Hand_Written_Digit_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyORxk3gmYeEi+zr5mliFRIN",
      "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/TitikshyaChanda/HandwrittenDigitClassificatipn/blob/main/Hand_Written_Digit_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HAND WRITTEN DIGIT CLASSIFIACTION"
      ],
      "metadata": {
        "id": "DCD11UICyCxQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The digits dataset consisits of 8x8 pixel images of digits.The images attribute of the dataset stores 8x8 arrays of grayscale values for each image.We will use the arrays to visualize the first 4 images.The target attribute of the dataset stores the digit each image represents."
      ],
      "metadata": {
        "id": "fgqsTfiHyd0w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Library"
      ],
      "metadata": {
        "id": "lCHrI0GPzToK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Zs_im0jfn156"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "hbXv3ybjopt9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "vESveCqSoqJK"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "56ksfx-mocBD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "bznnLeHLo-Yv"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Data"
      ],
      "metadata": {
        "id": "fquLwUs_zdTB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_digits"
      ],
      "metadata": {
        "id": "pSWElZIXpHYR"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = load_digits()"
      ],
      "metadata": {
        "id": "O1CsVS4FpRot"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "_, axes = plt.subplots(nrows=1, ncols=4, figsize=(10,3))\n",
        "for ax, image, label in zip(axes, df.images, df.target):\n",
        "    ax.imshow(image,cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
        "    ax.set_title(\"Training: %i\" % label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "1VWJwQ2lpplv",
        "outputId": "ebbe2e2a-3139-4b2a-9b07-da41677b7469"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 4 Axes>"
            ],
            "image/png": 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ZklRYWGh8ZjAYND7TK5xpAQAAAGA1SgsAAAAAq1FaAAAAAFiN0gIAAADAapQWAAAAAFajtAAAAACwGqUFAAAAgNUoLQAAAACsRmkBAAAAYDVKCwAAAACrUVoAAAAAWI3SAgAAAMBqlBYAAAAAVqO0AAAAALAapQUAAACA1SgtAAAAAKxGaQEAAABgNUoLAAAAAKtRWgAAAABYjdICAAAAwGpxXi9gSmFhofGZe/bsMT7zv//9r/GZnTt3Nj5Tkm688UbjMyPxcxo1apTxmQhdUVGR8ZkFBQXGZ0ZKr169vF4BPrB69WrjM3v27Gl85siRI43PlKRZs2ZFZC68M2HCBOMzc3Nzjc/s27ev8ZmdOnUyPlOSgsFgROZGC860AAAAALAapQUAAACA1SgtAAAAAKxGaQEAAABgNUoLAAAAAKtRWgAAAABYLazSkpeXp379+ikxMVGpqakaOXKkPvvss0jtBvgSOQHckRMgNGQFOCus0rJ582ZlZ2dr69atys/P1+nTpzV06FAdP348UvsBvkNOAHfkBAgNWQHOCuvNJd96660aHy9ZskSpqakqLCzUgAEDjC4G+BU5AdyREyA0ZAU4K6zS8r/KysokSS1atDjvdSoqKlRRUVH9cXl5+cXcJeA75ARwR06A0LhlhZwgWtX7ifhVVVWaOnWqsrKylJGRcd7r5eXlKTk5ufqSnp5e37sEfIecAO7ICRCaULJCThCt6l1asrOztXPnTq1YseKC15s+fbrKysqqLyUlJfW9S8B3yAngjpwAoQklK+QE