Favorite Book Prediction System Using Machine Learning Algorithms

Recent years have seen the rapid deployment of Artificial Intelligence (AI) which allows systems to take intelligent decisions. AI breakthroughs could radically change modern libraries' operations. However, introducing in libraries is a challenging task. This research explores potential for smart improve caliber user services through use machine learning (ML) techniques. The proposed work investigates methods such as Random Forest (RF) and boosting algorithms, including Light Gradient Boosting Machine (LGBM), Histogram-based gradient (HGB), Extreme (XGB), CatBoost (CB), AdaBoost (AB), (GB) task identifying classifying Favorite books compares their performances. Comprehensive experiments performed on publicly available dataset (Art Garfunkel's Library) show that model can effectively handle books. Experimental results LGBM has achieved outstanding performance with an accuracy rate 94.9367% than other ML algorithms. empirical takes advantage adoption using To best our knowledge, we are first develop application library automatically identify classify