This project aims to predict vehicle traffic flow on the Ventura Highway in Los Angeles using machine learning models. We will utilize a comprehensive dataset extracted from the California Performance Measurement System (PeMS) that combines traffic, weather, and calendar information from February 1 to May 31, 2020. Our goal is to develop and compare multiple predictive models, employing traditional machine learning algorithms such as Linear Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forests, to estimate the number of vehicles passing a specific point at 5-minute intervals. The project will investigate the impact of various factors, including weather conditions, holidays, and time-based features, on traffic patterns.