2_Linear_Regression_Notebook.ipynb

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      "name": "2.Linear Regression Notebook (1).ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/harshchan/AI-ML-python-codes/blob/main/2_Linear_Regression_Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skqtzJLhRssQ"
      },
      "source": [
        "#Get the essential libraries\n",
        "from sklearn import linear_model\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import io\n",
        "import seaborn as sns\n",
        "import math "
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDBkdcLJRssT"
      },
      "source": [
        "#Import the dataset\n",
        "#or_data = pd.read_csv(\"/home/sagar/Downloads/archive/car data.csv\")\n",
        "#choose all the files\n",
        "from  google.colab  import  files \n",
        "uploaded = files.upload()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-Y-UtamSG8I"
      },
      "source": [
        "or_data = pd.read_csv(io.BytesIO(uploaded['car data.csv']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uQkQvaHR-IA"
      },
      "source": [
        "or_data.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ijGfYsj3RssU",
        "outputId": "4552969f-8b16-4dce-8a01-fd197cde3ec0"
      },
      "source": [
        "# Get the details of the dataset\n",
        "or_data.info()\n",
        "#we are trying to predict the final price or selling price for the car\n",
        "#car name is very wide and long list so it wont contribute much to data"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 301 entries, 0 to 300\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Car_Name       301 non-null    object \n",
            " 1   Year           301 non-null    int64  \n",
            " 2   Selling_Price  301 non-null    float64\n",
            " 3   Present_Price  301 non-null    float64\n",
            " 4   Kms_Driven     301 non-null    int64  \n",
            " 5   Fuel_Type      301 non-null    object \n",
            " 6   Seller_Type    301 non-null    object \n",
            " 7   Transmission   301 non-null    object \n",
            " 8   Owner          301 non-null    int64  \n",
            "dtypes: float64(2), int64(3), object(4)\n",
            "memory usage: 21.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "azMZ2ZwoV488"
      },
      "source": [
        "whereas year is most important  factor\r\n",
        "fuel type\r\n",
        "transmission \r\n",
        "are the categorical data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "5tPi7yisRssV",
        "outputId": "88aa7728-e89b-4ad4-b8d6-d505e78b82dc"
      },
      "source": [
        "#Look at the statistics of dataset\n",
        "or_data.describe()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Owner</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "      <td>301.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2013.627907</td>\n",
              "      <td>4.661296</td>\n",
              "      <td>7.628472</td>\n",
              "      <td>36947.205980</td>\n",
              "      <td>0.043189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.891554</td>\n",
              "      <td>5.082812</td>\n",
              "      <td>8.644115</td>\n",
              "      <td>38886.883882</td>\n",
              "      <td>0.247915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2003.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2012.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>1.200000</td>\n",
              "      <td>15000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2014.000000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>32000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>9.900000</td>\n",
              "      <td>48767.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>92.600000</td>\n",
              "      <td>500000.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Year  Selling_Price  Present_Price     Kms_Driven       Owner\n",
              "count   301.000000     301.000000     301.000000     301.000000  301.000000\n",
              "mean   2013.627907       4.661296       7.628472   36947.205980    0.043189\n",
              "std       2.891554       5.082812       8.644115   38886.883882    0.247915\n",
              "min    2003.000000       0.100000       0.320000     500.000000    0.000000\n",
              "25%    2012.000000       0.900000       1.200000   15000.000000    0.000000\n",
              "50%    2014.000000       3.600000       6.400000   32000.000000    0.000000\n",
              "75%    2016.000000       6.000000       9.900000   48767.000000    0.000000\n",
              "max    2018.000000      35.000000      92.600000  500000.000000    3.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-yo1CaUWTz2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w-xlxaUjWTwi"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2AbVQoaqRssV",
        "outputId": "4eb929c7-b734-4d31-c8a3-6111cee680c0"
      },
      "source": [
        "#Figure out whether we have any missing values in dataset\n",
        "#no null values in this records \n",
        "#if very large records drop the data having null values\n",
        "#if very small values then take minimum value\n",
        "or_data.isna().sum()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Car_Name         0\n",
              "Year             0\n",
              "Selling_Price    0\n",
              "Present_Price    0\n",
              "Kms_Driven       0\n",
              "Fuel_Type        0\n",
              "Seller_Type      0\n",
              "Transmission     0\n",
