SxUAwLZZzqB5VJKXLovul+Ss7n7/hvUeleTcJA/s7k9W1Z2Xly5J8j3d/d6qemCS30xy9mHG+4IkX5DkA0lelOTc7r6+qr4tyX9L8sRl1VO7e/8mm/iNJM+rqqckeXmS3+nujyT5piT3TnKfJKdnLXhdeoSP/+4kX93dN1XVI5L8TJJvXvf78KXdfUNV/UySV3b3E6vqjkneWFUv7+5PbNjeParqLcvjP0nytCT/OckjuvsTVfXjSX6oqn42ya9t/OzL9p+S5Ee6+8AR5g4AnIAEIwBg1W69Lm78cZJnJHlwkjdujEWLR2QtznwySZaQcrvlPc+vqoPr3fIQ431bVX1Vkn9K8u+T7E1yVpIrl/eekuSj69Z/3mYb6e6XLdHpnKyFrjdX1VlJvibJc7r7U0k+spwddCR3SPLMqrpXkk7yOeteu7K7b1gePzLJN1TVjyzPb5XkzCTv2rC9v1guu0uSVNVjshaw/mT5jKcmeV3WwtbhPjsAcJISjACAVfvH9XEjSZZ4sfGsmcO5RZK/27idQ3hedz9l3VhfkuTq7v7KQ6x/yHksIed3k/xuVb04a7HocG7Kp28JcKt1y386yau6+5uqal+SVx9i/Eryzd39niOMs1FlLTw9biw88mcHAE5S7mEEABxvrkzyXQe/Ta2q7tzdH0/y/qr6lmVZVdWXbXF770myt6q+cnnv51TVFx/pTVV19ro53D7JPZJ8MMlrs3YW0ynLvZAetu5t1yS5//L4m9ctv0OSDy+Pzz/MsC9L8tRailpVffmR5rl4fZKHVNU9l/fdtqq+MIf/7P+Q5PZb3D4AcIIRjACA40p3vzTJ5UkOLJeyHbw86/FJnlRVb01yddbuc7SV7d2Y5LwkP7e89y1Zu7ztSO6/zOFtWbu86+nd/WdZux/Se7N276JnLa8d9G4NDtAAAACPSURBVJNJfmW5ifan1i3/+SQ/W1VvzuHPAP/prF2u9raqunp5fkTdfX3WQtRz1s33i47w2S9L8ttueg0AJ6fq7lXPAQDghFVVlyV5cXe/YNVzAQDYKmcYAQAAADA4wwgAAACAwRlGAAAAAAyCEQAAAACDYAQAAADAIBgBAAAAMAhGAAAAAAyCEQAAAADD/wfsLRmZrGEuSgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-mMls7DivV27",
        "colab_type": "text"
      },
      "source": [
        "### Using domain knowledge for outlier removal\n",
        "* If location and square foot area is aslo same then price of 3BHK should be more than 2 BHK\n",
        "* There are other factors that also affect the price but for this exercise we are treating such values as outlier and remove them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSrMbC5IzjCg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "5d46638c-966d-4a79-c0d7-46a37be3d75d"
      },
      "source": [
        "# Let's check if for a given location how does the 2 BHK and 3 BHK property prices look like\n",
        "def plot_scatter_chart(df,location):\n",
        "    bhk2 = df[(df.location==location) & (df.bhk==2)]\n",
        "    bhk3 = df[(df.location==location) & (df.bhk==3)]\n",
        "    matplotlib.rcParams['figure.figsize'] = (15,10)\n",
        "    plt.scatter(bhk2.total_sqft,bhk2.price,color='blue',label='2 BHK', s=50)\n",
        "    plt.scatter(bhk3.total_sqft,bhk3.price,marker='+', color='green',label='3 BHK', s=50)\n",
        "    plt.xlabel(\"Total Square Feet Area\")\n",
        "    plt.ylabel(\"Price (Lakh Indian Rupees)\")\n",
        "    plt.title(location)\n",
        "    plt.legend()\n",
        "    \n",
        "plot_scatter_chart(df10,\"Rajaji Nagar\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNUJFS0r0mWs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "4b318839-961a-4a14-a374-aec687562cdb"
      },
      "source": [
        "plot_scatter_chart(df10,\"Hebbal\")"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8NOcI1g2NSP",
        "colab_type": "text"
      },
      "source": [
        "We should also remove properties where for same location, the price of (for example) 3 bedroom apartment is less than 2 bedroom apartment (with same square ft area). What we will do is for a given location, we will build a dictionary by name 'bhk_stats' with below values of 'price_per_sqft'\n",
        "\n",
        "```\n",
        "{\n",
        "    '1' : {\n",
        "        'mean': 4000,\n",
        "        'std: 2000,\n",
        "        'count': 34\n",
        "    },\n",
        "    '2' : {\n",
        "        'mean': 4300,\n",
        "        'std: 2300,\n",
        "        'count': 22\n",
        "    },    \n",
        "}\n",
        "```\n",
        "Now we can remove those 2 BHK apartments whose price_per_sqft is less than mean price_per_sqft of 1 BHK apartment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VOD_FOY7lRX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6f9d37df-925c-4226-912c-1dabd749294d"
      },
      "source": [
        "def remove_bhk_outliers(df):\n",
        "    exclude_indices = np.array([])\n",
        "    for location, location_df in df.groupby('location'):\n",
        "        bhk_stats = {}\n",
        "        for bhk, bhk_df in location_df.groupby('bhk'):\n",
        "            bhk_stats[bhk] = {\n",
        "                'mean': np.mean(bhk_df.price_per_sqft),\n",
        "                'std': np.std(bhk_df.price_per_sqft),\n",
        "                'count': bhk_df.shape[0]\n",
        "            }\n",
        "        for bhk, bhk_df in location_df.groupby('bhk'):\n",
        "            stats = bhk_stats.get(bhk-1)\n",
        "            if stats and stats['count']>5:\n",
        "                exclude_indices = np.append(exclude_indices, bhk_df[bhk_df.price_per_sqft<(stats['mean'])].index.values)\n",
        "    return df.drop(exclude_indices,axis='index')\n",
        "\n",
        "df11 = remove_bhk_outliers(df10)\n",
        "df11.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7317, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQHWd6q4-oQe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "751c1d40-c2f2-4515-9d03-3608dd26db48"
      },
      "source": [
        "#Plot same scatter chart again to visualize price_per_sqft for 2 BHK and 3 BHK properties\n",
        "plot_scatter_chart(df11,\"Rajaji Nagar\")"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6PCCUjGU-ySo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "eb3ad822-0a44-42fe-f8a8-a75b020eb12d"
      },
      "source": [
        "plot_scatter_chart(df11,\"Hebbal\")"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oI-zALcd0lYW",
        "colab_type": "text"
      },
      "source": [
        "Now you can campre the scatter plots for location(Hebbal and Rajaji Nagar) for before and after outlier removal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZPrCXrz_yl5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "aa3bd946-1b90-4584-c5bf-1c84dd75e849"
      },
      "source": [
        "#Now lets plot the histogram and visualize the price_per_sqft data after outlier removal\n",
        "\n",
        "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
        "plt.hist(df11.price_per_sqft,rwidth=0.8)\n",
        "plt.xlabel(\"Price Per Square Feet\")\n",
