{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"1595fc9e-57f5-be2b-95aa-6e868f92d009"},"source":"Expedia hotel recommandations"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"47c2e213-7f2c-8e03-fb8e-eccf2f26eda2"},"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\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"7edaebc5-bf96-6f3c-5f9d-115467733431"},"outputs":[],"source":"#destinations = pd.read_csv(\"../input/destinations.csv\")\ntest = pd.read_csv(\"../input/test.csv\")\ntrain = pd.read_csv(\"../input/train.csv\")"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"a6487229-0bf0-9bc5-80d9-e32f5b2d8c79"},"outputs":[],"source":"test.shape"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"1b43d395-c73f-5603-2677-979f9f99f4ad"},"outputs":[],"source":"test.head(5)"}],"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}