{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"markdown","source":"1. Importing Packages\n We will use the below Python packages to inspect and manipulate our data."},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import numpy as np \nimport pandas as pd \n\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\n%matplotlib inline\nsns.set_style(\"whitegrid\")\n\nimport os\nprint(os.listdir(\"../input\"))\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"232a0f5438ad63336face2b37b89337817f8a718"},"cell_type":"markdown","source":"2. Exploring and Visualizing Data"},{"metadata":{"trusted":true,"_uuid":"b7a320fb40908b4cb3eff3ff62653257eb3dbbbf"},"cell_type":"code","source":"training = pd.read_csv(\"../input/train.csv\")\ntesting = pd.read_csv(\"../input/test.csv\")\ntraining.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6736fd439fd13b84d3859dc4731e241ef612025a"},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8e10b67115c98ac5fece6b05cd3ae38ba64b8bd0"},"cell_type":"markdown","source":""}],"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}