How to Predict Traffic Volume Using Machine Learning

In this project, we'll build a machine learning system that predicts traffic volume on roadways using historical data and environmental factors. Working with real-world traffic data that includes timestamps, weather conditions, holiday indicators, and hourly vehicle counts, we'll create regression models that forecast congestion patterns and help urban planners make data-driven decisions. The project covers the complete machine learning workflow from data cleaning and visualization to model training, evaluation, and deployment preparation.

We'll start by loading the dataset, removing duplicates, and exploring traffic patterns through seaborn visualizations to understand how weather and time affect road congestion. Next, we'll preprocess the data by extracting meaningful features from timestamps, converting categorical weather conditions into numerical formats, and splitting the dataset for training and testing. We'll then build three regression models:

• linear regression for baseline predictions,

• decision tree regressor for capturing non-linear relationships, and

• random forest regressor for ensemble accuracy.

We'll compare their performance using standard evaluation metrics.

By the end, we'll have trained models saved with joblib and ready for real-time traffic predictions. This project demonstrates essential data science skills including pandas data manipulation, feature engineering, model comparison, and model persistence, providing hands-on experience with scikit-learn workflows applicable to any regression or time-series forecasting problem.