Credit_Card_Fraud

Data science project:

• EDA on the dataset with visually appealing graphs
• Model built:
• 1. SVM with grid search for hyperparameters tuning
• 2. SVM with feature selection
• 3. SVM with SMOTE upsampling on the frauds
• 4. Random Forest with grid search for hyperparameter tuning
• 5. 3-layer ANN with Keras
• Metrics used: AUC, Precision, Recall
• Sample model results (for 3-layer ANN): AUC 98%, Precision 93%, Recall 74%

---

File organization

• Data: folder containing credit card transactions by Kaggle https://www.kaggle.com/dalpozz/creditcardfraud
• ProgressCheck: preliminary results of proposal, EDAs, inferential stats
• FinalResults: code and graphs wrapped together in jupyterbooks: Part1 for EDA and inferential stats, Part2 for predictive modeling, and a separate report

Important packages

• [Sklearn]
• [Tensorflow]
• [Keras]
• [statsmodel]
• [matplotlib]
• [seaborn]
• [pandas]
• [numpy]
• [scipy]

Contact

• Ginny -- author and organizer of this repo, [contact] (https://github.com/chocolocked)

Acknowledgments

• Special thanks to Amir Ziai, Jenny Hung for feedbacks on the project