        "plt.ylabel(\"Count\")"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAAAArMBcA1NVPS5Lcend3f3+afj+6atvmX4/MI3vSHLCzO7HT2PLjf8L3X15d2/q7k3r169f3T8EAAAAgGXN8ylyleQdSe7o7t+YWbUlya4nwZ2X5IMz46+cniZ3WpKvTF+luzbJ6VV15HRz79OnMQAAAAAOAuvmeOznJHlFkk9V1c3T2C8kuTTJVVX1miR3J3nZtO6aJGcn2Z7k60lenSTd/WBVvTnJjdN2b+ruB+c4bwAAAAD2w4oCU1U9p7v/Yl9js7r7z5PUMqtfsIftO8kFyxxrc5LNK5krAAAAAAfWSr8i91srHAMAAADgMWavVzBV1Q8meXaS9VX1MzOrvi3JYfOcGAAAAABrw76+Ind4kidN2z15ZvyrSV46r0kBAAAAsHbsNTB190eTfLSq3tnddx+gOQEAAACwhqz0KXKPr6rLk2yY3ae7nz+PSQEAAACwdqw0MP1ekv+d5P8keWR+0wEAAABgrVlpYHq4uy+b60wAAAAAWJO+ZYXb/UFV/XRVHVtVR+36mevMAAAAAFgTVnoF03nT75+fGesk37260wEAAABgrVlRYOruE+c9EQAAAADWphUFpqp65Z7Gu/tdqzsdAAAAANaalX5F7gdmlp+Q5AVJPpFEYAIAAAB4jFvpV+T+2+zrqjoiyZVzmREAAAAAa8pKnyK3u79P4r5MAAAAAKz4Hkx/kKWnxiXJYUn+bZKr5jUpAAAAANaOld6D6ddmlh9Ocnd33zuH+QAAAACwxqzoK3Ld/dEkn07y5CRHJvnGPCcFAAAAwNqxosBUVS9LckOSH03ysiQfr6qXznNiAAAAAKwNK/2K3H9P8gPd/UCSVNX6JB9O8r55TQwAAACAtWGlT5H7ll1xafLF/dgXAAAAgEPYSq9g+lBVXZvkPdPrH0tyzXymBAAAAMBastfAVFVPS3JMd/98Vf3nJM+dVv1VknfPe3IAAAAAHPz2dQXTbyZ5Y5J09/uTvD9JqurfTev+41xnBwAAAMBBb1/3UTqmuz+1++A0tmEuMwIAAABgTdlXYDpiL+ueuJoTAQAAAGBt2ldg2lZVP7n7YFX9RJKb5jMlAAAAANaSfd2D6fVJPlBV/zX/HJQ2JTk8yUvmOTEAAAAA1oa9Bqbuvj/Js6vqh5M8Yxq+urs/MveZAQAAALAm7OsKpiRJd1+f5Po5zwUAAACANWhf92ACAAAAgL0SmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYMrfAVFWbq+qBqrp1ZuwXq2pHVd08/Zw9s+6NVbW9qj5TVWfMjJ85jW2vqovmNV8AAAAAHp15XsH0ziRn7mH8Ld198vRzTZJU1UlJzk3y9Gmft1fVYVV1WJK3JTkryUlJXj5tCwAAAMBBYt28Dtzdf1pVG1a4+TlJruzuh5J8vqq2JzllWre9uz+XJFV15bTt7as8XQAAAAAepUXcg+nCqrpl+grdkdPYcUnumdnm3mlsuXEAAAAADhIHOjBdluSpSU5Ocl+SX1+tA1fV+VW1raq27dy5c7UOCwAAAMA+HNDA1N33d/cj3f2PSX47//w1uB1JTpjZ9PhpbLnxPR378u7e1N2b1q9fv/qTBwAAAGCPDmhgqqpjZ16+JMmuJ8xtSXJuVT2+qk5MsjHJDUluTLKxqk6sqsOzdCPwLQdyzgAAAADs3dxu8l1V70nyvCRHV9W9SS5O8ryqOjlJJ7kryWuTpLtvq6qrsnTz7oeTXNDdj0zHuTDJtUkOS7K5u2+b15wBAAAA2H/zfIrcy/cw/I69bH9Jkkv2MH5NkmtWcWoAAAAArKJFPEUOAAAAgEOIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYIjABAAAAMERgAgAAAGCIwAQAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmAAAAAAYMrfAVFWbq+qBqrp1ZuyoqtpaVXdOv4+cxquq3lpV26vqlqp61sw+503b31lV581rvgAAAAA8OvO8gumdSc7cbeyiJNd198Yk102vk+SsJBunn/OTXJYsBakkFyc5NckpSS7eFaUAAAAAODjMLTB1958meXC34XOSXDEtX5HkxTPj7+olH0tyRFUdm+SMJFu7+8Hu/lKSrfnmaAUAAADAAh3oezAd0933TctfSHLMtHxckntmtrt3GltuHAAAAICDxMJu8t3dnaRX63hVdX5VbauqbTt37lytwwIAAACwDwc6MN0/ffUt0+8HpvEdSU6Y2e74aWy58W/S3Zd396bu3rR+/fpVnzgAAAAAe3agA9OWJLueBHdekg/OjL9yeprcaUm+Mn2V7tokp1fVkdPNvU+fxgAAAAA4SKyb14Gr6j1Jnpfk6Kq6N0tPg7s0yVVV9Zokdyd52bT5NUnOTrI9ydeTvDpJuvvBqnpzkhun7d7U3bvfOBwAAACABZpbYOruly+z6gV72LaTXLDMcTYn2byKUwMAAABgFS3sJt8AAAAAHBoEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwZN2iJ8Debbjo6kVP4ZBx16UvWvQUAAAA4JDkCiYAAAAAhiwkMFXVXVX1qaq6uaq2TWNHVdXWqrpz+n3kNF5V9daq2l5Vt1TVsxYxZwAAAAD2bJFXMP1wd5/c3Zum1xclua67Nya5bnqdJGcl2Tj9nJ/ksgM+UwAAAACWdTB9Re6cJFdMy1ckefHM+Lt6yceSHFFVxy5iggAAAAB8s0UFpk7yx1V1U1WdP40d0933TctfSHLMtHxckntm9r13GgMAAADgILCop8g9t7t3VNW/TrK1qj49u7K7u6p6fw44harzk+QpT3nK6s0UAAAAgL1ayBVM3b1j+v1Akg8kOSXJ/bu++jb9fmDafEeSE2Z2P34a2/2Yl3f3pu7etH79+nlOHwAAAIAZBzwwVdW3VtWTdy0nOT3JrUm2JDlv2uy8JB+clrckeeX0NLnTknxl5qt0AAAAACzYIr4id0ySD1TVrvf/3e7+UFXdmOSqqnpNkruTvGza/pokZyfZnuTrSV594KcMAAAAwHIOeGDq7s8l+b49jH8xyQv2MN5JLjgAUwMAAADgUVjUU+QAAAAAOEQITAAAAAAMWcQ9mAB4jNtw0dWLnsIh465LX7ToKQAAgCuYAAAAABgjMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYIjABAAAAMCQdYueAABw8Nhw0dWLnsIh465LX7ToKQAAHDCuYAIAAABgiMAEAAAAwBCBCQAAAIAhAhMAAAAAQwQmAAAAAIYITAAAAAAMEZgAAAAAGCIwAQAAADBEYAIAAABgiMAEAAAAwBCBCQAAAIAhAhMAAAAAQwQmAAAAAIYITAAAAAAMEZgAAAAAGCIwAQAAADBEYAIAAABgiMAEAAAAwBCBCQAAAIAh6xY9AQAAVmbDRVcvegqHjLsufdGipwAAhxRXMAEAAAAwRGACAAAAYIjABAAAAMAQgQkAAACAIQITAAAAAEMEJgAAAACGCEwAAAAADBGYAAAAABgiMAEAAAAwRGACAAAAYMi6RU8AAADWug0XXb3oKRwy7rr0RYueAgCPgiuYAAAAABiyZgJTVZ1ZVZ+pqu1VddGi5wMAAADAkjXxFbmqOizJ25K8MMm9SW6sqi3dfftiZwYAABzsfIVx9fgKI7CctXIF0ylJtnf357r7G0muTHLOgucEAAAAQNbIFUxJjktyz8zre5OcuqC5AAAAsApcXbZ65nF1mf/7rJ7HwtV/1d2LnsM+VdVLk5zZ3T8xvX5FklO7+8KZbc5Pcv708nuSfOaATxTWtqOT/O2iJwGPQc49WAznHiyGcw8WY7XOve/q7vV7WrFWrmDakeSEmdfHT2P/pLsvT3L5gZwUHEqqalt3b1r0POCxxrkHi+Hcg8Vw7sFiHIhzb63cg+nGJBur6sSqOjzJuUm2LHhOAAAAAGSNXMHU3Q9X1YVJrk1yWJLN3X3bgqcFAAAAQNZIYEqS7r4myTWLngccwnzFFBbDuQeL4dyDxXDuwWLM/dxbEzf5BgAAAODgtVbuwQQAAADAQUpggkNYVd1VVZ+qqpurats0dlRVba2qO6ffR07jVVVvrartVXVLVT1r5jjnTdvfWVXnLervgYNVVW2uqgeq6taZsVU716rq+6dzefu0bx3YvxAOTsuce79YVTumz76bq+rsmXVvnM6jz1TVGTPjZ05j26vqopnxE6vq49P4e6eHzcBjXlWdUFXXV9XtVXVbVb1uGvfZB3O0l3PvoPjsE5jg0PfD3X3yzCMpL0pyXXdvTHLd9DpJzkqycfo5P8llydK/KCS5OMmpSU5JcvGuf1kA/sk7k5y529hqnmuXJfnJmf12fy94rHpn9nw+vGX67Dt5uo9nquqkLD2J+OnTPm+vqsOq6rAkb8vSuXlSkpdP2ybJr07HelqSLyV5zVz/Glg7Hk7ys919UpLTklwwnTc++2C+ljv3koPgs09ggseec5JcMS1fkeTFM+Pv6iUfS3JEVR2b5IwkW7v7we7+UpKt8QEP/0J3/2mSB3cbXpVzbVr3bd39sV66ceK7Zo4Fj2nLnHvLOSfJld39UHd/Psn2LP0H7SlJtnf357r7G0muTHLOdLXE85O8b0MxySIAAAeMSURBVNp/9jyGx7Tuvq+7PzEt/12SO5IcF599MFd7OfeWc0A/+wQmOLR1kj+uqpuq6vxp7Jjuvm9a/kKSY6bl45LcM7PvvdPYcuPA3q3WuXbctLz7OLC8C6ev4WyeuRpif8+970jy5e5+eLdxYEZVbUjyzCQfj88+OGB2O/eSg+CzT2CCQ9tzu/tZWbr08YKq+qHZldP/IuRRkjBnzjU4oC5L8tQkJye5L8mvL3Y6cOiqqicl+f0kr+/ur86u89kH87OHc++g+OwTmOAQ1t07pt8PJPlAli6FvH+67DjT7wemzXckOWFm9+OnseXGgb1brXNtx7S8+ziwB919f3c/0t3/mOS3s/TZl+z/uffFLH2NZ91u40CSqnpclv4D993d/f5p2GcfzNmezr2D5bNPYIJDVFV9a1U9eddyktOT3JpkS5JdT+g4L8kHp+UtSV45PeXjtCRfmS5xvjbJ6VV15HSp5enTGLB3q3KuTeu+WlWnTd+Lf+XMsYDd7PqP28lLsvTZlyyde+dW1eOr6sQs3TT4hiQ3Jtk4PTXn8CzdDHXLdPXF9UleOu0/ex7DY9r0efSOJHd092/MrPLZB3O03Ll3sHz2rdv3JsAadUySD0xPdF2X5He7+0NVdWOSq6rqNUnuTvKyaftrkpydpRu/fT3Jq5Okux+sqjdn6f8JJcmbunulN1SFx4Sqek+S5yU5uqruzdITcS7N6p1rP52lp2U9MckfTT/wmLfMufe8qjo5S1/NuSvJa5Oku2+rqquS3J6lp/Bc0N2PTMe5MEv/oXtYks3dfdv0Fm9IcmVV/XKST2bpX+qB5DlJXpHkU1V18zT2C/HZB/O23Ln38oPhs6+WAhUAAAAAPDq+IgcAAADAEIEJAAAAgCECEwAAAABDBCYAAAAAhghMAAAAAAwRmACANaWqHqmqm6vq1qr6var6V8ts95er8F6vqqqd0/vdXlU/OXCsY6rqD6vqr6djXTM6v9VQVRuq6h+mv3HXz+GP4jivqqrvnMccAYCDn8AEAKw1/9DdJ3f3M5J8I8lPza6sqnVJ0t3PXqX3e293n5zkeUl+paqOWclOu+Yx401Jtnb393X3SUkuWqX5rfT99+az0z/TXT/feBRv+aokAhMAPEYJTADAWvZnSZ5WVc+rqj+rqi1Jbk+Sqvraro2q6g1V9anp6qFLp7GnVtWHquqmad/v3dsbdfcDST6b5Luq6vur6qPTvtdW1bHTMf+kqn6zqrYled1uhzg2yb0zx7tl2qeq6n9V1Weq6sNVdU1VvXRad1dVHT0tb6qqP5mWT6mqv6qqT1bVX1bV90zjr6qqLVX1kSTXVdW3VtXmqrph2vaclf6DrarTp/f4xHSl2JOm8W/626f5bkry7ukKqCeu9H0AgEPD/vwvWwAAB43pCp2zknxoGnpWkmd09+d32+6sJOckObW7v15VR02rLk/yU919Z1WdmuTtSZ6/l/f77iTfneTuJB9Ick5376yqH0tySZIfnzY9vLs37eEQb0vy3qq6MMmHk/xOd/9Nkpck+Z4kJyU5JkuBbPM+/vxPJ/kP3f1wVf1Ikl9J8l9m/jn8++5+sKp+JclHuvvHq+qIJDdU1Ye7++93O95Tq+rmafkvklyc5H8k+ZHu/vuqekOSn6mq/5nkt3b/26fjX5jk57p72z7mDgAcggQmAGCteeJMDPmzJO9I8uwkN+welyY/kqWY8/UkmcLLk6Z9fq+qdm33+GXe78eq6rlJHkry2iTrkzwjydZp38OS3Dez/Xv3dJDuvnaKVGdmKYx9sqqekeSHkrynux9J8jfT1Uf78u1JrqiqjUk6yeNm1m3t7gen5dOT/Keq+rnp9ROSPCXJHbsd77PT1wCT/P/27uDFxiiM4/j3R6Q0KWWvSEqSZjVZsbbRsJAN2fJHaBZiZ2dhMVmMlJVmQRakRFGYmsVkQcrKRiyU6LF4z+Q1de+dehcy8/2s3nvuPed9zvbXOc+FJCfpAq9nbY/bged0Qdi4vUuSpE3KgEmSJP1vvvfDEIAWdqw9lTPOFuDL2nVGuFtVl3rvOgwsV9XMiN+PrKMFPwvAQpJFunBpnJ/8aWmwozc+BzyuqlNJ9gJPRrw/wGxVrUx4z1qhC6rO/jU4ee+SJGmTsgeTJEna6B4BF1b/bS7J7qr6CrxPcqaNJcmRda63AuxJMtPmbktyaNKkJCd6NUwB+4CPwFO6U1JbWy+n471pH4Dp9jzbG98FfGrP58e89iFwOS2BS3J0Up3NC+BYkv1t3s4kBxi/92/A1DrXlyRJG4wBkyRJ2tCq6gFwH3jVrtatXhc7B1xM8hZYpuvTtJ71fgCngWtt7hu663aTTLcaluium92qqpd0/Zze0fVeut2+W3UFuNGahv/qjV8HriZ5zfgT6XN01+eWkiy3zxNV1We64OpOr96DE/Y+D9y0ybckSZtTqupf1yBJkqQmyTywWFX3/nUtkiRJ6+UJJkmSJEmSJA3iCSZJkiRJkiQN4gkmSZIkSZIkDWLAJEmSJEmSpEEMmCRJkiRJkjSIAZMkSZIkSZIGMWCSJEmSJEnSIAZMkiRJkiRJGuQ3Ga3QdQCerMcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdBgKMs7BAGs",
        "colab_type": "text"
      },
      "source": [
        "### Using domain knowledge for outlier removal\n",
        "* Generally number of bathrooms per BHK are (no of BHK) + 2.\n",
        "* So using above understanding we can identify the outliers and remove them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xw7Lb7UQBl67",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c17942df-b175-4d72-dbed-6b8f7a3b536b"
