Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods
This addresses the challenge for startup companies and investors in making predictions with limited early-stage data, though it is incremental as it applies existing methods to this domain.
The paper tackled the data sparsity problem in predicting startup success by applying machine learning algorithms to a large Crunchbase dataset, achieving F1 scores of 53.03% with LightGBM and 52.96% with XGBoost.
Predicting the success of startup companies is of great importance for both startup companies and investors. It is difficult due to the lack of available data and appropriate general methods. With data platforms like Crunchbase aggregating the information of startup companies, it is possible to predict with machine learning algorithms. Existing research suffers from the data sparsity problem as most early-stage startup companies do not have much data available to the public. We try to leverage the recent algorithms to solve this problem. We investigate several machine learning algorithms with a large dataset from Crunchbase. The results suggest that LightGBM and XGBoost perform best and achieve 53.03% and 52.96% F1 scores. We interpret the predictions from the perspective of feature contribution. We construct portfolios based on the models and achieve high success rates. These findings have substantial implications on how machine learning methods can help startup companies and investors.