Building accurate legal case outcome prediction models

The legal community is very interested in and highly valued by legal predictive models. Lawyers and judges in particular can use these models' current benefits to forecast case difficulties to further complicate their future courses of action, such as faster decision-making, providing evidence for arguments, supporting the opposition, etc.. Yet it takes a long time and involves a lot of complex stages to correctly predict court rulings and case issues, including data mining, sanctification, engineering, and the chance discovery of appropriate bulk case papers. However, the legal complexities make everything more challenging. Here used several machine learning algorithms to predict the legal case. Naïve Bayes, Logistic Regression and Gradient Boosting algorithms are used. Here the accuracy and the confusion matrix of classifiers is evaluated.