Machine learning and data-driven techniques have become very famous and significant in several areas in recent times. In this paper, we discuss the performances of some machine learning methods with case of CatBoost classifier algorithm on both loan aproval and staff promotion. We compared the algorithm’s performance with other classifiers. After some feature engineering on both data, CatBoost algorithm outperforms other classifiers implemented in this paper. In analysis one, features such as loan amount, loan type, applicant income and loan purpose are major factors to predict mortgage loan approvals. And in the second analysis, features such as Division, Foreign schooled, geopolitical zones, Qualification and working years had high impact towards staff promotion. Hence, based on the performance of CatBoost in both analysis, we recommend this algorithm for better prediction of loan approvals and staff promotion.