{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as  np\nimport matplotlib.pyplot as plt\n\n\nfrom tensorflow import keras\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\n\nfrom keras.utils.np_utils import to_categorical\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense,Conv2D,Flatten,MaxPool2D,Dropout,BatchNormalization\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ReduceLROnPlateau\n\nfrom sklearn.metrics import confusion_matrix","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"train= pd.read_csv('../input/Kannada-MNIST/train.csv')\ntest=pd.read_csv('../input/Kannada-MNIST/test.csv')\nsample_sub=pd.read_csv('../input/Kannada-MNIST/sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_images = train.drop('label', axis = 1)\ntrain_labels = train.label","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_id = test.id\ntest_images = test.drop('id', axis=1)\ntest_images = test_images.values\ntest_images = test_images/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_images = train_images.values\ntrain_images = train_images/255\nrow_num = test_images.shape[0]\nTrain_labels_list = train_labels.tolist()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def KNN(TrainData, TestData, labels, k=3):\n    row_num = TestData.shape[0]\n    predict_value = np.zeros(row_num)\n    for i in range(row_num):\n        Square = (TrainData - TestData[i,:])**2\n        Sum_square = np.sum(Square, axis=1)\n        distance = Sum_square**0.5\n        Sortdistance = distance.argsort()\n        count = {}\n        for j in range(k):\n            ind = labels[Sortdistance[j]]\n            count[ind] = count.get(ind, 0)+1\n        Sortcount = sorted(count.items(), key=lambda item:item[1], reverse=True)\n        predict_value[i] = Sortcount[0][0]\n    return predict_value","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def Accuracy(predict, ground_turth):\n    row_num = predict.shape[0]\n    correct = 0\n    for i in range(row_num):\n        if predict[i] == ground_turth[i]:\n            correct += 1\n    accuracy = correct/row_num\n    return accuracy","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predict_value = KNN(train_images, \n                    test_images, \n                    Train_labels_list)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_sub['label'] = predict_value\nsample_sub.to_csv('submission.csv',index=False)\n\nsample_sub.head()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}