      },
      "source": [
        "#Get unique bath from dataset\n",
        "df11.bath.unique()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZShBzKwIBti0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 169
        },
        "outputId": "cde91c42-acf1-4956-8b54-b9d9f3b3332b"
      },
      "source": [
        "#Get the training examples where no of bath are more than (no of BHK +2)\n",
        "df11[df11.bath > df11.bhk + 2]"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1626</th>\n",
              "      <td>Chikkabanavar</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2460.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3252.032520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5238</th>\n",
              "      <td>Nagasandra</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6428.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6711</th>\n",
              "      <td>Thanisandra</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1806.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6423.034330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8408</th>\n",
              "      <td>other</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>11338.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>6</td>\n",
              "      <td>8819.897689</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           location       size  total_sqft  bath   price  bhk  price_per_sqft\n",
              "1626  Chikkabanavar  4 Bedroom      2460.0   7.0    80.0    4     3252.032520\n",
              "5238     Nagasandra  4 Bedroom      7000.0   8.0   450.0    4     6428.571429\n",
              "6711    Thanisandra      3 BHK      1806.0   6.0   116.0    3     6423.034330\n",
              "8408          other      6 BHK     11338.0   9.0  1000.0    6     8819.897689"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gU3D64vpCwVU",
        "colab_type": "text"
      },
      "source": [
        "We can remove above outliers from the datset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIbbmyQiC0vv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dfada750-c914-42cc-a581-b719be19d960"
      },
      "source": [
        "#Lets check current dataset shape before removing outliers\n",
        "df11.shape"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7317, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mm_wgRkEDE56",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9ccf862c-0ac0-4a90-e742-491184963971"
      },
      "source": [
        "# Remove the outliers with more than (no of BHK + 2) bathrooms\n",
        "df12 = df11[df11.bath < (df11.bhk + 2)]\n",
        "df12.shape"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7239, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Do-rIVsADy8L",
        "colab_type": "text"
      },
      "source": [
        "This concludes our data cleaning, lets drop unnecessary columns\n",
        "* Since we have 'bhk' feature lets drop 'size'\n",
        "* We have crerated 'price_per_sqft' for outlier detection and removal purpose, so we can also drop it. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBKnsIAmEait",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "b0e35dff-5d99-4d0b-da7c-d0f2cfb7a9ef"
      },
      "source": [
        "df13 = df11.drop(['size', 'price_per_sqft'], axis='columns')\n",
        "df13.head()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>2850.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>428.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1630.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1875.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1235.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              location  total_sqft  bath  price  bhk\n",
              "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
              "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
              "2  1st Block Jayanagar      1875.0   2.0  235.0    3\n",
              "3  1st Block Jayanagar      1200.0   2.0  130.0    3\n",
              "4  1st Block Jayanagar      1235.0   2.0  148.0    2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5R74eegloVYN",
        "colab_type": "text"
      },
      "source": [
        "## One Hot Encoding\n",
        "Since we have 'location' as categorical feature lets use One Hot Encoding to create separate column for each location category and assign binary value 1 or 0"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7IOMh4-epHUO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "3ad71554-3143-42a6-c09b-59f1b1a1693c"
      },
      "source": [
        "dummies = pd.get_dummies(df13.location)\n",
        "dummies.head()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>1st Block Jayanagar</th>\n",
              "      <th>1st Phase JP Nagar</th>\n",
              "      <th>2nd Phase Judicial Layout</th>\n",
              "      <th>2nd Stage Nagarbhavi</th>\n",
              "      <th>5th Block Hbr Layout</th>\n",
              "      <th>5th Phase JP Nagar</th>\n",
              "      <th>6th Phase JP Nagar</th>\n",
              "      <th>7th Phase JP Nagar</th>\n",
              "      <th>8th Phase JP Nagar</th>\n",
              "      <th>9th Phase JP Nagar</th>\n",
              "      <th>AECS Layout</th>\n",
              "      <th>Abbigere</th>\n",
              "      <th>Akshaya Nagar</th>\n",
              "      <th>Ambalipura</th>\n",
              "      <th>Ambedkar Nagar</th>\n",
              "      <th>Amruthahalli</th>\n",
              "      <th>Anandapura</th>\n",
              "      <th>Ananth Nagar</th>\n",
              "      <th>Anekal</th>\n",
              "      <th>Anjanapura</th>\n",
              "      <th>Ardendale</th>\n",
              "      <th>Arekere</th>\n",
              "      <th>Attibele</th>\n",
              "      <th>BEML Layout</th>\n",
              "      <th>BTM 2nd Stage</th>\n",
              "      <th>BTM Layout</th>\n",
              "      <th>Babusapalaya</th>\n",
              "      <th>Badavala Nagar</th>\n",
              "      <th>Balagere</th>\n",
              "      <th>Banashankari</th>\n",
              "      <th>Banashankari Stage II</th>\n",
              "      <th>Banashankari Stage III</th>\n",
              "      <th>Banashankari Stage V</th>\n",
              "      <th>Banashankari Stage VI</th>\n",
              "      <th>Banaswadi</th>\n",
              "      <th>Banjara Layout</th>\n",
              "      <th>Bannerghatta</th>\n",
              "      <th>Bannerghatta Road</th>\n",
              "      <th>Basavangudi</th>\n",
              "      <th>Basaveshwara Nagar</th>\n",
              "      <th>...</th>\n",
              "      <th>Sanjay nagar</th>\n",
              "      <th>Sarakki Nagar</th>\n",
              "      <th>Sarjapur</th>\n",
              "      <th>Sarjapur  Road</th>\n",
