v1.0.0

Release Notes - v1.0.0 🚀

Overview

This is the initial release of the Credit Card Fraud Detection project. The model is built using Random Forest Classifier to predict fraudulent credit card transactions based on various features. The release includes the implementation of the fraud detection model, model evaluation, and feature importance visualization.

Features 🎉

• Data Preprocessing: Includes data cleaning, handling missing values, and class distribution analysis.
• Model Training: The project uses a Random Forest Classifier to classify transactions as fraudulent or not.
• Evaluation Metrics: The model performance is evaluated using:
  • Confusion Matrix
  • Classification Report
  • ROC-AUC Score
• Feature Importance Visualization: Displays the importance of each feature in the model’s decision-making process.
• Confusion Matrix Visualization: Visual representation of the true positives, true negatives, false positives, and false negatives.

Improvements ✔️

• Performance Tuning: Random Forest hyperparameters (like n_estimators and max_depth) are optimized for better performance.
• Visualization: Added clear and easy-to-interpret plots for confusion matrix and feature importance.

Bug Fixes 🐞

• Dataset Handling: Fixed issues related to reading the dataset from zip files for smooth loading.
• Code Clean-up: Removed redundant code snippets and optimized the overall structure for better readability.

Known Issues ⚠️

• Performance on Larger Dataset: The current dataset is sampled (10%) for faster training. The full dataset might require additional computational resources for training.

Installation and Setup 📦

To install and run the project:

1. Clone the repository:

   git clone https://github.com/QuantumCoderrr/CreditCardFraudDetection.git
   cd CreditCardFraudDetection