Machine learning internship - Random Seeds for ML

As part of a machine learning lab at the University of Siegen[2], the code of the paper "The Effect of Random Seeds for Data Splitting on Recommendation Accuracy"[6] was replicated and reproduced. While the original paper focused on recommender systems, the code was adapted to investigate the effect of random seeds on general machine learning algorithms. Decision tree, KNN, and linear regression algorithms were evaluated on multiple datasets. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics were used for evaluation. In addition, comparative analyses were performed with the results from the original paper to confirm the consistency and validity of the results.