KhushiPunia_Handwritten-Digit-Classification.ipynb

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    "colab": {
      "name": "Uacelt.ipynb",
      "provenance": [],
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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "KadTmw6coCME"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "vTpRDJ_9qAEc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c1dee67-fc15-46a0-d5f8-7f33ff92e24a"
      },
      "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/train.csv\")\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "4CadKRaZY4KZ",
        "outputId": "67142268-72f3-4b54-ccc0-f61d574dd742"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<p>5 rows × 785 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c387079b-5763-453b-bc0f-c1e0fd8f3cce')\"\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-c387079b-5763-453b-bc0f-c1e0fd8f3cce 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-c387079b-5763-453b-bc0f-c1e0fd8f3cce');\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=data.values"
      ],
      "metadata": {
        "id": "nqKKIsr7Y4jr"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Cleaning data\n",
        "pixels=data[:,1:]\n",
        "label=data[:,0]"
      ],
      "metadata": {
        "id": "xLsGowizqCq9"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Splitting data into train and test \n",
        "pixels_train=pixels[:41000,:]\n",
        "pixels_test=pixels[41000:,:]\n",
        "label_train=label[:41000]\n",
        "label_test=label[41000:]"
      ],
      "metadata": {
        "id": "PAN_RxYQqyyx"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to calculate distance between various points\n",
        "def distance(x,x1):\n",
        "  dis=np.sqrt(sum((x-x1)**2))\n",
        "  return dis"
      ],
      "metadata": {
        "id": "8gK4E_4mvQAl"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#KNN model\n",
        "def knn(pixels,label,querypoint,k=10):\n",
        "  store=[]\n",
        "  size=pixels.shape[0]\n",
        "  for i in range(size):\n",
        "    d=distance(querypoint,pixels[i])\n",
        "    store.append((d,label[i]))\n",
        "  store=sorted(store)\n",
        "  store=store[:k]\n",
        "  store=np.array(store)\n",
        "  array=np.unique(store[:,1],return_counts=True)\n",
        "  index=array[1].argmax()\n",
        "  predicted=array[0][index]\n",
        "  return predicted"
      ],
      "metadata": {
        "id": "OKUvoICTwkA0"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Function to draw image using values of pixels in array\n",
        "def draw(arr):\n",
        "  img=arr.reshape(28,28)\n",
        "  plt.imshow(img,cmap='gray')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "xArw7q1gZDEs"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating accuracy of array\n",
        "def accuracy(pix_train,lab_train,pix_test,lab_test):\n",
        "  size=pix_test.shape[0]\n",
        "  cnt=0\n",
        "  for i in range(size):\n",
        "    pred=knn(pix_train,lab_train,pix_test[i])\n",
        "    if(pred==lab_test[i]):\n",
        "      cnt=cnt+1\n",
        "  acc=cnt/size\n",
        "  return acc"
      ],
      "metadata": {
        "id": "8DGELkUWZFpr"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting an image to array\n",
        "def imgtoarr(img):\n",
        "  imggray=ImageOps.grayscale(img)\n",
        "  small=imggray.resize((28,28))\n",
        "  testing=asarray(small)\n",
        "  tempo=testing.reshape(784,)\n",
        "  return tempo"
      ],
      "metadata": {
        "id": "_c-8TQFcZI2x"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ans=knn(pixels_train,label_train,pixels_test[0])\n",
        "ans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lr47Mee1yz5E",
        "outputId": "6167be30-bacd-4631-9108-207a8906aaf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "draw(pixels_test[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "BMoUtycT1h1s",
        "outputId": "5c2bd28d-dd9f-46b4-9c36-d5840eac40f4"
      },
      "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": [
        "acc=accuracy(pixels_train,label_train,pixels_test,label_test)"
      ],
      "metadata": {
        "id": "PRJ98UEL3g9f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "on_4DkqZP2aS",
        "outputId": "a0f613f1-4fa6-4663-e4fc-12ef70457931"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.967"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input=Image.open(\"/content/drive/MyDrive/22.jpeg\")"
      ],
      "metadata": {
        "id": "y-Pbn1pOQWyZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inp=imgtoarr(input)\n",
        "inp"
      ],
      "metadata": {
        "id": "MTzMW1zsRQDq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb495e7c-9e31-4f6b-9219-149cef816efe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,\n",
              "         4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,   4,\n",
              "         4,   4,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   1,  46,  92, 103,  72,  25,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,  75, 191, 249, 255, 255, 255, 234,\n",
              "       149,  17,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,  12, 152, 254, 255, 255, 255, 255,\n",
              "       255, 255, 255, 145,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   8, 174, 255, 255, 255, 232,\n",
              "       165, 141, 217, 255, 255, 187,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,  95, 255, 255, 253,\n",
              "       152,  29,   0,   7, 210, 255, 255, 118,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 168, 255,\n",
              "       255, 137,   0,   0,   0, 146, 255, 255, 224,  21,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "       150, 255, 255,  55,   0,   0,  83, 252, 255, 255,  90,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,  24, 131,  97,   3,   0,  40, 231, 255, 255, 162,   1,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,  16, 201, 255, 255, 214,\n",
              "        21,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   4, 168, 255, 255,\n",
              "       241,  56,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 136, 255,\n",
              "       255, 253,  95,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 102,\n",
              "       254, 255, 255, 132,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "        65, 244, 255, 255, 164,   3,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,  36, 225, 255, 255, 195,  14,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,  16, 199, 255, 255, 217,  25,   0,   0,   0,   0,   2,\n",
              "         7,   7,   2,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   6, 169, 255, 255, 250, 124,  82, 114, 142, 169,\n",
              "       188, 204, 215, 215, 203, 181, 136,  49,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   4, 149, 255, 255, 255, 254, 252, 255, 255,\n",
              "       255, 255, 255, 255, 255, 255, 255, 255, 255, 238,  37,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,  71, 255, 255, 255, 255, 255, 255,\n",
              "       255, 255, 255, 255, 255, 250, 244, 243, 250, 255, 255, 255,  50,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,  78, 255, 255, 255, 246,\n",
              "       217, 188, 161, 134, 104,  79,  59,  44,  34,  33,  45,  82, 150,\n",
              "        93,   2,   0,   0,   0,   0,   0,   0,   0,   0,   6, 117, 167,\n",
              "       109,  39,   9,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         0,   0,   0,   0], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ans=knn(pixels_train,label_train,inp)"
      ],
      "metadata": {
        "id": "XW8pInQnRVmw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HNajk9RnRYnX",
        "outputId": "76e53878-693e-42cf-dfb6-2c4128240c9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "draw(inp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "mgoNkdJjRfCv",
        "outputId": "116730cf-5f0f-4872-922c-980598e1e5de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QBB/QAUEQdiAIwg4EQdiBIAg7EARhB4Ig7EAQ/w9VZHXd6WyY5AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}