Air Quality Index Prediction System

Developed an AI-powered Air Quality Index (AQI) Prediction System utilizing environmental dataset inputs. The system uses a machine learning approach to provide real-time AQI forecasts, demonstrating proficiency in data science pipeline development and deployment.

Key Features:

• AI Model: Core prediction logic built using the XGBoost algorithm for high accuracy.
• Data Processing: Used Pandas and Scikit-learn for efficient data preprocessing, feature engineering, and model training on environmental data.
• Deployment: Deployed the prediction interface using Streamlit for easy user interaction and visualization.
• Visualization: Integrated Matplotlib for visualizing prediction results and data patterns.

Data Science / ML Developer

• Model Development: Trained and fine-tuned the XGBoost model for accurate AQI prediction.
• Data Engineering: Managed the entire data pipeline from cleaning raw environmental data to feature selection using Pandas and Scikit-learn.
• Deployment: Built and hosted the interactive prediction web application using Streamlit.
• Analysis: Performed exploratory data analysis (EDA) and model performance evaluation.