Logistic Regression-Based Legal Prediction Model

As the legal maxim states that “Justice delayed is justice denied”, if the legal redress to a plaintiff is available, it should be done on a timely manner. But delayed justice has become a bane in the Indian justice system. It has been observed through the years, there are endless number of pending cases where the plaintiff has been waiting for the final judicial verdict. In this paper, authors have made an effort to reduce the number of cases beforehand so that the case is not filed or moved to the court. This is accomplished by helping lawyers in making predictions about case outcomes based on historical data. This paper addresses “Dowry Death”, or IPC 498A and 304B. By applying the logistic regression method to forecast the accuracy of court argument-based analysis, it seeks to uphold justice. The following stages have been performed: (i) From West Bengal’s trial courts, hard copies of case files with declared dowry judgements are gathered. (ii) Datasets are manually constructed depending on criteria such as (a) “Victim Name” (b) “Number of years married”, whether or not more than seven years. The essential components of “Dowry”-related cases in India are identified using these criteria. If the action is brought within seven years after the defendant’s marriage and the defendant has received dowry, the case is categorised as a “dowry case”; otherwise, it is classified as (c) “Dowry taken within seven years of marriage (Yes/No)”. (d) “Incident occurred within seven years of marriage”, which indicates that if a death occurs within seven years of a marriage, the circumstance most relatedly a “Dowry Death.” (e) “Postmortem Report (Usual/Unusual Death)”, as well as several other specified criteria. (iii) Through a prediction system, a supervised machine learning algorithm called logistic regression is employed to support judicial judgement. This paper aims to use supervised learning to determine a person's guilt or innocence. The study uses logistic regression, a common classifier, to demonstrate the model’s effectiveness and accuracy.