The following is the peer review of the project proposal by [name of team completing peer review]. The team members that participated in this review are
Sunday Usman - @sundayusman
Tanya Evita George - @tanyageorge
Mellika Akbarsharifi - @mellica97
Divya Liladhar Dhole - @Divyadhole
Mohammad Ali Farmani - @mfarmani95
H M Abdul Fattah - @hmfattah
Gabriel Gedaliah Geffen - @gabegef
Describe the goal of the project.
The aim is to effectively detect fraudulent transactions by employing ensemble learning techniques such as bagging, boosting, and stacking. The focus lies in comprehending the underlying reasons behind the varying effectiveness of these models in different scenarios. This analytical approach aids in determining the optimal method for fraud detection.
Describe the data used or collected.
They analyzed a dataset of 550,000 credit card transactions by European cardholders in 2023. It includes 31 features, such as transaction IDs, anonymized attributes (V1-V28), transaction amounts, and a binary fraud classification. The anonymization protects privacy, while the binary classification suits supervised fraud detection methods.
Describe how the research question will be answered, e.g. what approaches / methods will be used.
The research question will be addressed through a comparative analysis of anomaly detection algorithms, focusing on machine learning models such as Random Forest, XGBoost, and Artificial Neural Networks, alongside ensemble techniques like stacking. This assessment will involve training and testing various models and ensemble techniques, comparing their performance metrics such as accuracy, precision, recall, and F1-score. Through this approach, the study aims to not only assess individual algorithm strengths but also uncover potential synergies leading to a more accurate and reliable fraud detection system.
Is there anything that is unclear from the proposal?
The proposal is well-structured and detailed, providing a clear overview of the project's objectives, motivation, dataset, research questions, and analysis plan.
Provide constructive feedback on how the team might be able to improve their project.
Explicitly Define Success Metrics: Clearly define the success metrics for evaluating the performance of different machine learning models and ensemble techniques. This could include metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Having well-defined success criteria will help in objectively assessing the effectiveness of the models.
Consider addressing the potential issue of imbalanced data, where fraudulent transactions may be significantly outnumbered by legitimate ones. Explore techniques such as oversampling, undersampling, or using algorithms specifically designed for imbalanced datasets to ensure that the models are not biased towards the majority class.
What aspect of this project are you most interested in and would like to see highlighted in the presentation.
We are interested about how they are going to implement the staking methods, which approach would be the best method and how are they going to deepen their understanding of how these models work.
Provide constructive feedback on any issues with file and/or code organization.
Add your repository link to the GitHub icon.
Add the link to the dataset sources.
Add a separate section for the Analysis plan and Plan of Attack.
The following is the peer review of the project proposal by [name of team completing peer review]. The team members that participated in this review are
Sunday Usman - @sundayusman
Tanya Evita George - @tanyageorge
Mellika Akbarsharifi - @mellica97
Divya Liladhar Dhole - @Divyadhole
Mohammad Ali Farmani - @mfarmani95
H M Abdul Fattah - @hmfattah
Gabriel Gedaliah Geffen - @gabegef
Describe the goal of the project.
The aim is to effectively detect fraudulent transactions by employing ensemble learning techniques such as bagging, boosting, and stacking. The focus lies in comprehending the underlying reasons behind the varying effectiveness of these models in different scenarios. This analytical approach aids in determining the optimal method for fraud detection.
They analyzed a dataset of 550,000 credit card transactions by European cardholders in 2023. It includes 31 features, such as transaction IDs, anonymized attributes (V1-V28), transaction amounts, and a binary fraud classification. The anonymization protects privacy, while the binary classification suits supervised fraud detection methods.
The research question will be addressed through a comparative analysis of anomaly detection algorithms, focusing on machine learning models such as Random Forest, XGBoost, and Artificial Neural Networks, alongside ensemble techniques like stacking. This assessment will involve training and testing various models and ensemble techniques, comparing their performance metrics such as accuracy, precision, recall, and F1-score. Through this approach, the study aims to not only assess individual algorithm strengths but also uncover potential synergies leading to a more accurate and reliable fraud detection system.
The proposal is well-structured and detailed, providing a clear overview of the project's objectives, motivation, dataset, research questions, and analysis plan.
We are interested about how they are going to implement the staking methods, which approach would be the best method and how are they going to deepen their understanding of how these models work.