Machine Learning Project -- Credit Card Fraud

This project built a supervised extra tree model to shape the issue.

Two most significant achievements:

• Built a feature selection function based on the correlation coefficient matrix and data visualization, which effectively reduced 78% of the noise (28 features down to 6) and maintained the overall model performance.
• Improved recall while maintaining precision by applying a customized algorithm and precision and recall curve.

Several libraries' experiences:

• Demonstrated dataset manipulation use case through Numpy and Pandas.
• Demonstrated data visualization use cases through Seaborn, matplotlib, Plotly.
• Demonstrated model evaluation metrics use cases through classification reports, confusion matrix, precision and recall curve.
• Demonstrated resampling methods use cases through SMOTE and Random Sampler.
• Demonstrated model building, evaluation, hyperparameter tuning and pipeline workflow use cases through Sklearn.
• Demonstrated dimension reduction use cases through Autoencoder and UMAP.

Libraries:

• Data process
  • Numpy
  • Pandas
• Data visualization
  • Matplotlib
  • Seaborn
  • Plotly
• Sampling
  • Pandas
    • Random sampling
  • Sklearn
    • Train test split
  • Imblearn
    • SMOTE
    • Random Sampler
• dimension reduction
  • UMAP
  • Autoencoder
• Model building & evaluation
  • Sklearn
    • Cross Validation
    • Grid search
    • Pipeline
    • Extra tree model
  • Classification report
  • Confusion matrix
  • Precision and recall curve
• Model selection
  • Pycaret