🏦 Credit Card Fraud Detection using Machine Learning

📌 Overview

This project focuses on detecting fraudulent credit card transactions using Machine Learning techniques in R. By leveraging classification models like Logistic Regression and Decision Trees, we aim to identify fraudulent transactions effectively.

🛠️ Technologies Used

• Language: R
• Libraries: ranger, caret, data.table, caTools, pROC, rpart, rpart.plot
• Algorithms: Logistic Regression, Decision Tree
• Dataset: Kaggle - Credit Card Fraud Detection

📂 Project Files

• Project Report: Click here
• Dataset: Download

🔍 Key Features

✔️ Data preprocessing and normalization for better model accuracy.
✔️ Exploratory Data Analysis (EDA) including statistical insights.
✔️ Implementation of Logistic Regression and Decision Trees for classification.
✔️ ROC Curve Analysis for model evaluation.
✔️ Fraud detection with high accuracy and minimal false positives.

📊 Results

• Logistic Regression: Achieved a good trade-off between precision and recall.
• Decision Tree: Provided interpretability with visual decision boundaries.
• ROC Curve Analysis: Evaluated the models based on AUC scores.

🚀 How to Run

1. Install required R packages:

   install.packages(c("ranger", "caret", "data.table", "caTools", "pROC", "rpart", "rpart.plot"))

2. Load the dataset in R.
3. Run the script to train and evaluate the models.

🤝 Contributing

💡 Want to improve this project? Feel free to fork, create a branch, and submit a pull request!

📞 Contact

🔗 LinkedIn: Ishan Gupta
🔗 GitHub: IshanGupta09