              "      <th>Sarjapura - Attibele Road</th>\n",
              "      <th>Sector 2 HSR Layout</th>\n",
              "      <th>Sector 7 HSR Layout</th>\n",
              "      <th>Seegehalli</th>\n",
              "      <th>Shampura</th>\n",
              "      <th>Shivaji Nagar</th>\n",
              "      <th>Singasandra</th>\n",
              "      <th>Somasundara Palya</th>\n",
              "      <th>Sompura</th>\n",
              "      <th>Sonnenahalli</th>\n",
              "      <th>Subramanyapura</th>\n",
              "      <th>Sultan Palaya</th>\n",
              "      <th>TC Palaya</th>\n",
              "      <th>Talaghattapura</th>\n",
              "      <th>Thanisandra</th>\n",
              "      <th>Thigalarapalya</th>\n",
              "      <th>Thubarahalli</th>\n",
              "      <th>Tindlu</th>\n",
              "      <th>Tumkur Road</th>\n",
              "      <th>Ulsoor</th>\n",
              "      <th>Uttarahalli</th>\n",
              "      <th>Varthur</th>\n",
              "      <th>Varthur Road</th>\n",
              "      <th>Vasanthapura</th>\n",
              "      <th>Vidyaranyapura</th>\n",
              "      <th>Vijayanagar</th>\n",
              "      <th>Vishveshwarya Layout</th>\n",
              "      <th>Vishwapriya Layout</th>\n",
              "      <th>Vittasandra</th>\n",
              "      <th>Whitefield</th>\n",
              "      <th>Yelachenahalli</th>\n",
              "      <th>Yelahanka</th>\n",
              "      <th>Yelahanka New Town</th>\n",
              "      <th>Yelenahalli</th>\n",
              "      <th>Yeshwanthpur</th>\n",
              "      <th>other</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 241 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   1st Block Jayanagar  1st Phase JP Nagar  ...  Yeshwanthpur  other\n",
              "0                    1                   0  ...             0      0\n",
              "1                    1                   0  ...             0      0\n",
              "2                    1                   0  ...             0      0\n",
              "3                    1                   0  ...             0      0\n",
              "4                    1                   0  ...             0      0\n",
              "\n",
              "[5 rows x 241 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYNOISttp7KR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "50b6011f-5c34-40bf-8530-bab038629460"
      },
      "source": [
        "#To avoid dummy variable trap problem lets delete the one of the dummy variable column\n",
        "dummies = dummies.drop(['other'],axis='columns')\n",
        "dummies.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>1st Block Jayanagar</th>\n",
              "      <th>1st Phase JP Nagar</th>\n",
              "      <th>2nd Phase Judicial Layout</th>\n",
              "      <th>2nd Stage Nagarbhavi</th>\n",
              "      <th>5th Block Hbr Layout</th>\n",
              "      <th>5th Phase JP Nagar</th>\n",
              "      <th>6th Phase JP Nagar</th>\n",
              "      <th>7th Phase JP Nagar</th>\n",
              "      <th>8th Phase JP Nagar</th>\n",
              "      <th>9th Phase JP Nagar</th>\n",
              "      <th>AECS Layout</th>\n",
              "      <th>Abbigere</th>\n",
              "      <th>Akshaya Nagar</th>\n",
              "      <th>Ambalipura</th>\n",
              "      <th>Ambedkar Nagar</th>\n",
              "      <th>Amruthahalli</th>\n",
              "      <th>Anandapura</th>\n",
              "      <th>Ananth Nagar</th>\n",
              "      <th>Anekal</th>\n",
              "      <th>Anjanapura</th>\n",
              "      <th>Ardendale</th>\n",
              "      <th>Arekere</th>\n",
              "      <th>Attibele</th>\n",
              "      <th>BEML Layout</th>\n",
              "      <th>BTM 2nd Stage</th>\n",
              "      <th>BTM Layout</th>\n",
              "      <th>Babusapalaya</th>\n",
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              "      <th>Banashankari Stage II</th>\n",
              "      <th>Banashankari Stage III</th>\n",
              "      <th>Banashankari Stage V</th>\n",
              "      <th>Banashankari Stage VI</th>\n",
              "      <th>Banaswadi</th>\n",
              "      <th>Banjara Layout</th>\n",
              "      <th>Bannerghatta</th>\n",
              "      <th>Bannerghatta Road</th>\n",
              "      <th>Basavangudi</th>\n",
              "      <th>Basaveshwara Nagar</th>\n",
              "      <th>...</th>\n",
              "      <th>Sahakara Nagar</th>\n",
              "      <th>Sanjay nagar</th>\n",
              "      <th>Sarakki Nagar</th>\n",
              "      <th>Sarjapur</th>\n",
              "      <th>Sarjapur  Road</th>\n",
              "      <th>Sarjapura - Attibele Road</th>\n",
              "      <th>Sector 2 HSR Layout</th>\n",
              "      <th>Sector 7 HSR Layout</th>\n",
              "      <th>Seegehalli</th>\n",
              "      <th>Shampura</th>\n",
              "      <th>Shivaji Nagar</th>\n",
              "      <th>Singasandra</th>\n",
              "      <th>Somasundara Palya</th>\n",
              "      <th>Sompura</th>\n",
              "      <th>Sonnenahalli</th>\n",
              "      <th>Subramanyapura</th>\n",
              "      <th>Sultan Palaya</th>\n",
              "      <th>TC Palaya</th>\n",
              "      <th>Talaghattapura</th>\n",
              "      <th>Thanisandra</th>\n",
              "      <th>Thigalarapalya</th>\n",
              "      <th>Thubarahalli</th>\n",
              "      <th>Tindlu</th>\n",
              "      <th>Tumkur Road</th>\n",
              "      <th>Ulsoor</th>\n",
              "      <th>Uttarahalli</th>\n",
              "      <th>Varthur</th>\n",
              "      <th>Varthur Road</th>\n",
              "      <th>Vasanthapura</th>\n",
              "      <th>Vidyaranyapura</th>\n",
              "      <th>Vijayanagar</th>\n",
              "      <th>Vishveshwarya Layout</th>\n",
              "      <th>Vishwapriya Layout</th>\n",
              "      <th>Vittasandra</th>\n",
              "      <th>Whitefield</th>\n",
              "      <th>Yelachenahalli</th>\n",
              "      <th>Yelahanka</th>\n",
              "      <th>Yelahanka New Town</th>\n",
              "      <th>Yelenahalli</th>\n",
              "      <th>Yeshwanthpur</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 240 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   1st Block Jayanagar  1st Phase JP Nagar  ...  Yelenahalli  Yeshwanthpur\n",
              "0                    1                   0  ...            0             0\n",
              "1                    1                   0  ...            0             0\n",
              "2                    1                   0  ...            0             0\n",
