Traffic Forecasting With Python: Lstm & Graph Neural NetWork

Python-driven traffic forecasting with Keras: LSTM and Graph Convolutional Networks for spatiotemporal data modeling

What you'll learn

Understand and analyze real-world traffic data using Python.

Implement and apply Graph Convolutional Networks (GCNs) for traffic data.

Combine LSTM networks with GCNs for time series forecasting.

Preprocess and normalize large datasets for machine learning.

Build, train, and evaluate predictive models using TensorFlow and Keras.

Visualize and interpret model results for traffic prediction.

Requirements

Basic proficiency in Python programming.

Access to a computer with an internet connection for coding and data analysis.

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.

Overview

Section 1: Fundamentals

Lecture 1 Introduction

Lecture 2 About this Project

Lecture 3 Applications

Lecture 4 Job Opportunities

Lecture 5 Why Python, Keras, and Google Colab?

Section 2: Building and Training Model

Lecture 6 Set up the working directory

Lecture 7 What is inside dataset folder?

Lecture 8 What is inside code.ipynb?

Lecture 9 Launch Project

Lecture 10 Activate GPU

Lecture 11 Mounting Google Drive

Lecture 12 Upgrading the Keras library

Lecture 13 Importing necessary libraries

Lecture 14 Defining the directory

Lecture 15 Reading route distances and speeds data

Lecture 16 Shapes of our data

Lecture 17 Selecting a subset of routes

Lecture 18 Shapes of our data (again)

Lecture 19 Data visualization-1

Lecture 20 Data visualization-2

Lecture 21 Data preprocessing and splitting

Lecture 22 Data preprocessing and its outcomes

Lecture 23 Defining parameters

Lecture 24 Creating TensorFlow datasets

Lecture 25 Creating train, validation, and test datasets

Lecture 26 Compute the adjacency matrix

Lecture 27 Store graph information

Lecture 28 Compute the adjacency matrix for a graph

Lecture 29 Implement a graph convolutional layer

Lecture 30 Combine a graph convolutional layer with an LSTM layer

Lecture 31 Model parameters

Lecture 32 Instance of the LSTMGC model

Lecture 33 Inputs and outputs of the model

Lecture 34 Compiling the model

Lecture 35 Training the model

Lecture 36 Evaluating the model

Lecture 37 Visualizing the actual and forecasted values

Lecture 38 Mean absolute errors (MAE)

Data scientists and machine learning engineers interested in time series forecasting.,Python programmers looking to enhance their skills in deep learning and graph-based models.,Researchers and students in the fields of transportation, urban planning, or smart cities.,Professionals working with traffic data or other spatiotemporal datasets.,AI enthusiasts seeking to understand and implement advanced neural network architectures like LSTM and graph convolutional networks.,Individuals with a background in data analysis who want to apply machine learning to real-world datasets.