0apeDw/LycnRG2+8oS1btqh9+/YXvG4gEFAgEKjXcoCfkRPAHTkBQhNqVsgJolVYpcVxHP32t7/VqlWrVFBQoE6dOkVqL8C3yAngjpwAoSErwFlhlZbs7Gy9+OKLWrNmjRITE3Xw4EFJUnJyspo2bRqRBQG/ISeAO3IChIasAGeF9ZyWBQsWqKysTIMGDVJaWlr1ZeXKlZHaD/AdcgK4IydAaMgKcFbYDw8DcGHkBHBHToDQkBXgrHq/ehgAAAAANARKCwAAAACrUVoAAAAAWK1e79Nio9LSUuMz+/TpY3xm586djc+MlL59+3q9AgybO3eu8ZkzZ840PrOsrMz4zEgZNGiQ1yvAB6ZOnWp8ZseOHY3PjMSekjRixIiIzIV3IvH7zO7du43P3LNnj/GZwWDQ+EwpMr/LpqSkGJ/pFc60AAAAALAapQUAAACA1SgtAAAAAKxGaQEAAABgNUoLAAAAAKtRWgAAAABYjdICAAAAwGqUFgAAAABWo7QAAAAAsBqlBQAAAIDVKC0AAAAArEZpAQAAAGA1SgsAAAAAq1FaAAAAAFiN0gIAAADAapQWAAAAAFajtAAAAACwGqUFAAAAgNUoLQAAAACsRmkBAAAAYLU4rxcwpbS01PjMG2+80fhMP4nE9zQlJcX4TIRu6tSpxmeOGzfO+Ew//Tn57rvvvF4BhkXiZzp37lzjM1evXm18ZqQsWbLE6xXgA507dzY+89tvvzU+MxgMGp8Zqbnr1683PtOrf6M50wIAAADAapQWAAAAAFajtAAAAACwGqUFAAAAgNUoLQAAAACsRmkBAAAAYDVKCwAAAACrXVRpefzxxxUTExOR934AogU5AdyRE8AdOUFjVu/Ssm3bNi1atEg9evQwuQ8QVcgJ4I6cAO7ICRq7epWWY8eO6e6779bixYt99c7VQEMiJ4A7cgK4IydAPUtLdna2hg0bpmAw6HrdiooKlZeX17gAjQE5AdyRE8AdOQGkuHBvsGLFCu3YsUPbtm0L6fp5eXmaNWtW2IsBfkZOAHfkBHBHToCzwjrTUlJSoilTpmj58uVKSEgI6TbTp09XWVlZ9aWkpKReiwJ+QU4Ad+QEcEdOgB+EdaalsLBQhw8fVp8+faqPVVZWasuWLZo3b54qKioUGxtb4zaBQECBQMDMtoAPkBPAHTkB3JET4AdhlZYhQ4boww8/rHFs/Pjx6tatm3Jzc2sFB2iMyAngjpwA7sgJ8IOwSktiYqIyMjJqHLv00kvVsmXLWseBxoqcAO7ICeCOnAA/uKg3lwQAAACASAv71cP+V0FBgYE1gOhGTgB35ARwR07QWHGmBQAAAIDVKC0AAAAArEZpAQAAAGC1i35Oiy1SUlKMzywsLDQ+MxJKS0sjMnf79u3GZ44ePdr4TMBLRUVFxmf26tXL+EyEbubMmcZn/uUvfzE+MxJWr14dkbnNmzePyFzATSR+P1y/fr3xmZI0ceJE4zOfeOIJ4zMff/xx4zNDwZkWAAAAAFajtAAAAACwGqUFAAAAgNUoLQAAAACsRmkBAAAAYDVKCwAAAACrUVoAAAAAWI3SAgAAAMBqlBYAAAAAVqO0AAAAALAapQUAAACA1SgtAAAAAKxGaQEAAABgNUoLAAAAAKtRWgAAAABYjdICAAAAwGqUFgAAAABWo7QAAAAAsBqlBQAAAIDVKC0AAAAArBbn9QKmdO7c2fjM7du3G5/5yiuv+GJmpOTm5nq9AgBc0Lhx44zPLCgoMD7zgw8+MD5z5MiRxmdK0ogRI4zPHD9+vPGZkdgToXvggQeMzwwGg8ZnlpaWGp8pSfn5+cZnjh492vhMr3CmBQAAAIDVKC0AAAAArEZpAQAAAGA1SgsAAAAAq1FaAAAAAFiN0gIAAADAamGXln379mnMmDFq2bKlmjZtqquvvjoiLw0M+Bk5AdyREyA0ZAUI831aSktLlZWVpcGDB2vt2rVq3bq1iouLlZKSEqn9AN8hJ4A7cgKEhqwAZ4VVWp544gmlp6fr+eefrz7WqVMn40sBfkZOAHfkBAgNWQHOCuvhYa+99poyMzM1atQopaamqnfv3lq8ePEFb1NRUaHy8vIaFyCakRPAHTkBQhNuVsgJolVYpWX37t1asGCBunbtqnXr1mnSpEmaPHmyli5det7b5OXlKTk5ufqSnp5+0UsDNiMngDtyAoQm3KyQE0SrsEpLVVWV+vTpozlz5qh3796aMGGC7rvvPi1cuPC8t5k+fbrKysqqLyUlJRe9NGAzcgK4IydAaMLNCjlBtAqrtKSlpal79+41jl155ZXau3fveW8TCASUlJRU4wJEM3ICuCMnQGjCzQo5QbQKq7RkZWXps88+q3Hs888/V4cOHYwuBfgZOQHckRMgNGQFOCus0nL//fdr69atmjNnjnbt2qUXX3xRzz33nLKzsyO1H+A75ARwR06A0JAV4KywSku/fv20atUqvfTSS8rIyNCjjz6quXPn6u67747UfoDvkBPAHTkBQkNWgLPCep8WSRo+fLiGDx8eiV2AqEFOAHfkBAgNWQHCPNMCAAAAAA2N0gIAAADAapQWAAAAAFYL+zktturcubPxmU888YTxmbm5ucZnZmZmGp8pSYWFhRGZi+jSvHlz4zNHjBhhfOaaNWuMz5SkgoIC4zPHjRtnfCZC16tXL+Mzi4qKfDFz5syZxmdKkclfx44djc+MxN89CF1KSorxmRMmTDA+M1JGjx5tfOaiRYuMz/QKZ1oAAAAAWI3SAgAAAMBqlBYAAAAAVqO0AAAAALAapQUAAACA1SgtAAAAAKxGaQEAAABgNUoLAAAAAKtRWgAAAABYjdICAAAAwGqUFgAAAABWo7QAAAAAsBqlBQAAAIDVKC0AAAAArEZpAQAAAGA1SgsAAAAAq1FaAAAAAFiN0gIAAADAapQWAAAAAFaLa+g7dBxHklReXt7Qdx2277//3vjMqqoq4zNPnz5tfKbkj5+Raee+5nN/Tr3ip5xEQqT+TEfCqVOnjM+0/edOTuxw7Ngx4zP9lL2KigrjM03+WSIn4Tt58qTxmZH4vStSGuO/J1LoWYlxGjhNX331ldLT0xvyLoGwlZSUqH379p7dPzmBH5ATwB05AULjlpUGLy1VVVXav3+/EhMTFRMTc97rlZeXKz09XSUlJUpKSmrADcPDnmZ5vafjODp69KjatWunJk28e/QkOfGOX3b1ck9yEjl+2ZU93fktJxI/V9PYMzShZqXBHx7WpEmTsP6PQ1JSktU/6HPY0ywv90xOTvbkfn+MnHjPL7t6tSc5iSy/7MqeF+bHnEj8XE1jT3ehZIUn4gMAAACwGqUFAAAAgNWsLS2BQEAzZsxQIBDwepULYk+z/LKnLfzy/fLLnpJ/dvXLnjbw0/fKL7uyZ3Tyy/eLPc3yy54N/kR8AAAAAAiHtWdaAAAAAECitAAAAACwHKUFAAAAgNUoLQAAAACsRmkBAAAAYDVrS8v8+fPVsWNHJSQk6Nprr9V7773n9Uo15OXlqV+/fkpMTFRqaqpGjhypzz77zOu1XD3++OOKiYnR1KlTvV6lln379mnMmDFq2bKlmjZtqquvvlrbt2/3ei2rkZPIICfRhZxEBjmJLuQkMmzOieSvrFhZWlauXKlp06ZpxowZ2rFjh3r27KmbbrpJhw8f9nq1aps3b1Z2dra2bt2q/Px8nT59WkOHDtXx48e9Xu28tm3bpkWLFqlHjx5er1JLaWmpsrKydMkll2jt2rX6+OOP9fTTTyslJcXr1axFTiKDnEQXchIZ5CS6kJPIsDknkg+z4ljommuucbKzs6s/rqysdNq1a+fk5eV5uNWFHT582JHkbN682etV6nT06FGna9euTn5+vjNw4EBnypQpXq9UQ25urnP99dd7vYavkBPzyEn0ISfmkZPoQ07Msz0njuO/rFh3puXUqVMqLCxUMBisPtakSRMFg0G9++67Hm52YWVlZZKkFi1aeLxJ3bKzszVs2LAa31ebvPbaa8rMzNSoUaOUmpqq3r17a/HixV6vZS1yEhnkJLqQk8ggJ9GFnESG7TmR/JcV60rLkSNHVFlZqTZt2tQ43qZNGx08eNCjrS6sqqpKU6dOVVZWljIyMrxep5YVK1Zox44dysvL83qV89q9e7cWLFigrl27at26dZo0aZImT56spUuXer2alciJeeQk+pAT88hJ9CEn5vkhJ5L/shLn9QLRIDs7Wzt37tQ777zj9Sq1lJSUaMqUKcrPz1dCQoLX65xXVVWVMjMzNWfOHElS7969tXPnTi1cuFBjx471eDuYQE4uHjmJfuTk4pGT6EdOzPBbVqw709KqVSvFxsbq0KFDNY4fOnRIbdu29Wir88vJydEbb7yhTZs2qX379l6vU0thYaEOHz6sPn36KC4uTnFxcdq8ebOeeeYZxcXFqbKy0usVJUlpaWnq3r17jWNXXnml9u7d69FGdiMnZpGT6EROzCIn0YmcmOWXnEj+y4p1pSU+Pl59+/bVhg0bqo9VVVVpw4YN6t+/v4eb1eQ4jnJycrRq1Spt3LhRnTp18nqlOg0ZMkQffvihioqKqi+ZmZm6++67VVRUpNjYWK9XlCRlZWXVeunCzz//XB06dPBoI7uRE7PISXQiJ2aRk+hETszyS04kH2bF29cBqNuKFSucQCDgLFmyxPn444+dCRMmOM2bN3cOHjzo9WrVJk2a5CQnJzsFBQXOgQMHqi8nTpzwejVXNr6KxXvvvefExcU5jz32mFNcXOwsX77cadasmbNs2TKvV7MWOYkschIdyElkkZPoQE4iy8acOI7/smJlaXEcx3n22Wedyy+/3ImPj3euueYaZ+vWrV6vVIOkOi/PP/+816u5sjU8r7/+upORkeEEAgGnW7duznPPPef1StYjJ5FDTqIHOYkcchI9yEnk2JoTx/FXVmIcx3Ea7rwOAAAAAITHuue0AAAAAMCPUVoAAAAAWI3SAgAAAMBqlBYAAAAAVqO0AAAAALAapQUAAACA1SgtAAAAAKxGaQEAAABgNUoLAAAAAKtRWgAAAABYjdICAAAAwGr/D2PC9UywgsqOAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing"
      ],
      "metadata": {
        "id": "U_eVriEyzqDC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.images.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WO96_EHHqlMa",
        "outputId": "148509b4-4bee-43fd-f287-115802f6aff6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1797, 8, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.images[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXN7ZBW_qxyZ",
        "outputId": "34e42c06-ec00-4fa6-a5c8-fec2fbbe3c29"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.],\n",
              "       [ 0.,  0., 13., 15., 10., 15.,  5.,  0.],\n",
              "       [ 0.,  3., 15.,  2.,  0., 11.,  8.,  0.],\n",
              "       [ 0.,  4., 12.,  0.,  0.,  8.,  8.,  0.],\n",
              "       [ 0.,  5.,  8.,  0.,  0.,  9.,  8.,  0.],\n",
