{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.svm import LinearSVC\nimport os","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ftrain = \"../input/Kannada-MNIST/train.csv\"\nftest = \"../input/Kannada-MNIST/test.csv\"\nsubmission = pd.read_csv('../input/Kannada-MNIST/sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train = pd.read_csv(ftrain)\ndf_test = pd.read_csv(ftest)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train.head(10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_test.head(10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nX = df_train[df_train.columns[1:]].values\nY = df_train.label.values","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Using linear SVM \nlin_clf = LinearSVC()\nlin_clf.fit(X,Y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds = lin_clf.predict(df_test[df_test.columns[1:]].values)\nsubmission['label'] = preds\nsubmission.to_csv('submission.csv', index=False)\nsubmission.head(20)","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.4"}},"nbformat":4,"nbformat_minor":1}