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UMASS BOSTON 2023 CS CLUB HACKATHON

Team: NP-completing

Team members:

krishna teja kaspe
kyshan Neheeth
yeshaswiniVasudev
Srikar kodavati

Project Description

Developing a decision-making model for MBTA commuters by allowing users to determine their optimal commute strategy. Users can make informed decisions by considering factors such as MBTA crowding levels based on season and average commute on the day The availability of Uber or Lyft Predicted prices This model empowers users to select the most suitable commuting option based on crowd conditions, timing, and cost-efficiency.

How to setup on Mac

git clone : https://github.com/YeshaswiniVasudev/Hackathon.git
python3 -m venv venv
pip3 install -r requirements.txt
streamlit run template.py

How to setup on windows

git clone : https://github.com/YeshaswiniVasudev/Hackathon.git
python3 -m venv henv
.\henv\Scripts\activate
pip install -r requirements.txt
streamlit run template.py

Link to model file

ride_price.joblib file

https://drive.google.com/file/d/1b8uqCFw_Kc2bBQD2eOOQaTcU5TQjAUSb/view?usp=drive_link

ride.csv file

https://drive.google.com/file/d/1EnVX5j7l1bc_QpzjK5niOF_Ha978WEmO/view?usp=drive_link

Use case

  • MBTA : Select the MBTA line to know the expected crowd level
  • Cab : Select the Source and Destination address to compare the predicted prices on Uber and Lyft
  • Diffusion: We just wanted to explore diffusion. It has no specific use case with respect to our project.