Shrishti_Singh_handwritten_digit_classification.ipynb

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    "colab": {
      "name": "handwritten_digit_classification.ipynb",
      "provenance": [],
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      "mount_file_id": "1SeqURZgZjwfQpId3ytW8iDqHvfy_aOUh",
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      "include_colab_link": true
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      "name": "python3",
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      "cell_type": "markdown",
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        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ss-shrishi2000/Handwritten-Digit-Classification-1/blob/main/handwritten_digit_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U -q PyDrive\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials"
      ],
      "metadata": {
        "id": "sW_txO1SAuir"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "metadata": {
        "id": "Tw53NnKXBfSI"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "downloaded = drive.CreateFile({'id':'1botJS1K3yidi_02fbrwxwPDMB5pzQoqQ'}) # replace the id with id of file you want to access\n",
        "downloaded.GetContentFile('mnist_train.csv') "
      ],
      "metadata": {
        "id": "AL2SHjKWGF_Z"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('mnist_train.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "jjX_ILsXHSZz",
        "outputId": "8441e808-b35a-4496-f6ed-5ec872126f96"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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",
              "      <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-4300b977-1dc4-41fd-8631-bbb9d7c24fb6')\"\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-4300b977-1dc4-41fd-8631-bbb9d7c24fb6 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-4300b977-1dc4-41fd-8631-bbb9d7c24fb6');\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  1x1  1x2  1x3  1x4  1x5  ...  28x23  28x24  28x25  28x26  28x27  28x28\n",
              "0      5    0    0    0    0    0  ...      0      0      0      0      0      0\n",
              "1      0    0    0    0    0    0  ...      0      0      0      0      0      0\n",
              "2      4    0    0    0    0    0  ...      0      0      0      0      0      0\n",
              "3      1    0    0    0    0    0  ...      0      0      0      0      0      0\n",
              "4      9    0    0    0    0    0  ...      0      0      0      0      0      0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def dist(x1, x2):\n",
        "    return np.sqrt(sum((x1-x2)**2))"
      ],
      "metadata": {
        "id": "JoCUMtDhJc43"
      },
      "execution_count": 21,
      "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": "us1mpHF3Jgq8"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GQu7HAOKVts",
