Credit-Card-Fraud-Analysis-Detection

Credit Card Fraud Analysis and Detection

Credit Card Fraud Detection- Anonymized credit card transactions labeled as fraudulent or genuine About the Dataset

Project Summary

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]
• Models
  • Logistic Regression
  • Support vector Machine
  • Decission Tree
  • Autoencoder
• Results