Club Mahindra DataOlympics(ML Hackathon)

Club Mahindra presents DataOlympics in association with Analytics Vidhya.

DataOlympics reflects the data-driven culture we have in Club Mahindra where we take up the most critical and impactful business challenges and try to solve them using data insights and advanced predictive models. In our journey to become a data-driven decision making organization, we invite you to DataOlympics, our flagship hackathon to identify the best talents in the industry to work on challenging business problems.

Problem Statement

Food & Beverages Spend Prediction in Club Mahindra Resorts

Club Mahindra makes significant revenue from Food and Beverages (F&B) sales in their resorts.The members of Club Mahindra are offered a wide variety of items in either buffet or A la carte form.Following are some benefits that the model to predict the spend by a member in their next visit to a resort will bring:

• Predicting the F&B spend of a member in a resort would help in improving the pre-sales during resort booking through web and mobile app
• Targeted campaigns to suit the member taste and preference of F&B
• Providing members in the resort with a customized experience and offers
• Help resort kitchen to plan the inventory and food quantity to be prepared in advance

Given the information related to resort,club member,reservation etc. The task is to predict average spend per room night on food and beverages for the each reservation in the test set.

Data Description

• train.zip contains train.csv and data_dictionary.csv.

• train.csv contains the training data with details on resort as described in the last section

• data_dictionary.csv contains a brief description on each variable provided in the training and test set.

• test.csv contains details of all member and average spend per room night on food and beverages for which the participants are to submit probability of default.

• sample_submission.csv contains the submission format for the predictions against the test set. A single csv needs to be submitted as a solution.

Evaluation Metric

Submissions are evaluated on Root Mean Square Error (RMSE) between the predicted probability and the observed target.

Public and Private Split

Test data is further randomly divided into Public (30%) and Private (70%) data. Your initial responses will be checked and scored on the Public data. The final rankings would be based on your private score which will be published once the competition is over.