              "Owner            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BnzZdHmZRssW",
        "outputId": "50ce6a85-69d0-4ed6-8c32-ecf6c7e8f4b1"
      },
      "source": [
        "#Find the correlation of different features of the dataset with its label\n",
        "or_data.corr()['Selling_Price']\n",
        "#selling price is 1 cuz it is correlation of variable with itself"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year             0.236141\n",
              "Selling_Price    1.000000\n",
              "Present_Price    0.878983\n",
              "Kms_Driven       0.029187\n",
              "Owner           -0.088344\n",
              "Name: Selling_Price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VUMegUNYF1J"
      },
      "source": [
        "best value for correlation is 1\r\n",
        "\r\n",
        "the values which are not correlated with each other as negatives\r\n",
        "\r\n",
        "so correlation of entity with itselfis very high ie 1\r\n",
        "\r\n",
        "so we can look at selling price and ignore crrelation with itself \r\n",
        "\r\n",
        "so we can see that selling price is moderately correlated with prresent price\r\n",
        "\r\n",
        "your independent variables should not be correlated with each other \r\n",
        "\r\n",
        "ex take year it is not correlated very well with other independent variables like present price kms driven owner so it  is a goodthing for our model\r\n",
        "\r\n",
        "so definitely we dont have problem of multi colliinearity\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "3I7csSUSXx2s",
        "outputId": "158026e5-620e-49ae-9d17-db104f589e13"
      },
      "source": [
        "cor = or_data.corr()\r\n",
        "sns.heatmap(cor)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f63eee45390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zn4rm6sMbb4B"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUiXeBsjbN2-"
      },
      "source": [
        "conclusion is we dont have multi linear data\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "ujX3_dqYRssW",
        "outputId": "5e115eda-7dd7-4b7a-d315-c8d6bb57af68"
      },
      "source": [
        "#Data Preprocessing and Preparation\n",
        "#replacing column year with age\n",
        "#what is age of our car\n",
        "\n",
        "or_data['Age'] = 2020 - or_data['Year']\n",
        "or_data.drop('Year',axis = 1 , inplace = True)\n",
        "or_data.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>Car_Name</th>\n",
              "      <th>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ritz</td>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sx4</td>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ciaz</td>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>swift</td>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Car_Name  Selling_Price  Present_Price  ...  Transmission Owner Age\n",
              "0     ritz           3.35           5.59  ...        Manual     0   6\n",
              "1      sx4           4.75           9.54  ...        Manual     0   7\n",
              "2     ciaz           7.25           9.85  ...        Manual     0   3\n",
              "3  wagon r           2.85           4.15  ...        Manual     0   9\n",
              "4    swift           4.60           6.87  ...        Manual     0   6\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "Gpr8bFv3RssX",
        "outputId": "33dd9988-5bbd-4c98-c04b-3175cfd84679"
      },
      "source": [
        "or_data.drop('Car_Name',axis = 1 , inplace = True)\n",
        "or_data.head()\n",
        "#car name is not much significant so we can drop it"
      ],
      "execution_count": 24,
      "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>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Fuel_Type</th>\n",
              "      <th>Seller_Type</th>\n",
              "      <th>Transmission</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>Petrol</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>Diesel</td>\n",
              "      <td>Dealer</td>\n",
              "      <td>Manual</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Selling_Price  Present_Price  Kms_Driven  ... Transmission Owner Age\n",
              "0           3.35           5.59       27000  ...       Manual     0   6\n",
              "1           4.75           9.54       43000  ...       Manual     0   7\n",
              "2           7.25           9.85        6900  ...       Manual     0   3\n",
              "3           2.85           4.15        5200  ...       Manual     0   9\n",
              "4           4.60           6.87       42450  ...       Manual     0   6\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "6CgvgLvpRssY",
        "outputId": "31d547be-d197-440e-f076-6ef04c9bff92"
      },
      "source": [
        "or_data = pd.get_dummies(data = or_data,drop_first=True)\n",
        "or_data.head()\n",
        "#to remove the redundant variables"
      ],
      "execution_count": 25,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>Selling_Price</th>\n",
              "      <th>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.35</td>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.75</td>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.25</td>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.85</td>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.60</td>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Selling_Price  Present_Price  ...  Seller_Type_Individual  Transmission_Manual\n",
