A Comparative Study of Start-Up Success Prediction with Random Forest and Traditional Models

The effectiveness or otherwise of a startup ecosystem is often dictated by the intricate interplay of several factors such as finance, participation, milestones, or market dynamics. Here we introduce RF-SSP: A Randomized Forest-Driven Framework of Start-up Success Prediction, a tool that may help to avoid blind investments and their inherent uncertainty. Missing value management, feature normalization, and class imbalance correction utilizing the ADASYN algorithm are some of the robust preprocessing techniques that this framework incorporates to improve upon traditional machine learning methods and guarantee highquality training data. A Flask-based deployment component for real-time prediction and recommendation, multiple algorithm assessment for comparative performance evaluation, balanced dataset preparation through preprocessing and feature engineering, and dataset investigation and visualization for meaningful insights are the four main modules that make up the proposed system. Despite the evaluation of other classifiers such as Logistic Regression, KNN, SVM, or Naïve Bayes, the Random Forest method proved to be the most effective, maintaining an accuracy level of over 94 % and surpassing the others in terms of precision, recall, the F1-score. With RF-SSP, investors and entrepreneurs have access to a trustworthy, interpretable, & analyst-friendly tool for forecasting startup results and producing practical recommendations for success-all thanks to the combination of ensemble learning combined structured preprocessing and deployment. By analyzing the Kaggle start-up dataset, we can see that the framework is useful for identifying trends in entrepreneurial success and providing actionable advice for making competitive strategic decisions.