Describe the goal of the project.
The project aims to identify whether credit-card transactions are genuine (a binary classification problem) through stack generalization or other model ensembles of different ML models.
Describe the data used or collected.
The data consists of 550,000 credit card transactions made by European cardholders in 2023. It contains
transaction details such as Transaction ID, time, location, amount, several deidentified variables, and a class label associated with the type of transaction.
Describe how the research question will be answered, e.g. what approaches / methods will be used.
Explores the effectiveness of Random Forest, XGBoost, etc.. and compares them with ensemble techniques like stacking, bagging, boosting, etc.
Build a meta-classifier to check whether there is any improvement in fraud detection from base classifiers.
Is there anything that is unclear from the proposal?
1: Model Comparison Metrics: Clarify which performance metrics will be used to compare the models and justify the selection of these metrics. Explain how these metrics effectively identify this task's 'best model'. While the ROC Curve is mentioned, its limitations in the context of imbalanced classification should be addressed by incorporating additional, more suitable metrics.
2: Class Imbalance Strategy: The inherent class imbalance in credit card transaction datasets is not addressed. Specify the strategies that will be employed to manage this imbalance, enhancing the models' ability to accurately detect anomalies.
3: Stacked Generalization Considerations: Detail the criteria for including models in the stacked generalization ensemble. Address the potential for some models to negatively impact the overall performance and how this will be evaluated and mitigated.
4: Clarity on the Second Research Question: The description of the second research question lacks clarity. Provide a concise and clear statement of the question, outlining the objectives and how it contributes to the research goals.
Provide constructive feedback on how the team might be able to improve their project.
Synthetic Minority Over-sampling Technique (SMOTE), enhancing the model's sensitivity to minority classes.
The evaluation of models for imbalanced binary classification incorporates metrics like AUC-PR, MCC, and the F1-MCC plot, offering a comprehensive view of performance beyond conventional accuracy.
Shapley Additive explanations (SHAP) are employed for model interpretation, identifying key features that significantly influence the detection of credit card fraud, thereby improving model transparency and effectiveness
What aspect of this project are you most interested in and would like to see highlighted in the presentation.
Strategies for Managing Class Imbalance.
How model ensembling improves the classification task.
Provide constructive feedback on any issues with file and/or code organization.
You can add the required libraries for this project in the requirements.txt file
The following is the peer review of the project proposal by [KG Competitors]. The team members who participated in this review are:
[]Gorantla Sai Laasya] - @[Sailaasya-1]
[Surya Vardhan Dama] - @[suryavardhandama]
[Maksim Kulik] - @[@h-akston]
[Remi Hendershott] - @[remisublette]
[Monica Tejaswi Kommareddy] - @[KommareddyMonicaTejaswi]
[Shashank Yadav] - @[xinformatics]
[Priom Mahmud] - @[Priom1996]
Describe the goal of the project. The project aims to identify whether credit-card transactions are genuine (a binary classification problem) through stack generalization or other model ensembles of different ML models.
Describe the data used or collected.
The data consists of 550,000 credit card transactions made by European cardholders in 2023. It contains transaction details such as Transaction ID, time, location, amount, several deidentified variables, and a class label associated with the type of transaction.
1: Model Comparison Metrics: Clarify which performance metrics will be used to compare the models and justify the selection of these metrics. Explain how these metrics effectively identify this task's 'best model'. While the ROC Curve is mentioned, its limitations in the context of imbalanced classification should be addressed by incorporating additional, more suitable metrics.
2: Class Imbalance Strategy: The inherent class imbalance in credit card transaction datasets is not addressed. Specify the strategies that will be employed to manage this imbalance, enhancing the models' ability to accurately detect anomalies.
3: Stacked Generalization Considerations: Detail the criteria for including models in the stacked generalization ensemble. Address the potential for some models to negatively impact the overall performance and how this will be evaluated and mitigated.
4: Clarity on the Second Research Question: The description of the second research question lacks clarity. Provide a concise and clear statement of the question, outlining the objectives and how it contributes to the research goals.
Provide constructive feedback on any issues with file and/or code organization. You can add the required libraries for this project in the requirements.txt file
(Optional) Any further comments or feedback?