MultipleRegression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MultipleRegression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP1Y+PHj+jLa2ajOXVyqSYi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PRAN20/Multiple_Regression/blob/main/MultipleRegression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Multiple Linear Regression**"
      ],
      "metadata": {
        "id": "ASuzF1Nfu5rS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IMPORTING LIBRARIES**"
      ],
      "metadata": {
        "id": "ZeoKQyZwum_h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as ps\n",
        "import matplotlib.pyplot as mp\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "G926SCfBupJA"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing Datasets From Csv File**"
      ],
      "metadata": {
        "id": "rU9DhSN7wIbP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = ps.read_csv('50_Startups.csv')\n",
        "\n",
        "x = dataset.iloc [:, : -1].values\n",
        "y = dataset.iloc [:, -1].values"
      ],
      "metadata": {
        "id": "BbQU2gWCwQxH"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcyvM-O9xCpG",
        "outputId": "c36fcf35-f885-4732-fd4c-d390de5934ba"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[165349.2 136897.8 471784.1 'New York']\n",
            " [162597.7 151377.59 443898.53 'California']\n",
            " [153441.51 101145.55 407934.54 'Florida']\n",
            " [144372.41 118671.85 383199.62 'New York']\n",
            " [142107.34 91391.77 366168.42 'Florida']\n",
            " [131876.9 99814.71 362861.36 'New York']\n",
            " [134615.46 147198.87 127716.82 'California']\n",
            " [130298.13 145530.06 323876.68 'Florida']\n",
            " [120542.52 148718.95 311613.29 'New York']\n",
            " [123334.88 108679.17 304981.62 'California']\n",
            " [101913.08 110594.11 229160.95 'Florida']\n",
            " [100671.96 91790.61 249744.55 'California']\n",
            " [93863.75 127320.38 249839.44 'Florida']\n",
            " [91992.39 135495.07 252664.93 'California']\n",
            " [119943.24 156547.42 256512.92 'Florida']\n",
            " [114523.61 122616.84 261776.23 'New York']\n",
            " [78013.11 121597.55 264346.06 'California']\n",
            " [94657.16 145077.58 282574.31 'New York']\n",
            " [91749.16 114175.79 294919.57 'Florida']\n",
            " [86419.7 153514.11 0.0 'New York']\n",
            " [76253.86 113867.3 298664.47 'California']\n",
            " [78389.47 153773.43 299737.29 'New York']\n",
            " [73994.56 122782.75 303319.26 'Florida']\n",
            " [67532.53 105751.03 304768.73 'Florida']\n",
            " [77044.01 99281.34 140574.81 'New York']\n",
            " [64664.71 139553.16 137962.62 'California']\n",
            " [75328.87 144135.98 134050.07 'Florida']\n",
            " [72107.6 127864.55 353183.81 'New York']\n",
            " [66051.52 182645.56 118148.2 'Florida']\n",
            " [65605.48 153032.06 107138.38 'New York']\n",
            " [61994.48 115641.28 91131.24 'Florida']\n",
            " [61136.38 152701.92 88218.23 'New York']\n",
            " [63408.86 129219.61 46085.25 'California']\n",
            " [55493.95 103057.49 214634.81 'Florida']\n",
            " [46426.07 157693.92 210797.67 'California']\n",
            " [46014.02 85047.44 205517.64 'New York']\n",
            " [28663.76 127056.21 201126.82 'Florida']\n",
            " [44069.95 51283.14 197029.42 'California']\n",
            " [20229.59 65947.93 185265.1 'New York']\n",
            " [38558.51 82982.09 174999.3 'California']\n",
            " [28754.33 118546.05 172795.67 'California']\n",
            " [27892.92 84710.77 164470.71 'Florida']\n",
            " [23640.93 96189.63 148001.11 'California']\n",
