Start-up Acquisition Status Prediction Using Machine Learning

Abstract

Startup acquisition is the process where companies interested in investing in startups buy and own some or all of the startup's shares. However, investing in startups requires extensive research as many startups fail within a short period of time, and other factors such as IPO and acquisition by other companies also need to be considered. The recent advances in machine learning have made it possible to predict a startup's acquisition status based on financial statistics, historic data and market trends. The model uses supervised learning to find patterns and correlations between the input features (startup characteristics) and the related acquisition status by learning from labelled data. The prediction model is constructed using machine learning algorithms like KNN and random forests which can save time and costs for investors and speed up their investment process. Also, it aims to classify a startup's acquisition status as Operating, Acquired, IPO, or Closed based on input data. When using the Random Forest approach, the suggested system for predicting acquisition status achieves an accuracy score of 93%, whereas using the KNN (K-Nearest Neighbours) strategy, it earns a score of 91%.