Handwritten_Digit_Recognition_Using_MNIST.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOInt4gVhN+mzUoPAIuxE0U",
      "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/dibyarupnath/CodeClause_Hand_Written_Digit_Recognization/blob/main/Handwritten_Digit_Recognition_Using_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fetching the required libraries"
      ],
      "metadata": {
        "id": "FeyQLh8mXi_W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Emo57Azve_zs"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.datasets import fetch_openml\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import cross_val_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fetching the MNIST dataset"
      ],
      "metadata": {
        "id": "yQEFlR2VXoWI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mnist = fetch_openml('mnist_784')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ytqd6JVLqAn",
        "outputId": "f1c295bd-606d-4816-919d-ba7b240cd719"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:968: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
            "  warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dividing the dataset and plotting some data"
      ],
      "metadata": {
        "id": "HiPhsY1dXrVA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x, y = mnist['data'], mnist['target']\n",
        "\n",
        "some_digit = x.to_numpy()[3601]\n",
        "some_digit_image = some_digit.reshape(28, 28)  # reshaping for plotting\n",
        "\n",
        "plt.imshow(some_digit_image, cmap=matplotlib.cm.binary,\n",
        "           interpolation='nearest')\n",
        "plt.axis(\"off\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "UQvOy_5-JnGW",
        "outputId": "ea6d6764-7392-4ada-8e4b-f5365e469bde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x, y = mnist['data'], mnist['target']\n",
        "\n",
        "some_digit = x.to_numpy()[3602]\n",
        "some_digit_image = some_digit.reshape(28, 28)  # reshaping for plotting\n",
        "\n",
        "plt.imshow(some_digit_image, cmap=matplotlib.cm.binary,\n",
        "           interpolation='nearest')\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "2mwVyIpvKNhd",
        "outputId": "f19e90a3-bf5a-401f-cf33-716f41ce10f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking an index without plotting"
      ],
      "metadata": {
        "id": "tcCPffXaX09H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y[3602]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "NDiRzgGGLdxn",
        "outputId": "bb0633c9-4718-4a41-b182-2552bb1ed4db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting data into test and train sets"
      ],
      "metadata": {
        "id": "M1dmvZIcX8ur"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test = x[0:6000], x[6000:7000]\n",
        "y_train, y_test = y[0:6000], y[6000:7000]\n",
        "\n",
        "# Creating a checker for the digit 5\n",
        "y_train = y_train.astype(np.int8)\n",
        "y_test = y_test.astype(np.int8)\n",
        "y_train_5 = (y_train == 5)\n",
        "y_test_5 = (y_test == 5)\n"
      ],
      "metadata": {
        "id": "Lkg32nhzJoT5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uLydgwmhNZ0o",
        "outputId": "7069a493-218d-418a-8f78-bf40c6949ea1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       5\n",
              "1       0\n",
              "2       4\n",
              "3       1\n",
              "4       9\n",
              "       ..\n",
              "5995    7\n",
              "5996    6\n",
              "5997    8\n",
              "5998    6\n",
              "5999    9\n",
              "Name: class, Length: 6000, dtype: int8"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression"
      ],
      "metadata": {
        "id": "KyFfDdaeRBop"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = LogisticRegression(tol=0.1, solver='lbfgs', max_iter=1000)  # Using 1000 max iters so that the algo can converge\n",
        "clf.fit(x_train, y_train_5)\n",
        "example = clf.predict([some_digit])\n",
        "print(example)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZ2LPbAsJrdZ",
        "outputId": "0598dbe0-e4ee-4eca-abfb-d2ea108d51b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Evaluation"
      ],
      "metadata": {
        "id": "raLUDkdQRFQh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = cross_val_score(clf, x_train, y_train_5, cv=3, scoring=\"accuracy\")\n",
        "print(a.mean())\n",
        "\n",
        "# Getting an accuracy of almost 95%"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CccLh6tSM2Gq",
        "outputId": "186d1a3f-59a7-4ac5-8910-46b5299c5fa3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9558333333333332\n"
          ]
        }
      ]
    }
  ]
}