Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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Brac University
2021
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| גישה מקוונת: | http://dspace.bracu.ac.bd/xmlui/handle/10361/14445 |
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10361-144452022-01-26T10:13:22Z Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction Chowdhury, Ahnaf Shahriyar Abdullah, Nayeem Mamun, Hasan Al Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Transaction fraud Neural Network machine learning classifiers Overfit Stacking technique K-fold cross validation Grid Search Hyperparameter tuning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 83-86). Transaction fraud has become a fast growing issue in the world of modern technology which has become a serious threat to the financial sectors. Although these fraudulent actions have several categories or type but online financial fraud has been a dominant issue so far. In reality, a profoundly precise procedure of identification of fraudulent transaction is required since it is causing a extensive wealth related depletion. Therefore, we have conducted research on financial fraud record using machine learning models and proposed a procedure for precise misrepresentation recognition dependent on the points of interest and restrictions of each exploration. In our initial stage, we implemented machine learning classifiers such as Logistic Regression, K-Nearest Neighbor, Support Vector Classifier, Na¨ıve Bayes, Gaussian Na¨ıve Bayes Classifier, Random Forest Classifier, Extra Tree Classifier, Neural Network and Adaptive Boosting to see how all of them performs separately. We also balanced the dataset that we used in order to overcome the overfit issue. Then again we tested the above mentioned classifiers on the balanced dataset. After that we tried our final step which is the implementation of Stacking technique. The accuracy that stacking method came up with were the best along with very less overfitting issues since K-fold cross validation was applied. To further boost the accuracy, we implemented Grid Search Hyperparameter tuning to get the best possible outcome at a much lower error rate. Therefore, to give a superior outcome for different sorts of online money transaction frauds, we have been keen on working with this issue and build a solid and defensive platform for safe transactions of money. Ahnaf Shahriyar Chowdhury Nayeem Abdullah Hasan Al Mamun B. Computer Science 2021-05-29T10:11:45Z 2021-05-29T10:11:45Z 2020 2020-04 Thesis ID: 15201009 ID: 15201027 ID: 15101065 http://dspace.bracu.ac.bd/xmlui/handle/10361/14445 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 86 Pages application/pdf Brac University |
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Brac University |
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Institutional Repository |
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en_US |
| topic |
Transaction fraud Neural Network machine learning classifiers Overfit Stacking technique K-fold cross validation Grid Search Hyperparameter tuning |
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Transaction fraud Neural Network machine learning classifiers Overfit Stacking technique K-fold cross validation Grid Search Hyperparameter tuning Chowdhury, Ahnaf Shahriyar Abdullah, Nayeem Mamun, Hasan Al Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Hossain, Muhammad Iqbal |
| author_facet |
Hossain, Muhammad Iqbal Chowdhury, Ahnaf Shahriyar Abdullah, Nayeem Mamun, Hasan Al |
| format |
Thesis |
| author |
Chowdhury, Ahnaf Shahriyar Abdullah, Nayeem Mamun, Hasan Al |
| author_sort |
Chowdhury, Ahnaf Shahriyar |
| title |
Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction |
| title_short |
Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction |
| title_full |
Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction |
| title_fullStr |
Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction |
| title_full_unstemmed |
Performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction |
| title_sort |
performance analysis of stacking neural network and machine learning model for detecting fraudulent transaction |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://dspace.bracu.ac.bd/xmlui/handle/10361/14445 |
| work_keys_str_mv |
AT chowdhuryahnafshahriyar performanceanalysisofstackingneuralnetworkandmachinelearningmodelfordetectingfraudulenttransaction AT abdullahnayeem performanceanalysisofstackingneuralnetworkandmachinelearningmodelfordetectingfraudulenttransaction AT mamunhasanal performanceanalysisofstackingneuralnetworkandmachinelearningmodelfordetectingfraudulenttransaction |
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1814308227600351232 |