{"cells":[{"metadata":{},"cell_type":"markdown","source":"Based on the following kernel: https://www.kaggle.com/heibankeli/pca-svm1\n\nIf you find it useful, pleause upvote."},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nfrom sklearn.decomposition import PCA\nfrom sklearn import svm\n\n# The competition datafiles are in the directory ../input\n# Read competition data files:\ntrain = pd.read_csv(\"../input/Kannada-MNIST/train.csv\")\ntest  = pd.read_csv(\"../input/Kannada-MNIST/test.csv\")\nsubmission  = pd.read_csv(\"../input/Kannada-MNIST/sample_submission.csv\")\n\n\ntrain_x = train.values[:,1:]\ntrain_y = train.values[:,0]\ntest_x = test.values[:,1:]\n\npca = PCA(n_components=0.65,whiten=True)\ntrain_x = pca.fit_transform(train_x)\ntest_x = pca.transform(test_x)\n\n\nsvc = svm.SVC(kernel='rbf',C=13)\nsvc.fit(train_x, train_y)\n\npreds = svc.predict(test_x)\nsubmission['label'] = preds\nsubmission.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"code","source":"","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":1}