{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"3d6b03b8-5174-bf80-f7da-9f3dd7ac4f80"},"source":"This serves as my introduction to Kaggle competitions using AI, using references from Jeremy's FastAI"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9af93e94-ea74-9dba-318c-4ae018a3bedb"},"outputs":[],"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\nfrom subprocess import check_output, call\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# A quick method to execute shell commands\ndef runCmd(cmd):\n    if type(cmd) is str:\n        print(call(cmd.split(\" \")))\n    else:\n        print(call(cmd))\n# Any results you write to the current directory are saved as output.\nrunCmd(\"ls -l ../input\")"},{"cell_type":"markdown","metadata":{"_cell_guid":"22b356a6-4b11-e10c-524d-099e5c24c6f1"},"source":"##Check the data"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"fddab5ba-3eef-5b5b-6abf-8fb4d4b9ae11"},"outputs":[],"source":"TRAIN_DIR = '../input/train'\nTEST_DIR = '../input/test'\n"}],"metadata":{"_change_revision":0,"_is_fork":false,"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":"3.6.0"}},"nbformat":4,"nbformat_minor":0}