Deep Learning to Predict the Success of Startup Companies based on Smote and XB-RFE Model

The success of these high-risk startups can provide significant returns to venture capital businesses, and startups in general are a major factor for economic development. Investors can get a significant advantage over their competition if they can accurately forecast the success of startups. The overarching goal of this study is to identify the critical success factors for new businesses and to develop a model to categorize start-ups. Preprocessing, feature extraction, and model training make up the proposed method. Information cleansing, missing value handling, and N-gram analysis are all components of preprocessing. Subject Recognition Feature extraction makes use of the LDA algorithm. When training the model, we opted for the SMOTE-XGBoost-RFE. Our suggested model achieves an average accuracy rate of 90.35 percent, which is better than state-of-the-art alternatives such as RFE and XGBoost.