              "       [ 0.,  4., 11.,  0.,  1., 12.,  7.,  0.],\n",
              "       [ 0.,  2., 14.,  5., 10., 12.,  0.,  0.],\n",
              "       [ 0.,  0.,  6., 13., 10.,  0.,  0.,  0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df.images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a4V_bGePq3Kl",
        "outputId": "1c57a57b-6e7a-4a59-cdd8-484fcfa36e02"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1797"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_samples= len(df.images)\n",
        "data = df.images.reshape((n_samples, -1))"
      ],
      "metadata": {
        "id": "g5wjkKeiq79h"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data [0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfBgtUB0rCDX",
        "outputId": "32222051-dfb6-40d4-a0a4-c9be11cb8505"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
              "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
              "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
              "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
              "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHrd1A0BrX0d",
        "outputId": "dc77869e-b1e5-4862-e41a-33b6dd4edcdc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(64,)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Szos6EIIrdqQ",
        "outputId": "d7e7818c-b65e-44ae-cad7-106403c84348"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1797, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Scaling Image Data"
      ],
      "metadata": {
        "id": "1OPcWDc2zzb3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "06T_7s8MrlRN",
        "outputId": "f90308f3-13f6-4e2e-b60c-4ed567d08251"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPvST5Hwrt1U",
        "outputId": "09492602-8a2f-42a9-a338-b55618830d48"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16.0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=data/16"
      ],
      "metadata": {
        "id": "b2sTZwHRruV6"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Or5p8UUrul5",
        "outputId": "6ddca9e5-bdac-4986-c95d-50a8fe2b3bc1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SoaQ0g0zr5J3",
        "outputId": "5a3ec010-cc02-4151-e319-0e1ce2041f1d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6DrlBK4r-Ih",
        "outputId": "8d6af4de-334d-4798-c98a-5c9a3f61625b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.    , 0.    , 0.3125, 0.8125, 0.5625, 0.0625, 0.    , 0.    ,\n",
              "       0.    , 0.    , 0.8125, 0.9375, 0.625 , 0.9375, 0.3125, 0.    ,\n",
              "       0.    , 0.1875, 0.9375, 0.125 , 0.    , 0.6875, 0.5   , 0.    ,\n",
              "       0.    , 0.25  , 0.75  , 0.    , 0.    , 0.5   , 0.5   , 0.    ,\n",
              "       0.    , 0.3125, 0.5   , 0.    , 0.    , 0.5625, 0.5   , 0.    ,\n",
              "       0.    , 0.25  , 0.6875, 0.    , 0.0625, 0.75  , 0.4375, 0.    ,\n",
