ASuresh_Handwritten_Digit_Classification_KNN.ipynb

{
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  "metadata": {
    "colab": {
      "name": "ASuresh_Handwritten-Digit-Classification-KNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### **Name :** Anuganti Suresh\n",
        "\n",
        "### **Project :** HandWritten Digit Classifier"
      ],
      "metadata": {
        "id": "PrM4hksEcUTw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "----------------"
      ],
      "metadata": {
        "id": "LPckVoTacfdd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Data Description**"
      ],
      "metadata": {
        "id": "jFQh8pqEdlx1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The data files **train.csv** contain **gray-scale images** of hand-drawn digits, from zero through nine."
      ],
      "metadata": {
        "id": "BHo1CgtkdojG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Each image is **28 pixels in height and 28 pixels in width**, for a total of **784 pixels** in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This **pixel-value** is an integer **between 0 and 255**, inclusive."
      ],
      "metadata": {
        "id": "IM4LQd6-Tcha"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The training data set, (train.csv), has **785 columns**.\n",
        "\n",
        " - The **first column**, called **\"label\"**, is the **digit** that was **drawn by the user**.\n",
        " \n",
        " - The **rest of the columns** contain the **pixel-values** of the associated image."
      ],
      "metadata": {
        "id": "2HXWbuj_S3Z5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Each pixel column in the training set has a name like **pixelx**, where **x** is an integer **between 0 and 783**, inclusive. To locate this pixel on the image, suppose that we have decomposed x as x = i * 28 + j, where i and j are integers between 0 and 27, inclusive. Then pixelx is located on row i and column j of a 28 x 28 matrix, (indexing by zero)."
      ],
      "metadata": {
        "id": "Wbt7gpr_S3PC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- For example, **pixel31** indicates the pixel that is in the **fourth column from the left**, and the **second row from the top**, as in the ascii-diagram below."
      ],
      "metadata": {
        "id": "r5qQx_RqSuaq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Visually, if we omit the \"pixel\" prefix, the pixels make up the image like this:"
      ],
      "metadata": {
        "id": "gMksryAgSvBp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "_n0n8HtVSmvc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7cGQWx6QZtdH"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.options.display.max_info_columns = 800"
      ],
      "metadata": {
        "id": "Sxx7fwyeoGd6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dist(x1, x2):\n",
        "    return np.sqrt(sum((x1-x2)**2))"
      ],
      "metadata": {
        "id": "OXiwuO5EZu75"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def knn(x, y, queryPoint, k=5):\n",
        "    vals = []\n",
        "    m = x.shape[0]\n",
        "    for i in range(m):\n",
        "        d = dist(queryPoint, x[i])\n",
        "        vals.append((d, y[i]))\n",
        "    vals = sorted(vals)\n",
        "    vals = vals[:k]\n",
        "    vals = np.array(vals)\n",
        "    new_vals = np.unique(vals[:, 1], return_counts = True)\n",
        "    index = new_vals[1].argmax()\n",
        "    pred = new_vals[0][index]\n",
        "    return pred"
