This research addresses the challenge of predicting individuals’ credit scores accurately using Python and machine learning libraries (NumPy, Pandas, Matplotlib, and Seaborn). Using a simulated dataset from the Rohan Paris financial company, which is available on the Kaggle website, it explores the potentials of various machine learning models after performing exploratory data analysis and preprocessing steps. The study aims to improve credit score prediction based on financial history and personal information, emphasizing the significance of creditworthiness assessment. After rigorous model training and evaluation, the study employs metrics like accuracy, precision, recall, and F1 score to select the best model. The chosen model is then applied to predict credit scores for new data, and its performance is assessed. The research ultimately aims to contribute to more robust credit assessment methods and enhance the financial decision-making process.