{"cells":[{"metadata":{},"cell_type":"markdown","source":"So your only goal is to score high and nothing else?\n"},{"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},"cell_type":"code","source":"df= pd.read_csv('../input/best-score-ever/best_score.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.read_csv('../input/LANL-Earthquake-Prediction/sample_submission.csv')\nsubmission['time_to_failure'] = df.time_to_failure\nsubmission.to_csv('submission.csv',index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"We all know whats coming. 20+ kernels of dudes blending everything up. So here is how to make the best out of it:"},{"metadata":{},"cell_type":"markdown","source":"Steps to achieve high score:\n    \n    1. Create an over-fitted submission\n    2. Make sure it has good LB score\n    3. Make it public\n    4. Count the forks\n    5. Choose your own conservative model as actual submission (optional, you could just choose some other better public ones)\n    6. At the end of the day, if you only wanna score high, it really does not matter what you did. \n    Only thing that mathers is that others do it worse, right?"},{"metadata":{},"cell_type":"markdown","source":"BETTER, how to make it less suspicious than this. Gather everything in a private set, make some not so trivial computations and coding, just to mask the fact that in the private set there is a bunch of over fitted solutions. Blend it all up. Submit."},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}