Naive_Bayes_And_Decision_Tree_Classifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Naive Bayes.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNhiOsOfNHaQ7w2Vuxh+Pn0",
      "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/osamazafar980/Handwriting-Digits-Prediction-Naive-Bayes/blob/master/Naive_Bayes_And_Decision_Tree_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNPZgcTdspYK",
        "colab_type": "text"
      },
      "source": [
        "Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytZaNWWFsu7R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.datasets import fetch_openml\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XcyUh-Ssph9A",
        "colab_type": "text"
      },
      "source": [
        "# Preparing Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEv31aNuptkk",
        "colab_type": "code",
        "outputId": "1b04121d-458c-4d02-b264-b75725d4ab2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        }
      },
      "source": [
        "mnist = fetch_openml('mnist_784', version=1)\n",
        "x = mnist['data']\n",
        "y = mnist['target']\n",
        "print(x.dtype, y.dtype)\n",
        "print(x.shape,y.shape)\n",
        "\n",
        "#Ploting Data\n",
        "\n",
        "def plot_images(images , labels):\n",
        "  n_cols = min(5,len(images))\n",
        "  n_rows = len(images)//n_cols\n",
        "  fig = plt.figure(figsize=(8,8))\n",
        "\n",
        "  for i  in range (n_rows * n_cols):\n",
        "    sp = fig.add_subplot(n_rows,n_cols,i+1)\n",
        "    plt.axis(\"off\")\n",
        "    plt.imshow(images[i], cmap=plt.cm.gray)\n",
        "    sp.set_title(labels[i])\n",
        "  plt.show()\n",
        "\n",
        "p = np.random.permutation(len(x))\n",
        "p = p[:20]\n",
        "plot_images(x[p].reshape(-1,28,28),y[p])\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "float64 object\n",
            "(70000, 784) (70000,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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pom1+nSClxr333gsAuOiii7TtyCOPjCqdUqtWrZrG9vxJex5iqs+gtNuH2bWs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\nMyny6N/DH4wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGJLrtMf2eVU",
        "colab_type": "code",
        "outputId": "628a4179-029d-4143-a3ab-bb2a4559a70f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "y=y.astype(\"int32\")\n",
        "x = x/255.0\n",
        "from sklearn.model_selection import train_test_split\n",
        "tx,tesx,ty,tesy = train_test_split(x,y, test_size=0.15)\n",
        "print(\"Training Data Shape : \",tx.shape,\"\\nTesting Data Shape : \",tesx.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data Shape :  (59500, 784) \n",
            "Testing Data Shape :  (10500, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_C-dC9r3k4U",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "Training Naive Bayes Classifier Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkkF0peX3nj9",
        "colab_type": "code",
        "outputId": "7e45d0d9-4836-456e-edb8-681ca544ca34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.naive_bayes import BernoulliNB\n",
        "\n",
        "cls = BernoulliNB()\n",
        "cls.fit(tx,ty)\n"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MGcAZhH341F",
        "colab_type": "text"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e02XPp7P365d",
        "colab_type": "code",
        "outputId": "292a5d29-ee1b-44fc-8f4a-e5a6af1c8259",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        }
      },
      "source": [
        "print(\"Testing Accuracy Score: \",cls.score(tesx,tesy))\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "pre = cls.predict(tesx)\n",
        "print(\"\\n\\nClassification Report\")\n",
        "print(classification_report(tesy,pre))\n",
        "print(\"\\n\\nConfusion metrics\")\n",
        "print(confusion_matrix(tesy,pre))\n",
        "\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing Accuracy Score:  0.8322857142857143\n",
            "\n",
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.88      0.90      1017\n",
            "           1       0.87      0.96      0.91      1210\n",
            "           2       0.87      0.83      0.85      1046\n",
            "           3       0.77      0.81      0.79      1071\n",
