linear_reg.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOGA3pglWIiOKTj9JaLm5Hq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mathurshab2210/Data_sci_Python/blob/main/linear_reg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hR-xHewevCHf"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn import datasets,linear_model\n",
        "from sklearn.metrics import mean_squared_error\n"
      ],
      "metadata": {
        "id": "1OeoZj2evC3a"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes=datasets.load_diabetes()\n",
        "print(diabetes.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VpZJT4WZvix7",
        "outputId": "c5905921-5abb-402f-d74f-a63fd4ddf1fc"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename', 'data_module'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diabetes_X=diabetes.data\n",
        "diabetes_X_train=diabetes_X[:-30]\n",
        "diabetes_X_test=diabetes_X[-30:]\n",
        "diabetes_Y_train=diabetes.target[:-30]\n",
        "diabetes_Y_test=diabetes.target[-30:]"
      ],
      "metadata": {
        "id": "_2ip2PYDwKPY"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=linear_model.LinearRegression()\n"
      ],
      "metadata": {
        "id": "gKvFMRNdxKUw"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(diabetes_X_train,diabetes_Y_train)\n",
        "diabetes_Y_pred=model.predict(diabetes_X_test)\n",
        "print(\"mean sq error is :\", mean_squared_error(diabetes_Y_test,diabetes_Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qa3saQxjyKC1",
        "outputId": "fa26f255-965e-4dc8-e394-945b11fecf64"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean sq error is : 1826.5364191345423\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"weights\", model.coef_)\n",
        "print(\"intercept\", model.intercept_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w71Yzc6GzzNf",
        "outputId": "48d53c6f-796c-48da-ecc3-7336fed334a4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "weights [  -1.16924976 -237.18461486  518.30606657  309.04865826 -763.14121622\n",
            "  458.90999325   80.62441437  174.32183366  721.49712065   79.19307944]\n",
            "intercept 153.05827988224112\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(diabetes_X_test,diabetes_Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "L-t7ZGG10IFp",
        "outputId": "6f0a7145-b83b-41b3-d333-0d5b37d6efe9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f6976c8a210>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(diabetes_X_test,diabetes_Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "f1neTVbOy-4e",
        "outputId": "05a57212-6d69-47b8-81bc-7eb78dc9d64c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6962ab4310>]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(diabetes_Y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EENGGDfIz36z",
        "outputId": "1b9e9237-c0d6-488c-ced8-230b3279a158"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[234.04069476 122.92246614 166.35433115 174.39222892 226.94851442\n",
            " 151.82190227 100.94358512  83.08956073 143.1298667  192.67823683\n",
            " 197.98434449 154.37965196 173.16544327 112.38886823 164.20528828\n",
            " 131.63666326 258.66439447 101.4126313  117.87101994 123.5370635\n",
            " 219.5123662   62.09123359 133.09435929 121.33048447  53.51502538\n",
            " 193.125764   104.69007361 123.68983031 211.27336049  53.87045485]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6HuEyvsp0YAo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}