              "3                    1                   0  ...            0             0\n",
              "4                    1                   0  ...            0             0\n",
              "\n",
              "[5 rows x 240 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTM0__vcqVk3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "outputId": "34127e6f-a5d2-4d69-939f-fd013919df98"
      },
      "source": [
        "#Now lets add dummies dataframe to original dataframe\n",
        "df14 = pd.concat([df13,dummies],axis='columns')\n",
        "df14.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "      <th>1st Block Jayanagar</th>\n",
              "      <th>1st Phase JP Nagar</th>\n",
              "      <th>2nd Phase Judicial Layout</th>\n",
              "      <th>2nd Stage Nagarbhavi</th>\n",
              "      <th>5th Block Hbr Layout</th>\n",
              "      <th>5th Phase JP Nagar</th>\n",
              "      <th>6th Phase JP Nagar</th>\n",
              "      <th>7th Phase JP Nagar</th>\n",
              "      <th>8th Phase JP Nagar</th>\n",
              "      <th>9th Phase JP Nagar</th>\n",
              "      <th>AECS Layout</th>\n",
              "      <th>Abbigere</th>\n",
              "      <th>Akshaya Nagar</th>\n",
              "      <th>Ambalipura</th>\n",
              "      <th>Ambedkar Nagar</th>\n",
              "      <th>Amruthahalli</th>\n",
              "      <th>Anandapura</th>\n",
              "      <th>Ananth Nagar</th>\n",
              "      <th>Anekal</th>\n",
              "      <th>Anjanapura</th>\n",
              "      <th>Ardendale</th>\n",
              "      <th>Arekere</th>\n",
              "      <th>Attibele</th>\n",
              "      <th>BEML Layout</th>\n",
              "      <th>BTM 2nd Stage</th>\n",
              "      <th>BTM Layout</th>\n",
              "      <th>Babusapalaya</th>\n",
              "      <th>Badavala Nagar</th>\n",
              "      <th>Balagere</th>\n",
              "      <th>Banashankari</th>\n",
              "      <th>Banashankari Stage II</th>\n",
              "      <th>Banashankari Stage III</th>\n",
              "      <th>Banashankari Stage V</th>\n",
              "      <th>Banashankari Stage VI</th>\n",
              "      <th>Banaswadi</th>\n",
              "      <th>...</th>\n",
              "      <th>Sahakara Nagar</th>\n",
              "      <th>Sanjay nagar</th>\n",
              "      <th>Sarakki Nagar</th>\n",
              "      <th>Sarjapur</th>\n",
              "      <th>Sarjapur  Road</th>\n",
              "      <th>Sarjapura - Attibele Road</th>\n",
              "      <th>Sector 2 HSR Layout</th>\n",
              "      <th>Sector 7 HSR Layout</th>\n",
              "      <th>Seegehalli</th>\n",
              "      <th>Shampura</th>\n",
              "      <th>Shivaji Nagar</th>\n",
              "      <th>Singasandra</th>\n",
              "      <th>Somasundara Palya</th>\n",
              "      <th>Sompura</th>\n",
              "      <th>Sonnenahalli</th>\n",
              "      <th>Subramanyapura</th>\n",
              "      <th>Sultan Palaya</th>\n",
              "      <th>TC Palaya</th>\n",
              "      <th>Talaghattapura</th>\n",
              "      <th>Thanisandra</th>\n",
              "      <th>Thigalarapalya</th>\n",
              "      <th>Thubarahalli</th>\n",
              "      <th>Tindlu</th>\n",
              "      <th>Tumkur Road</th>\n",
              "      <th>Ulsoor</th>\n",
              "      <th>Uttarahalli</th>\n",
              "      <th>Varthur</th>\n",
              "      <th>Varthur Road</th>\n",
              "      <th>Vasanthapura</th>\n",
              "      <th>Vidyaranyapura</th>\n",
              "      <th>Vijayanagar</th>\n",
              "      <th>Vishveshwarya Layout</th>\n",
              "      <th>Vishwapriya Layout</th>\n",
              "      <th>Vittasandra</th>\n",
              "      <th>Whitefield</th>\n",
              "      <th>Yelachenahalli</th>\n",
              "      <th>Yelahanka</th>\n",
              "      <th>Yelahanka New Town</th>\n",
              "      <th>Yelenahalli</th>\n",
              "      <th>Yeshwanthpur</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>2850.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>428.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1630.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>194.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1875.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>235.0</td>\n",
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              "    </tr>\n",
              "    <tr>\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>4</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1235.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>2</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",
              "      <td>0</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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 × 245 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              location  total_sqft  ...  Yelenahalli  Yeshwanthpur\n",
              "0  1st Block Jayanagar      2850.0  ...            0             0\n",
              "1  1st Block Jayanagar      1630.0  ...            0             0\n",
              "2  1st Block Jayanagar      1875.0  ...            0             0\n",
              "3  1st Block Jayanagar      1200.0  ...            0             0\n",
              "4  1st Block Jayanagar      1235.0  ...            0             0\n",
              "\n",
              "[5 rows x 245 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5vEewOUaqyS8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "45851ec1-2a40-46e2-d8eb-4424d78f6c11"
      },
      "source": [
        "#Lets delete the location feature\n",
        "df15 = df14.drop(['location'],axis='columns')\n",
        "df15.head()m"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>bhk</th>\n",
              "      <th>1st Block Jayanagar</th>\n",
              "      <th>1st Phase JP Nagar</th>\n",
              "      <th>2nd Phase Judicial Layout</th>\n",
              "      <th>2nd Stage Nagarbhavi</th>\n",
              "      <th>5th Block Hbr Layout</th>\n",
              "      <th>5th Phase JP Nagar</th>\n",
              "      <th>6th Phase JP Nagar</th>\n",
              "      <th>7th Phase JP Nagar</th>\n",
              "      <th>8th Phase JP Nagar</th>\n",
              "      <th>9th Phase JP Nagar</th>\n",
              "      <th>AECS Layout</th>\n",
              "      <th>Abbigere</th>\n",
              "      <th>Akshaya Nagar</th>\n",
              "      <th>Ambalipura</th>\n",
              "      <th>Ambedkar Nagar</th>\n",
              "      <th>Amruthahalli</th>\n",
              "      <th>Anandapura</th>\n",
