Linear_Regression_Metrics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOLIRsE7t5/pXZ9oOdpoSgi",
      "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/riyajaiswal25/MLProjects/blob/main/Linear_Regression_Metrics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Regression Metrics\n"
      ],
      "metadata": {
        "id": "EVekQtoAI3Y1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "n7BGCBNfI9KR"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('FoodTruck.csv')"
      ],
      "metadata": {
        "id": "EcSX5AJrJRR3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nsr9NgB_Jaf8",
        "outputId": "770a6219-192f-4461-cfac-b8b8e037827a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     6.1101    17.592\n",
              "0    5.5277   9.13020\n",
              "1    8.5186  13.66200\n",
              "2    7.0032  11.85400\n",
              "3    5.8598   6.82330\n",
              "4    8.3829  11.88600\n",
              "..      ...       ...\n",
              "91   5.8707   7.20290\n",
              "92   5.3054   1.98690\n",
              "93   8.2934   0.14454\n",
              "94  13.3940   9.05510\n",
              "95   5.4369   0.61705\n",
              "\n",
              "[96 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-adb5f822-f117-4e85-9c69-65426e4dd29d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>6.1101</th>\n",
              "      <th>17.592</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.5277</td>\n",
              "      <td>9.13020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8.5186</td>\n",
              "      <td>13.66200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.0032</td>\n",
              "      <td>11.85400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.8598</td>\n",
              "      <td>6.82330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8.3829</td>\n",
              "      <td>11.88600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>5.8707</td>\n",
              "      <td>7.20290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>5.3054</td>\n",
              "      <td>1.98690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>8.2934</td>\n",
              "      <td>0.14454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>13.3940</td>\n",
              "      <td>9.05510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>5.4369</td>\n",
              "      <td>0.61705</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-adb5f822-f117-4e85-9c69-65426e4dd29d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-adb5f822-f117-4e85-9c69-65426e4dd29d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-adb5f822-f117-4e85-9c69-65426e4dd29d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-79bdca6b-0daa-4a05-a739-afa162c79e45\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79bdca6b-0daa-4a05-a739-afa162c79e45')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-79bdca6b-0daa-4a05-a739-afa162c79e45 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "0c8KGY_SJuou"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Segregate into X and Y\n",
        "X = data['6.1101']\n",
        "Y = data['17.592']\n"
      ],
      "metadata": {
        "id": "PkvCSYV6Jw5d"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDbamQnrKL8E",
        "outputId": "b9560147-728e-4af2-acff-3874681ec332"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      5.5277\n",
              "1      8.5186\n",
              "2      7.0032\n",
              "3      5.8598\n",
              "4      8.3829\n",
              "       ...   \n",
              "91     5.8707\n",
              "92     5.3054\n",
              "93     8.2934\n",
              "94    13.3940\n",
              "95     5.4369\n",
              "Name: 6.1101, Length: 96, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOXWulibKO7o",
        "outputId": "ae50a61d-432f-43e7-c108-768e3637097a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      9.13020\n",
              "1     13.66200\n",
              "2     11.85400\n",
              "3      6.82330\n",
              "4     11.88600\n",
              "        ...   \n",
              "91     7.20290\n",
              "92     1.98690\n",
              "93     0.14454\n",
              "94     9.05510\n",
              "95     0.61705\n",
              "Name: 17.592, Length: 96, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatterplot for Feature1 vs. Feature2\n",
        "plt.scatter(X, Y)\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('Y')\n",
        "plt.title('Scatterplot of Feature1 vs. Feature2')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "QAsTGRRwKSSG",
        "outputId": "52a7e242-edda-4df8-d94a-2b7f5bee651c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "# Create a pairplot\n",
        "sns.pairplot(data)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "ulQ9qyj3Ksd2",
        "outputId": "a5524098-069b-4320-e4c2-c0a19787fe94"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy import stats\n"
      ],
      "metadata": {
        "id": "TqWU7ExDLGFw"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a correlation matrix\n",
        "correlation_matrix = data.corr()\n",
        "\n",
        "# Print the correlation matrix\n",
        "print(\"Correlation Matrix:\")\n",
        "print(correlation_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-zMGLGtILHiQ",
        "outputId": "e27bc7d5-d135-4065-c374-3308f88b018c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation Matrix:\n",
            "          6.1101    17.592\n",
            "6.1101  1.000000  0.872157\n",
            "17.592  0.872157  1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "slope, intercept, r_value, p_value, std_err = stats.linregress(X, Y)\n",
        "\n",
        "print(f\"Linear Regression Parameters:\")\n",
        "print(f\"Slope (Regression Coefficient): {slope}\")\n",
        "print(f\"Intercept: {intercept}\")\n",
        "print(f\"R-squared (Coefficient of Determination): {r_value**2}\")\n",
        "print(f\"P-value: {p_value}\")\n",
        "print(f\"Standard Error: {std_err}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTeFdqwDLY-n",
        "outputId": "7777cb4e-552c-46f3-f5ca-1372b4fb6ada"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Parameters:\n",
            "Slope (Regression Coefficient): 1.2135472539083587\n",
            "Intercept: -4.211504005424093\n",
            "R-squared (Coefficient of Determination): 0.7606583419339852\n",
            "P-value: 6.110460481642768e-31\n",
            "Standard Error: 0.07021131328791101\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "slope, intercept, r_value, p_value, std_err = stats.linregress(X, Y)\n",
        "\n",
        "# Compute the predicted values\n",
        "predicted_Y = slope * X + intercept\n",
        "\n",
        "# Compute the number of data points\n",
        "n = len(X)\n",
        "\n",
        "# Calculate the mean squared error (cost)\n",
        "cost = np.mean((predicted_Y - Y) ** 2)\n",
        "\n",
        "# Calculate the sum of squared errors (SSE)\n",
        "sse = np.sum((predicted_Y - Y) ** 2)\n",
        "\n",
        "# Calculate the sum of squared regression (SSR)\n",
        "ssr = np.sum((predicted_Y - np.mean(Y)) ** 2)\n",
        "\n",
        "# Calculate the sum of squared total (SST)\n",
        "sst = np.sum((Y - np.mean(Y)) ** 2)\n",
        "\n",
        "# Calculate the coefficient of determination (R-squared)\n",
        "r_squared = 1 - (sse / sst)\n",
        "\n",
        "print(f\"Cost (Mean Squared Error): {cost}\")\n",
        "print(f\"SSE (Sum of Squared Errors): {sse}\")\n",
        "print(f\"SSR (Sum of Squared Regression): {ssr}\")\n",
        "print(f\"SST (Sum of Squared Total): {sst}\")\n",
        "print(f\"R-squared (Coefficient of Determination): {r_squared}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZNiPR3oKMBmo",
        "outputId": "ae2868ed-245a-4abe-c746-65b4660c635c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cost (Mean Squared Error): 6.91916380631601\n",
            "SSE (Sum of Squared Errors): 664.2397254063369\n",
            "SSR (Sum of Squared Regression): 2111.0386393116273\n",
            "SST (Sum of Squared Total): 2775.2783647179626\n",
            "R-squared (Coefficient of Determination): 0.7606583419339846\n"
          ]
        }
      ]
    }
  ]
}