Developed a machine learning model using XGBoost to predict LN_IC50 for cancer drug responses with an impressive R² score of 99.41%. Leveraged SHAP for model interpretability, providing clear insights into feature contributions. Deployed the solution using Gradio, enabling real-time predictions through an interactive web interface. This project aims to assist researchers and pharmaceutical companies in evaluating drug efficacy and advancing personalized cancer treatment strategies.