      ],
      "metadata": {
        "id": "csFZnl2BZu3h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JfaEaY8nZ49x",
        "outputId": "4f70033f-f34f-424b-9fdc-8e1d7fd82904"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/5_UAcelt/Hand-written-digit.zip')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "tJPtfTkUZuyh",
        "outputId": "adbe5f03-6273-4277-c5cb-0c8d2a222874"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1a96a2d1-07e0-4ad8-9539-1e3f16fff285 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1a96a2d1-07e0-4ad8-9539-1e3f16fff285');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   label  pixel0  pixel1  pixel2  ...  pixel780  pixel781  pixel782  pixel783\n",
              "0      1       0       0       0  ...         0         0         0         0\n",
              "1      0       0       0       0  ...         0         0         0         0\n",
              "2      1       0       0       0  ...         0         0         0         0\n",
              "3      4       0       0       0  ...         0         0         0         0\n",
              "4      0       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZoAwnueaEdZ",
        "outputId": "827bfd33-0d63-4116-d2a2-101b316616be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42000, 785)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **EDA**"
      ],
      "metadata": {
        "id": "_uW12Xhyprf1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.columns.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zF32Q9DYZut5",
        "outputId": "53ec1965-1606-4f67-8a07-f3f75d1fb339"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['label' 'pixel0' 'pixel1' 'pixel2' 'pixel3' 'pixel4' 'pixel5' 'pixel6'\n",
            " 'pixel7' 'pixel8' 'pixel9' 'pixel10' 'pixel11' 'pixel12' 'pixel13'\n",
            " 'pixel14' 'pixel15' 'pixel16' 'pixel17' 'pixel18' 'pixel19' 'pixel20'\n",
            " 'pixel21' 'pixel22' 'pixel23' 'pixel24' 'pixel25' 'pixel26' 'pixel27'\n",
            " 'pixel28' 'pixel29' 'pixel30' 'pixel31' 'pixel32' 'pixel33' 'pixel34'\n",
            " 'pixel35' 'pixel36' 'pixel37' 'pixel38' 'pixel39' 'pixel40' 'pixel41'\n",
            " 'pixel42' 'pixel43' 'pixel44' 'pixel45' 'pixel46' 'pixel47' 'pixel48'\n",
            " 'pixel49' 'pixel50' 'pixel51' 'pixel52' 'pixel53' 'pixel54' 'pixel55'\n",
            " 'pixel56' 'pixel57' 'pixel58' 'pixel59' 'pixel60' 'pixel61' 'pixel62'\n",
            " 'pixel63' 'pixel64' 'pixel65' 'pixel66' 'pixel67' 'pixel68' 'pixel69'\n",
            " 'pixel70' 'pixel71' 'pixel72' 'pixel73' 'pixel74' 'pixel75' 'pixel76'\n",
            " 'pixel77' 'pixel78' 'pixel79' 'pixel80' 'pixel81' 'pixel82' 'pixel83'\n",
            " 'pixel84' 'pixel85' 'pixel86' 'pixel87' 'pixel88' 'pixel89' 'pixel90'\n",
            " 'pixel91' 'pixel92' 'pixel93' 'pixel94' 'pixel95' 'pixel96' 'pixel97'\n",
            " 'pixel98' 'pixel99' 'pixel100' 'pixel101' 'pixel102' 'pixel103'\n",
            " 'pixel104' 'pixel105' 'pixel106' 'pixel107' 'pixel108' 'pixel109'\n",
            " 'pixel110' 'pixel111' 'pixel112' 'pixel113' 'pixel114' 'pixel115'\n",
            " 'pixel116' 'pixel117' 'pixel118' 'pixel119' 'pixel120' 'pixel121'\n",
            " 'pixel122' 'pixel123' 'pixel124' 'pixel125' 'pixel126' 'pixel127'\n",
            " 'pixel128' 'pixel129' 'pixel130' 'pixel131' 'pixel132' 'pixel133'\n",
            " 'pixel134' 'pixel135' 'pixel136' 'pixel137' 'pixel138' 'pixel139'\n",
            " 'pixel140' 'pixel141' 'pixel142' 'pixel143' 'pixel144' 'pixel145'\n",
