The rapid growth of startup ecosystems has created a strong need for reliable methods to evaluate the potential success of newly established companies. Traditional investment decisions often rely on subjective judgment, limited historical analysis, and manual evaluation, which may lead to inaccurate predictions and high financial risk. This research proposes an efficient and novel machine learning–based approach for predicting startup company success rates using advanced ML paradigms. The proposed system integrates multiple data sources such as financial metrics, founder experience, market trends, funding history, product innovation, and customer engagement indicators to build a comprehensive predictive model. The methodology involves data preprocessing, feature engineering, and the application of supervised learning algorithms including Random Forest, Support Vector Machine, Gradient Boosting, and Neural Networks. Ensemble learning techniques are employed to improve prediction accuracy and reduce model bias. The system utilizes historical startup datasets to train and validate models, enabling identification of critical success factors influencing startup growth and sustainability. Performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics to ensure robust model performance. Experimental results demonstrate that the proposed hybrid ML framework significantly improves prediction reliability compared to traditional statistical methods. The model assists investors, entrepreneurs, and policymakers in making datadriven decisions by providing early insights into startup viability. Overall, the proposed approach enhances risk assessment, optimizes investment strategies, and contributes to the development of intelligent decision-support systems within entrepreneurial ecosystems.
Keywords :
Author : Mr. L. N. V. Rao1 , KONDETI ANAND PAL2 ,RAVULAKOLLU LAKSHMI NARASAMMA3 ,METHUKUMILLI DIVYA4 ,KUNUKU SRI LAKSHMI5
Title : TRANSFORMING HAND GESTURE INTO WORDS A NOVEL APPROCH FOR ASSISTIVE COMMUNICATIONS USING ML TECHNIQUES
Volume/Issue : 2026;03(04)
Page No : 746-754