You will get Production-Ready ML Fraud Detection System with Flask API Deployment
Project details
Built an end-to-end credit card fraud detection system with
production-level code architecture.
🎯 Objective:
Develop a scalable ML system to identify fraudulent transactions
in real-time with modular, maintainable code structure.
🔧 Technical Implementation:
Key Features:
✅ Custom exception handling (exceptions.py)
✅ Centralized logging system (logger.py)
✅ Modular component design
✅ setup.py for package installation
✅ Docker containerization ready
✅ Git version control with proper .gitignore
ML Workflow:
• Data preprocessing with outlier handling
• Feature engineering and scaling
• Model training (Logistic Regression/Random Forest)
• Hyperparameter tuning
• Model evaluation (Precision, Recall, F1-Score)
• Model serialization using pickle
Deployment:
• Flask REST API for predictions
• JSON input/output format
• Error handling and validation
• Ready for Docker deployment
📊 Results:
• Achieved 95%+ accuracy on test data
• Fast inference time (<100ms per prediction)
• Production-ready codebase following industry standards
Tech Stack: Python, scikit-learn, Pandas, NumPy, Flask, Docker, Git
production-level code architecture.
🎯 Objective:
Develop a scalable ML system to identify fraudulent transactions
in real-time with modular, maintainable code structure.
🔧 Technical Implementation:
Key Features:
✅ Custom exception handling (exceptions.py)
✅ Centralized logging system (logger.py)
✅ Modular component design
✅ setup.py for package installation
✅ Docker containerization ready
✅ Git version control with proper .gitignore
ML Workflow:
• Data preprocessing with outlier handling
• Feature engineering and scaling
• Model training (Logistic Regression/Random Forest)
• Hyperparameter tuning
• Model evaluation (Precision, Recall, F1-Score)
• Model serialization using pickle
Deployment:
• Flask REST API for predictions
• JSON input/output format
• Error handling and validation
• Ready for Docker deployment
📊 Results:
• Achieved 95%+ accuracy on test data
• Fast inference time (<100ms per prediction)
• Production-ready codebase following industry standards
Tech Stack: Python, scikit-learn, Pandas, NumPy, Flask, Docker, Git
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$50
|
Standard
$120
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | - | - | |
Data Source Connectivity | - | ||
Source Code | - | - |
Frequently asked questions
About Ayan
Full-Stack Developer + ML/NLP Engineer | Build & Deploy AI Systems
Ujjain, India - 2:39 pm local time
🔹 Data Science: ML model development (regression, classification, NLP),
EDA, feature engineering, Flask API deployment
🔹 Web Development: MERN stack applications, React dashboards,
MongoDB/backend integration, RESTful APIs
Recent work:
- Credit Card Fraud Detection system (production-ready structure)
- War Impact Analysis - Economic trends analysis with ML benchmarking
- Full-stack applications with ML model integration
Tech Stack: Python, scikit-learn, Pandas, NumPy, Flask, React, Node.js,
MongoDB, Docker, Git
I focus on clean code, modular architecture, and deployable solutions.
Available for both short-term tasks and long-term projects.
Steps for completing your project
After purchasing the project, send requirements so Ayan can start the project.
Delivery time starts when Ayan receives requirements from you.
Ayan works on your project following the steps below.
Revisions may occur after the delivery date.
Development & Coding
Objective: Begin frontend development by translating the approved design into a functional website, ensuring it’s mobile-responsive and fully optimized. Outcome: A fully coded website prototype.