{"cells":[{"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":{},"cell_type":"markdown","source":"[https://www.kaggle.com/c/expedia-hotel-recommendations/data](http://)\n\nExpedia has provided you logs of customer behavior. These include what customers searched for, how they interacted with search results (click/book), whether or not the search result was a travel package. The data in this competition is a random selection from Expedia and is not representative of the overall statistics.\n\nExpedia is interested in predicting which hotel group a user is going to book. Expedia has in-house algorithms to form hotel clusters, where similar hotels for a search (based on historical price, customer star ratings, geographical locations relative to city center, etc) are grouped together. These hotel clusters serve as good identifiers to which types of hotels people are going to book, while avoiding outliers such as new hotels that don't have historical data.\n\nYour goal of this competition is to predict the booking outcome (hotel cluster) for a user event, based on their search and other attributes associated with that user event.\n\nThe train and test datasets are split based on time: training data from 2013 and 2014, while test data are from 2015. The public/private leaderboard data are split base on time as well. Training data includes all the users in the logs, including both click events and booking events. Test data only includes booking events. \n\ndestinations.csv data consists of features extracted from hotel reviews text. \n\nNote that some srch_destination_id's in the train/test files don't exist in the destinations.csv file. This is because some hotels are new and don't have enough features in the latent space. Your algorithm should be able to handle this missing information.\n\n**File descriptions**\n\ntrain.csv - the training set\n\ntest.csv - the test set\n\ndestinations.csv - hotel search latent attributes\n\nsample_submission.csv - a sample submission file in the correct format\n\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv(\"/input/Expedia Hotel Recommendations/train.csv\")","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}