The Wildfire Data Analysis and Prediction project aims to provide learners with hands-on experience in data analytics and predictive modeling using real-world environmental data. The project involves exploring and analyzing two comprehensive wildfire datasets: NASA’s FIRMS and SatFire. Learners will focus on understanding regional distribution and identifying trends in wildfire occurrences. The primary goal is to create an interactive visualization that effectively communicates these insights. Additionally, learners will develop a statistical or machine learning model to predict future wildfire occurrences. This project will enhance learners' skills in data visualization, statistical analysis, and predictive modeling, contributing to improved wildfire management strategies.

Data Analysis Report

Summary of exploratory data analysis, including data preprocessing, feature engineering, and key insights from the NASA FIRMS and SatFire datasets.

Interactive Visualization Dashboard

An interactive visualization focused on regional wildfire distribution and trends, using tools like Plotly.

Predictive Model

A trained statistical or machine learning model for wildfire prediction, with documentation of the model development process.

Final Presentation

Presentation summarizing key findings, demonstrating the interactive visualization, and showcasing the predictive model results.

Technical Documentation

Documentation of code, model development, and deployment instructions for the dashboard and model.