Ml-based Ensemble Learning Data Model for Classification Problems in Bank Marketing Prediction
R K Reddy
Department of Information Technology, Sreenidhi Institute of science and Technology (Autonomous), Hyderabad, India.
G. Charishma *
Department of Information Technology, Sreenidhi Institute of science and Technology (Autonomous), Hyderabad, India.
V. Nikitha
Department of Information Technology, Sreenidhi Institute of science and Technology (Autonomous), Hyderabad, India.
G. Sameeksha
Department of Information Technology, Sreenidhi Institute of science and Technology (Autonomous), Hyderabad, India.
*Author to whom correspondence should be addressed.
Abstract
This new data modelling strategy is aimed at improving predictions for telemarketing campaigns targeting potential customers for long-term deposit products at a Portuguese retail bank. The dataset includes detailed information about clients, the bank’s products, and various socio-economic factors, some of which reflect the impact of the financial crisis. Starting from an initial pool of 150 features, the model narrows this down to 21 key variables, including the target label. Our approach leverages ensemble learning and treats each feature type independently during preprocessing, followed by normalization to enhance overall predictive accuracy. To evaluate the efficiency of this technique, we compare the throughput of five widely-used classification algorithms, both individually and as part of an ensemble. The results demonstrate that integrating these techniques within an ensemble framework leads to consistently higher accuracy across all models.
Keywords: AI, ensemble learning, data model, classification problems, bank marketing prediction