Product Recommendation Engines
Model Exploration, Evaluation, Development, and Deployment of Recommendation Engines with Collaborative Filtering models like implicit and lightFM.
- Data & Feature Engineering
- Model Evaluation
- Machine Learning – Pipeline Development (Microservices with Docker)
- API Development to efficiently serve personalized website content (Flask)
- Integration into Customers Websites to drive revenue
Data Science Platform with RShiny & Python
Data Science Platform with RShiny as Frontend & Python as Backend (Product Owner, Data Engineering, Development & Deployment)
…. for deep analysis of websites and customer behaviour used for client consulting on growth hacking
- Seamless Integration of Website Customer Behaviour Data
- Database Architecture & Pipelines
- Analytics & Visualizations
- Machine Learning & Predictive Modeling like e.g. Customer Clustering, Time Series Forecasting, Causal Effect Estimation with Bayesian Time Series Modeling, …
- What drives a customer to buy? (Catboost & Feature Importance)
- Deployment & Maintenance
© 2024 Stefan Brunhuber | Impressum