atmospheric data.
The project involved:
Data collection and preprocessing (handling missing values, normalization, etc.)
Feature engineering and selection
Applying supervised learning algorithms such as Random Forest and SVM
Evaluating model performance using metrics like Accuracy and F1-score
Visualizing insights using Python libraries like Seaborn and Matplotlib
This project demonstrates my ability to process real-world data and develop predictive models that provide meaningful insights.
