Primary Objective

Develop and validate machine learning models that can predict customer satisfaction for e-commerce transactions with 85%+ accuracy, enabling automated payment decision support for satisfaction-based commerce platforms.

Specific Learning Objectives

1. Apply Machine Learning Theory: Implement supervised learning algorithms for binary classification (satisfied/unsatisfied)
2. Data Science Practice: Clean, process, and analyze large-scale e-commerce transaction datasets
3. Model Evaluation: Use industry-standard metrics to validate prediction accuracy and reliability
4. Software Development: Build a working prototype system that demonstrates real-time prediction capabilities

Project Scope (Computer Science Focus)

Core Tasks (4-5 Related Activities)

Task 1: Data Collection & Preprocessing (Week 1-2)

• Collect simulated e-commerce transaction data (customer profiles, product details, purchase context)
• Clean and normalize datasets for machine learning applications
• Create training/validation/test splits following best practices
• Deliverable: Clean dataset with 10,000+ labeled transactions

Task 2: Feature Engineering (Week 3-4)

• Extract relevant features from customer behavior data
• Implement feature selection techniques to identify key satisfaction predictors
• Create customer personality profiles and purchase pattern indicators
• Deliverable: Feature engineering pipeline with documented methodology

Task 3: Model Development (Week 5-8)

• Implement multiple ML algorithms (Random Forest, SVM, Neural Networks)
• Train models using different feature combinations and hyperparameters
• Apply cross-validation techniques to prevent overfitting
• Deliverable: Trained models achieving 85%+ accuracy on validation set

Task 4: System Integration (Week 9-10)

• Build web-based interface for real-time satisfaction prediction
• Integrate trained models into working prototype application
• Implement confidence scoring and prediction explanation features
• Deliverable: Functional web application demonstrating satisfaction prediction

Task 5: Evaluation & Documentation (Week 11-12)

• Test system performance with new transaction data
• Conduct error analysis and identify improvement opportunities
• Document methodology, results, and future research directions
• Deliverable: Complete technical report and system demonstration

Required Skills & Knowledge (Single Discipline)

Computer Science Prerequisites

• Programming: Python, pandas, scikit-learn, Flask/Django
• Machine Learning: Supervised learning, classification algorithms, model evaluation
• Data Science: Data preprocessing, feature engineering, statistical analysis
• Software Development: API development, web applications, version control

Classroom Knowledge Applied

• CS 485 - Machine Learning: Classification algorithms, model validation
• CS 470 - Data Mining: Feature extraction, pattern recognition
• CS 405 - Software Engineering: System design, testing, documentation
• CS 350 - Database Systems: Data management, query optimization

Technical Deliverables

• Machine Learning Models3+ trained classification models (Random Forest, SVM, Neural Network)
• Model performance comparison with accuracy, precision, recall metrics
• Saved model files ready for production deployment
• Prediction SystemWeb-based application for real-time satisfaction prediction
• REST API endpoints for model integration
• User interface showing prediction confidence and reasoning
• Dataset & CodeCleaned and processed dataset with 10,000+ transaction records
• Complete source code with documentation and comments
• Installation and usage instructions for system deployment

Academic Deliverables

• Technical Report (20-25 pages)Literature review of satisfaction prediction research
• Methodology and experimental design documentation
• Results analysis with statistical significance testing
• Future work recommendations and system limitations
• Presentation & Demo15-minute presentation of methodology and results
• Live demonstration of working prediction system
• Q&A session addressing technical implementation details
• Code DocumentationComprehensive README with setup instructions
• Inline code comments explaining algorithms and design decisions
• API documentation for system integration

Success Metrics (Measurable)

Technical Performance

• Prediction Accuracy: ≥85% on held-out test set
• Processing Speed: <500ms response time for real-time predictions
• System Reliability: 99%+ uptime during demonstration period
• Code Quality: Pass automated testing with 90%+ code coverage

Learning Outcomes

• ML Implementation: Successfully implement 3+ different classification algorithms
• Data Science Skills: Demonstrate proficiency in data preprocessing and feature engineering
• Software Development: Build functional web application with proper documentation
• Research Skills: Conduct literature review and experimental validation

Project Management

• Timeline Adherence: Complete all milestones within 12-week timeframe
• Team Collaboration: Effective use of version control and project management tools
• Documentation Quality: Clear, comprehensive documentation for all deliverables

Team Structure (3-4 Computer Science Students)

Team Roles

• ML Engineer: Lead model development and algorithm implementation
• Data Scientist: Handle data preprocessing, feature engineering, and validation
• Software Developer: Build web application and API integration
• Research Coordinator: Literature review, documentation, and testing

Shared Responsibilities

• Weekly team meetings and progress reviews
• Collaborative code review and quality assurance
• Joint presentation preparation and system demonstration
• Collective problem-solving and technical decision-making

Expected Outcomes (Realistic)

Student Learning

• Hands-on experience with real-world machine learning applications
• Understanding of end-to-end ML system development lifecycle
• Practical skills in data science tools and frameworks
• Experience with collaborative software development practices

Technical Innovation

• Working prototype demonstrating satisfaction prediction capability
• Comparative analysis of different ML approaches for satisfaction prediction
• Reusable codebase for future satisfaction prediction research
• Documentation contributing to satisfaction prediction knowledge base
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