            " 'pixel146' 'pixel147' 'pixel148' 'pixel149' 'pixel150' 'pixel151'\n",
            " 'pixel152' 'pixel153' 'pixel154' 'pixel155' 'pixel156' 'pixel157'\n",
            " 'pixel158' 'pixel159' 'pixel160' 'pixel161' 'pixel162' 'pixel163'\n",
            " 'pixel164' 'pixel165' 'pixel166' 'pixel167' 'pixel168' 'pixel169'\n",
            " 'pixel170' 'pixel171' 'pixel172' 'pixel173' 'pixel174' 'pixel175'\n",
            " 'pixel176' 'pixel177' 'pixel178' 'pixel179' 'pixel180' 'pixel181'\n",
            " 'pixel182' 'pixel183' 'pixel184' 'pixel185' 'pixel186' 'pixel187'\n",
            " 'pixel188' 'pixel189' 'pixel190' 'pixel191' 'pixel192' 'pixel193'\n",
            " 'pixel194' 'pixel195' 'pixel196' 'pixel197' 'pixel198' 'pixel199'\n",
            " 'pixel200' 'pixel201' 'pixel202' 'pixel203' 'pixel204' 'pixel205'\n",
            " 'pixel206' 'pixel207' 'pixel208' 'pixel209' 'pixel210' 'pixel211'\n",
            " 'pixel212' 'pixel213' 'pixel214' 'pixel215' 'pixel216' 'pixel217'\n",
            " 'pixel218' 'pixel219' 'pixel220' 'pixel221' 'pixel222' 'pixel223'\n",
            " 'pixel224' 'pixel225' 'pixel226' 'pixel227' 'pixel228' 'pixel229'\n",
            " 'pixel230' 'pixel231' 'pixel232' 'pixel233' 'pixel234' 'pixel235'\n",
            " 'pixel236' 'pixel237' 'pixel238' 'pixel239' 'pixel240' 'pixel241'\n",
            " 'pixel242' 'pixel243' 'pixel244' 'pixel245' 'pixel246' 'pixel247'\n",
            " 'pixel248' 'pixel249' 'pixel250' 'pixel251' 'pixel252' 'pixel253'\n",
            " 'pixel254' 'pixel255' 'pixel256' 'pixel257' 'pixel258' 'pixel259'\n",
            " 'pixel260' 'pixel261' 'pixel262' 'pixel263' 'pixel264' 'pixel265'\n",
            " 'pixel266' 'pixel267' 'pixel268' 'pixel269' 'pixel270' 'pixel271'\n",
            " 'pixel272' 'pixel273' 'pixel274' 'pixel275' 'pixel276' 'pixel277'\n",
            " 'pixel278' 'pixel279' 'pixel280' 'pixel281' 'pixel282' 'pixel283'\n",
            " 'pixel284' 'pixel285' 'pixel286' 'pixel287' 'pixel288' 'pixel289'\n",
            " 'pixel290' 'pixel291' 'pixel292' 'pixel293' 'pixel294' 'pixel295'\n",
            " 'pixel296' 'pixel297' 'pixel298' 'pixel299' 'pixel300' 'pixel301'\n",
            " 'pixel302' 'pixel303' 'pixel304' 'pixel305' 'pixel306' 'pixel307'\n",
            " 'pixel308' 'pixel309' 'pixel310' 'pixel311' 'pixel312' 'pixel313'\n",
            " 'pixel314' 'pixel315' 'pixel316' 'pixel317' 'pixel318' 'pixel319'\n",
            " 'pixel320' 'pixel321' 'pixel322' 'pixel323' 'pixel324' 'pixel325'\n",
            " 'pixel326' 'pixel327' 'pixel328' 'pixel329' 'pixel330' 'pixel331'\n",
            " 'pixel332' 'pixel333' 'pixel334' 'pixel335' 'pixel336' 'pixel337'\n",
            " 'pixel338' 'pixel339' 'pixel340' 'pixel341' 'pixel342' 'pixel343'\n",
            " 'pixel344' 'pixel345' 'pixel346' 'pixel347' 'pixel348' 'pixel349'\n",
            " 'pixel350' 'pixel351' 'pixel352' 'pixel353' 'pixel354' 'pixel355'\n",
            " 'pixel356' 'pixel357' 'pixel358' 'pixel359' 'pixel360' 'pixel361'\n",
            " 'pixel362' 'pixel363' 'pixel364' 'pixel365' 'pixel366' 'pixel367'\n",
            " 'pixel368' 'pixel369' 'pixel370' 'pixel371' 'pixel372' 'pixel373'\n",
            " 'pixel374' 'pixel375' 'pixel376' 'pixel377' 'pixel378' 'pixel379'\n",
            " 'pixel380' 'pixel381' 'pixel382' 'pixel383' 'pixel384' 'pixel385'\n",
            " 'pixel386' 'pixel387' 'pixel388' 'pixel389' 'pixel390' 'pixel391'\n",
            " 'pixel392' 'pixel393' 'pixel394' 'pixel395' 'pixel396' 'pixel397'\n",
