Predictive Analysis and Judgement Forecasting of Government Employee’s Service Matters under purview of Machine Learning

Abstract

The integration of Machine Learning (ML) into the legal system represents a significant advancement, reshaping how judgments are delivered and cases are managed. This paper explores the transformative potential of ML techniques in enhancing the Indian legal system, focusing on predicting case outcomes and improving legal research efficiency. The study specifically addresses the application of ML in predicting judgments within the services tribunal court of Uttar Pradesh, a novel area of research in India. We design and evaluate a machine learning model to classify four types of petitions: Minor Punishment Cases, Major Punishment Cases, Recovery (Financial Irregularity/Loss) Cases, and Retirement/Pensionary Benefits Cases. By training the model on a comprehensive labeled dataset of case characteristics, we employ various ML techniques including Naive Bayes Classifier, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Neural Networks, and Ensemble Learning. The model's performance is assessed through metrics such as accuracy, precision, recall, and F1 score. This ML framework aims to aid judges in predicting case outcomes and streamline the decision-making process, offering valuable insights to both legal professionals and non-specialists. The results indicate that ML can significantly reduce workload, enhance prediction accuracy, and facilitate better case management in the judiciary.