• Cleaned, transformed, and standardized raw data using Pandas; progressed data quality checks and validation methods that refurbished data consistency and accuracy by 25%. • Generated the random forest model by fine-tuning hyper parameters with Bayesian optimization techniques, increasing accuracy rate by 15% and reducing processing time by 40%.