Скачать или смотреть A Suspicious Financial Transaction Detection Model Using Autoencoder and Risk Based Approach

🎓 A Suspicious Financial Transaction Detection Model Using Autoencoder and Risk-Based Approach | Final Year ML Project 🔍

Welcome to our project showcase on Suspicious Financial Transaction Detection using Machine Learning! 🚨💰

In this video, we present a powerful ML-based fraud detection model that leverages Autoencoders for anomaly detection and a risk-based approach for evaluating financial transactions. This hybrid model helps identify potentially fraudulent activities with higher accuracy and faster response times — a crucial tool in today's digital financial ecosystem.

✅ Project Highlights:

🔍 Unsupervised anomaly detection with Autoencoders

📊 Risk scoring using transaction features (amount, duration, location, etc.)

📈 Performance evaluation with ensemble learning (XGBoost, Random Forest)

🧠 Implemented in Python using Scikit-learn, TensorFlow & Pandas

💻 Suitable for B.Tech, M.Tech, AI, and Data Science projects

Whether you're a student looking for inspiration, a researcher exploring fraud detection techniques, or a developer aiming to understand hybrid ML models, this video is packed with practical insights and complete implementation support.

📦 Includes:

Full source code 💻

Dataset preprocessing steps 📂

Model training & evaluation walkthrough 📊

Code explanation and output demo 🔁

🚀 Perfect For:
Final Year Projects | AI Projects | Mini & Major Projects | Student Demos | Financial Fraud Research | Python-Based ML Systems

🔖 Tags:
#FinalYearProject #ColorectalCancerDetection #MachineLearning #EnsembleLearning #PythonProject #BTechProjects #MTechProjects #AIProjects #DLProjects #StudentProjects #MiniProject #MajorProject #HealthcareAI #CancerPrediction #XGBoost #RandomForest #ProjectWithCode #ExecutionSupport