Welcome to Cab Fare Prediction AI Challenge!

The aim of the challenge is to predict the cab fare using the given dataset. The dataset consists of 3 CSV files:

TRAIN.csv
TEST.csv
sample_submission.csv

TRAIN.csv consists of 9 attributes:

index
time_stamp - epoch time (in seconds) when the cab was booked
cab_provider - company (Uber/Lyft)
source - the starting point of the cab ride
destination - the destination of the cab ride
distance - the distance between source and destination
surge_multiplier - multiplier by which price increased
cab_type - the type of cab (Uber Pool, Uber XL, etc. )
fare - cab fare in USD (Target Attribute)

This data should be used to train the model, no additional data is allowed to be used for the training process.

TEST.csv consists of the testing data required for prediction and consists of 8 attributes:

index
time_stamp - epoch time (in seconds) when the cab was booked
cab_provider - company (Uber/Lyft)
source - the starting point of the cab ride
destination - the destination of the cab ride
distance - the distance between source and destination
surge_multiplier - multiplier by which price increased
cab_type - the type of cab (Uber Pool, Uber XL, etc. )

A "sample_submission".csv is also provided for ease of the participants. To know the exact submission format, please check out "Submission Guidelines".

Shreenidhi Hipparagi's Certificate

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