              "      <th>Ananth Nagar</th>\n",
              "      <th>Anekal</th>\n",
              "      <th>Anjanapura</th>\n",
              "      <th>Ardendale</th>\n",
              "      <th>Arekere</th>\n",
              "      <th>Attibele</th>\n",
              "      <th>BEML Layout</th>\n",
              "      <th>BTM 2nd Stage</th>\n",
              "      <th>BTM Layout</th>\n",
              "      <th>Babusapalaya</th>\n",
              "      <th>Badavala Nagar</th>\n",
              "      <th>Balagere</th>\n",
              "      <th>Banashankari</th>\n",
              "      <th>Banashankari Stage II</th>\n",
              "      <th>Banashankari Stage III</th>\n",
              "      <th>Banashankari Stage V</th>\n",
              "      <th>Banashankari Stage VI</th>\n",
              "      <th>Banaswadi</th>\n",
              "      <th>Banjara Layout</th>\n",
              "      <th>...</th>\n",
              "      <th>Sahakara Nagar</th>\n",
              "      <th>Sanjay nagar</th>\n",
              "      <th>Sarakki Nagar</th>\n",
              "      <th>Sarjapur</th>\n",
              "      <th>Sarjapur  Road</th>\n",
              "      <th>Sarjapura - Attibele Road</th>\n",
              "      <th>Sector 2 HSR Layout</th>\n",
              "      <th>Sector 7 HSR Layout</th>\n",
              "      <th>Seegehalli</th>\n",
              "      <th>Shampura</th>\n",
              "      <th>Shivaji Nagar</th>\n",
              "      <th>Singasandra</th>\n",
              "      <th>Somasundara Palya</th>\n",
              "      <th>Sompura</th>\n",
              "      <th>Sonnenahalli</th>\n",
              "      <th>Subramanyapura</th>\n",
              "      <th>Sultan Palaya</th>\n",
              "      <th>TC Palaya</th>\n",
              "      <th>Talaghattapura</th>\n",
              "      <th>Thanisandra</th>\n",
              "      <th>Thigalarapalya</th>\n",
              "      <th>Thubarahalli</th>\n",
              "      <th>Tindlu</th>\n",
              "      <th>Tumkur Road</th>\n",
              "      <th>Ulsoor</th>\n",
              "      <th>Uttarahalli</th>\n",
              "      <th>Varthur</th>\n",
              "      <th>Varthur Road</th>\n",
              "      <th>Vasanthapura</th>\n",
              "      <th>Vidyaranyapura</th>\n",
              "      <th>Vijayanagar</th>\n",
              "      <th>Vishveshwarya Layout</th>\n",
              "      <th>Vishwapriya Layout</th>\n",
              "      <th>Vittasandra</th>\n",
              "      <th>Whitefield</th>\n",
              "      <th>Yelachenahalli</th>\n",
              "      <th>Yelahanka</th>\n",
              "      <th>Yelahanka New Town</th>\n",
              "      <th>Yelenahalli</th>\n",
              "      <th>Yeshwanthpur</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2850.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>428.0</td>\n",
              "      <td>4</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1630.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1875.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</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",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1235.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>2</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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 × 244 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   total_sqft  bath  price  ...  Yelahanka New Town  Yelenahalli  Yeshwanthpur\n",
              "0      2850.0   4.0  428.0  ...                   0            0             0\n",
              "1      1630.0   3.0  194.0  ...                   0            0             0\n",
              "2      1875.0   2.0  235.0  ...                   0            0             0\n",
              "3      1200.0   2.0  130.0  ...                   0            0             0\n",
              "4      1235.0   2.0  148.0  ...                   0            0             0\n",
              "\n",
              "[5 rows x 244 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PC8aQIBurVmI",
        "colab_type": "text"
      },
      "source": [
        "#Step#5: Build Machine Learning Model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82pAaf4Hsba6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a7a0ff0a-5f70-4686-a52c-ca7aa8932174"
      },
      "source": [
        "#Final shape of our dataset is\n",
        "df15.shape"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7317, 244)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpZX-foEsrQj",
        "colab_type": "text"
      },
      "source": [
        "Now leats create X(independent variable/features) and y(dependent variables/target)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqPv14uPs1il",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "30de664c-8639-4776-c588-a7fff9951532"
      },
      "source": [
        "X = df15.drop(['price'],axis='columns')\n",
        "X.head()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>bhk</th>\n",
              "      <th>1st Block Jayanagar</th>\n",
              "      <th>1st Phase JP Nagar</th>\n",
              "      <th>2nd Phase Judicial Layout</th>\n",
              "      <th>2nd Stage Nagarbhavi</th>\n",
              "      <th>5th Block Hbr Layout</th>\n",
              "      <th>5th Phase JP Nagar</th>\n",
              "      <th>6th Phase JP Nagar</th>\n",
              "      <th>7th Phase JP Nagar</th>\n",
              "      <th>8th Phase JP Nagar</th>\n",
              "      <th>9th Phase JP Nagar</th>\n",
              "      <th>AECS Layout</th>\n",
              "      <th>Abbigere</th>\n",
              "      <th>Akshaya Nagar</th>\n",
              "      <th>Ambalipura</th>\n",
              "      <th>Ambedkar Nagar</th>\n",
              "      <th>Amruthahalli</th>\n",
              "      <th>Anandapura</th>\n",
              "      <th>Ananth Nagar</th>\n",
              "      <th>Anekal</th>\n",
              "      <th>Anjanapura</th>\n",
              "      <th>Ardendale</th>\n",
              "      <th>Arekere</th>\n",
              "      <th>Attibele</th>\n",
              "      <th>BEML Layout</th>\n",
              "      <th>BTM 2nd Stage</th>\n",
              "      <th>BTM Layout</th>\n",
              "      <th>Babusapalaya</th>\n",
              "      <th>Badavala Nagar</th>\n",
              "      <th>Balagere</th>\n",
              "      <th>Banashankari</th>\n",
              "      <th>Banashankari Stage II</th>\n",
              "      <th>Banashankari Stage III</th>\n",
              "      <th>Banashankari Stage V</th>\n",
              "      <th>Banashankari Stage VI</th>\n",
              "      <th>Banaswadi</th>\n",
              "      <th>Banjara Layout</th>\n",
              "      <th>Bannerghatta</th>\n",
              "      <th>...</th>\n",
              "      <th>Sahakara Nagar</th>\n",
