Traffic Forecasting with Python: LSTM & Graph Neural Network

Description:

This course offers an in-depth journey into the world of advanced time series forecasting, specifically tailored for traffic data analysis using Python. Throughout the course, learners will engage with the PeMSD7 dataset, a real-world traffic speed dataset, to develop predictive models that can forecast traffic conditions with high accuracy. The course focuses on integrating Long Short-Term Memory (LSTM) networks with Graph Convolutional Networks (GCNs), enabling learners to understand and apply cutting-edge techniques in spatiotemporal data analysis.

Key topics include data preprocessing, feature engineering, model building, and evaluation, with hands-on coding in Python to solidify understanding. Learners will also gain practical experience in using popular libraries such as TensorFlow and Keras for deep learning applications.

This course is ideal for those looking to advance their careers in data science, machine learning, or AI-driven industries. The practical skills acquired will be highly valuable for roles in smart city planning, transportation analysis, and any field that relies on predictive modeling. By the end of the course, learners will not only have a strong grasp of advanced forecasting techniques but will also be well-prepared for job opportunities in data science and related fields, where they can contribute to innovative solutions in traffic management and urban development.