| Technical details | |
|---|---|
| Submit files | dataset_analysis.ipynb - presentation.txt - a_fool_fraud_tradi.ipynb - a_fool_fraud_data_augmentation.ipynb |
| Languages | It needs to be completed in the language you are working on right now. If you are doing Bootcamp Javascript, then javascript (file extension will be .js). If you are doing Bootcamp Ruby, then Ruby (file extension will be .rb). It goes the same for Python, Java, C++, Rust, ... |
Email sounds You've Got Mail. This is a transcript from a meeting that senior leader at your company just had:
[Location: Conference Room]
[Attendees: Project Manager, Data Scientist (You), Chief Information Officer (CIO), Chief Risk Officer (CRO), and Head of Fraud Detection Department]
Project Manager (PM): Good morning, everyone! Thank you for joining us today. We have gathered here to discuss an exciting project that aims to enhance our credit card fraud detection capabilities using cutting-edge data augmentation techniques. I'll hand it over to our brilliant Data Scientist (DS) to present the project overview.
Data Scientist (You): Good morning, everyone! As PM mentioned, I'll be leading this project. Our primary goal is to improve the performance of our credit card fraud detection system by leveraging new data augmentation techniques. As we all know, credit card fraud is a growing concern, and we want to stay ahead of the curve.
CIO: That sounds promising. Can you tell us more about the data augmentation techniques you plan to use?
You: Of course! We'll explore state-of-the-art techniques like Synthetic Minority Over-sampling Technique (SMOTE), Adaptive Synthetic (ADASYN), and even delve into Generative Adversarial Networks (GANs) to generate synthetic samples of the minority class, which in our case, are fraudulent transactions. By increasing the diversity of our training data, we expect our models to better generalize and detect fraud with higher accuracy.
CRO: That's excellent! But how do we ensure that the augmented data doesn't introduce any biases?
You: An important question, indeed. We'll carefully examine the augmented data to mitigate any potential biases. Additionally, we'll monitor the performance of the models on a separate validation set to make sure we're not overfitting or introducing any unwanted biases.
Head of Fraud Detection Department: Will this project impact our existing fraud detection system?
You: Great question! Our intention is to enhance the current system, not replace it. We'll run experiments in parallel and compare the augmented models' performance with our baseline models. If the results are promising, we can integrate the improved model into our existing system.
CIO: Budget-wise, what do you need for this project?
You: The budget will be allocated for data acquisition, computational resources, and any other necessary expenses related to experimentation and analysis. We'll ensure to optimize costs while maintaining high-quality results.
CRO: What about ethical considerations? How will you address potential data privacy concerns?
You: Ethical considerations are paramount in this project. We'll strictly adhere to data privacy and confidentiality guidelines. All sensitive information will be anonymized and protected. Moreover, we'll address any potential biases introduced during data augmentation and ensure fairness and transparency in our approach.
PM: Fantastic! This project has the potential to significantly improve our fraud detection capabilities. Let's work together to make this a success. Are there any other questions or concerns before we wrap up?
[Attendees discuss any additional questions or clarifications]
PM: Thank you all for your valuable input. Let's get started on this exciting project. We'll schedule regular update meetings to keep everyone informed about the progress. If there are no further questions, this meeting is adjourned. Have a great day, everyone!
[The meeting ends with a sense of enthusiasm and anticipation for the successful completion of the project]
This project aims to enhance the classification performance in credit card fraud detection by utilizing cutting-edge data augmentation techniques. The primary objective is to address the challenges posed by imbalanced datasets, where fraudulent transactions are significantly outnumbered by genuine ones, leading to biased model predictions and reduced accuracy.
The proposed methodology includes data collection, preprocessing, and exploration of state-of-the-art data augmentation techniques such as SMOTE, ADASYN, and GANs. These techniques will generate synthetic samples of the minority class, i.e., fraudulent transactions, increasing the diversity of the training data and improving model generalization.
A variety of machine learning algorithms will be evaluated, including Logistic Regression, Random Forest, SVM, and GBM. The models will be trained on both the original imbalanced dataset and the augmented dataset, with performance evaluation using accuracy, precision, recall, F1 score, and area under the ROC curve.
The project emphasizes ethical considerations, ensuring data privacy and addressing potential biases in the augmented data. Interpretability and explainability of the selected models will be explored to gain insights into fraud detection processes.
By integrating the improved model into the existing fraud detection system, this project will contribute to a safer financial environment and better protection against credit card fraud for consumers and financial institutions.
We would like you to complete the following:
- An analysis of the dataset.
- A prediction model. (jupyter notebook)
- A presentation reports comparing the model's performance against traditional traffic prediction methods.
https://storage.googleapis.com/qwasar-public/track-ds/creditcard.csv
Make sure you don't push the dataset(s) to git, it will be an automatic rejection.