Predicting the cab booking cancellations
In Yourcabs, not all drivers are coming up for the scheduled calls and it increases the discontented customers. And, other competitor, Uber is coming in India soon so Yourcabs need to manage their drivers by arranging available resources and reduce the cab cancellations. The dataset sums up a varied set of features about customers collected by Yourcabs from 2011 to through 2013. The goal is to predict whether a cab booking will get cancelled. It is needed to explore the variables which may affect the cancellation of the ride and suggests a revolutionary approach to predict the cancellations. The target variable is the binary indicator of whether a ride was canceled. Because the problem is supervised learning the important key is to build a concise model of the distribution of class labels in terms of predictor features.
1. Visualize the data to understand the categories of each attribute and their influence on the dependent variable.
2. Build the classification models (Decision Tree, Bagging, Boosting, and Random Forest) to predict the dependent variable. Explain all results of these different techniques on classifying the cab cancellation and choose the best prediction model with proper explanation
Data Descriptions:
user_id: the ID of the customer (based on mobile number)
vehicle_model_id: vehicle model type.
travel_type_id: type of travel (1 = long distance, 2 = point to point, 3 = hourly rental).
from_area_id: unique identifier of area. Applicable only for point-to-point travel and packages
to_area_id: unique identifier of area. Appplcable only for point-to-point travel
from_date: time stamp of requested trip start
online_booking: if booking was done on desktop website
mobile_site_booking: if booking was done on mobile website
booking_created: time stamp of booking
from_lat: latitude of from area
from_long: longitude of from area
to_lat: latitude of to area
to_long: longitude of to area
Car_cancellation: whether the booking was cancelled (1) or not (0) due to unavailability of a car.