              "0           3.35           5.59  ...                       0                    1\n",
              "1           4.75           9.54  ...                       0                    1\n",
              "2           7.25           9.85  ...                       0                    1\n",
              "3           2.85           4.15  ...                       0                    1\n",
              "4           4.60           6.87  ...                       0                    1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "AHzF5WH3RssY",
        "outputId": "322543ce-a251-4d29-83ec-be88db9f1d81"
      },
      "source": [
        "X = or_data.drop('Selling_Price',axis = 1)\n",
        "X.head()\n",
        "#breaking down data into independent and dependent dataset\n",
        "#all indepenedent varaibles xcept selling price"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>Present_Price</th>\n",
              "      <th>Kms_Driven</th>\n",
              "      <th>Owner</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fuel_Type_Diesel</th>\n",
              "      <th>Fuel_Type_Petrol</th>\n",
              "      <th>Seller_Type_Individual</th>\n",
              "      <th>Transmission_Manual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.59</td>\n",
              "      <td>27000</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.54</td>\n",
              "      <td>43000</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.85</td>\n",
              "      <td>6900</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.15</td>\n",
              "      <td>5200</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.87</td>\n",
              "      <td>42450</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Present_Price  Kms_Driven  ...  Seller_Type_Individual  Transmission_Manual\n",
              "0           5.59       27000  ...                       0                    1\n",
              "1           9.54       43000  ...                       0                    1\n",
              "2           9.85        6900  ...                       0                    1\n",
              "3           4.15        5200  ...                       0                    1\n",
              "4           6.87       42450  ...                       0                    1\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "rxXky1pYRssZ",
        "outputId": "e68b3d5a-d9e0-41f6-862b-57d16554bb28"
      },
      "source": [
        "Y = or_data[['Selling_Price']]\n",
        "Y.head()\n",
        "#this is dependent variable data\n",
        "#ie selling price depends on all x "
      ],
      "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>Selling_Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Selling_Price\n",
              "0           3.35\n",
              "1           4.75\n",
              "2           7.25\n",
              "3           2.85\n",
              "4           4.60"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YILzC3vtRssa",
        "outputId": "b74c41ac-ac43-4635-95bc-646c887a2430"
      },
      "source": [
        "#Divide the dataset into train and test datasets\n",
        "\n",
        "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=1)\n",
        "print(\"X train: \",X_train.shape)\n",
        "print(\"X test: \",X_test.shape)\n",
        "print(\"Y train: \",Y_train.shape)\n",
        "print(\"Y test: \",Y_test.shape)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X train:  (240, 8)\n",
            "X test:  (61, 8)\n",
            "Y train:  (240, 1)\n",
            "Y test:  (61, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SvgRzDDWRssa",
        "outputId": "5ddb69fd-e608-48f7-b965-bf36482f1161"
      },
      "source": [
        "#Fit the Linear regressor on the training data\n",
        "\n",
        "lm = linear_model.LinearRegression()\n",
        "\n",
        "lm.fit(X_train,Y_train)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9ns1TlWRssb",
        "outputId": "a4e6d3b3-e1bd-4052-d48d-667d307deb10"
      },
      "source": [
        "#Get the evaluation metrics for the prediction\n",
        "\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "\n",
        "y_pred = lm.predict(X_test)\n",
        "\n",
        "#Mean Absolute Error\n",
        "print(\"Mean Absolute Error : \",mean_absolute_error(Y_test, y_pred))\n",
        "\n",
        "#Mean Squared Error\n",
        "print(\"Mean Squared Error : \",mean_squared_error(Y_test, y_pred))\n",
        "\n",
        "#root mean squared error\n",
        "print(\"Root mean Squared Error :\",math.sqrt(mean_squared_error(Y_test,y_pred)))\n",
        "#R2 score\n",
        "print(\"R2 Score : \",r2_score(Y_test, y_pred))\n",
        "#so our model is 86% accurate\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error :  1.0998575552990957\n",
            "Mean Squared Error :  2.9823848618597513\n",
            "Root mean Squared Error : 1.726958268708237\n",
            "R2 Score :  0.8625260513315252\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "F-2ZYkoDfnLE",
        "outputId": "1e0bd9ca-c185-4d72-8179-b7e0baed23df"
      },
      "source": [
        "#look at residual plots\r\n",
        "sns.residplot(x=Y_test,y=y_pred,color=\"g\")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f63e567d518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "xORIdg7xg8D7",
        "outputId": "49a72522-b20f-4a1e-80e3-004dd3d7a1dd"
      },
      "source": [
        "residuals=Y_test- y_pred\r\n",
        "plt.scatter(y_pred,residuals)\r\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}