A Legal Prediction Model Using Support Vector Machine and K-Means Clustering Algorithm for Predicting Judgements and Making Decisions

The legal community has displayed significant interest in harnessing predictive models to anticipate case outcomes, providing judges and attorneys with valuable tools to enhance their decision-making processes. These models offer the potential to expedite choices, offer support for arguments, and strengthen defence strategies. However, accurately predicting legal rulings and case results is a complex endeavour, involving multifaceted stages such as sourcing appropriate bulk case documents, extracting and refining data, and navigating the intricate nature of legal paper complexities. In this innovative machine learning-powered legal prediction model is introduced, named "Legal Acumen." Beyond predictions, this model also offers informed decisions based on the appropriate laws, providing an added layer of insight and value. "Legal Acumen" not only generates predictions about judgments from the Supreme Court but also incorporates a feature that aids end users: upon uploading a new case description document to a designated folder, the system rapidly comprehends its contents and generates both a prediction and a legally informed decision. The understanding indicates that "Legal Acumen" stands among the pioneering legal prediction models striving to anticipate decisions from the Indian Supreme Court. For automatic prediction of Legal Acumen used different types of machine learning algorithms such as Support vector machine, Decision Tree algorithm, Etc., But this paper proposed two-step process. First step used Support vector machine for the prediction of judgement. Then the second step explained about the punishment of the criminal based on K-Means Clustering algorithm.