            " [15505.73 127382.3 35534.17 'New York']\n",
            " [22177.74 154806.14 28334.72 'California']\n",
            " [1000.23 124153.04 1903.93 'New York']\n",
            " [1315.46 115816.21 297114.46 'Florida']\n",
            " [0.0 135426.92 0.0 'California']\n",
            " [542.05 51743.15 0.0 'New York']\n",
            " [0.0 116983.8 45173.06 'California']]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTrC8EpCxGE_",
        "outputId": "db2b0039-5b15-4c90-f5da-fbbec09f27f2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[192261.83 191792.06 191050.39 182901.99 166187.94 156991.12 156122.51\n",
            " 155752.6  152211.77 149759.96 146121.95 144259.4  141585.52 134307.35\n",
            " 132602.65 129917.04 126992.93 125370.37 124266.9  122776.86 118474.03\n",
            " 111313.02 110352.25 108733.99 108552.04 107404.34 105733.54 105008.31\n",
            " 103282.38 101004.64  99937.59  97483.56  97427.84  96778.92  96712.8\n",
            "  96479.51  90708.19  89949.14  81229.06  81005.76  78239.91  77798.83\n",
            "  71498.49  69758.98  65200.33  64926.08  49490.75  42559.73  35673.41\n",
            "  14681.4 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using Encoding and convert it into Binary**"
      ],
      "metadata": {
        "id": "95tL4kRAyBac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')\n",
        "x = np.array(ct.fit_transform(x))"
      ],
      "metadata": {
        "id": "qf35uMvAyI6A"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Creating Training sets and Test sets**"
      ],
      "metadata": {
        "id": "0SgfLjApxKYC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.2, random_state = 1)"
      ],
      "metadata": {
        "id": "LJK5PbnMxTFa"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QvZsISoypqq",
        "outputId": "23f2c77d-c2c0-4a18-90d6-17d5a89a588b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.0 0.0 0.0 63408.86 129219.61 46085.25]\n",
            " [1.0 0.0 0.0 38558.51 82982.09 174999.3]\n",
            " [0.0 0.0 1.0 78389.47 153773.43 299737.29]\n",
            " [0.0 1.0 0.0 28663.76 127056.21 201126.82]\n",
            " [0.0 0.0 1.0 86419.7 153514.11 0.0]\n",
            " [1.0 0.0 0.0 23640.93 96189.63 148001.11]\n",
            " [1.0 0.0 0.0 0.0 116983.8 45173.06]\n",
            " [0.0 1.0 0.0 75328.87 144135.98 134050.07]\n",
            " [0.0 1.0 0.0 73994.56 122782.75 303319.26]\n",
            " [1.0 0.0 0.0 91992.39 135495.07 252664.93]\n",
            " [0.0 1.0 0.0 27892.92 84710.77 164470.71]\n",
            " [0.0 0.0 1.0 94657.16 145077.58 282574.31]\n",
            " [0.0 0.0 1.0 1000.23 124153.04 1903.93]\n",
            " [0.0 0.0 1.0 77044.01 99281.34 140574.81]\n",
            " [0.0 1.0 0.0 67532.53 105751.03 304768.73]\n",
            " [0.0 1.0 0.0 142107.34 91391.77 366168.42]\n",
            " [0.0 1.0 0.0 55493.95 103057.49 214634.81]\n",
            " [0.0 1.0 0.0 119943.24 156547.42 256512.92]\n",
            " [0.0 1.0 0.0 61994.48 115641.28 91131.24]\n",
            " [0.0 1.0 0.0 101913.08 110594.11 229160.95]\n",
            " [0.0 1.0 0.0 66051.52 182645.56 118148.2]\n",
            " [1.0 0.0 0.0 22177.74 154806.14 28334.72]\n",
            " [1.0 0.0 0.0 46426.07 157693.92 210797.67]\n",
            " [0.0 1.0 0.0 91749.16 114175.79 294919.57]\n",
            " [1.0 0.0 0.0 76253.86 113867.3 298664.47]\n",
            " [1.0 0.0 0.0 64664.71 139553.16 137962.62]\n",
            " [1.0 0.0 0.0 134615.46 147198.87 127716.82]\n",
            " [0.0 1.0 0.0 130298.13 145530.06 323876.68]\n",
            " [1.0 0.0 0.0 0.0 135426.92 0.0]\n",
            " [1.0 0.0 0.0 162597.7 151377.59 443898.53]\n",
            " [1.0 0.0 0.0 78013.11 121597.55 264346.06]\n",
            " [0.0 0.0 1.0 165349.2 136897.8 471784.1]\n",