        "outputId": "fb799647-e00f-4fb0-943a-53b10eaf0208"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 785)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('mnist_train.csv')\n",
        "data = np.array(df)"
      ],
      "metadata": {
        "id": "vvtF0sIvJ2wI"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-DKuER1DK0dJ",
        "outputId": "0a56efb8-3b89-47ac-98af-5be3e2847163"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [4, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [5, 0, 0, ..., 0, 0, 0],\n",
              "       [6, 0, 0, ..., 0, 0, 0],\n",
              "       [8, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pixels\n",
        "x = data[:, 1:]\n",
        "# label\n",
        "y = data[:, 0]\n",
        "\n",
        "print(x.shape, y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWWErk_7LG06",
        "outputId": "a7cf606d-20e6-4c9a-ccc7-164122d08746"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 784) (60000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label = data[:, 0]"
      ],
      "metadata": {
        "id": "ThkXhbhkM6fv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sLYJagj3NCM1",
        "outputId": "12264444-45d4-4d05-e56a-9a2362490a37"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "split = 50000\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": "W52Su_piNMJr",
        "outputId": "a31dcc1d-1420-49d9-cbb8-0e896e6e30f3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape :  (50000, 784) (50000,)\n",
            "Testing data shape :  (10000, 784) (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def drawImg(sample):\n",
        "    img = sample.reshape((28, 28))\n",
        "    plt.imshow(img, cmap='Reds_r')\n",
        "    plt.show()\n",
        "\n",
        "drawImg(x_test[108])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "WHLB-yIKIvNU",
        "outputId": "5283fa40-7fbc-4be4-89c6-d7f1849b3b9a"
      },
      "execution_count": 19,
      "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[108])\n",
        "print(int(Prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKl5rRIWJUg1",
        "outputId": "133adc24-9fd0-49d5-ca0f-0cf38b46a01d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def drawImg(sample):\n",
        "    img = sample.reshape((28, 28))\n",
        "    plt.imshow(img, cmap='Reds_r')\n",
        "    plt.show()\n",
        "\n",
        "drawImg(x_test[16])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "VT-dFQ_FJ0G9",
        "outputId": "a149be6c-0f51-4fe7-8288-db599153b8fd"
      },
      "execution_count": 25,
      "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[16])\n",
        "print(int(Prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92nxKWtKJ7s0",
        "outputId": "a6800847-0c67-4d95-9c32-4b44230a7254"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def drawImg(sample):\n",
        "    img = sample.reshape((28, 28))\n",
