Data-Driven Impact Analysis of Climate Change on Agriculture Using Machine Learning

A data-driven machine learning analysis of how climate change affects agriculture.

A data-driven machine learning analysis of how climate change affects agriculture. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to turn raw data into decisions: collecting and cleaning data, exploring it, modelling it and presenting the findings in a dashboard or report.

The main parts are data collection and cleaning, exploratory analysis and feature building, modelling and validation, and an interactive dashboard of the results. The reference build uses Python, Pandas, Apache Spark, and Power BI, with a clear project structure, setup instructions and sample data so that it can be run and extended.