SVM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SVM.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM6l/E07vjTM72adp6kIOUh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sumitdahiya619/Digit-Recognition-System/blob/main/SVM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2y4tboXFw8tN",
        "outputId": "018de49e-ec4d-44bf-ae37-8791a1ab58a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "(x_train,y_train),(x_test,y_test) = tf.keras.datasets.mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9eZDe9BpPOI"
      },
      "source": [
        "x_train=x_train[:12000]\n",
        "y_train=y_train[:12000]\n",
        "x_test=x_test[:5000]\n",
        "y_test=y_test[:5000]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J86EGE2azROT",
        "outputId": "7279ed22-b923-40aa-b542-dcc357537130",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12000, 28, 28)\n",
            "(12000,)\n",
            "(5000, 28, 28)\n",
            "(5000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8_l8MKRy6ev"
      },
      "source": [
        "x_train=x_train.reshape(x_train.shape[0],784)\n",
        "x_test=x_test.reshape(x_test.shape[0],784)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JiRwQqYIz8Oe"
      },
      "source": [
        "from sklearn import svm\n",
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yX12XCzefpPY",
        "outputId": "451b0f78-06dd-4bb6-df44-b05c8b9a50a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "model=svm.SVC(kernel=\"poly\")\n",
        "\n",
        "model.fit(x_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='poly',\n",
              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
              "    tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x13SoXQM0J6S"
      },
      "source": [
        "y_pred=model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHnm9I7siAlw",
        "outputId": "c9c9a307-2eb7-4aa6-f5b2-0267198d5374",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "metrics.accuracy_score(y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9374"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmLFzm8eQ3f4",
        "outputId": "c66240b0-a1ad-480e-d230-df591360ad1e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "cm=metrics.confusion_matrix(y_test,y_pred,[0,1,2,3,4,5,6,7,8,9])\n",
        "print(cm)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[446   0   2   0   0   8   3   0   1   0]\n",
            " [  0 563   3   2   0   0   2   0   1   0]\n",
            " [  7   7 491   3   3   2   3  10   3   1]\n",
            " [  0   5   3 468   0  10   1  10   2   1]\n",
            " [  0   3   1   0 474   0   6   0   1  15]\n",
            " [  2   2   0  10   4 426   4   0   5   3]\n",
            " [  6   6   0   0   8   4 436   0   2   0]\n",
            " [  0  18  10   5   3   0   0 466   2   8]\n",
            " [  5   6   3  11   5   8   1   4 440   6]\n",
            " [  5  10   0   6  14   1   0   3   4 477]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sb8OBBMHZZRg"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTihDvopaohc",
        "outputId": "5afbbe2d-64a1-4813-fa1f-c3acd70be597",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#metrics.classification_report(y_test,y_pred)\n",
        "print(\"Classification report for classifier %s:\\n%s\\n\" % (model, metrics.classification_report(y_test, y_pred)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification report for classifier SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='poly',\n",
            "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "    tol=0.001, verbose=False):\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.97      0.96       460\n",
            "           1       0.91      0.99      0.95       571\n",
            "           2       0.96      0.93      0.94       530\n",
            "           3       0.93      0.94      0.93       500\n",
            "           4       0.93      0.95      0.94       500\n",
            "           5       0.93      0.93      0.93       456\n",
            "           6       0.96      0.94      0.95       462\n",
            "           7       0.95      0.91      0.93       512\n",
            "           8       0.95      0.90      0.93       489\n",
            "           9       0.93      0.92      0.93       520\n",
            "\n",
            "    accuracy                           0.94      5000\n",
            "   macro avg       0.94      0.94      0.94      5000\n",
            "weighted avg       0.94      0.94      0.94      5000\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFzk96AXSMFc",
        "outputId": "5befc226-da83-42f6-e0a0-0ab5c3a41445",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "metrics.plot_confusion_matrix(model,x_test,y_test,[0,1,2,3,4,5,6,7,8,9],normalize='true')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IL_T1hKmST2l",
        "outputId": "dbd66d21-f3b7-473f-d86e-e6ccdb080841",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "precision=metrics.precision_score(y_test,y_pred,average=None)\n",
        "recall=metrics.recall_score(y_test,y_pred,average=None)\n",
        "print(precision,\"\\n\",recall)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.94692144 0.90806452 0.95711501 0.92673267 0.92759295 0.92810458\n",
            " 0.95614035 0.94523327 0.95444685 0.9334638 ] \n",
            " [0.96956522 0.98598949 0.92641509 0.936      0.948      0.93421053\n",
            " 0.94372294 0.91015625 0.8997955  0.91730769]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUeDMgWicvR2"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}