Credit Card Fraud Analysis and Detection
Credit Card Fraud Detection- Anonymized credit card transactions labeled as fraudulent or genuine About the Dataset
The project was intended to detect fraudulent transactions from a highly imbalanced dataset. Performed data analysis and developed machine learning models for a credit card fraud detection using ULB credit card dataset.
- To solve the imbalance dataset problem random undersampling, oversampling and SMOTE techniques were used.
- Performed data cleansing (with the help of correlation matrix, box plot and interquartile range) and dimensionality reduction using PCA, t-SNE and truncated SVD.
- Created Logistic regression (0.94), support vector machine (0.94) and decision tree (0.92) and neural network (0.97) based classifier.
- F1scores along with ROC cure was used to measure the performance generalization of various classification models.
- Neural network model with SMOTE based oversampling generated the best model with an F1 score of 0.97.
- Also created a Gaussian distribution based anomaly detection model with an F1 score of 0.65.
- [Exploratory Data Analysis]
- Logistic Regression
- Support vector Machine
- Decission Tree