Software Startups Success Prediction: Machine Learning Methods of Comparative Analysis

In recent years, there has been a significant rise in the number of software startups globally, driven by advances in technology and increasing reliance on digital solutions. These startups are crucial in shaping the future of industries. This study examines several machine learning models—Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and XGBoost—to predict the success of software startups based on key factors such as team dynamics and organizational characteristics. Using 473 data records and the top 19 features, the models were tested with an 80-20 train-test split, yielding strong performance in terms of accuracy, precision, recall, and F1 scores. Logistic Regression achieved the highest accuracy at 0.97, followed closely by KNN at 0.96. The findings reveal essential factors for startup success, such as team size, company age, and industry type, providing valuable insights for entrepreneurs, investors, and startup accelerators to adopt datadriven strategies that improve their chances of success.