archit03 / creditcard-fraud-detection

Credit Card Fraud Detection

Overview

This project focuses on building and evaluating machine learning models for credit card fraud detection. The dataset used contains transactions labeled as fraudulent or non-fraudulent.

Files

• ML.py: Python script containing the machine learning code.
• creditcard.csv: Dataset file containing transaction data.

Libraries Used

• numpy
• pandas
• matplotlib
• seaborn
• warnings
• sklearn (various modules for models, preprocessing, and metrics)
• imbalanced-learn (for oversampling)
• xgboost (if used, based on the conversation)
• streamlit for model deployment

Usage

1. Create a Virtual env to run ML.py.
2. Install the required libraries using pip install -r requirements.txt.
3. Ensure the dataset file (creditcard.csv) is in the same directory.
4. Run the ML.py script to train and evaluate machine learning models.

Machine Learning Models

The following machine learning models were implemented and evaluated:

• Logistic Regression
• Decision Tree
• Random Forest
• AdaBoost
• Gradient Boosting
• Linear Support Vector Machine (SVM)
• Support Vector Machine with Polynomial kernel and rbf kernel.

Results

Evaluation metrics such as precision, recall, F1-score, and accuracy were computed for each model. Model performance varies, and it's crucial to consider business requirements when choosing the best model.

Future Improvements

• Hyperparameter tuning for models.
• Feature engineering to enhance model performance.
• Exploration of other algorithms and ensemble methods.

Notes

• The dataset is imbalanced, impacting model performance.
• Consider the trade-off between precision and recall based on business needs.
• Experiment with different approaches to handle class imbalance.