Air Quality Prediction Using Machine Learning

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.