            " [0.0 0.0 1.0 114523.61 122616.84 261776.23]\n",
            " [0.0 0.0 1.0 131876.9 99814.71 362861.36]\n",
            " [1.0 0.0 0.0 100671.96 91790.61 249744.55]\n",
            " [1.0 0.0 0.0 123334.88 108679.17 304981.62]\n",
            " [0.0 0.0 1.0 120542.52 148718.95 311613.29]\n",
            " [0.0 1.0 0.0 93863.75 127320.38 249839.44]\n",
            " [0.0 0.0 1.0 15505.73 127382.3 35534.17]\n",
            " [1.0 0.0 0.0 44069.95 51283.14 197029.42]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1bFynofyvFB",
        "outputId": "edfa36e4-d621-4a8e-f311-fbca365fa1ba"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.0 0.0 1.0 72107.6 127864.55 353183.81]\n",
            " [0.0 0.0 1.0 46014.02 85047.44 205517.64]\n",
            " [1.0 0.0 0.0 28754.33 118546.05 172795.67]\n",
            " [0.0 0.0 1.0 20229.59 65947.93 185265.1]\n",
            " [0.0 1.0 0.0 153441.51 101145.55 407934.54]\n",
            " [0.0 0.0 1.0 144372.41 118671.85 383199.62]\n",
            " [0.0 0.0 1.0 542.05 51743.15 0.0]\n",
            " [0.0 0.0 1.0 65605.48 153032.06 107138.38]\n",
            " [0.0 1.0 0.0 1315.46 115816.21 297114.46]\n",
            " [0.0 0.0 1.0 61136.38 152701.92 88218.23]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSx4r3ttyxiN",
        "outputId": "e5082b23-6b8b-424f-8473-1631944da2be"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 97427.84  81005.76 111313.02  90708.19 122776.86  71498.49  14681.4\n",
            " 105733.54 110352.25 134307.35  77798.83 125370.37  64926.08 108552.04\n",
            " 108733.99 166187.94  96778.92 132602.65  99937.59 146121.95 103282.38\n",
            "  65200.33  96712.8  124266.9  118474.03 107404.34 156122.51 155752.6\n",
            "  42559.73 191792.06 126992.93 192261.83 129917.04 156991.12 144259.4\n",
            " 149759.96 152211.77 141585.52  69758.98  89949.14]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gKQqxGSxy0vS",
        "outputId": "d12d9771-adc9-4349-ec80-ac41cd12faea"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[105008.31  96479.51  78239.91  81229.06 191050.39 182901.99  35673.41\n",
            " 101004.64  49490.75  97483.56]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Training our Model**"
      ],
      "metadata": {
        "id": "8__CwIiHy8Ei"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBHdRN2Xy_qX",
        "outputId": "0e31a63e-27de-4251-ce99-3cd3dedd77bc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Prediciting Results**"
      ],
      "metadata": {
        "id": "FQCMPDu_3GJu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "metadata": {
        "id": "SUGC97q13Ksh"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SBy9u9uI3Y4Z",
        "outputId": "a62d4826-ee09-4604-ebaf-27b0ac7ab736"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[114664.41715867  90593.15531621  75692.84151575  70221.88679652\n",
            " 179790.25514872 171576.9201852   49753.5875203  102276.65888936\n",
            "  58649.37795761  98272.02561131]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.set_printoptions(precision=2)\n",
        "print(np.concatenate((y_pred.reshape(len(y_pred),1), Y_test.reshape(len(Y_test),1)),1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "siVeg56e3lvA",
        "outputId": "3a318494-0a0c-437f-b4c8-bced86e06b22"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[114664.42 105008.31]\n",
            " [ 90593.16  96479.51]\n",
            " [ 75692.84  78239.91]\n",
            " [ 70221.89  81229.06]\n",
            " [179790.26 191050.39]\n",
            " [171576.92 182901.99]\n",
            " [ 49753.59  35673.41]\n",
            " [102276.66 101004.64]\n",
            " [ 58649.38  49490.75]\n",
            " [ 98272.03  97483.56]]\n"
          ]
        }
      ]
    }
  ]
}