{"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"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":99552,"databundleVersionId":13190393,"sourceType":"competition"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission = pd.DataFrame({\n    'ID': ['img001', 'img002', 'img003'],\n    'Aneurysm Present': [0.85, 0.01, 0.92],\n    'label_1': [0.1, 0.03, 0.02],\n    'label_2': [0.05, 0.02, 0.07],\n    'label_3': [0.03, 0.04, 0.06],\n    'label_4': [0.09, 0.05, 0.11],\n    'label_5': [0.07, 0.11, 0.03],\n    'label_6': [0.02, 0.06, 0.09],\n    'label_7': [0.05, 0.03, 0.12],\n    'label_8': [0.15, 0.12, 0.08],\n    'label_9': [0.01, 0.01, 0.02],\n    'label_10': [0.03, 0.05, 0.09],\n    'label_11': [0.10, 0.03, 0.07],\n    'label_12': [0.02, 0.12, 0.10],\n    'label_13': [0.08, 0.06, 0.05]\n})","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}