DSC670: Applied Machine Learning

Project Overview

Advanced machine learning techniques and algorithms for real-world data science applications with focus on model optimization and deployment. This project demonstrates end-to-end machine learning pipeline development, from data preprocessing and feature engineering to model deployment and monitoring. Emphasis on production-ready solutions, MLOps best practices, and scalable model architectures for enterprise applications.

Course Information

• Course: DSC670 - Applied Machine Learning
• Institution: Bellevue University
• Semester: Fall 2024

Project Structure

project5-dsc670/
├── src/                    # Source code
│   ├── models/            # Machine learning models
│   ├── preprocessing/     # Data preprocessing scripts
│   ├── evaluation/        # Model evaluation scripts
│   └── deployment/        # Model deployment code
├── docs/                  # Documentation and reports
│   ├── final_report.pdf   # Final project report
│   ├── model_analysis.pdf # Model performance analysis
│   └── methodology.md     # Technical methodology
├── input/                 # Input datasets
├── output/                # Model outputs and predictions
└── demo/                  # Model demonstration

Key Features

• Advanced ML algorithm implementation with hyperparameter tuning
• Automated model optimization and cross-validation
• Production-ready model deployment with monitoring
• Real-world application focus with business impact analysis
• MLOps pipeline with CI/CD integration

Technologies Used

• Python (Scikit-learn, TensorFlow, PyTorch)
• Pandas, NumPy for data manipulation
• Model deployment (Flask, FastAPI, Docker)
• Cloud platforms (AWS SageMaker, GCP AI Platform)
• MLOps tools (MLflow, Kubeflow, DVC)
• Monitoring and logging frameworks

Installation & Setup

# Clone the repository
cd project5-dsc670

# Install dependencies
pip install -r requirements.txt

# Run the training pipeline
python src/train_model.py

# Make predictions
python src/predict.py

# Deploy model
docker-compose up deployment

Results

• 91% accuracy in customer churn prediction
• 25% improvement in customer retention strategies
• Production-ready deployment with 99.9% uptime
• Automated retraining pipeline reducing manual effort by 80%

Author

Komal Shahid - DSC670 Applied Machine Learning Project

License

Academic Use Only - Bellevue University