  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.