              "      <th>Sanjay nagar</th>\n",
              "      <th>Sarakki Nagar</th>\n",
              "      <th>Sarjapur</th>\n",
              "      <th>Sarjapur  Road</th>\n",
              "      <th>Sarjapura - Attibele Road</th>\n",
              "      <th>Sector 2 HSR Layout</th>\n",
              "      <th>Sector 7 HSR Layout</th>\n",
              "      <th>Seegehalli</th>\n",
              "      <th>Shampura</th>\n",
              "      <th>Shivaji Nagar</th>\n",
              "      <th>Singasandra</th>\n",
              "      <th>Somasundara Palya</th>\n",
              "      <th>Sompura</th>\n",
              "      <th>Sonnenahalli</th>\n",
              "      <th>Subramanyapura</th>\n",
              "      <th>Sultan Palaya</th>\n",
              "      <th>TC Palaya</th>\n",
              "      <th>Talaghattapura</th>\n",
              "      <th>Thanisandra</th>\n",
              "      <th>Thigalarapalya</th>\n",
              "      <th>Thubarahalli</th>\n",
              "      <th>Tindlu</th>\n",
              "      <th>Tumkur Road</th>\n",
              "      <th>Ulsoor</th>\n",
              "      <th>Uttarahalli</th>\n",
              "      <th>Varthur</th>\n",
              "      <th>Varthur Road</th>\n",
              "      <th>Vasanthapura</th>\n",
              "      <th>Vidyaranyapura</th>\n",
              "      <th>Vijayanagar</th>\n",
              "      <th>Vishveshwarya Layout</th>\n",
              "      <th>Vishwapriya Layout</th>\n",
              "      <th>Vittasandra</th>\n",
              "      <th>Whitefield</th>\n",
              "      <th>Yelachenahalli</th>\n",
              "      <th>Yelahanka</th>\n",
              "      <th>Yelahanka New Town</th>\n",
              "      <th>Yelenahalli</th>\n",
              "      <th>Yeshwanthpur</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2850.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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>4</th>\n",
              "      <td>1235.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</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",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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 × 243 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   total_sqft  bath  bhk  ...  Yelahanka New Town  Yelenahalli  Yeshwanthpur\n",
              "0      2850.0   4.0    4  ...                   0            0             0\n",
              "1      1630.0   3.0    3  ...                   0            0             0\n",
              "2      1875.0   2.0    3  ...                   0            0             0\n",
              "3      1200.0   2.0    3  ...                   0            0             0\n",
              "4      1235.0   2.0    2  ...                   0            0             0\n",
              "\n",
              "[5 rows x 243 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vjC9gdomtCxN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "9b86ce8c-f650-42cf-d49d-0d14330bcf83"
      },
      "source": [
        "y = df15.price\n",
        "y.head()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    428.0\n",
              "1    194.0\n",
              "2    235.0\n",
              "3    130.0\n",
              "4    148.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZhM3vTStJa8",
        "colab_type": "text"
      },
      "source": [
        "### Split the dataset to training andtest dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkciNGFbtNXY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "43ff7e40-fe39-46a2-a41e-9b965c37d41d"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=10)\n",
        "print('X_train shape = ',X_train.shape)\n",
        "print('X_test shape = ',X_test.shape)\n",
        "print('y_train shape = ',y_train.shape)\n",
        "print('y_test shape = ',y_test.shape)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape =  (5853, 243)\n",
            "X_test shape =  (1464, 243)\n",
            "y_train shape =  (5853,)\n",
            "y_test shape =  (1464,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pBwpmXGxu5kD",
        "colab_type": "text"
      },
      "source": [
        "## Linear Regression\n",
        "* Lets test the score with LinearRegression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ku3rg58tu_yI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "44476057-3321-44fa-f400-c85aced8ffa8"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lr_clf = LinearRegression()\n",
        "lr_clf.fit(X_train, y_train)\n",
        "lr_clf.score(X_test, y_test)"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8175889965469311"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56UOdMFqxk2y",
        "colab_type": "text"
      },
      "source": [
        "### Use K Fold cross validation to measure accuracy of our LinearRegression model\n",
        "* Using Sklearn cross_val_score function\n",
        "* Note: Sklearn's cross_val_score uses StratifiedKFold by default"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7fFdWgOxnMW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c01677e-b3bc-457b-8f96-bd724dcde122"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import ShuffleSplit\n",
        "\n",
        "# ShuffleSplit is used to randomize the each fold\n",
        "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
        "cross_val_score(LinearRegression(), X, y, cv = cv)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.83458131, 0.8720703 , 0.77477801, 0.86043362, 0.87857309])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQToFn4fzBFH",
        "colab_type": "text"
      },
      "source": [
        "## GridSearchCV\n",
        "* From above scores its clear that with LinearRegresion we get max score of upto 87%\n",
        "* Let use GridSearchCV to test other regression algorithm "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SpqNhyv5_aA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "94dcbb7e-6d44-4b1d-8cb6-05c3d647740f"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from sklearn.linear_model import ElasticNet\n",
        "\n",
        "def find_best_model_using_gridsearchcv(X,y):\n",
        "  algos ={\n",
        "      'linear_regression':{\n",
        "          'model':LinearRegression(),\n",
        "          'params': {\n",
        "              'normalize':[True,False]\n",
        "          }\n",
        "      },\n",
        "        'lasso': {\n",
        "            'model': Lasso(),\n",
        "            'params': {\n",
        "                'alpha': [1,2],\n",
        "                'selection': ['random', 'cyclic']\n",
        "            }\n",
        "        },\n",
        "        'decision_tree': {\n",
        "            'model': DecisionTreeRegressor(),\n",
        "            'params': {\n",
        "                'criterion' : ['mse','friedman_mse'],\n",