            " 'pixel398' 'pixel399' 'pixel400' 'pixel401' 'pixel402' 'pixel403'\n",
            " 'pixel404' 'pixel405' 'pixel406' 'pixel407' 'pixel408' 'pixel409'\n",
            " 'pixel410' 'pixel411' 'pixel412' 'pixel413' 'pixel414' 'pixel415'\n",
            " 'pixel416' 'pixel417' 'pixel418' 'pixel419' 'pixel420' 'pixel421'\n",
            " 'pixel422' 'pixel423' 'pixel424' 'pixel425' 'pixel426' 'pixel427'\n",
            " 'pixel428' 'pixel429' 'pixel430' 'pixel431' 'pixel432' 'pixel433'\n",
            " 'pixel434' 'pixel435' 'pixel436' 'pixel437' 'pixel438' 'pixel439'\n",
            " 'pixel440' 'pixel441' 'pixel442' 'pixel443' 'pixel444' 'pixel445'\n",
            " 'pixel446' 'pixel447' 'pixel448' 'pixel449' 'pixel450' 'pixel451'\n",
            " 'pixel452' 'pixel453' 'pixel454' 'pixel455' 'pixel456' 'pixel457'\n",
            " 'pixel458' 'pixel459' 'pixel460' 'pixel461' 'pixel462' 'pixel463'\n",
            " 'pixel464' 'pixel465' 'pixel466' 'pixel467' 'pixel468' 'pixel469'\n",
            " 'pixel470' 'pixel471' 'pixel472' 'pixel473' 'pixel474' 'pixel475'\n",
            " 'pixel476' 'pixel477' 'pixel478' 'pixel479' 'pixel480' 'pixel481'\n",
            " 'pixel482' 'pixel483' 'pixel484' 'pixel485' 'pixel486' 'pixel487'\n",
            " 'pixel488' 'pixel489' 'pixel490' 'pixel491' 'pixel492' 'pixel493'\n",
            " 'pixel494' 'pixel495' 'pixel496' 'pixel497' 'pixel498' 'pixel499'\n",
            " 'pixel500' 'pixel501' 'pixel502' 'pixel503' 'pixel504' 'pixel505'\n",
            " 'pixel506' 'pixel507' 'pixel508' 'pixel509' 'pixel510' 'pixel511'\n",
            " 'pixel512' 'pixel513' 'pixel514' 'pixel515' 'pixel516' 'pixel517'\n",
            " 'pixel518' 'pixel519' 'pixel520' 'pixel521' 'pixel522' 'pixel523'\n",
            " 'pixel524' 'pixel525' 'pixel526' 'pixel527' 'pixel528' 'pixel529'\n",
            " 'pixel530' 'pixel531' 'pixel532' 'pixel533' 'pixel534' 'pixel535'\n",
            " 'pixel536' 'pixel537' 'pixel538' 'pixel539' 'pixel540' 'pixel541'\n",
            " 'pixel542' 'pixel543' 'pixel544' 'pixel545' 'pixel546' 'pixel547'\n",
            " 'pixel548' 'pixel549' 'pixel550' 'pixel551' 'pixel552' 'pixel553'\n",
            " 'pixel554' 'pixel555' 'pixel556' 'pixel557' 'pixel558' 'pixel559'\n",
            " 'pixel560' 'pixel561' 'pixel562' 'pixel563' 'pixel564' 'pixel565'\n",
            " 'pixel566' 'pixel567' 'pixel568' 'pixel569' 'pixel570' 'pixel571'\n",
            " 'pixel572' 'pixel573' 'pixel574' 'pixel575' 'pixel576' 'pixel577'\n",
            " 'pixel578' 'pixel579' 'pixel580' 'pixel581' 'pixel582' 'pixel583'\n",
            " 'pixel584' 'pixel585' 'pixel586' 'pixel587' 'pixel588' 'pixel589'\n",
            " 'pixel590' 'pixel591' 'pixel592' 'pixel593' 'pixel594' 'pixel595'\n",
            " 'pixel596' 'pixel597' 'pixel598' 'pixel599' 'pixel600' 'pixel601'\n",
            " 'pixel602' 'pixel603' 'pixel604' 'pixel605' 'pixel606' 'pixel607'\n",
            " 'pixel608' 'pixel609' 'pixel610' 'pixel611' 'pixel612' 'pixel613'\n",
            " 'pixel614' 'pixel615' 'pixel616' 'pixel617' 'pixel618' 'pixel619'\n",
            " 'pixel620' 'pixel621' 'pixel622' 'pixel623' 'pixel624' 'pixel625'\n",
            " 'pixel626' 'pixel627' 'pixel628' 'pixel629' 'pixel630' 'pixel631'\n",
            " 'pixel632' 'pixel633' 'pixel634' 'pixel635' 'pixel636' 'pixel637'\n",
            " 'pixel638' 'pixel639' 'pixel640' 'pixel641' 'pixel642' 'pixel643'\n",
            " 'pixel644' 'pixel645' 'pixel646' 'pixel647' 'pixel648' 'pixel649'\n",
            " 'pixel650' 'pixel651' 'pixel652' 'pixel653' 'pixel654' 'pixel655'\n",
