Car-Accident-Prediction

Project Goals:

1. Apply machine learning regression techniques to a very large dataset in order to see how methods must change when working with big data
2. Create heatmaps of car accident severity across the US and predict weather/road condition factors that cause accidents

Dataset: https://www.kaggle.com/datasets/sobhanmoosavi/us-accidents

• 2.8 million datapoints about car accidents from 2016-2021 in the continental US
• Predictors: mix of quantitative (weather related), boolean (road condition), and location data
• Response: Numeric variable 1-4 measuring car accident severity (severity measured in road disruption time)

Model Choices

• Linear Model
• Feature Selection Models (Lasso/Ridge/Elastic Net)
• Boruta
• GAM with tensors
• Regression Tree
• Support Vector Regression
• Gradient Boosting Algorithm