In this code-along, we'll build a neural network to predict traffic volume on a Minnesota highway. Traffic patterns fluctuate, making prediction challenging, but deep learning can uncover hidden patterns. Such a system can help reduce congestion, improve road design, enhance safety, or even plan a smoother commute!
Predicting traffic volume is a complex challenge due to fluctuating patterns influenced by time and external factors. In this code-along, we’ll build a neural network to analyze and predict traffic volume on a highway in Minnesota, USA. Using deep learning, we’ll extract abstract patterns from historical traffic data to improve prediction accuracy. This hands-on project walks you through data preparation, model building, training, and evaluation using PyTorch. You’ll work with real-world traffic datasets, preprocess data, and fine-tune a neural network for optimal predictions. Along the way, you’ll gain insights into handling time-series data and using AI to make informed traffic predictions. By the end, you’ll have a functional traffic volume predictor that can help with congestion management, road planning, and commuting efficiency. Create your own DataLab workbook for this code along: (https://www.datacamp.com/datalab/new?accountType=personal&_tag=workspace&workspaceId=a04557f6-e047-4564-a838-bbfcbdeac2fc&title=Building%20a%20Traffic%20Volume%20Predictor&visibility=private&openIfExists=true) This course is ideal for individuals with a foundational understanding of machine learning and data science concepts. Familiarity with Python and basic neural network principles will help, but prior experience with deep learning or time-series analysis is not required. It’s suitable for data scientists, transportation analysts, and machine learning practitioners looking to apply their skills to real-world challenges. In this hands-on course, you’ll work directly with real-world traffic datasets to build and train an LSTM model for traffic volume prediction. You’ll learn essential steps, including data preprocessing, model training, and evaluation. Each phase is designed to help you understand time-series forecasting, with guided exercises to ensure a clear understanding of deep learning techniques in practice. This course is based on Building a Traffic Volume Predictor, by DataLab DataLab and Bernd Schrooten. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.