            " 'pixel656' 'pixel657' 'pixel658' 'pixel659' 'pixel660' 'pixel661'\n",
            " 'pixel662' 'pixel663' 'pixel664' 'pixel665' 'pixel666' 'pixel667'\n",
            " 'pixel668' 'pixel669' 'pixel670' 'pixel671' 'pixel672' 'pixel673'\n",
            " 'pixel674' 'pixel675' 'pixel676' 'pixel677' 'pixel678' 'pixel679'\n",
            " 'pixel680' 'pixel681' 'pixel682' 'pixel683' 'pixel684' 'pixel685'\n",
            " 'pixel686' 'pixel687' 'pixel688' 'pixel689' 'pixel690' 'pixel691'\n",
            " 'pixel692' 'pixel693' 'pixel694' 'pixel695' 'pixel696' 'pixel697'\n",
            " 'pixel698' 'pixel699' 'pixel700' 'pixel701' 'pixel702' 'pixel703'\n",
            " 'pixel704' 'pixel705' 'pixel706' 'pixel707' 'pixel708' 'pixel709'\n",
            " 'pixel710' 'pixel711' 'pixel712' 'pixel713' 'pixel714' 'pixel715'\n",
            " 'pixel716' 'pixel717' 'pixel718' 'pixel719' 'pixel720' 'pixel721'\n",
            " 'pixel722' 'pixel723' 'pixel724' 'pixel725' 'pixel726' 'pixel727'\n",
            " 'pixel728' 'pixel729' 'pixel730' 'pixel731' 'pixel732' 'pixel733'\n",
            " 'pixel734' 'pixel735' 'pixel736' 'pixel737' 'pixel738' 'pixel739'\n",
            " 'pixel740' 'pixel741' 'pixel742' 'pixel743' 'pixel744' 'pixel745'\n",
            " 'pixel746' 'pixel747' 'pixel748' 'pixel749' 'pixel750' 'pixel751'\n",
            " 'pixel752' 'pixel753' 'pixel754' 'pixel755' 'pixel756' 'pixel757'\n",
            " 'pixel758' 'pixel759' 'pixel760' 'pixel761' 'pixel762' 'pixel763'\n",
            " 'pixel764' 'pixel765' 'pixel766' 'pixel767' 'pixel768' 'pixel769'\n",
            " 'pixel770' 'pixel771' 'pixel772' 'pixel773' 'pixel774' 'pixel775'\n",
            " 'pixel776' 'pixel777' 'pixel778' 'pixel779' 'pixel780' 'pixel781'\n",
            " 'pixel782' 'pixel783']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-zGc26Z1oeQw",
        "outputId": "53931384-ff8f-4f01-d0f5-ec0c9209bbe8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 42000 entries, 0 to 41999\n",
            "Data columns (total 785 columns):\n",
            " #   Column    Non-Null Count  Dtype\n",
            "---  ------    --------------  -----\n",
            " 0   label     42000 non-null  int64\n",
            " 1   pixel0    42000 non-null  int64\n",
            " 2   pixel1    42000 non-null  int64\n",
            " 3   pixel2    42000 non-null  int64\n",
            " 4   pixel3    42000 non-null  int64\n",
            " 5   pixel4    42000 non-null  int64\n",
            " 6   pixel5    42000 non-null  int64\n",
            " 7   pixel6    42000 non-null  int64\n",
            " 8   pixel7    42000 non-null  int64\n",
            " 9   pixel8    42000 non-null  int64\n",
            " 10  pixel9    42000 non-null  int64\n",
            " 11  pixel10   42000 non-null  int64\n",
            " 12  pixel11   42000 non-null  int64\n",
            " 13  pixel12   42000 non-null  int64\n",
            " 14  pixel13   42000 non-null  int64\n",
            " 15  pixel14   42000 non-null  int64\n",
            " 16  pixel15   42000 non-null  int64\n",
            " 17  pixel16   42000 non-null  int64\n",
            " 18  pixel17   42000 non-null  int64\n",
            " 19  pixel18   42000 non-null  int64\n",
            " 20  pixel19   42000 non-null  int64\n",
            " 21  pixel20   42000 non-null  int64\n",
            " 22  pixel21   42000 non-null  int64\n",
            " 23  pixel22   42000 non-null  int64\n",
            " 24  pixel23   42000 non-null  int64\n",
