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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Predicting the Future Transaction from Large and Imbalanced Banking Dataset

Author 1: Sadaf Ilyas
Author 2: Sultan Zia
Author 3: Umair Muneer Butt
Author 4: Sukumar Letchmunan
Author 5: Zaib un Nisa

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110134

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.

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Abstract: Machine learning (ML) algorithms are being adopted rapidly for a range of applications in the finance industry. In this paper, we used a structured dataset of Santander bank, which is published on a data science and machine learning competition site (kaggle.com) to predict whether a customer would make a transaction or not? The dataset consists of two classes, and it is imbalanced. To handle imbalance as well as to achieve the goal of prediction with the least log loss, we used a variety of methods and algorithms. The provided dataset is partitioned into two sets of 200,000 entries each for training and testing. 50% of data is kept hidden on their server for evaluation of the submission. A detailed exploratory data analysis (EDA) of datasets is performed to check the distributions of values. Correlation between features and importance of characteristics is calculated. To calculate the feature importance, random forest and decision trees are used. Furthermore, principal component analysis and linear discriminant analysis are used for dimensionality reduction. We have used 9 different algorithms including logistic regression (LR), Random forests (RF), Decision tree (DT), Multilayer perceptron (MLP), Gradient boosting method (GBM), Category boost (CatBoost), Extreme gradient boosting (XGBoost), Adaptive boosting (Adaboost) and Light gradient boosting (LigtGBM) method on the dataset. We proposed LighGBM as a regression problem on the dataset and it outperforms the state-of-the-art algorithms with 85% accuracy. Later, we have used fine-tune hyperparameters for our dataset and implemented them in combination with the LighGBM. This tuning improves performance, and we have achieved 89% accuracy.

Keywords: Machine Learning (ML); banking; Santander; transactions; prediction; imbalanced; unbalanced; skewed; hyperparameter; oversampling; undersampling; EDA; dimensionality reduction; PCA; LDA; LR; RF; DT; MLP; GBM; CatBoost; XGBoost; AdaBoost; LigtGBM

Sadaf Ilyas, Sultan Zia, Umair Muneer Butt, Sukumar Letchmunan and Zaib un Nisa, “Predicting the Future Transaction from Large and Imbalanced Banking Dataset” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110134

@article{Ilyas2020,
title = {Predicting the Future Transaction from Large and Imbalanced Banking Dataset},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110134},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110134},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Sadaf Ilyas and Sultan Zia and Umair Muneer Butt and Sukumar Letchmunan and Zaib un Nisa}
}


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