Air Quality Prediction
PM2.5 pollution level prediction using regression models and KNN classification with feature engineering and categorical encoding.
About the Project
Air Quality Prediction is a machine learning project focused on forecasting PM2.5 pollution levels using advanced regression models and K-Nearest Neighbors (KNN) classification. The project addresses the critical environmental challenge of air quality monitoring and prediction, providing tools for understanding and forecasting pollution levels.
The implementation combines multiple machine learning approaches, utilizing both regression for continuous pollution level prediction and classification for categorizing air quality conditions. Feature engineering and categorical encoding play crucial roles in improving model accuracy and reliability.
Key Features
- PM2.5 Prediction: Accurate forecasting of particulate matter pollution levels
- Hybrid Approach: Combination of regression models and KNN classification for comprehensive analysis
- Feature Engineering: Advanced data transformation and feature creation for improved predictions
- Categorical Encoding: Proper handling of categorical variables in environmental data
- Statistical Analysis: Integration with statsmodels for detailed statistical insights
- Model Comparison: Evaluation of multiple approaches to identify the most effective prediction method
Technical Highlights
The project implements sophisticated feature engineering techniques to extract meaningful patterns from raw environmental data. This includes temporal features, meteorological factors, and geographical information that influence air quality levels.
Regression models provide continuous predictions of PM2.5 concentrations, while KNN classification categorizes air quality into discrete levels (e.g., good, moderate, unhealthy), offering different perspectives for decision-making and public health advisories.
The use of statsmodels alongside scikit-learn enables both traditional statistical analysis and modern machine learning approaches, providing comprehensive insights into factors affecting air quality and the reliability of predictions.
This project demonstrates expertise in environmental data science, feature engineering, and the application of diverse machine learning techniques to real-world prediction problems.