Machine learning in detecting and interpreting business incubator success data and datasets

This research contributes to creating a proposed architectural model by utilizing several machine learning (ML) algorithms, heatmap correlation, and ML interpretation. Several algorithms are used, such as K-nearest neighbors (KNN) to the adaptive boosting (AdaBoost) algorithm, and heatmap correlation is used to see the relationship between variables. Finally, select K-best is used in the results, showing that several proposed model ML algorithms such as AdaBoost, CatBoost, and XGBoost have accuracy, precision, and recall of 94% and an F1-score of 93%. However, the computing time the best ML is AdaBoost with 0.081s. Then, finally, the proposed model results of the interpretation of AdaBoost using select K-best are the best features “last revenue” and “first revenue” with k feature values of 0.58 and 0.196, these features influence the success of the business. The results show that the proposed model successfully utilized model classification, correlation, and interpretation. The proposed model still has weaknesses, such as the ML model being outdated and not having too many interpretation features. The future research might maximize with ML models and the latest interpretations. These improvements could be in the form of ML algorithms that are more immune to data uncertainty, and interpretation of results with wider data.