From 74e5d1abe0084d62a9d26c5b7f80207d40e459bb Mon Sep 17 00:00:00 2001
From: Prashant Singh Rana <prashant.singh@thapar.edu>
Date: Wed, 19 Jun 2024 09:10:05 +0530
Subject: [PATCH] Created using Colab

---
 Linear_Model.ipynb | 447 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 447 insertions(+)
 create mode 100644 Linear_Model.ipynb

diff --git a/Linear_Model.ipynb b/Linear_Model.ipynb
new file mode 100644
index 0000000..5e78939
--- /dev/null
+++ b/Linear_Model.ipynb
@@ -0,0 +1,447 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNKMPDP85TxLBcFdUQmoEqD",
+      "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/psrana/Machine-Learning-using-PyCaret/blob/main/Linear_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**1. Import Libraries**"
+      ],
+      "metadata": {
+        "id": "R7_OeMy1Q8eE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "iX51Kn3CQ5Hg"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.metrics import mean_squared_error, r2_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**2. Creating random dataset**"
+      ],
+      "metadata": {
+        "id": "Ixt1BJl4RPC-"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = np.random.rand(20).reshape(-1,1)\n",
+        "Y = np.random.rand(20)\n",
+        "\n",
+        "print(\"X = \", X)\n",
+        "print(\"\\nY = \", Y)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N25KYrILRSnL",
+        "outputId": "919a6186-f515-4ab8-d1bb-d148e5fa3066"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X =  [[0.78353344]\n",
+            " [0.99928113]\n",
+            " [0.72295665]\n",
+            " [0.31040895]\n",
+            " [0.28749016]\n",
+            " [0.37163667]\n",
+            " [0.98226679]\n",
+            " [0.50707929]\n",
+            " [0.47808077]\n",
+            " [0.51092768]\n",
+            " [0.28925052]\n",
+            " [0.48766622]\n",
+            " [0.30722435]\n",
+            " [0.93064507]\n",
+            " [0.91562345]\n",
+            " [0.5489895 ]\n",
+            " [0.91544885]\n",
+            " [0.49987293]\n",
+            " [0.40493747]\n",
+            " [0.14684876]]\n",
+            "\n",
+            "Y =  [0.48674845 0.01009211 0.03883565 0.64845882 0.1020579  0.86302998\n",
+            " 0.42414442 0.15472004 0.6642622  0.64368917 0.79475244 0.74610127\n",
+            " 0.25411568 0.41302081 0.30833708 0.75945603 0.49482498 0.10741174\n",
+            " 0.76733307 0.91431607]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**3. Splitting data into train and test dataset**"
+      ],
+      "metadata": {
+        "id": "XuJIYpOWRdm2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 20 )\n",
+        "\n",
+        "print(\"X_train\",X_train)\n",
+        "\n",
+        "print(\"\\nX_test\",X_test)\n",
+        "\n",
+        "print(\"\\nY_train\",Y_train)\n",
+        "\n",
+        "print(\"\\nY_test\",Y_test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W5Sj-Y8_Rdm2",
+        "outputId": "db4a29e3-db28-45a4-b182-fca0d6af8060"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X_train [[0.49987293]\n",
+            " [0.30722435]\n",
+            " [0.14684876]\n",
+            " [0.37163667]\n",
+            " [0.47808077]\n",
+            " [0.40493747]\n",
+            " [0.78353344]\n",
+            " [0.72295665]\n",
+            " [0.50707929]\n",
+            " [0.98226679]\n",
+            " [0.48766622]\n",
+            " [0.51092768]\n",
+            " [0.5489895 ]\n",
+            " [0.31040895]]\n",
+            "\n",
+            "X_test [[0.91544885]\n",
+            " [0.93064507]\n",
+            " [0.28925052]\n",
+            " [0.28749016]\n",
+            " [0.99928113]\n",
+            " [0.91562345]]\n",
+            "\n",
+            "Y_train [0.10741174 0.25411568 0.91431607 0.86302998 0.6642622  0.76733307\n",
+            " 0.48674845 0.03883565 0.15472004 0.42414442 0.74610127 0.64368917\n",
+            " 0.75945603 0.64845882]\n",
+            "\n",
+            "Y_test [0.49482498 0.41302081 0.79475244 0.1020579  0.01009211 0.30833708]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**4. Creating an instance for the linear regression model**\n"
+      ],
+      "metadata": {
+        "id": "R9azRaIsVtHV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "lr = LinearRegression()\n",
+        "lr"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 94
+        },
+        "id": "2SEmE08uV1ta",
+        "outputId": "f64f8c23-1ac6-4508-a81e-e208b8ecae1c"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**5. Creating/Train Linear Model**"
+      ],
+      "metadata": {
+        "id": "PbPgkB0ZV816"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Training the model by passing the dependent and independent features of the training dataset\n",
+        "lr.fit(X_train, Y_train)\n",
+        "lr"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 94
+        },
+        "id": "AHvhr9ABWBYX",
+        "outputId": "4739df9a-617d-435c-84b8-86387101e666"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**6. Make prediction on test dataset**"
+      ],
+      "metadata": {
+        "id": "By_a7suiZV8c"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Y_pred = lr.predict( X_test)\n",
+        "\n",
+        "print(Y_pred)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PgGa5n_eZYvN",
+        "outputId": "44b33718-afd7-45c0-c74e-0f2497f17b72"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0.29712591 0.28837648 0.65766811 0.65868166 0.24885834 0.29702538]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**7. Model Evaluation**"
+      ],
+      "metadata": {
+        "id": "iJUoVUsIjXWY"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**7.1 Get the coefficients**"
+      ],
+      "metadata": {
+        "id": "MMHK-dHGjiVE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# The value of the coefficients for the independent feature through the multiple regression model\n",
+        "print(\"Value of the oefficients: \\n\", lr.coef_)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DjBh16z-Zy2t",
+        "outputId": "a76fbadd-07de-4b03-a9f5-e2e9f779729e"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Value of the oefficients: \n",
+            " [-0.57576358]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**7.2 Get Mean squared error between Actual and Predicted**"
+      ],
+      "metadata": {
+        "id": "706dGL7MjxWd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# The value of the mean squared error\n",
+        "print(f\"Mean square error: {mean_squared_error( Y_test, Y_pred)}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mfI6QqPzjx1-",
+        "outputId": "dbafe8ce-83d0-4af1-f3d0-bdb5385dc884"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean square error: 0.07339675249663291\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**7.3 Get R Square between Actual and Predicted**"
+      ],
+      "metadata": {
+        "id": "1Dty9dAAj-m1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# The value of the coefficient of determination, i.e., R-square score of the model\n",
+        "print(f\"Coefficient of determination: {r2_score( Y_test, Y_pred )}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Yp7EcVIYj-yI",
+        "outputId": "d841082b-0438-4b8b-df3e-51567244179b"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Coefficient of determination: -0.09708334672591712\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**7.4 Scatter Plot**"
+      ],
+      "metadata": {
+        "id": "RAcx9vPQkOA3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plotting the output\n",
+        "plt.scatter(X_test, Y_test, color = \"black\", label = \"original data\")\n",
+        "plt.plot(X_test, Y_pred, color = \"blue\", linewidth=3, label = \"regression line\")\n",
+        "plt.xlabel(\"Independent Feature\")\n",
+        "plt.ylabel(\"Target Values\")\n",
+        "plt.title(\"Simple Linear Regression\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 492
+        },
+        "id": "be-I_2-dkOLK",
+        "outputId": "3d72ab91-5204-4e33-c62e-f83c7a9859e1"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file