Ordinary_Least_Square_using_statsmodel_prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNBHbWHFWQri6OtQSUJaI8P",
      "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/sfansaria/Machine-Learning/blob/main/Ordinary_Least_Square_using_statsmodel_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVEJYfiLtirZ",
        "outputId": "0d30e0c0-5c57-4732-ef93-8d4cc16d1990"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_c9VB6mp_rN",
        "outputId": "69479822-52a3-4d83-afcf-7407b81ea404"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (0.14.0)\n",
            "Requirement already satisfied: numpy>=1.18 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.23.5)\n",
            "Requirement already satisfied: scipy!=1.9.2,>=1.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.10.1)\n",
            "Requirement already satisfied: pandas>=1.0 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (1.5.3)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (0.5.3)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels) (23.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0->statsmodels) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0->statsmodels) (2023.3.post1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "pip install statsmodels"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import libraries\n",
        "'''\n",
        "import statsmodels.api as sm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "#read the data\n",
        "\n",
        "data = pd.read_csv('/content/drive/My Drive/ml_datasets/train_ols.csv')\n",
        "\n",
        "data.head()\n",
        "'''\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "6P8hsZ0arATS",
        "outputId": "313e7fdd-cf70-4209-87da-87a71f1911d0"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nimport statsmodels.api as sm\\nimport pandas as pd\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\n#read the data\\n\\ndata = pd.read_csv('/content/drive/My Drive/ml_datasets/train_ols.csv')\\n\\ndata.head()\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#define the variables\n",
        "'''\n",
        "x = data['x'].tolist()\n",
        "print(x)\n",
        "y = data['y'].tolist()\n",
        "print(y)\n",
        "'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "uN8Po9IJt76z",
        "outputId": "f2cedb52-cc54-4518-e00c-756afdb82fe6"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nx = data['x'].tolist()\\nprint(x)\\ny = data['y'].tolist()\\nprint(y)\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#adding the constant term\n",
        "#x = sm.add_constant(x)\n",
        "#print(x)"
      ],
      "metadata": {
        "id": "kDqpFKVYucxJ"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Perform the regression and fitting the model\n",
        "\n",
        "#result = sm.OLS(y, x).fit()\n",
        "\n",
        "\n",
        "#print the summary table\n",
        "#print(result.summary())\n"
      ],
      "metadata": {
        "id": "r-O452MSurxO"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediction of the model\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# reading data from the csv\n",
        "data = pd.read_csv('/content/drive/My Drive/ml_datasets/train_ols.csv')\n",
        "#data = pd.read_csv('train.csv')\n",
        "\n",
        "# plotting the original values\n",
        "x = data['x'].tolist()\n",
        "y = data['y'].tolist()\n",
        "plt.scatter(x, y)\n",
        "\n",
        "# finding the maximum and minimum\n",
        "# values of x, to get the\n",
        "# range of data\n",
        "max_x = data['x'].max()\n",
        "min_x = data['x'].min()\n",
        "\n",
        "# range of values for plotting\n",
        "# the regression line\n",
        "x = np.arange(min_x, max_x, 1)\n",
        "\n",
        "# the substituted equation\n",
        "y = 1.0143 * x - 0.4618\n",
        "\n",
        "# plotting the regression line\n",
        "plt.plot(y, 'r')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "b84iZdopySgS",
        "outputId": "7251d2bd-9355-4c32-9c2f-887267578223"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}