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Chetan-Lande / repository
This repository presents an AQI prediction project using machine learning, developed during an internship. It employs environmental data and modern algorithms to create a reliable model for air pollution management. Components include a dataset, random forest regression model, Jupyter notebook, Python scripts, and improvement recommendations.
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This repository presents an AQI prediction project using machine learning, developed during an internship. It employs environmental data and modern algorithms to create a reliable model for air pollution management. Components include a dataset, random forest regression model, Jupyter notebook, Python scripts, and improvement recommendations.
This project predicts the Air Quality Index (AQI) based on key pollutants and environmental parameters using machine learning algorithms. The goal is to provide actionable insights into air quality, supporting data-driven decision-making for environmental management.
The project includes data preprocessing, exploratory data analysis, and the implementation of a predictive model using Random Forest Regression. Results are visualized to understand patterns and evaluate model performance.
Selected from shared topics, language and repository description—not editorial ratings.
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43/100 healthRun the Notebook Execute the AQI_Prediction.ipynb file in the notebooks/ folder to explore the analysis and model development. Results Training R²: 97.75% Testing R²: 84.71% Best Model: Random Forest Regression These results indicate that the model effectively predicts AQI based on the provided dataset, demonstrating its potential for practical applications.
Real-Time Integration: Integrate with IoT sensors for live AQI monitoring. Better Algorithms: Explore advanced techniques like XGBoost or LSTM for improved accuracy. Application Development: Build a user-friendly web app using Streamlit for visualizing predictions. Incorporate Weather Data: Include meteorological factors like humidity and wind speed.
This project is licensed under the MIT License.
Contributions are welcome! Feel free to fork this repository, create a branch, and submit a pull request.
This project was developed as part of a virtual internship by Edunet Foundation in collaboration with Shell, under the title:
"Artificial Intelligence with Green Technology | 4-weeks Virtual Internship"
For more details, visit the AICTE Internship Portal.
Project Author: Chetan Lande
📧 Email: landevaibhav14@gmail.com
📂 GitHub: Chetan-Lande
Keywords:
AI, Python, Data Analytics, Sustainability, Green Technology, Machine Learning, Deep Learning, AQI Prediction
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