{"cells":[{"metadata":{"_uuid":"741358b58e51e38458788a0671bcb5bd355de3d5"},"cell_type":"markdown","source":""},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"61a8a7a7a55a4861397f03dbcd9e3dfc29f1938c"},"cell_type":"code","source":"print(os.listdir(\"../input/embeddings\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"480c64059fada732fcddb6af1bf22c1cd39fc87a"},"cell_type":"code","source":"train = pd.read_csv('../input/train.csv')\ntest = pd.read_csv('../input/test.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"aafb044acc5371874cb3ff6c763e512baccc43fd"},"cell_type":"code","source":"train.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"88621a54098fa3ab932402b38633386be2f9e95b"},"cell_type":"code","source":"test.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"ef6b395bfe06564de22362ffadbbf3487051c92d"},"cell_type":"code","source":"train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"affd4b28f552d4cd41e9dcc4936d159a2dec785d"},"cell_type":"code","source":"test.shape","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"sample_submission = pd.read_csv('../input/sample_submission.csv')\nsample_submission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"adb4de8126ac8c4052d405ea8b726c7404732ece"},"cell_type":"code","source":"sample_submission.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"7bb4479f0e4d600e972f9a0e4afe24b82d8d1e41"},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}