Python-Based Startup Evaluation Framework: Data-Driven Analysis for Venture Capital

Abstract

This research project developed Python-based scoring models and data analysis pipelines for startup evaluation in the venture capital context. The work focused on researching and evaluating AI startups, with particular emphasis on companies in generative AI and LLM implementations, while creating technical reports analyzing pros and cons of various LLM approaches.

Key Contributions

• Data Analysis Pipelines: Developed Python-based scoring models for startup evaluation
• AI Startup Research: Focused on generative AI and LLM implementation companies
• Technical Reports: Created comprehensive analysis of LLM approaches and methodologies
• Venture Capital Integration: Applied data science to VC decision-making processes

Technical Framework

• Python Development: Custom scoring models and analysis pipelines
• Data Science: Statistical analysis and data visualization for startup evaluation
• AI Research: Deep dive into generative AI and LLM technologies
• Business Intelligence: Integration of technical analysis with business decision-making

Impact

This work demonstrates ability to apply technical skills to business contexts, specifically in venture capital evaluation of AI startups, showcasing practical understanding of both technology and business applications.

Recommended citation: Mkhitaryan, D. (2024). Python-Based Startup Evaluation Framework: Data-Driven Analysis for Venture Capital. Innovis VC Research.