A startup is a new company started by a person or a group of people to solve a real-world problem uniquely. Startups have dynamic economics and initially require tremendous effort, time, and money. Along with this venture, capitalists have a high amount of risk while investing their money. So, there is necessary to find essential factors that help the startup's success.We propose a machine-learning-based solution for this challenge by keeping a data set of 923 rows and 45 columns, and ensemble methods usingLGM Classifier, XGBoosts, and AdaBoost Classifier are used for the training, while SVM is used for classification instead of the traditional softmax function.