              "       0.    , 0.125 , 0.875 , 0.3125, 0.625 , 0.75  , 0.    , 0.    ,\n",
              "       0.    , 0.    , 0.375 , 0.8125, 0.625 , 0.    , 0.    , 0.    ])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Test Split Data"
      ],
      "metadata": {
        "id": "-EscPUMwz6ks"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "E1YFy-ersTRa"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data, df.target, test_size=0.3)"
      ],
      "metadata": {
        "id": "S3GUbOYvsTZ_"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gM613-50sTi1",
        "outputId": "69880716-1ff6-4391-9ef7-e124f17fe0dd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1257, 64), (540, 64), (1257,), (540,))"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest Model"
      ],
      "metadata": {
        "id": "mwrAgoh90ApG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "vIPr2X-AuAhY"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier()"
      ],
      "metadata": {
        "id": "ldsdmjEluA0Y"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "yNwMXJ94umVK",
        "outputId": "888dc458-00a2-4d1c-90ee-1d110307fc0b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predict Test Data"
      ],
      "metadata": {
        "id": "VaG2ti4P0LDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rf.predict(X_test)"
      ],
      "metadata": {
        "id": "fwTpCVFGu4vw"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVmoSKcbu_F7",
        "outputId": "240077f5-5c8a-4b23-a180-5c0ae7a87cfb"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 2, 4, 0, 1, 9, 6, 9, 7, 9, 7, 9, 4, 7, 3, 0, 7, 4, 7, 9, 4, 0,\n",
              "       6, 1, 5, 1, 3, 3, 3, 5, 4, 6, 3, 2, 8, 9, 4, 5, 8, 0, 6, 7, 4, 5,\n",
              "       3, 1, 3, 2, 0, 0, 8, 2, 5, 0, 8, 0, 5, 2, 8, 7, 2, 0, 5, 4, 6, 7,\n",
              "       1, 0, 7, 9, 7, 3, 9, 6, 6, 2, 4, 9, 7, 6, 4, 0, 5, 4, 3, 0, 1, 5,\n",
              "       5, 8, 7, 4, 1, 5, 8, 3, 0, 8, 7, 4, 6, 8, 8, 1, 4, 1, 4, 2, 5, 6,\n",
              "       2, 5, 0, 5, 2, 5, 9, 5, 0, 9, 4, 9, 8, 9, 0, 5, 3, 8, 5, 9, 8, 2,\n",
              "       2, 7, 8, 8, 3, 8, 2, 6, 9, 3, 3, 8, 6, 8, 4, 0, 9, 9, 6, 3, 5, 7,\n",
              "       1, 2, 9, 0, 7, 9, 0, 9, 2, 6, 8, 5, 2, 9, 7, 4, 2, 4, 0, 5, 5, 9,\n",
              "       3, 4, 9, 0, 4, 8, 6, 1, 5, 1, 7, 2, 1, 8, 9, 8, 2, 8, 6, 4, 1, 8,\n",
              "       1, 1, 1, 2, 1, 2, 7, 7, 5, 6, 3, 4, 9, 0, 2, 8, 1, 0, 5, 6, 2, 2,\n",
              "       3, 6, 8, 6, 1, 5, 3, 3, 2, 4, 1, 4, 5, 6, 6, 4, 1, 7, 1, 0, 0, 6,\n",
              "       6, 6, 4, 5, 6, 1, 3, 0, 9, 5, 1, 5, 3, 0, 3, 9, 6, 9, 3, 8, 6, 9,\n",
              "       6, 5, 2, 0, 5, 8, 2, 8, 7, 7, 9, 7, 7, 5, 2, 2, 7, 3, 1, 6, 6, 8,\n",
              "       3, 5, 8, 6, 8, 2, 9, 4, 3, 9, 4, 8, 4, 8, 1, 0, 4, 2, 6, 3, 3, 9,\n",
              "       2, 1, 2, 3, 1, 9, 6, 9, 5, 5, 2, 7, 4, 8, 2, 6, 1, 5, 2, 9, 0, 4,\n",