            " 25  pixel24   42000 non-null  int64\n",
            " 26  pixel25   42000 non-null  int64\n",
            " 27  pixel26   42000 non-null  int64\n",
            " 28  pixel27   42000 non-null  int64\n",
            " 29  pixel28   42000 non-null  int64\n",
            " 30  pixel29   42000 non-null  int64\n",
            " 31  pixel30   42000 non-null  int64\n",
            " 32  pixel31   42000 non-null  int64\n",
            " 33  pixel32   42000 non-null  int64\n",
            " 34  pixel33   42000 non-null  int64\n",
            " 35  pixel34   42000 non-null  int64\n",
            " 36  pixel35   42000 non-null  int64\n",
            " 37  pixel36   42000 non-null  int64\n",
            " 38  pixel37   42000 non-null  int64\n",
            " 39  pixel38   42000 non-null  int64\n",
            " 40  pixel39   42000 non-null  int64\n",
            " 41  pixel40   42000 non-null  int64\n",
            " 42  pixel41   42000 non-null  int64\n",
            " 43  pixel42   42000 non-null  int64\n",
            " 44  pixel43   42000 non-null  int64\n",
            " 45  pixel44   42000 non-null  int64\n",
            " 46  pixel45   42000 non-null  int64\n",
            " 47  pixel46   42000 non-null  int64\n",
            " 48  pixel47   42000 non-null  int64\n",
            " 49  pixel48   42000 non-null  int64\n",
            " 50  pixel49   42000 non-null  int64\n",
            " 51  pixel50   42000 non-null  int64\n",
            " 52  pixel51   42000 non-null  int64\n",
            " 53  pixel52   42000 non-null  int64\n",
            " 54  pixel53   42000 non-null  int64\n",
            " 55  pixel54   42000 non-null  int64\n",
            " 56  pixel55   42000 non-null  int64\n",
            " 57  pixel56   42000 non-null  int64\n",
            " 58  pixel57   42000 non-null  int64\n",
            " 59  pixel58   42000 non-null  int64\n",
            " 60  pixel59   42000 non-null  int64\n",
            " 61  pixel60   42000 non-null  int64\n",
            " 62  pixel61   42000 non-null  int64\n",
            " 63  pixel62   42000 non-null  int64\n",
            " 64  pixel63   42000 non-null  int64\n",
            " 65  pixel64   42000 non-null  int64\n",
            " 66  pixel65   42000 non-null  int64\n",
            " 67  pixel66   42000 non-null  int64\n",
            " 68  pixel67   42000 non-null  int64\n",
            " 69  pixel68   42000 non-null  int64\n",
            " 70  pixel69   42000 non-null  int64\n",
            " 71  pixel70   42000 non-null  int64\n",
            " 72  pixel71   42000 non-null  int64\n",
            " 73  pixel72   42000 non-null  int64\n",
            " 74  pixel73   42000 non-null  int64\n",
            " 75  pixel74   42000 non-null  int64\n",
            " 76  pixel75   42000 non-null  int64\n",
            " 77  pixel76   42000 non-null  int64\n",
            " 78  pixel77   42000 non-null  int64\n",
            " 79  pixel78   42000 non-null  int64\n",
            " 80  pixel79   42000 non-null  int64\n",
            " 81  pixel80   42000 non-null  int64\n",
            " 82  pixel81   42000 non-null  int64\n",
            " 83  pixel82   42000 non-null  int64\n",
            " 84  pixel83   42000 non-null  int64\n",
            " 85  pixel84   42000 non-null  int64\n",
            " 86  pixel85   42000 non-null  int64\n",
            " 87  pixel86   42000 non-null  int64\n",
            " 88  pixel87   42000 non-null  int64\n",
            " 89  pixel88   42000 non-null  int64\n",
            " 90  pixel89   42000 non-null  int64\n",
            " 91  pixel90   42000 non-null  int64\n",
            " 92  pixel91   42000 non-null  int64\n",
            " 93  pixel92   42000 non-null  int64\n",
            " 94  pixel93   42000 non-null  int64\n",
            " 95  pixel94   42000 non-null  int64\n",
            " 96  pixel95   42000 non-null  int64\n",
            " 97  pixel96   42000 non-null  int64\n",
            " 98  pixel97   42000 non-null  int64\n",
            " 99  pixel98   42000 non-null  int64\n",
            " 100 pixel99   42000 non-null  int64\n",
            " 101 pixel100  42000 non-null  int64\n",
            " 102 pixel101  42000 non-null  int64\n",
            " 103 pixel102  42000 non-null  int64\n",
            " 104 pixel103  42000 non-null  int64\n",
            " 105 pixel104  42000 non-null  int64\n",
            " 106 pixel105  42000 non-null  int64\n",
            " 107 pixel106  42000 non-null  int64\n",
            " 108 pixel107  42000 non-null  int64\n",
            " 109 pixel108  42000 non-null  int64\n",
            " 110 pixel109  42000 non-null  int64\n",
            " 111 pixel110  42000 non-null  int64\n",
            " 112 pixel111  42000 non-null  int64\n",
            " 113 pixel112  42000 non-null  int64\n",
            " 114 pixel113  42000 non-null  int64\n",
            " 115 pixel114  42000 non-null  int64\n",
            " 116 pixel115  42000 non-null  int64\n",
            " 117 pixel116  42000 non-null  int64\n",
            " 118 pixel117  42000 non-null  int64\n",
            " 119 pixel118  42000 non-null  int64\n",
            " 120 pixel119  42000 non-null  int64\n",
            " 121 pixel120  42000 non-null  int64\n",
            " 122 pixel121  42000 non-null  int64\n",
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            " 784 pixel783  42000 non-null  int64\n",
            "dtypes: int64(785)\n",
            "memory usage: 251.5 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.values\n",
        "# data.shape\n",
        "print(type(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9smSUYLQaEZo",
        "outputId": "caa3f226-a898-4d88-bad2-8f16f5fa751a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Target Variable**"
      ],
      "metadata": {
        "id": "rhAMXIDbmwhi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "set(df.label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tmBOr2umpAx",
        "outputId": "44aff467-21cf-4fd4-82d9-7ecbb9ec5d97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zrg4Xtv2m09K",
        "outputId": "56e8b319-62aa-479e-dd52-029f8b46b13e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    4684\n",
              "7    4401\n",
              "3    4351\n",
              "9    4188\n",
              "2    4177\n",
              "6    4137\n",
              "0    4132\n",
              "4    4072\n",
              "8    4063\n",
              "5    3795\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the distribuation of labels\n",
        "sns.countplot(x=df[\"label\"]);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "WPgwqTdjm7S7",
        "outputId": "fedda38c-b14d-468a-e255-c07ce4cbe3fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Model Building and Evaluation**"
      ],
      "metadata": {
        "id": "7qIjO64cpy0L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# pixels\n",
        "x = data[:, 1:]\n",
        "\n",
        "# label\n",
        "y = data[:, 0]\n",
        "\n",
        "print(x.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGrQ5dcYaNph",
