From Data to Decision: Machine Learning Techniques for Financial Deposit Prediction

It is now necessary for most financial institutions in strategizing their futures, especially with new consumer trends and market needs, to continue providing the data or forecasting term deposits. In terms of the research on predictive modelling with regard to term deposits in banks, different machine learning methods have been employed. Prior studies have implied where most traditional methods like logistic regression have failed to deliver high standards of accuracy and interpretability in motivating imposition of advanced methods such as decision tree or random forests. The analysis emphasizes such activities as feature selection, exploration, and model evaluation in improving the reliability of predictions while eliminating generalizability-compromising variables, which takes place within the scope of a data cell created from a telemarketing campaign of a Portuguese bank. Random forests outperformed logistic regression in terms of result output, recording a higher test accuracy and score compared to logistic regression. The better performance is especially obvious in predicting the minority "yes" class and rather points out the usefulness of ensemble learning in appraising imbalanced datasets. The present results suggest that the power of machine learning can be harnessed for increased predictive accuracies and to guide marketers in their strategies. It provides a reminder of the constraint that must be commandeered when trying for balance among performance, interpretability, and applicability in prediction models for real-world use.