SVM_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SVM_Model.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# Standard scientific Python imports\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Import datasets, classifiers and performance metrics\n",
        "from sklearn import datasets, svm, metrics\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "YaaCUCopEiJS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "digits = datasets.load_digits()"
      ],
      "metadata": {
        "id": "XrY0ZvEyEkUn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# flatten the images\n",
        "n_samples = len(digits.images)\n",
        "data = digits.images.reshape((n_samples, -1))\n",
        "\n",
        "# Create a classifier: a support vector classifier\n",
        "clf = svm.SVC(gamma=0.001)\n",
        "\n",
        "# Split data into 50% train and 50% test subsets\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    data, digits.target, test_size=0.5, shuffle=False\n",
        ")\n",
        "\n",
        "# Learn the digits on the train subset\n",
        "clf.fit(X_train, y_train)\n",
        "\n",
        "# Predict the value of the digit on the test subset\n",
        "predicted = clf.predict(X_test)"
      ],
      "metadata": {
        "id": "J02sPsMRFaen"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\n",
        "    f\"Classification report for classifier {clf}:\\n\"\n",
        "    f\"{metrics.classification_report(y_test, predicted)}\\n\"\n",
        ")"
      ],
      "metadata": {
        "id": "ZROAhviJFekq",
        "outputId": "e3a9ceb8-0d91-4e1e-c75c-1bb68330b17f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification report for classifier SVC(gamma=0.001):\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99        88\n",
            "           1       0.99      0.97      0.98        91\n",
            "           2       0.99      0.99      0.99        86\n",
            "           3       0.98      0.87      0.92        91\n",
            "           4       0.99      0.96      0.97        92\n",
            "           5       0.95      0.97      0.96        91\n",
            "           6       0.99      0.99      0.99        91\n",
            "           7       0.96      0.99      0.97        89\n",
            "           8       0.94      1.00      0.97        88\n",
            "           9       0.93      0.98      0.95        92\n",
            "\n",
            "    accuracy                           0.97       899\n",
            "   macro avg       0.97      0.97      0.97       899\n",
            "weighted avg       0.97      0.97      0.97       899\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted)\n",
        "disp.figure_.suptitle(\"Confusion Matrix\")\n",
        "print(f\"Confusion matrix:\\n{disp.confusion_matrix}\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "WYl11hrJFiSF",
        "outputId": "a2fecf5b-d4b6-4e98-9933-13d0e6fb4497",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix:\n",
            "[[87  0  0  0  1  0  0  0  0  0]\n",
            " [ 0 88  1  0  0  0  0  0  1  1]\n",
            " [ 0  0 85  1  0  0  0  0  0  0]\n",
            " [ 0  0  0 79  0  3  0  4  5  0]\n",
            " [ 0  0  0  0 88  0  0  0  0  4]\n",
            " [ 0  0  0  0  0 88  1  0  0  2]\n",
            " [ 0  1  0  0  0  0 90  0  0  0]\n",
            " [ 0  0  0  0  0  1  0 88  0  0]\n",
            " [ 0  0  0  0  0  0  0  0 88  0]\n",
            " [ 0  0  0  1  0  1  0  0  0 90]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}