Description

This project focused on using machine learning to analyze historical weather data from European weather stations, with the goal of improving weather prediction accuracy for ClimateWins. Key tasks included data preparation, exploration, and optimization to refine predictive models. Findings were summarized with visualizations and presented to support ClimateWins' objective of forecasting weather and extreme events more accurately.

Topics Covered

Machine Learning, data optimization, supervised learning, unsupervised learning, deep learning, gradient descent, K-Nearest Neighbor, Decision Tree, Artificial Neural Network, Random Forest, Hierarchical Clustering, Generative Adversarial Network, Convultional Neural Network, Recurrent Neural Network

Tools Used

Anaconda, Jupyter Notebook, Python

Presentations

https://www.canva.com/design/DAGWSwD2GoE/IjOlEH03EpEnD44hobSsAw/view?utm_content=DAGWSwD2GoE&utm_campaign=designshare&utm_medium=link&utm_source=editor

https://www.canva.com/design/DAGZHU8CNpo/TwNjFxEJzoMYy3SrNknIVg/view?utm_content=DAGZHU8CNpo&utm_campaign=designshare&utm_medium=link2&utm_source=uniquelinks&utlId=h016b9d1dc5