Likelihood of Company Acquisition ML Predictions

This presentation explores a machine learning approach to predicting the likelihood of acquisition for venture-backed technology companies in the US market. Using the CRISP-DM framework, the project evaluates the business opportunity, defines success metrics, outlines a validation strategy, and discusses key ML system design decisions and production risks.

The proposed solution uses a Random Forest classification model trained on factors such as revenue growth, funding activity, sector M&A trends, valuation multiples, hiring momentum, and market signals to estimate the probability of acquisition within the next 24 months.

The project focuses on how investment banks, private equity firms, venture capital firms, and corporate strategy teams can use machine learning to improve deal origination, reduce analyst research time, and anticipate market changes more effectively.

Видео Likelihood of Company Acquisition ML Predictions канала R Perry SPG