SohaliBaisla_Handwritten_Digit_Classification.ipynb

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      "name": "SohaliBaisla_Handwritten-Digit-Classification.ipynb",
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      "authorship_tag": "ABX9TyM+hMrXbckat8DcdKTdD3te",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SohaliBaisla/Handwritten-Digit-Classification/blob/main/SohaliBaisla_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",
      "execution_count": 1,
      "metadata": {
        "id": "8GVZSN0cmZaP"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "from PIL import Image, ImageOps\n",
        "from numpy import asarray\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmActVeOneii",
        "outputId": "ea68f2cc-f805-4171-fe7d-785c93936919"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"/content/drive/MyDrive/Dataset/Mnist/digit-recognizer/train.csv\")\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "RfNbg3QtnrBj",
        "outputId": "9dc2700a-8a18-47ed-8470-a40790c7e661"
      },
      "execution_count": 3,
      "outputs": [
        {
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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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b68c996e-7da2-4dc4-b052-3d39fcb30510')\"\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-b68c996e-7da2-4dc4-b052-3d39fcb30510 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-b68c996e-7da2-4dc4-b052-3d39fcb30510');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgNoZVhPoDYN",
        "outputId": "1848f63c-4517-42fd-b3e6-5ad87978198e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42000, 785)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data1 = data.values\n",
        "print(type(data1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "14CMAz4MoGlf",
        "outputId": "ec5557a8-6ebf-4418-d319-27cb1e086198"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Cleaning data\n",
        "pixels=data1[:,1:]\n",
        "labels=data1[:,0]"
      ],
      "metadata": {
        "id": "U9dBpgGFoSzw"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(pixels.shape, labels.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqJZwpW2pLn2",
        "outputId": "d4204022-f53f-4827-98a5-c47a8cd5df5e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(42000, 784) (42000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(pixels, labels, test_size=0.2)"
      ],
      "metadata": {
        "id": "cD9YCkX9ojnw"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training data shape : \",X_train.shape, y_train.shape)\n",
        "print(\"Testing data shape : \",X_test.shape, y_test.shape)"
      ],
      "metadata": {
        "id": "ChmZwAkUo8-D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0169c41-72bc-4ab1-aca8-c784f539c92f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape :  (33600, 784) (33600,)\n",
            "Testing data shape :  (8400, 784) (8400,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to calculate distance between various points\n",
        "def distance(x1,x2):\n",
        "  return np.sqrt(sum((x1-x2)**2))"
      ],
      "metadata": {
        "id": "nUiAkjVVqK_J"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def knn(pixels, labels, queryPoint, k=10):\n",
        "    value = []\n",
        "    m = pixels.shape[0]\n",
        "    for i in range(m):\n",
        "        d = distance(queryPoint, pixels[i])\n",
        "        value.append((d, labels[i]))\n",
        "    value = sorted(value)\n",
        "    value = value[:k]\n",
        "    value = np.array(value)\n",
        "    new_value = np.unique(value[:, 1], return_counts = True)\n",
        "    index = new_value[1].argmax()\n",
        "    predict = new_value[0][index]\n",
        "    return predict"
      ],
      "metadata": {
        "id": "VFCwnQhNrDv7"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "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[120])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "uIhX4EBksxNZ",
        "outputId": "2e49caa2-b0c3-4620-9ff9-f56593530ba7"
      },
      "execution_count": 12,
      "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": "ojF81IhptDc6",
        "outputId": "532873c2-d4be-4d88-d34f-4f745c737194"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drawImg(X_test[11])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Y9f7mo8ftgOK",
        "outputId": "4fb93463-370c-4a03-a122-da44407e1580"
      },
      "execution_count": 14,
      "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": [
        "def accuracy(X_train,y_train,X_test,y_test):\n",
        "  size=X_test.shape[0]\n",
        "  cnt=0\n",
        "  for i in range(size):\n",
        "    pred=knn(X_train,y_train,X_test[i])\n",
        "    if(pred==y_test[i]):\n",
        "      cnt=cnt+1\n",
        "  acc=cnt/size\n",
        "  return acc"
      ],
      "metadata": {
        "id": "PDdHLXM-tlsI"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc=accuracy(X_train,y_train,X_test,y_test)\n",
        "acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cicJ_cBLZFsU",
        "outputId": "5a1eea30-aff0-4611-cb12-e3a814002d5e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9633333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(5):\n",
        "    result = knn(X_train, y_train, X_test[i])\n",
        "    drawImg(X_test[i])\n",
        "    print(f\"Label : {result}\\n\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ntlOyus93AC8",
        "outputId": "4784ce46-6265-41a4-9120-eadcb94d5a9e"
      },
      "execution_count": 22,
      "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 : 8.0\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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r74z8/0r69zp6yOnrR5Lezb7eq7s3SS+o77Du/9R3buM2SedJ2iTpA0l/ljS2jXr7L/VN7b1NfcGaUFNvV6rvEH2bpHeyr/l1v3aJvlryunG5LBAEJ+iAIAg7EARhB4Ig7EAQhB0IgrADQRB2IIj/B8eTpLAaKI+TAAAAAElFTkSuQmCC\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 : 6.0\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label : 5.0\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nStrYcBOZdqb"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}