🛡️ Credit Card Fraud Detection using Machine Learning – Final Year BE Project

In the modern digital economy, where cashless transactions are a norm, credit card fraud has become a critical concern for consumers and financial institutions alike. To address this, my final year engineering project aimed to build a Credit Card Fraud Detection System using Machine Learning – blending data science, real-time security, and user-friendly design into a powerful fraud prevention solution.

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🧠 Why This Project?

Financial fraud contributes to billions of dollars in losses annually. With more people relying on digital payments, it has become crucial to:

• Detect suspicious transactions instantly,

• Prevent losses before they occur, and

• Stay ahead of evolving fraud techniques.

Our project was driven by this motivation to combine machine learning algorithms with real-time transaction monitoring.

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🎯 Project Goal

"To develop a machine learning model that accurately classifies transactions as legitimate or fraudulent, minimizing false positives and false negatives."

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🔍 Dataset Used

We used the Kaggle Credit Card Fraud Dataset, which contains:

• 284,807 transactions

• 492 fraudulent cases (only ~0.17%)

• Anonymized numerical features (V1-V28), Amount, and Time

This dataset is highly imbalanced – a common challenge in real-world fraud detection problems.

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⚙️ Tech Stack

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🧪 Machine Learning Approach

We experimented with several ML techniques and finalized Support Vector Machine (SVM) due to its high precision in binary classification tasks. Our pipeline included:

1. Data Cleaning

   • Removing nulls (none in the dataset)

   • Standardizing the Amount feature

2. Handling Imbalance

   • Random under-sampling

   • Stratified data splitting

3. Model Training

   • Trained the SVM model using labeled data

   • Evaluated using Accuracy, Precision, Recall, F1-score

4. Real-Time Prediction

   • Built a GUI to simulate transaction entry and view fraud detection results

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🧱 System Modules

🔐 Admin Panel

• Authorize users

• Upload dataset

• Monitor activity logs

👤 User Panel

• Register/Login

• Submit transaction info

• View predictions

🧠 Detection Engine

• Receives transaction data

• Uses trained SVM model

• Returns prediction: Fraud or Legitimate

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🎨 GUI Snapshots

Here's a glimpse of our simple, intuitive user interface:

• 🔐 Login Page

  

• 📝 Registration Page

  

• 📊 Output Page (Fraud/Legit)

  

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✅ Results

After training and testing, here are our outcomes:

💡 Insight: The combination of proper preprocessing and SVM allowed the system to detect fraud with excellent performance and almost zero false alarms.

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🛠️ Tools Used

• Python 3.5+

• Spyder (IDE via Anaconda)

• SQLite for lightweight database handling

• Tkinter for the GUI interface

• Scikit-learn for ML modeling

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📚 Lessons Learned

• Working with imbalanced datasets is tricky – we had to apply balancing techniques to avoid bias.

• SVM performed well but is computationally heavier on large data.

• GUI development gave a real-world application feel to the project.

• Testing with real use cases was essential for catching edge case issues.

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📁 GitHub Repository

👉 View Project on GitHub

Includes complete source code, dataset link, GUI scripts, and documentation.

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🚀 Future Scope

• Replace SVM with deep learning or ensemble methods (Random Forest + XGBoost)

• Implement real-time API integration

• Add OTP-based or biometric authentication

• Use cloud deployment for production scalability

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🙌 Acknowledgments

• Special thanks to our internal guide and faculty at Savitribai Phule Pune University

• Gratitude to Kaggle for the public dataset

• My incredible team for bringing the project to life

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🧾 Final Thoughts

This project is more than just a machine learning exercise – it's a real-world solution to a pressing financial problem. With strong results and simple UI, it's a practical foundation for next-gen fraud detection systems.

If you're a student, developer, or enthusiast, I hope this inspires you to build meaningful machine learning applications. Feel free to fork the project on GitHub and experiment with it!

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Let’s connect!
💼 LinkedIn – https://www.linkedin.com/in/saksham-kamble-651678226/
🛠️ GitHub – https://github.com/SakshamCloudOps