Lead Scoring Machine Learning Project Workflow

Lead scoring system that leverages modern MLOps practices to help identify and prioritize potential customers. This project demonstrates the implementation of a complete machine learning pipeline, from data processing to production deployment.

Architecture Overview: The system is built on a robust tech stack that includes:

Data Storage: SQLite database housing five key tables including email subscribers, products, customer transactions, event tags, and website analytics
• Data Processing: Pandas for efficient data manipulation and preparation
• Containerization: Docker for ensuring consistency across development and deployment environments
• Machine Learning: Scikit-learn for model development
• MLOps Pipeline: MLflow for experiment tracking and model management
• Deployment: Streamlit for creating an interactive front-end application
• Version Control & CI/CD: GitLab for source control and continuous integration
• Cloud Infrastructure: Google Cloud Platform for scalable deployment

The workflow begins with comprehensive business understanding, followed by data analysis and preparation phases. The machine learning component processes the prepared data to generate lead scores, which are then tracked and versioned through MLflow. The entire system is packaged into a user-friendly Streamlit application, making it accessible to business users.

Key Technical Achievements:

– Implemented an automated data pipeline handling multiple data sources
• – Created a reproducible machine learning workflow with version control
• – Deployed a production-ready application with real-time scoring capabilities
• – Established continuous integration and deployment practices

This project demonstrates practical experience with modern data science tools and MLOps best practices, showcasing the ability to deliver end-to-end machine learning solutions in a production environment.