https://github.com/theo-liang/python-project-analysis-for-climatewins

This project analyzes historical weather data to identify patterns and predict future weather conditions, focusing on extreme events and temperature trends across Europe.
https://github.com/theo-liang/python-project-analysis-for-climatewins

artificial-neural-networks backpropagation confusion-matrix-heatmap convolutional-neural-networks data-optimization decision-tree-classifier deep-learning generative-adversarial-network gradient-descent hierarchical-clustering hyperparameter-tuning jupyter-notebook k-nearest-neighbor learning-rate machine-learning-algorithms overfitting python random-forest recurrent-neural-networks supervised-machine-learning

Last synced: about 9 hours ago
JSON representation

This project analyzes historical weather data to identify patterns and predict future weather conditions, focusing on extreme events and temperature trends across Europe.

• Host: GitHub
• URL: https://github.com/theo-liang/python-project-analysis-for-climatewins
• Owner: theo-liang
• Created: 2024-11-14T21:00:11.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-19T21:58:39.000Z (about 1 month ago)
• Last Synced: 2025-01-24T01:52:08.691Z (about 9 hours ago)
• Topics: artificial-neural-networks, backpropagation, confusion-matrix-heatmap, convolutional-neural-networks, data-optimization, decision-tree-classifier, deep-learning, generative-adversarial-network, gradient-descent, hierarchical-clustering, hyperparameter-tuning, jupyter-notebook, k-nearest-neighbor, learning-rate, machine-learning-algorithms, overfitting, python, random-forest, recurrent-neural-networks, supervised-machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 76.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
##### 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