Machine Learning Model to Predict Results of Law Cases

The judicial system is an important component of the nation's democratic system, protecting the rights and liberties of its residents while preserving the rule of law. With time, many necessary changes are made in the judiciary system to maintain peace, trust, and order in the country. But court hearings and proceedings take too much time for a decision. Despite the fact that the legal industry is developing faster than ever with the aid of developing technologies, there are still many unexplored areas and there is always potential for improvement. In this paper, we present a simplified approach, "AI in Law Practice". The model is developed by utilizing, the most disruptive technology - Machine Learning. The dataset is stored in IPFS for legal and ethical considerations. The algorithm can forecast case results, giving departments and attorneys useful information. Predicting the case output with accuracy is the model's problem. The F1-score, accuracy, precision, recall, support, and recall are used to evaluate the system's performance, which also demonstrates the system's practical applicability. The model is trained on 3304 U.S. Supreme Court cases and achieves 95% accuracy. The model that is currently being built will be utilized by legal practitioners, law departments, end users, etc.