            "           4       0.82      0.79      0.81      1067\n",
            "           5       0.80      0.70      0.75       934\n",
            "           6       0.88      0.90      0.89      1058\n",
            "           7       0.92      0.82      0.87      1051\n",
            "           8       0.78      0.78      0.78      1017\n",
            "           9       0.72      0.81      0.76      1029\n",
            "\n",
            "    accuracy                           0.83     10500\n",
            "   macro avg       0.83      0.83      0.83     10500\n",
            "weighted avg       0.83      0.83      0.83     10500\n",
            "\n",
            "\n",
            "\n",
            "Confusion metrics\n",
            "[[ 892    0    6    7    3   55   21    0   33    0]\n",
            " [   0 1163   10    3    0   10    7    1   12    4]\n",
            " [  13   14  873   26   26    4   45    8   34    3]\n",
            " [  10   25   48  864    2   21   11    9   48   33]\n",
            " [   2   11    8    0  847    8   26    5   20  140]\n",
            " [  23   14   10  141   26  653   19    6   19   23]\n",
            " [   7   41   16    0    4   31  952    0    6    1]\n",
            " [   3   29   12    3   29    0    1  864   27   83]\n",
            " [  11   28   18   68   17   29    4    2  797   43]\n",
            " [  11   18    3   13   79    7    0   41   23  834]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30V5Vu1SeR2q",
        "colab_type": "text"
      },
      "source": [
        "Decision Tree Classifier\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYMMTP79eUgG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d5489819-6702-47dd-f9f4-c5b33de77d01"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "clf = DecisionTreeClassifier(random_state=0)\n",
        "clf.fit(tx,ty)\n",
        "print(\"Testing Accuracy Score: \",clf.score(tesx,tesy))\n",
        "from sklearn.metrics import classification_report\n",
        "pre = clf.predict(tesx)\n",
        "print(\"\\n\\nClassification Report\")\n",
        "print(classification_report(tesy,pre))\n",
        "print(\"\\n\\nConfusion metrics\")\n",
        "print(confusion_matrix(tesy,pre))\n",
        "matrix = confusion_matrix(tesy,pre)\n",
        "\n",
        "figurePlot = plt.figure(figsize=(10,10))\n",
        "axis = figurePlot.add_subplot(111)\n",
        "axis.set_title('Confusion Matrix')\n",
        "plt.imshow(matrix)\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing Accuracy Score:  0.8671428571428571\n",
            "\n",
            "\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.92      0.92      1017\n",
            "           1       0.94      0.96      0.95      1210\n",
            "           2       0.85      0.85      0.85      1046\n",
            "           3       0.84      0.83      0.84      1071\n",
            "           4       0.85      0.86      0.86      1067\n",
            "           5       0.84      0.81      0.82       934\n",
            "           6       0.93      0.92      0.93      1058\n",
            "           7       0.89      0.87      0.88      1051\n",
            "           8       0.80      0.79      0.80      1017\n",
            "           9       0.81      0.85      0.83      1029\n",
            "\n",
            "    accuracy                           0.87     10500\n",
            "   macro avg       0.87      0.87      0.87     10500\n",
            "weighted avg       0.87      0.87      0.87     10500\n",
            "\n",
            "\n",
            "\n",
            "Confusion metrics\n",
            "[[ 935    2   13   10    6   11   13    3   17    7]\n",
            " [   0 1156    8    9    5    4    7    3   11    7]\n",
            " [  17   14  885   39   15   10   13   18   25   10]\n",
            " [   8    8   44  892    6   44    4   13   36   16]\n",
            " [  13    6   13   13  916    8   10   14   25   49]\n",
            " [  12    5   12   34   16  754   16   13   42   30]\n",
            " [  14    8   12    4    8   23  973    2   12    2]\n",
            " [   5   11   11   14   24    6    2  911   14   53]\n",
            " [   9   24   39   32   29   27    5   11  808   33]\n",
            " [   7    2    2   16   47   13    2   41   24  875]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}