Logistic_Regression1ipynb.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Logistic Regression1ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMqUHoS4JfYmbGWPCHTCHlP",
      "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/sridevibonthu/FML/blob/main/Logistic_Regression1ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Logistic Regression with scikit-learn - Example 1\n",
        "\n",
        "There are several general steps you’ll take when you’re preparing your classification models:\n",
        "\n",
        "- Import packages, functions, and classes\n",
        "- Get data to work with and, if appropriate, transform it\n",
        "- Create a classification model and train (or fit) it with your existing data\n",
        "- Evaluate your model to see if its performance is satisfactory"
      ],
      "metadata": {
        "id": "bebKf4oNxdEV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S86zAjKFxX8Z"
      },
      "outputs": [],
      "source": [
        "#1. Import Packages, Functions and classes\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get data\n",
        "x = np.arange(15).reshape(-1, 1)\n",
        "y = np.array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1])"
      ],
      "metadata": {
        "id": "yHsqX3ipx7O4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x, y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvu0ksWRyBl8",
        "outputId": "3e3644bd-70b6-4f3a-c513-90a06042cfc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[ 0],\n",
              "        [ 1],\n",
              "        [ 2],\n",
              "        [ 3],\n",
              "        [ 4],\n",
              "        [ 5],\n",
              "        [ 6],\n",
              "        [ 7],\n",
              "        [ 8],\n",
              "        [ 9],\n",
              "        [10],\n",
              "        [11],\n",
              "        [12],\n",
              "        [13],\n",
              "        [14]]), array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1]))"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3. Create model and train it\n",
        "model = LogisticRegression(C=0.5)\n",
        "model.fit(x,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVm4_LqfyCKL",
        "outputId": "4b61e20a-2e41-4d90-dd2f-0badd79f3ea6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.5)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#attributes of your model\n",
        "model.classes_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dTOwzoYyjnq",
        "outputId": "4ee1cf18-6bdd-4d3d-c593-1ac54dbf7d86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3zNOlIAbyrx-",
        "outputId": "b0d1522d-7d1c-46d6-8ab6-ffbe37223b2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-5.96039406])"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-NKTNrmLyu1D",
        "outputId": "8bd78296-81d6-46fe-9729-b8502647b211"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.7944447]])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#4. Evaluate the model\n",
        "model.predict_proba(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b_t867ySyw9e",
        "outputId": "039a7487-d456-4569-d063-fdce7b8f39eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.99742774, 0.00257226],\n",
              "       [0.99432475, 0.00567525],\n",
              "       [0.98752536, 0.01247464],\n",
              "       [0.9728026 , 0.0271974 ],\n",
              "       [0.94172912, 0.05827088],\n",
              "       [0.87954946, 0.12045054],\n",
              "       [0.76740677, 0.23259323],\n",
              "       [0.59851494, 0.40148506],\n",
              "       [0.40247491, 0.59752509],\n",
              "       [0.23332901, 0.76667099],\n",
              "       [0.12088746, 0.87911254],\n",
              "       [0.05849725, 0.94150275],\n",
              "       [0.02730655, 0.97269345],\n",
              "       [0.01252546, 0.98747454],\n",
              "       [0.00569853, 0.99430147]])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get the actual predictions\n",
        "y_pred = model.predict(x)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v2zhJWB7zAt7",
        "outputId": "e578cc07-1601-4ee9-efee-cb6f1aee4ac2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 0 1 1 1 1 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#accuracy\n",
        "model.score(x, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMyodUsvzJ9c",
        "outputId": "6e741551-7f15-4420-8c2f-838aa97e83e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8666666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KJjnftizWJf",
        "outputId": "df941383-4c26-41fb-d8ec-6e687c0609d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7, 1],\n",
              "       [1, 6]])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualization of confusion matrix\n",
        "cm = confusion_matrix(y, y_pred)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "ax.imshow(cm)\n",
        "ax.grid(False)\n",
        "ax.xaxis.set(ticks=(0, 1), ticklabels=('Predicted 0s', 'Predicted 1s'))\n",
        "ax.yaxis.set(ticks=(0, 1), ticklabels=('Actual 0s', 'Actual 1s'))\n",
        "ax.set_ylim(1.5, -0.5)\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        ax.text(j, i, cm[i, j], ha='center', va='center', color='red')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "jDGrBm5Nz9GT",
        "outputId": "adbc884f-bf04-4302-f6a9-15bc45f9bf8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#more comprehensive report on the classification \n",
        "print(classification_report(y, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTsATcIB0WjP",
        "outputId": "b9388ad3-bfb9-4341-8ed6-2ef976ed1706"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.88      0.88         8\n",
            "           1       0.86      0.86      0.86         7\n",
            "\n",
            "    accuracy                           0.87        15\n",
            "   macro avg       0.87      0.87      0.87        15\n",
            "weighted avg       0.87      0.87      0.87        15\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "WzucfxV61GPh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}