        "    plt.imshow(img, cmap='Reds_r')\n",
        "    plt.show()\n",
        "\n",
        "drawImg(x_test[35])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "QNf8GTQ2KI7W",
        "outputId": "7a52ab4c-9474-4aea-c309-12e1b0ebdcc2"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAObklEQVR4nO3df5BV9XnH8c+zG2AViIGKyBCSWEPjkDbBZkuTyDDaTA1qRlRmTOyUUmtm7YzOaGLSOOYPSdOZmk6I06mNLVGUpkbrBFE6ZYhIMmOMbcaFIuCPCDEYobiLIZZFgrC7T//YQ7qL93zv3XvPveeG5/2a2bl3z3O/e5+5w4dz7/nec77m7gJw6usouwEArUHYgSAIOxAEYQeCIOxAEO9o5ZN1mflU/n8BmmZAwzrqbpVqDYXdzBZL+ntJnZLucfc7Uo+fqg4t1emNPCWAhLU6klurezdrZp2S/lHSJZLmSbrGzObV+/cANFcj76kXSNrt7i+7+zFJD0laUkxbAIrWSNhnS3p11O97s21jmFmPmfWaWe9R8W09oCxNP1rm7qvcvdvdu7tU8bgBgBZoJOz7JM0Z9fu7s20A2lAjYX9G0lwzO8fMJkr6jKT1xbQFoGh1T725+6CZ3SjpexqZelvt7s8V1hmAQjU0z+7uGyRtKKgXAE3E19mAIAg7EARhB4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEIQdCKKlSzaj/cyamP4n8OW/XJSsd3713mTd+36WWzv2lS8lxz77xK5k/b6+N5J1jMWeHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeCYJ79FLfsrDOS9Y9uuCdZ7/jAHybr/vP0Kt02Y05ubcLHPpIc273sT5P1+xbflKxjrIbCbmZ7JA1IGpI06O7dRTQFoHhF7NkvcvfXC/g7AJqIz+xAEI2G3SU9bmZbzKyn0gPMrMfMes2s96i8wacDUK9G38YvdPd9ZnaWpE1m9qK7Pzn6Ae6+StIqSZphnaQdKElDe3Z335fd9ktaJ2lBEU0BKF7dYTezyWY29cR9SRdL2llUYwCK1cjb+JmS1pnZib/zHXffWEhXbei9kybk1o4MDyfHHjg+VHQ7Y3z8nV35tZ0/So71vS8l60+c88FkfcPBN5P1hYnervrp1uRYDRxM1zEudYfd3V+W9OECewHQREy9AUEQdiAIwg4EQdiBIAg7EASnuNboQ5Mn5dZ+MTiYHNvo1NuEkenNXH/yhcvzi1XG/mjxtcn62tcHkvVqNr1xJLd2VbWptdPemSyfd9rEZP3FXx1L//1g2LMDQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBDMs9fo+4n54sEmX3/n7ImdyXrn576eW/vVtYk5eEkP9P9vXT0VYejR1cl652dvT9bnMs8+LuzZgSAIOxAEYQeCIOxAEIQdCIKwA0EQdiAI5tlr9GaVy0U30/mT8y/HLEl+4Oe5ta8+uqPodsZlckf+/qRzacUVw/7fofR6oVsPH62npbDYswNBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEMyz/waY2JG+9ruGjueWBr3JJ9tX8bXbl+YX33V2cuzw9x9K1vcdS1+vH2NV3bOb2Woz6zeznaO2TTezTWa2K7ud1tw2ATSqlrfx90tafNK2WyVtdve5kjZnvwNoY1XD7u5PSjp5nZ4lktZk99dIuqLgvgAUrN7P7DPdfX92/zVJM/MeaGY9knokaYqqfPYE0DQNH413d5eUexTI3Ve5e7e7d3cRdqA09Ya9z8xmSVJ2219cSwCaod6wr5e0PLu/XNJjxbQDoFnMq8zDmtmDki6UdKakPkm3S3pU0sOS3iPpFUlXu3uVxbalGdbpS3V6gy3HM2dS+tDKbf27cmuHP31JcuwXN75YV08nXD59SrJ+yc8S59N3pK+H/8Ds30nWnzrE+ewnW6sjOuBDFT8vVz1A5+7X5JQ+0VBXAFqKr8sCQRB2IAjCDgRB2IEgCDsQBKe4/gZ49a30qZwDSz+ZW5v6r99Njr3qgxck6z88lL9UtSR98j/+OVlPTa8N3/c3yaFMrRWLPTsQBGEHgiDsQBCEHQiCsANBEHYgCMIOBFH1FNcicYprc7y/a0Ju7fNb1ifH2rQql3N+aUuy3vGRi5P1lEfmnJesb3ojPcePt0ud4sqeHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeC4Hz2Gi0647TcWrVlkZ9u8nnZu4/mL9n80pLluTVJOu/Zrcl6R3f+ufK1GLwl7+LEzKO3Gnt2IAjCDgRB2IEgCDsQBGEHgiDsQBCEHQiCefYandGZf/3zRX8wOzn26e/9pOh2ajbjzK5kver1DBq83kHHx/KvSz/1nqeTYweGhht6boxVdc9uZqvNrN/Mdo7atsLM9pnZtuzn0ua2CaBRtbyNv1/S4grb73T3+dnPhmLbAlC0qmF39yclHWxBLwCaqJEDdDea2fbsbf60vAeZWY+Z9ZpZ71G17np3AMaqN+x3SzpX0nxJ+yWtzHugu69y92537+5SxevgAWiBusLu7n3uPuTuw5K+JWlBsW0BKFpdYTezWaN+vVLSzrzHAmgPVefZzexBSRdKOtPM9kq6XdKFZjZfkkvaI+n6JvaIKiZY/sejd918bUN/e3jj/cm6/d7Hk/WOpTfk1m