Dynamic Pricing and Business Analytics

This industry-transfer project focuses on the use of data-driven methods for dynamic pricing, business analytics, and strategic decision-making.

The work connects scientific modeling, statistical analysis, and applied machine-learning workflows with real business data. The goal is to understand how pricing strategies, temporal patterns, demand behavior, and commercial variables interact in large transactional datasets.

Main topics

• Dynamic pricing.
• Business analytics.
• Time-series analysis.
• Demand and revenue modeling.
• Data-driven decision-making.
• Applied machine-learning workflows.

Technical profile

The project uses Python-based data analysis pipelines, exploratory modeling, feature engineering, and predictive approaches adapted to business and commercial datasets.