Abstract
Machine learning (ML) is a branch of artificial intelligence that enables computers to learn from data and make predictions based on patterns and statistics. ML applied to various domains, such as natural language processing, computer vision, and recommender systems. One of the emerging applications of ML is in the field of legal analytics, which aims to provide insights and guidance for legal professionals and stakeholders. Predictive legal analytics is a subfield of legal analytics that focuses on using ML to predict the outcomes of legal disputes, such as court cases, arbitrations, or negotiations. Predictive legal analytics can help lawyers and judges to assess the potential legal consequences of their actions, to align their decisions with past precedents, to identify the best strategies for resolving conflicts, and to improve the efficiency and quality of justice delivery. Predictive legal analytics can also help clients and policy makers to understand the legal risks and opportunities involved in their situations, to make informed decisions before initiating or pursuing a legal action, and to evaluate the impact of legal reforms and interventio
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