5c+e3k2L/d8T/JOsanatjdvdLVB+5tQi8AmoivywJBEHYgCMIOBEHYgSAIOxAEp7ieAhZPm5xb67jsuuTY4XV3J+sr/uJryfqcSel/Qp/9z7W5tfds2pgcq7M/lK5jXNizA0EQdiAIwg4EQdiBIAg7EARhB4Ig7EAQzLMXYOd/95X6/BdddG7dY3u/8M1k/cDxoYbq6xd9Ore2ZHd6ueiVV6bn2W9Ztz1Zx1js2YEgCDsQBGEHgiDsQBCEHQiCsANBEHYgCObZa/TvBw+X3UKuSdf+WW6t2qWg7+t7o+Buxtr4yzdza59a+cXk2NPv/Kf0H1+Xvow1xmLPDgRB2IEgCDsQBGEHgiDsQBCEHQiCsANBMM9+Chh8+N9ya++47LIWdjI+ux/+r2T9Az0taiSIqnt2M5tjZj8ws+fN7DkzuynbPt3MNpnZrux2WvPbBVCvWt7GD0q6xd3nSfqopBvMbJ6kWyVtdve5kjZnvwNoU1XD7u773X1rdn9A0guSZktaImlN9rA1kq5oVpMAGjeuz+xm9j5J50v6saSZ7r4/K70maWbOmB5JPZI0RVZvnwAaVPPReDObImmtpJvd/dDomru7JK80zt1XuXu3u3d3EXagNDWF3cwmaCToD7j7I9nmPjObldVnSepvTosAilD1bbyZmaR7Jb3g7t8YVVovabmkO7Lbx5rSIara/cOXc2vz/mFZcuxdK/ck67948IlkfeLE9P5i6h+dn1vrvGFFcqzeOpKuY1xq+cx+gaRlknaY2bZs220aCfnDZnadpFckXd2cFgEUoWrY3f0pKffD9ieKbQdAs/B1WSAIwg4EQdiBIAg7EARhB4LgFNdTwEN7f5lbW7E1PU/eef1fJ+tn9Xylrp6K8NZfcY5rkdizA0EQdiAIwg4EQdiBIAg7EARhB4Ig7EAQzLOfAg4cH8qtrb/qc8mxl696LVm3Dy9M12e9P1kf3vJ4bu2tu+5Kjv38d7cl6xgf9uxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EISNLObSGjOs05fq9JY9HxDNWh3RAR+qeDVo9uxAEIQdCIKwA0EQdiAIwg4EQdiBIAg7EETVsJvZHDP7gZk9b2bPmdlN2fYVZrbPzLZlP5c2v10A9arl4hWDkm5x961mNlXSFjPblNXudPevN689AEWpZX32/ZL2Z/cHzOwFSbOb3RiAYo3rM7uZvU/S+ZJ+nG260cy2m9lqM5uWM6bHzHrNrPeoWvfVXABj1Rx2M5siaa2km939kKS7JZ0rab5G9vwrK41z91Xu3u3u3V2q+JVdAC1QU9jNbIJGgv6Auz8iSe7e5+5D7j4s6VuSFjSvTQCNquVovEm6V9IL7v6NUdtnjXrYlZJ2Ft8egKLUcjT+AknLJO0wsxPX9r1N0jVmNl+SS9oj6fqmdAigELUcjX9Kqvhhe0Px7QBoFr5BBwRB2IEgCDsQBGEHgiDsQBCEHQiCsANBEHYgCMIOBEHYgSAIOxAEYQeCIOxAEIQdCKKlSzab2QFJr4zadKak11vWwPi0a2/t2pdEb/Uqsrf3uvuMSoWWhv1tT27W6+7dpTWQ0K69tWtfEr3Vq1W98TYeCIKwA0GUHfZVJT9/Srv21q59SfRWr5b0VupndgCtU/aeHUCLEHYgiFLCbmaLzewnZrbbzG4to4c8ZrbHzHZky1D3ltzLajPrN7Odo7ZNN7NNZrYru624xl5JvbXFMt6JZcZLfe3KXv685Z/ZzaxT0kuS/ljSXknPSLrG3Z9vaSM5zGyPpG53L/0LGGa2SNJhSf/i7r+bbfs7SQfd/Y7sP8pp7v6lNulthaTDZS/jna1WNGv0MuOSrpD05yrxtUv0dbVa8LqVsWdfIGm3u7/s7sckPSRpSQl9tD13f1LSwZM2L5G0Jru/RiP/WFoup7e24O773X1rdn9A0ollxkt97RJ9tUQZYZ8t6dVRv+9Ve6337pIeN7MtZtZTdjMVzHT3/dn91yTNLLOZCqou491KJy0z3javXT3LnzeKA3Rvt9Ddf1/SJZJuyN6utiUf+QzWTnOnNS3j3SoVlhn/tTJfu3qXP29UGWHfJ2nOqN/fnW1rC+6+L7vtl7RO7bcUdd+JFXSz2/6S+/m1dlrGu9Iy42qD167M5c/LCPszkuaa2TlmNlHSZyStL6GPtzGzydmBE5nZZEkXq/2Wol4vaXl2f7mkx0rsZYx2WcY7b5lxlfzalb78ubu3/EfSpRo5Iv9TSV8uo4ecvn5b0rPZz3Nl9ybpQY28rTuukWMb10n6LUmbJe2S9ISk6W3U27cl7ZC0XSPBmlVSbws18hZ9u6Rt2c+lZb92ib5a8rrxdVkgCA7QAUEQdiAIwg4EQdiBIAg7EARhB4Ig7EAQ/wchM1bM/QC1IQAAAABJRU5ErkJggg==\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[35])\n",
        "print(int(Prediction))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzJFwOeDKCfE",
        "outputId": "8d34f43b-cd62-4088-ee53-5cbff55ad42e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8\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": {
        "id": "OxHccuHvKcFF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": {
        "id": "u6u9FmgXKfW1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}