🌍 Air Quality Level Prediction

Project Objective

Build a machine learning model to predict air quality levels using pollutant data such as PM2.5, PM10, NO2, CO, and O3. The project helps in understanding pollution trends and environmental impact.

Dataset

Source: Kaggle – Air Quality Dataset
Features include:

• PM2.5, PM10, NO2, CO, O3
• AQI (Air Quality Index)
• Date and Location

Technologies Used

Python
Google Colab
Pandas, NumPy
Matplotlib, Seaborn
Scikit-learn
XGBoost

Project Workflow

Data collection and cleaning
Exploratory Data Analysis (EDA)
Feature engineering
Model training
Model evaluation
Result visualization

Results

Achieved good prediction accuracy
Identified major pollutants affecting air quality
Analyzed pollution trends

Folder Structure

Air-Quality-Level-Prediction/
├── README.md
├── Air_Quality_Prediction.ipynb
└── datasets/
└── air_quality.csv

Future Improvements

Deploy using Flask or Streamlit
Add real-time data integration
Enhance model with deep learning

Acknowledgements

Kaggle Dataset
Nan Mudhalvan Program
Mailam Engineering College

Author

Janani Gunasekaran
Department of AI & DS
Mailam Engineering College.