MNIST_Digit_Data_Set_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST Digit Data Set .ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOLi6fNCwECgsqs+y3XZjBi",
      "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/satyambhatt5/Satyam/blob/main/MNIST_Digit_Data_Set_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysD_YMeiFisI"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hu642k25GRNA"
      },
      "source": [
        "from sklearn.datasets import load_digits,fetch_openml"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MuAl6mbAHuqu"
      },
      "source": [
        "\n",
        "#split the data into training and testing \n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CE5EJ8AXIXXh"
      },
      "source": [
        "#find the metrics of the data \n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZaMHbfQJQQD"
      },
      "source": [
        "#loading the data load_digit package it should have images and data set  \n",
        "\n",
        "digits =load_digits()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQLD2Ow5Jwq9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c6e1b40-a933-4aad-9f70-cb46a8167386"
      },
      "source": [
        "#hence there is two form   1- data ,2- Target \n",
        "\n",
        "print(digits.data.shape)\n",
        "print(digits.target.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1797, 64)\n",
            "(1797,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHQ0YYpua8zv"
      },
      "source": [
        "# PRINT TOP  FIVE RECORD FROM THE DIGIT DATA SET \n",
        "\n",
        "def plot_images(data,target,img_shape=(8,8)):\n",
        "  \n",
        "  plt.figure(figsize=(20,5))\n",
        "  \n",
        "  for index,(image,label) in enumerate(zip(data[:5],target[:5])): \n",
        "    #Enter the inside the data into the target data set or manage image and label enumrate means show the data \n",
        "    \n",
        "    \n",
        "    plt.subplot(1,5,index+1)\n",
        "    #building the image with new image  cmap =plt.cm.gray will help  to find the accuracy   \n",
        "    \n",
        "    \n",
        "    plt.imshow(np.reshape(image,img_shape),cmap=plt.cm.gray)\n",
        "    plt.title(\"training %i\\n\" %label,frontsize=20)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIc5nhxJK72H"
      },
      "source": [
        "def plot_images(data,target,img_shape=(8,8)):\n",
        "    plt.figure(figsize=(20,5))\n",
        "    for index,(image,label) in enumerate(zip(data[:5],target[:5])):\n",
        "        plt.subplot(1,5,index+1)\n",
        "        plt.imshow(np.reshape(image,img_shape),cmap=plt.cm.gray)\n",
        "        plt.title(\"Training %i\\n\" %label,fontsize=20)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6o2LT1PiO4d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "189b984b-afef-4deb-a864-98ad3a223b98"
      },
      "source": [
        "plot_images(digits.data,digits.target)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cyY_PkLWLc-"
      },
      "source": [
        "#training the data with machine learning  model   \n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test= train_test_split(digits.data,digits.target,test_size=.25,random_state=100)\n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t1MVSOHrf0kv"
      },
      "source": [
        "#buiding the logistic regression   system   \n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vylltdh1gs67"
      },
      "source": [
        "lr= LogisticRegression()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9xIGmgjg1-B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c1fd86c-2717-42bf-a634-aa72cb6bcb56"
      },
      "source": [
        "#model had learn the data sucessfully  \n",
        "lr.fit(x_train,y_train)\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwk61Fr1hDoK"
      },
      "source": [
        "#now we predict the data while using x_test data \n",
        "\n",
        "y_pred=lr.predict(x_test)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVEdyMluhokH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63a842fa-5999-496f-88e0-fb8d5ac49b56"
      },
      "source": [
        "#now find the accuracy score of the model by trained model  \n",
        "score=accuracy_score(y_test,y_pred)\n",
        "score"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9688888888888889"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBXNFdzYicOi"
      },
      "source": [
        ""
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGTr0NEOipT6"
      },
      "source": [
        "#now find the confusion  matrix  \n",
        "\n",
        "cm=confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IkTIpzcejVsH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ec13eee-7564-447d-e8b9-89e83e2bd5d1"
      },
      "source": [
        "cm"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[49,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0, 36,  0,  0,  0,  0,  0,  0,  1,  0],\n",
              "       [ 0,  0, 51,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0, 37,  0,  0,  0,  0,  0,  2],\n",
              "       [ 0,  0,  0,  0, 52,  0,  0,  0,  0,  2],\n",
              "       [ 0,  0,  0,  0,  0, 42,  1,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0, 46,  0,  1,  0],\n",
              "       [ 0,  0,  0,  0,  1,  0,  0, 42,  0,  1],\n",
              "       [ 0,  2,  0,  0,  0,  1,  0,  0, 38,  0],\n",
              "       [ 0,  0,  0,  1,  0,  0,  0,  1,  0, 43]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1U_aQspjXsU"
      },
      "source": [
        "def get_misclassified_index(y_pred,y_test):\n",
        "  misclassification=[]\n",
        "  for index,(predicted,actual) in enumerate(zip(y_pred,y_test)):\n",
        "    if predicted!=actual:\n",
        "      misclassification.append(index)\n",
        "      return misclassification"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SzvHwRyDl5Yk"
      },
      "source": [
        "misclassification=get_misclassified_index(y_pred,y_test)\n"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnYb6kPgl8jK"
      },
      "source": [
        "def plot_misclassifications(misclassification,img_shape=(8,8)):\n",
        "    plt.figure(figsize=(20,4))\n",
        "    for index,wrong in enumerate(misclassification[0:5]):\n",
        "        plt.subplot(1,5,index+1)\n",
        "        plt.imshow(np.reshape(x_test[wrong],img_shape),cmap=plt.cm.gray)\n",
        "        plt.title(\"Predicted:{} Actual:{}\".format(y_pred[wrong],y_test[wrong]))"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "talxYEiFmBwX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "outputId": "12ebb8a7-bb37-4269-9714-dd67dd1bf55c"
      },
      "source": [
        "plot_misclassifications(misclassification)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wFthLiHpmHdB"
      },
      "source": [
        ""
      ],
      "execution_count": 20,
      "outputs": []
    }
  ]
}