In late 2013, the taxi company Yourcabs.com in Bangalore, India, was facing a problem with the drivers using their platform not showing up for their scheduled calls. Drivers would cancel their acceptance of a call, and, if the cancellation did not occur with adequate notice, the customer would be delayed or even left high and dry. Yourcabs.com featured an online booking system (though customers could phone in as well) and presented itself as a taxi booking portal. Yourcabs.com had collected data on its bookings from 2011 to 2013. The data presented for this case are a randomly selected subset of the original data, with 10,000 rows, one row for each booking. There are 17 input variables, including user (customer) ID, vehicle model, whether the booking was made online or via a mobile app, type of travel, type of booking package, geographic information, and the date and time of the scheduled trip. The target variable of interest is the binary indicator of whether a ride was canceled. The overall cancellation rate is between 7% and 8%.

Questions

1. Explore, prepare, and transform the data to facilitate predictive modeling. (5)

2. Fit the following predictive models. Discuss the results. (35) a) kNN b) NB c) Decision Tree d) ANN e) SVM

3. Report the predictive performance of your models in terms of error rates (the confusion matrix). How well does the model perform? (5)

4. Based on your analysis which model would you recommend and why? (5)

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