How to Predict the Traffic Volume Using Machine Learning

In this data-driven project, we aim to predict traffic volume on a given roadway by leveraging machine learning techniques and various environmental factors.

First, we’ll preprocess the data, address the missing values, and convert categorical variables into numerical representations for seamless processing. Next, we’ll harness the visualization capabilities of seaborn to gain valuable insights into the data. We’ll utilize historical data, including date and time, weather conditions, and holidays, to predict the traffic volume on the roads. We’ll employ three regression models: linear regression, decision tree regressor, and random forest regressor. Finally, we’ll save all three models using joblib for future deployment, allowing us to make real-time traffic volume predictions.