Sentimental Analysis on Drugs

• Role: Collected and preprocessed a Kaggle dataset comprising 161,218 drug reviews, optimized it for analysis, and contributed to a research paper. 

• Description: Developed an interactive Streamlit web application that enables healthcare providers to input patient drug reviews and receive real-time sentiment analysis, improving decision-making and achieving a 90% user satisfaction rating.

• Implemented LightGBM for sentiment classification, ensuring high accuracy and efficiency.

 • Designed data visualizations using Matplotlib and Seaborn for better interpretability.

 • Technologies: Python, LightGBM, Scikit-learn, Pandas, NumPy, Jupyter Notebook