              "       9, 8, 5, 2, 3, 8, 0, 4, 6, 2, 1, 7, 3, 6, 3, 5, 0, 7, 5, 7, 0, 9,\n",
              "       5, 8, 9, 4, 8, 8, 3, 3, 7, 6, 3, 1, 2, 5, 7, 6, 3, 0, 0, 0, 7, 3,\n",
              "       7, 3, 8, 7, 2, 2, 3, 2, 1, 8, 2, 4, 3, 7, 9, 6, 3, 6, 0, 5, 2, 7,\n",
              "       4, 2, 4, 0, 9, 3, 1, 7, 8, 8, 0, 7, 2, 4, 6, 6, 6, 4, 7, 0, 4, 3,\n",
              "       9, 1, 8, 5, 8, 8, 7, 8, 5, 6, 9, 3, 6, 0, 9, 8, 8, 0, 7, 6, 0, 3,\n",
              "       3, 6, 2, 2, 9, 5, 6, 4, 4, 6, 0, 2, 3, 8, 8, 0, 7, 1, 0, 4, 8, 3,\n",
              "       6, 2, 6, 1, 9, 9, 0, 3, 4, 0, 8, 4, 0, 7, 9, 2, 8, 0, 3, 9, 8, 1,\n",
              "       1, 7, 5, 0, 5, 6, 1, 7, 6, 1, 1, 6, 9, 5, 0, 6, 1, 2, 4, 0, 7, 6,\n",
              "       6, 5, 1, 1, 0, 2, 1, 9, 7, 9, 1, 6, 3, 8, 0, 8, 1, 2, 9, 9, 7, 1,\n",
              "       4, 6, 2, 6, 3, 3, 5, 6, 4, 3, 8, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Accuracy"
      ],
      "metadata": {
        "id": "IG0NIsph0W7N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "QtokF5uavlem"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LW5Xtma6v524",
        "outputId": "27e9a7f3-b96e-420d-bc7a-e18f6697c00c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[55,  0,  0,  0,  1,  0,  0,  0,  0,  0],\n",
              "       [ 0, 48,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0, 55,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0, 55,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0, 49,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0, 51,  0,  0,  0,  1],\n",
              "       [ 0,  0,  0,  0,  0,  0, 62,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0,  0,  0,  0, 49,  0,  0],\n",
              "       [ 0,  1,  0,  0,  0,  0,  0,  0, 58,  1],\n",
              "       [ 0,  0,  0,  1,  0,  0,  0,  0,  1, 52]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lpp6yVlQv6Gx",
        "outputId": "2f5ecf21-f8a1-4525-a72e-237f330432d0"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99        56\n",
            "           1       0.98      1.00      0.99        48\n",
            "           2       1.00      1.00      1.00        55\n",
            "           3       0.98      1.00      0.99        55\n",
            "           4       0.98      1.00      0.99        49\n",
            "           5       1.00      0.98      0.99        52\n",
            "           6       1.00      1.00      1.00        62\n",
            "           7       1.00      1.00      1.00        49\n",
            "           8       0.98      0.97      0.97        60\n",
            "           9       0.96      0.96      0.96        54\n",
            "\n",
            "    accuracy                           0.99       540\n",
            "   macro avg       0.99      0.99      0.99       540\n",
            "weighted avg       0.99      0.99      0.99       540\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "aR0mGjBFx4m0"
      }
    }
  ]
}