A Novel Approach on Argument based Legal Prediction Model using Machine Learning


“Justice delayed is justice denied” and this delaying of justice is a great bane for the Indian justice system. Every year, illimitable cases remain pending just for the final hearing of judicial verdict. Years pass-by keeping the plaintiff waiting for justice. For years, this is a major issue faced in the Indian judicial system. In this paper, authors have attempted to condense the problem by decreasing the number of cases before it reaches the Court. This is done by extending help to the legal professionals to predict a case output from previous records. This paper focuses on cases related to `Dowry Death' i.e. IPC section 498A and 304B. It aims to deliver justice by predicting judicial argument-based analysis using the Support Vector Machine (SVM) algorithm to find its accuracy. This model processes through the following steps: (i) Hard-copies of the case files with pronounced judgments related to dowry are collected from trial courts of West Bengal. (ii) Data set are generated manually based on certain parameters like:(a) `Victim Name' (b) `Number of years married (greater than seven years or not)'. In India, these parameters determine the key factors of `Dowry' related cases. If the case is filed within seven years of marriage and the defendant has taken dowry then the case falls under `dowry case' else not (c) `Dowry taken within seven years of marriage (Yes/No)' (d) `Incident occurred within seven years of marriage', this parameter shows that if the death has taken place within seven years of marriage it falls under 'Dowry Death' case. (e) `Postmortem Report (Usual/Unusual Death)' and many other documented parameters. (iii) A Supervised Machine Learning Algorithm namely, Support Vector Machine (SVM) is used to assist legal judgement through a prediction system. The objective of this paper is to predict whether a person is guilty or not, using a supervised learning approach. The paper shows the performance and accuracy of the model with a standard classifier i.e. Support Vector Machine (SVM).



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL