SaiPallavi_HandWritten_Digit_classification.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7iDA114s3Gb"
      },
      "source": [
        " **Importing libraries** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A-OpXBMtrPRk"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "# these are the three Python libraries that are imported with their local names as 'np' 'pd' and 'plt'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6oKm_Gds6Pj"
      },
      "source": [
        "Defining the **Euclidean distance function**\n",
        "\n",
        "This function is used for finding the distance between two given points"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GWSPLg9EtQhV"
      },
      "outputs": [],
      "source": [
        "def dist(x1, x2):\n",
        "    return np.sqrt(sum((x1-x2)**2))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78we-IxGu6h0"
      },
      "source": [
        "Defining the **K-NN Algorithm**:\n",
        "\n",
        "\n",
        "\n",
        "*   The function knn takes 3 parameters: pixel, label,  queryPoint and k\n",
        "*   'n' is the size of the list 'pixel' \n",
        "*   array is an empty list into which we append the differences of distances between the query point and every other point in the data.\n",
        "*   new_array is the new list which returns all the unique label elements and its count (number of occurances)\n",
        "*   index counts the most repeated elements in new_array and returns its value using the argmax() function\n",
        "*   prediction returns the value of label present for most number of times\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rBmLNKFovRGh"
      },
      "outputs": [],
      "source": [
        "def knn(pixel, label, queryPoint, k=5):\n",
        "    array = []\n",
        "    n = pixel.shape[0]\n",
        "    for i in range(n):\n",
        "        d = dist(queryPoint, pixel[i])\n",
        "        array.append((d, label[i]))\n",
        "    array = sorted(array)\n",
        "    array = array[:k]\n",
        "    array = np.array(array)\n",
        "    new_array = np.unique(array[:, 1], return_counts = True)\n",
        "    index = new_array[1].argmax()\n",
        "    prediction = new_array[0][index]\n",
        "    return prediction\n",
        "   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acYZnn98xLrx"
      },
      "source": [
        "Importing **MNIST data**\n",
        "\n",
        ".shape() function gives the lengths of array dimensions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2kI0VRTxTEw",
        "outputId": "70dba205-9762-402a-cd2f-d01dec9bc07e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(42000, 785)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/train.csv')\n",
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BEeHGqwc9fEv",
        "outputId": "817c9cf1-454a-4fa0-a18a-b8959a384635"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNaeyY6Hy4VE"
      },
      "source": [
        "Printing the **rows and columns** of the data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ag8POE7uzIQy",
        "outputId": "8d08bcd6-b97c-4fcc-e71e-2749edb20aed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['label', 'pixel0', 'pixel1', 'pixel2', 'pixel3', 'pixel4', 'pixel5',\n",
            "       'pixel6', 'pixel7', 'pixel8',\n",
            "       ...\n",
            "       'pixel774', 'pixel775', 'pixel776', 'pixel777', 'pixel778', 'pixel779',\n",
            "       'pixel780', 'pixel781', 'pixel782', 'pixel783'],\n",
            "      dtype='object', length=785)\n"
          ]
        }
      ],
      "source": [
        "print(df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "HP4Qs5O0zexv",
        "outputId": "5bd8ef54-558b-4425-9546-fa55086884e3"
      },
      "outputs": [
        {
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4c84a7b-1085-496f-aad7-18996f001f7d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      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-f4c84a7b-1085-496f-aad7-18996f001f7d 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-f4c84a7b-1085-496f-aad7-18996f001f7d');\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]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZUsE03QqzoBm"
      },
      "source": [
        "Storing all the values of the data in a variable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWm9vt3Nz7vy",
        "outputId": "d4b1690c-448e-4f97-f38b-634805a77c21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "data = df.values\n",
        "print(type(data))\n",
        "#converting the given data into a numpy array for performing required operations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UO-l4W3D0VCj"
      },
      "source": [
        "**Identifying or separating the labels and features of the data.**\n",
        "The MNIST data contains of labels(output) and features(inputs)\n",
        "\n",
        "we slice the data into pixels and labels where 'pixel' contain all the numbers in the form of pixels and 'label' conatins the numbers from '0 to 9' "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrolCcLz1UHO",
        "outputId": "6790f108-9df9-4290-f9c8-a6b4e3e409f4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]] [1 0 1 ... 7 6 9]\n",
            "(42000, 784) (42000,)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "pixel = data[:, 1:]\n",
        "label = data[:, 0]\n",
        "#the print statements are added for better understanding of slicing of data into pixels and labels \n",
        "print(pixel,label)\n",
        "print(pixel.shape, label.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eFYDPxoB5Whs"
      },
      "source": [
        "Dividing the data into **'training data' and 'testing data'**\n",
        "The given data is divided into two parts:\n",
        "*  Training data : this part of the data is trained according to the model\n",
        "*   Testing data : this part of the data is used to test the model.\n",
        "The built model is tested on 'testing data' and trained using 'training data'\n",
        "\n",
        "here, pixel is 2D array and label is a 1D array.\n",
        "\n",
        "in pixel_train we slice the pixel array till split that is till 41500 rows and have all the 784 columns\n",
        "\n",
        "in label_train we slice the label array till split that is till 41500 rows.\n",
        "so there are now 2 arrays which are mapped.\n",
        "\n",
        "in pixel_test we slice the other half/add the other half\n",
        "\n",
        "in label_test also we add the other half"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7rMhkYW7kS-",
        "outputId": "193c3f46-f3fc-4c45-87ab-c3dccdba5e4e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training data:  (41500, 784) (41500,)\n",
            "Testing data:  (500, 784) (500,)\n"
          ]
        }
      ],
      "source": [
        "# so we have 42000 pixels out of which 41500 pixels is used for 'training' and the rest for 'testomg'\n",
        "split = 41500 \n",
        "#training the data\n",
        "pixel_train = pixel[:split, :]\n",
        "label_train = label[:split]\n",
        "\n",
        "#testing the data\n",
        "pixel_test = pixel[split: , : ]\n",
        "label_test = label[split: ]\n",
        "\n",
        "print(\"Training data: \",pixel_train.shape,label_train.shape)\n",
        "print(\"Testing data: \",pixel_test.shape,label_test.shape)\n",
        "                   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JD4xhVU6F-q5"
      },
      "source": [
        "Defining a function for **displaying an image** of the data.\n",
        "\n",
        "imshow() is an inbuilt function used to show the image.\n",
        "\n",
        "we can give any random number from pixel_test as argument for the draw_img function as it just prints the image of number present at that place."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "sZ-_7nUiGjTI",
        "outputId": "b66312f4-7a4b-453c-8174-e21d58236790"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def drawImage(image):\n",
        "    img = image.reshape((28, 28))\n",
        "    plt.imshow(img, cmap='gray')\n",
        "    plt.show()\n",
        "\n",
        "drawImage(pixel_test[10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6YwDhBGG0H-",
        "outputId": "b224f045-ac4a-48e1-e18d-268fe1c64d6a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2\n"
          ]
        }
      ],
      "source": [
        "Pred = knn(pixel_train, label_train, pixel_test[10])\n",
        "print(int(Pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJk_hnnDRcTK"
      },
      "source": [
        "Checking the **Accuracy** of the Model\n",
        "\n",
        " \n",
        "\n",
        "*   the variable 'len1' is used to store all the values of the labels.\n",
        "*   'prediction[]' is an empty list which is used to store the value of the predicted label\n",
        "*   'predict' stores the result of knn algorithm, if 'predict' and label_test value is same the accuracy becomes  'one' showing the model runs efficiently.\n",
        "*    the accuracy is determined in percentage (%)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "fbahc1f8Beg7",
        "outputId": "b46f83c7-6d1d-4e73-91b7-7ca77899eb43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy of model : 98.6\n"
          ]
        }
      ],
      "source": [
        "len1 = (label_test.shape[0])\n",
        "corr_pred = 0.0\n",
        "prediction = []\n",
        "for i in range (len1):\n",
        "    predict = knn(pixel_train, label_train, pixel_test[i])\n",
        "    if predict == label_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*100}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3OylITo85oVM",
        "outputId": "4d4b837e-370b-44db-fce6-2cb612dcdaf1"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 7.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 1.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 9.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 3.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 9.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 4.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 8.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 8.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 0.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted output : 4.0\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "for i in range(10):\n",
        "    output = knn(pixel_train, label_train, pixel_test[i])\n",
        "    drawImage(pixel_test[i])\n",
        "    print(f\"Predicted output : {output}\\n\\n\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "SaiPallavi_HandWritten-Digit-classification.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}