Model_train.ipynb

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        "<a href=\"https://colab.research.google.com/github/Kaustubhprabhu98/prudentialproject/blob/main/Model_train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import datasets\n",
        "from sklearn import metrics\n",
        "import joblib\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#load data\n",
        "\n",
        "train = pd.read_csv('./train1.csv',index_col=0)\n",
        "test = pd.read_csv('./test1.csv',index_col=0)\n",
        "\n",
        "X_train = train.drop(['bmi'], axis = 1) \n",
        "y_train = train['bmi']\n",
        "\n",
        "X_test = test.drop(['bmi'], axis = 1) \n",
        "y_test = test['bmi']\n"
      ],
      "metadata": {
        "id": "4DSwHhtlYdUA"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Model Creation\n",
        "lm = LinearRegression()\n",
        "lm.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
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        "id": "BC7iaGzVb4er",
        "outputId": "66716b97-cda9-42ce-daab-78835ddbb21d"
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              "LinearRegression()"
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            ]
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    {
      "cell_type": "code",
      "source": [
        "with open('model.joblib', 'wb') as f:\n",
        "    joblib.dump(lm,f)\n",
        "\n",
        "\n",
        "with open('model.joblib', 'rb') as f:\n",
        "    predictor = joblib.load(f)"
      ],
      "metadata": {
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      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#test \n",
        "print(\"Testing following input: \")\n",
        "\n",
        "print(predictor.predict(X_test))"
      ],
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      "execution_count": 7,
      "outputs": [
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          "name": "stdout",
          "text": [
            "Testing following input: \n",
            "[24.91304173 24.62843967 29.45201059 28.86836441 18.90666238 27.14724088\n",
            " 28.58376235 24.21968044 24.11968877 24.32885855 21.90479339 30.14537188\n",
            " 23.53118087 25.146914   28.9732178  24.46367005 24.5584061  23.68043442\n",
            " 26.8583141  29.12300836 23.73023331 21.10657871 23.06683013 24.60334327\n",
            " 23.87516215 24.41873288 22.6580709  25.29670456 30.78367867 25.18698945]\n"
          ]
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