multiregression.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Saani765/machine_learning-basics/blob/main/multiregression.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",
      "execution_count": null,
      "metadata": {
        "id": "GHubR0gvC1-o"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "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": "esro3f4IFJcv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Encoding categorical data , only the state column needs to be encoded hence we use index 3\n"
      ],
      "metadata": {
        "id": "OQQcUp2RUqaz"
      }
    },
    {
      "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": "HLyVYjrUFKsc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": "0DHPh6J5FKek"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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/",
          "height": 74
        },
        "id": "HwvX0v1mFKS2",
        "outputId": "9bcd16cf-cb2f-4ad4-cff9-39949a929489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
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    {
      "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/"
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        "outputId": "89eed4e3-6e74-4fcd-c411-88c9506e427e"
      },
      "execution_count": null,
      "outputs": [
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          "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"
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      "metadata": {
        "id": "4rAqSkQoFJ7r"
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      "execution_count": null,
      "outputs": []
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}