Inspiration
I was inspired by some projects I did over the summer for Cognizant regarding AI classifiers and predictors, and wanted to do a project related to that using NJ transit data.
What it does
Predicts future delays and cancellations in trains based off of previous data. Visualizes data using mathplotlib.
How we built it
We leveraged scikit-learn's RandomForestRegressor to predict future data based off of previous values to a high accuracy.
Challenges we ran into
We had some difficulty with getting the model spun up and working; since this was the backend developer's second time working on it. We also had to clean the datasets and derive some data on our own to fit our purposes (specifically the cancellations data table).
Accomplishments that we're proud of
We are proud of creating a very accurate prediction model and the ability for it to be flexible in what exactly it's predicting - it can either be very specific or very general.
What we learned
We learned both how to leverage RandomForestRegressor as a very accurate prediction model and how to interface Flask with Python to create a working web application.
What's next for Delay Detective
In the future, we may add some more user-friendly features such as alerts to phones about possible delays for the day! We may also want to integrate the live data (GTFS data) to try and predict daily trends; we had trouble accessing the live API. Additionally, the data provided for the daily prediction is difficult to match up with the cancellation/delay data which is only tracked monthly. It could be possible to derive such a prediction from the monthly prediction if we had more time to analyze all the data. We also may want to update our frontend so that it looks nicer.
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