Multiple_Linear_Regression_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Multiple Linear Regression .ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMp8pOqDqkoqPXHFq9n7nRj"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the libraries**"
      ],
      "metadata": {
        "id": "cMKIJ8CfO6Vl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "ksM38xQX0Pmo"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importing the dataset**"
      ],
      "metadata": {
        "id": "og0yhBNaPC9t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset =pd.read_csv('50_Startups.csv')\n",
        "x=dataset.iloc[:,:-1].values\n",
        "y=dataset.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "kHqijGXuPIv2"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Encoding catergorial data**"
      ],
      "metadata": {
        "id": "QI04VJgQPZ-J"
      }
    },
    {
      "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))\n"
      ],
      "metadata": {
        "id": "eHbGfNEgPf1c"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the dataset into the traning set & test set**"
      ],
      "metadata": {
        "id": "yBVLOautQqoB"
      }
    },
    {
      "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=0)"
      ],
      "metadata": {
        "id": "_U_h5jIsQS5Z"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Traning the multiple linear Regression model on training set**"
      ],
      "metadata": {
        "id": "oGKA-M1MRFv8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "regressor=LinearRegression()\n",
        "regressor.fit(x_train,y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0NKv5AeSA7E",
        "outputId": "187f4554-e567-4907-8931-ddddc031f0fd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predicting the test Set result**"
      ],
      "metadata": {
        "id": "g8sapD9qStok"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=regressor.predict(x_test)\n",
        "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": "iQwRBadFTA1E",
        "outputId": "8c558d5e-c1ba-4584-b906-5d23d1465039"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[103015.2  103282.38]\n",
            " [132582.28 144259.4 ]\n",
            " [132447.74 146121.95]\n",
            " [ 71976.1   77798.83]\n",
            " [178537.48 191050.39]\n",
            " [116161.24 105008.31]\n",
            " [ 67851.69  81229.06]\n",
            " [ 98791.73  97483.56]\n",
            " [113969.44 110352.25]\n",
            " [167921.07 166187.94]]\n"
          ]
        }
      ]
    }
  ]
}