Automotive Retail Analytics

Car dealerships generate large amounts of operational and sales data, but turning that into clear insights for decision‑making is often complex. The challenge was to explore whether generative AI could help analyze these datasets, provide explanations, and support management decisions without requiring deep technical expertise.

The project set out to build an AI‑driven analytics solution tailored to automotive retail. The idea was to use large language models to interpret data, generate analytical code, and deliver insights in a way that felt accessible to non‑technical users.

• – Extract insights from large datasets
• – Support decision-making via AI-generated analysis

• – LLMs for data understanding and Python-based analytical code generation
• – Classical ML vs LLM-assisted analytics

The work showed that language models can help translate complex data into explanations, route tasks intelligently, and even generate code to speed up analysis. The project provided practical takeaways on how to blend AI with traditional methods to make data‑driven decisions easier and more effective.