Legal Court Case Classification and Prediction Using ML

Abstract

The increasing availability of digital legal documents has made it essential to develop intelligent systems that can efficiently process and analyze large volumes of legal text. This study proposes a machine learning-based framework for classifying legal documents and predicting case outcomes by combining both traditional algorithms and advanced deep learning techniques. Specifically, Logistic Regression and Random Forest are used as baseline models, while BERT (Bidirectional Encoder Representations from Transformers) is employed to capture deeper contextual relationships within the text. The models are evaluated on a legal dataset after applying standard preprocessing techniques. The findings indicate that although traditional models provide reasonable performance, the BERT-based approach significantly enhances accuracy and understanding of complex legal language. The proposed system achieved an overall accuracy of 91%, along with improved precision and recall in predicting case outcomes. This approach has the potential to support legal professionals by reducing manual effort and enabling faster, data-driven decision-making.

Introduction

The text discusses the use of machine learning (ML) and Natural Language Processing (NLP) to manage and analyze the growing volume of digital legal documents—including case records, judgments, and contracts—which are difficult to process manually. Legal decision-making is complex, requiring interpretation of facts, laws, and precedents, and traditional human-centric approaches are time-consuming and prone to inconsistency.

Key Points:

• Motivation: The rapid digitization of legal texts creates massive data that requires automated, intelligent processing for efficiency and consistency.
• Machine Learning in Law:
  • Traditional models (Logistic Regression, Random Forest, SVM) provide baseline performance for legal text classification and case outcome prediction but struggle with the contextual complexity of legal language.
  • Transformer-based models, particularly BERT and domain-specific variants like Legal-BERT, excel at capturing deep contextual and semantic relationships, improving prediction accuracy.
  • Graph-based and precedent-aware systems incorporate structured legal knowledge, further enhancing reliability.
• Applications: NLP is applied in document classification, judgment prediction, contract analysis, and regulatory compliance, demonstrating practical utility in legal analytics.
• Challenges:
  • Limited domain-specific datasets
  • High computational requirements
  • Limited interpretability (black-box nature)
  • Dependence on structured data, restricting cross-domain and cross-jurisdiction applicability
• Research Gap:
  • Most studies treat classification and outcome prediction separately; there is a lack of integrated frameworks.
  • Few studies compare traditional ML with transformer-based models under a unified approach.
• Objective of the Study: Develop a comparative, integrated ML framework using Logistic Regression, Random Forest, and BERT for legal document classification and case outcome prediction, aiming to improve accuracy, reduce manual workload, and provide scalable, practical solutions for legal decision-making.

Conclusion

This study presents a machine learning-based approach for legal document classification and case outcome prediction by integrating both traditional and advanced models. The comparative analysis demonstrates that while Logistic Regression and Random Forest provide reliable baseline performance, transformer-based models such as BERT significantly enhance accuracy by effectively capturing contextual relationships within legal text. The proposed framework successfully combines document classification and outcome prediction into a unified system, reducing manual effort and improving analytical efficiency. The results indicate that the use of deep learning techniques leads to better precision, recall, and overall predictive performance, making the system more suitable for real-world legal applications. Despite these improvements, certain challenges remain, including the need for large domain-specific datasets and the computational complexity associated with advanced models. Future work can focus on optimizing model efficiency, incorporating domain-specific variants such as Legal-BERT, and expanding the system to handle multilingual legal data. Overall, this research contributes to the field of legal informatics by demonstrating the effectiveness of machine learning in automating legal analysis and supporting data-driven decision-making. The proposed approach offers a scalable solution that can be further extended to other legal and analytical domains.

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Copyright

Copyright © 2026 Ms. Saloni Bhatt, Prof. Kiran Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.