Authors - Kajal Joseph, Deepa Parasar Abstract - This study conducts a predictive analysis of company status using various machine learning algorithms, aiming to identify the models that deliver the highest accuracy and reliability for decision-making in finance and business intelligence. The study employs a range of algorithms, including Logistic Regression, DecisionTreeClassifier, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Gradient Boosting Machines (GBM), XGBoost, AdaBoost, LightGBM, CatBoost, and Extra Trees Model, each rigorously tested on a preprocessed dataset split into training and testing sets to ensure robust validation. (Kunjir et al., 2020) Results indicate that ensemble models, particularly XGBoost and Random Forest, outperformed other methods, achieving accuracy rates exceeding 93%. This high level of performance highlights the value of ensemble techniques for handling complex predictive tasks, showcasing their suitability for applications where precise forecasting is critical. The study underscores the importance of model selection in predictive analytics, as it directly impacts the reliability of predictions in financial contexts. These findings suggest that machine learning, especially ensemble models like XGBoost and Random Forest, can significantly improve the accuracy of company status predictions, offering a dependable tool for stakeholders operating in uncertain environments. This research contributes valuable insights into the efficacy of machine learning in predictive tasks, advocating for data-driven decision-making approaches that can enhance business intelligence and strategic planning. (Liaw et al., 2019)
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