Inspiration
There is a huge variation in the cost of taxi rides charged by different companies. We wanted to find out if we are getting charged a fair price or not.
What it does
Uses regression predictive model to predict the cost of going from start to end point on a map.
How we built it
Backend: Used django to route urls and collect user info. Django also uses the model interface built in python and connects it to front for providing a prediction. Frontend: Styled using TailwindCSS. Added the map using MapboxAPI Model: Made a Regression model using historical data for cab rates from various states in America. Trained on wgs4 coordinate system and outputs the cost in usd
Challenges we ran into
Training the model, learning to use the MapboxAPI, unable to set up a pipeline that can use latest data to improve the model next, unsuccessful time series modelling
Accomplishments that we're proud of
Learnt new technology such as TailwindCSS and Django, successfully setting up the interface for the regression model
What we learned
Training the model, learning to use the MapboxAPI, unable to set up a pipeline that can use latest data to improve the model next, unsuccessful time series modelling
What's next for FairFare
Better modelling of the data, account for day and night difference in cab rates, better outlier detection in data, account for different currencies of fair amounts based on location data
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