Dworlock11/Understanding-EMS-Response-Times-in-NYC

# Understanding-EMS-Response-Times-in-NYC This project was collaborated on with Arthur Faynin, Roderick Joshua, and Harley Kaufman. ## Summary Our goal is to create a model that can analyze and predict EMS response time in seconds. Predicting and understanding the major factors behind EMS response time is very important and can help save both lives and large amounts of money. The EMS Incident Dispatch dataset from NYC Open Data was used and the models was trained to predict the listed incident response time. Included in the workflow are data preprocessing, exploratory analysis, model training, hyperparameter tuning, and evaluation. Multiple models were tested, including a baseline model that predicts the mean, a regularized linear regression model, and a random forest model. The results showed that the linear regression model performed the best overall. Feature importance analysis was used to interpret model behavior and identify key predictive variables. Dataset: [Exoplanet Dataset (Kaggle)](https://www.kaggle.com/datasets/chandrimad31/phl-exoplanet-catalog?resource=download) ## Structure Analysis.rmd # Main analysis Final Report # Report detailing project inspiration, workflow, and results ## Tech Stack - RMarkdown - rstudioapi, ranger, glmnet