        "outputId": "b2b037fe-730c-4767-b981-4701ef246043"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(42000, 784) (42000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# put 80 % data for training and rest 20 for testing\n",
        "# 28*28 pixels = 784\n",
        "# split = int(0.95*(x.shape[0]))\n",
        "\n",
        "split = 41000\n",
        "x_train = x[:split, :]\n",
        "x_test = x[split:, :]\n",
        "\n",
        "y_train = y[:split]\n",
        "y_test = y[split:]\n",
        "\n",
        "print(\"Training data shape : \", x_train.shape, y_train.shape)\n",
        "print(\"Testing data shape : \", x_test.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hi9U5qz3aNmB",
        "outputId": "8fd09287-ac5e-4e8f-bbf3-3a615cb842df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape :  (41000, 784) (41000,)\n",
            "Testing data shape :  (1000, 784) (1000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def drawImg(sample):\n",
        "    img = sample.reshape((28, 28))\n",
        "    plt.imshow(img, cmap='gray')\n",
        "    plt.show()\n",
        "\n",
        "drawImg(x_test[110])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "q6P-x6e-aNip",
        "outputId": "1adcaa86-68cc-429d-b327-f426f2aa6760"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Prediction = knn(x_train, y_train, x_test[11])\n",
        "print(int(Prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kjyiB1dpaVDw",
        "outputId": "8944e494-d9c7-419a-fbdb-b16bf2c0fe40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len1 = (y_test.shape[0])\n",
        "corr_pred = 0.0\n",
        "prediction = []\n",
        "for i in range (len1):\n",
        "    pred = knn(x_train, y_train, x_test[i])\n",
        "    if pred == y_test[i]:\n",
        "        corr_pred = corr_pred + 1\n",
        "\n",
        "# accuracy = correct_predictions/total_predictions\n",
        "accuracy = corr_pred/len1\n",
        "\n",
        "print(f\"Accuracy of model : {accuracy}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmI8mODdaU-Y",
        "outputId": "184df878-bf7b-47e0-dd64-7139b9f9b7a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of model : 0.972\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(10):\n",
        "    label = knn(x_train, y_train, x_test[i])\n",
        "    drawImg(x_test[i])\n",
        "    print(f\"Label : {label}\\n\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KzR34xg4aU6S",
        "outputId": "ce57c02e-eea6-47df-e4c3-7c8c0428ef05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 2.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 8.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 1.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gCDsQxP8DipZ1BX09zXcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 8.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NqKm3GWrtor8taVv2c33d712ir668b3xdFgiCA3RAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EMT/Aya3eoyO3IYdAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 0.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 1.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 3.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 8.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 1.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1BGt4Rb1dp55D9A8lbch+bq76tUv01ZTXjctlgSA4QQcEQdiBIAg7EARhB4Ig7EAQhB0IgrADQfwva+OO+6vb350AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 0.0\n",
            "\n",
            "\n"
          ]
        }
      ]
    }
  ]
}