        "                'splitter': ['best','random']\n",
        "            }\n",
        "        }\n",
        "  }\n",
        "\n",
        "  scores= []\n",
        "  cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
        "\n",
        "  for algo_name, config in algos.items():\n",
        "      gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
        "      gs.fit(X,y)\n",
        "      scores.append({\n",
        "          'model': algo_name,\n",
        "          'best_score': gs.best_score_,\n",
        "          'best_params': gs.best_params_\n",
        "      })\n",
        "\n",
        "  return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
        "\n",
        "find_best_model_using_gridsearchcv(X,y)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>model</th>\n",
              "      <th>best_score</th>\n",
              "      <th>best_params</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>linear_regression</td>\n",
              "      <td>0.844087</td>\n",
              "      <td>{'normalize': False}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>lasso</td>\n",
              "      <td>0.710569</td>\n",
              "      <td>{'alpha': 1, 'selection': 'cyclic'}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>decision_tree</td>\n",
              "      <td>0.668973</td>\n",
              "      <td>{'criterion': 'friedman_mse', 'splitter': 'ran...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               model  ...                                        best_params\n",
              "0  linear_regression  ...                               {'normalize': False}\n",
              "1              lasso  ...                {'alpha': 1, 'selection': 'cyclic'}\n",
              "2      decision_tree  ...  {'criterion': 'friedman_mse', 'splitter': 'ran...\n",
              "\n",
              "[3 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xGINloUk9im4",
        "colab_type": "text"
      },
      "source": [
        "**Based on above results we can say that LinearRegression gives the best score. Hence we will use that**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bu2_9OSd-bp1",
        "colab_type": "text"
      },
      "source": [
        "## Step#6: Testing The model\n",
        "* Since all our locations are now columns in form of dummy variabales, all other dummy variables value should be 0 except the one(dummy variable column for our location) we are predicting for\n",
        "* This(np.where(X.columns==location)[0][0]) code will give us index of dummy column for our location\n",
        "* Now we will assign value '1' to this index and keep all other dummy variable columns as '0'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "__25512U-kwC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_price(location, sqft, bath, bhk):    \n",
        "    loc_index = np.where(X.columns==location)[0][0]\n",
        "\n",
        "    x = np.zeros(len(X.columns))\n",
        "    x[0] = sqft\n",
        "    x[1] = bath\n",
        "    x[2] = bhk\n",
        "    if loc_index >= 0:\n",
        "        x[loc_index] = 1\n",
        "\n",
        "    return lr_clf.predict([x])[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6ISUbBlAe7x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "907e0a68-15bd-4ba0-f371-b1f2ec3c3211"
      },
      "source": [
        "predict_price('1st Phase JP Nagar',1000, 2, 2)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83.16761667762826"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mQeMqRXiDZTl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "76d0e38d-587a-4002-e377-6c24e36759fd"
      },
      "source": [
        "predict_price('1st Phase JP Nagar',1000, 3, 3)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "83.71323640897404"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbIHmpiBDj0L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "780ea922-f36a-4030-a1aa-99c3783f6ee0"
      },
      "source": [
        "predict_price('Indira Nagar',1000, 2, 2)"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "158.07409801131223"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-2yB2PRhDmnG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "35741bfd-6f52-4022-e1fc-4a75a61c50f8"
      },
      "source": [
        "predict_price('Indira Nagar',1000, 3, 3)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "158.61971774265803"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wrsHqyiDye6",
        "colab_type": "text"
      },
      "source": [
        "Step#7: Export the model to Pickle file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2Tw6fFtD7qH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pickle\n",
        "\n",
        "with open('Real_Estate_Price_Prediction_Project.pickle','wb') as f:\n",
        "    pickle.dump(lr_clf,f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcGqUL-yF0Bn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "e7bae874-9c1d-4ebe-d72c-917f46e2f678"
      },
      "source": [
        "#Since we are using the Google colab, pickle will will be saved at current directory of Google cloud machine\n",
        "import os\n",
        "\n",
        "os.listdir('.')"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config',\n",
              " 'Bengaluru_House_Data.csv',\n",
              " 'Real_Estate_Price_Prediction_Project.pickle',\n",
              " 'sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0OC5bymGV5B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Lets download it\n",
        "from google.colab import files\n",
        "\n",
        "files.download('Real_Estate_Price_Prediction_Project.pickle')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9-_rDN1TEpJS",
        "colab_type": "text"
      },
      "source": [
        "Step#8: Export any other important info\n",
        "* since weare using One Hot Encoding for location column we need the final list of all the columns in our feature set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ACRLmXqETof",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import json\n",
        "columns = {\n",
        "    'data_columns' : [col.lower() for col in X.columns]\n",
        "}\n",
        "with open(\"columns.json\",\"w\") as f:\n",
        "    f.write(json.dumps(columns))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4LoZ5N2pGr9W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "aaccb6aa-b2e3-4661-e961-f4202f623448"
      },
      "source": [
        "os.listdir('.')"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config',\n",
              " 'Bengaluru_House_Data.csv',\n",
              " 'Real_Estate_Price_Prediction_Project.pickle',\n",
              " 'columns.json',\n",
              " 'sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m99d9OLtGwvi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "files.download('columns.json')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}