Modeling Future Requirements for Retirement Homes in Canada
- Built as part of Borealis AI's 'Let's Solve It' program.
- Team: Shriya Kulkarni (s32k) Simi Colaco (scococola) Yijun Zhao (loveillusion) Theodora Turda
- Mentors: Vineel Nagisetty Shilpa Singh
- Blog Post: https://docs.google.com/document/d/15svH1YtH3lmgEctZQ5QwHP6YWpc2oz9g/edit?usp=sharing&ouid=114125579928179777153&rtpof=true&sd=true
- Report: https://docs.google.com/document/d/1TvnCblfuhkbmupR0OMu9VRqyMD-Y4tTjjYeiHbhXK2w/edit?usp=sharing
- Presentation: https://docs.google.com/presentation/d/1Wc6pS4ZY1kLhAruekaAcvOQy4cTHeNAr5R2KkPJbLeM/edit?usp=sharing
user_input.py (Shriya)
- Main file: uses the deployed models from 'models'.
- Takes user input for Population, Number of LTC Homes & Province of city.
- Returns an estimate for Number of LTC Beds needed using Linear Regression.
datasets (Simi/Kevin)
- Contains datasets for each Canadian province in CSV format.
- maxmin.csv contains max & min values of relevant features for every provinces.
models (Shriya)
- Contains a folder per province with 2 Multiple Linear Regression Models.
- m1 returns the Number of Beds based on the current data.
- m2 returns an adjustment to be added to the value above.
- building_model.py contains the code used to build & save the said models.
visualizations (Theodora)
- Contains visualizations for Ontario as sample.
- models_visualized: 3D graphs created using matplotlib with plane of fit.
- line_graphs: Line graphs (population vs Number of Beds/Homes etc.)
- Accuracies.png: Box-plots for 5 provinces showing accuracy of models.