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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