Explore the detailed process of model training and evaluation through backtesting, covering the period from 2016 to 2022.

This section explores the features used in the startup success model, including founders’ and investors’ data, investment rounds information, and text tags.

This section details the methodology for creating datasets of successful and unsuccessful companies.

This section details the use of Crunchbase's daily export data for training a deep learning model to classify startup success.

This section reviews recent studies on AI applications in startup prediction and venture capital.

Explore how novel deep learning models and extensive data from Crunchbase can predict startup success, including IPOs and unicorn status.

Discover how investors are ranked based on frequency, amount, and field of investments using an autoencoder model.

Explore the assessment of startup investment performance based on financial growth and valuation metrics.

Explore advanced methods to refine AI investor models by incorporating diverse data sources like social media and company websites.

Explore how integrating data-driven insights and AI enhances venture capital investment decisions.

Explore models for ranking founders based on their previous startup experience and success, and discover methods for predicting unicorns.