[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121812-en":3,"doc-seo-121812-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121812,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Revolutionizing Divorce Case Prediction in India - A Machine Learning Approach to Save Marriages and Enhance Decision Accuracy","Divorce rates in India have risen, creating significant concerns for the stability and well-being of marriages. Accurate divorce case prediction can support early risk identification and timely interventions, helping to preserve relationships and improve decision quality. The study proposes machine learning models to forecast divorce cases by combining socio-demographic data, marriage history, and psychological factors. After preprocessing for missing values and outliers, models including logistic regression, SVM, random forests, and gradient boosting are trained, evaluated using accuracy, precision, recall, and F1-score, and interpreted to reveal key predictors, while addressing privacy, fairness, and transparency for responsible deployment.","Revolutionizing Divorce Case Prediction in India: A Machine Learning Approach to Save Marriages and Enhance Decision Accuracy  \nPrithviraj Singh Solanki1 and Yamini Kunwar Solanki2  \n1Assistant Professor, Department of Computer Engineering Gokul Global University Siddhpur (Gujarat), INDIA 2Advocate District Court, Bikaner Sirohi (Rajasthan), INDIA  \n[1](1Corresponding Author: prithvisingh2488@gmail.com)[Corresponding Author: prithvisingh2488@gmail.com](1Corresponding Author: prithvisingh2488@gmail.com)  \nReceived: 30-03-2023 Revised: 17-04-2023 Accepted: 29-04-2023  \nABSTRACT  \nThe rising number of divorce cases in India has raised concerns about the stability and well-being of marriages. Predicting divorce cases accurately can be critical in identifying potential risks and implementing timely interventions to save marriages. This study proposes a novel approach that uses machine learning algorithms to forecast divorce cases in India. The primary goal is to use advanced predictive models to improve decision accuracy and marriage preservation. To begin, the paper establishes the importance of accurate divorce case prediction by investigating the social, emotional, and economic consequences of divorce on individuals and society as a whole. An extensive review of existing literature is conducted, shedding light on the limitations of traditional divorce methods. The paper goes over the process of gathering detailed socio-demographic information, marriage history, and psychological factors from various sources. Preprocessing is performed on the collected dataset to address missing values, outliers, and ensure data integrity. To train predictive models, various machine learning algorithms such as logistic regression, support vector machines, random forests, and gradient boosting are investigated. To identify the most relevant predictors contributing to divorce cases, feature selection techniques are used. The accuracy, precision, recall, and F1-score of these models are used to evaluate their performance. In addition, the interpretability of machine learning models is investigated in order to gain insights into the underlying factors that lead to divorce [7]. This analysis contributes to a better understanding of the critical factors that influence marital outcomes and provides useful information for policymakers, counsellors, and individuals looking to strengthen their marriages. The proposed machine learning approach's ethical considerationsand potential implications in the legal and counselling domains are thoroughly discussed. Concerns about privacy, fairness, and transparency are addressed in order to ensure responsible and accountable predictive model deployment in the divorce case prediction process. The study demonstrates how machine learning has the potential to revolutionise divorce case prediction in India. This approach can facilitate timely interventions, counselling, and support systems to preserve marriages and foster marital harmony by accurately identifying marriages at risk. This study's findings contribute to the burgeoning field of automatic court decision prediction and provide actionable insights for stakeholders involved in marriage counselling and family law in India [4].  \nKeywords-- Divorce Case Prediction, Machine Learning Algorithms, Revolutionizing Divorce, Marriage  \nCounselling, Family Law.  \nI. INTRODUCTION  \nThe History and Importance of Divorce Case Prediction in India: Divorce rates in India have steadily increased over the years, raising concerns about the country's marriage stability and well-being. Prediction of divorce cases has emerged as an important area of research, with the goal of identifying early warning signs and providing timely interventions to save marriages. Understanding the causes of divorce is critical for developing effective strategies and interventions. Cultural, social, economic, and psychological factors all play important roles in marital relationships in India. Traditiona","cbCaihg6opPJqgtm","https://ap.wps.com/l/cbCaihg6opPJqgtm","pdf",639526,1,9,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## History and Importance of Divorce Case Prediction in India\n## Role of Cultural, Social, Economic, and Psychological Factors\n## Limitations of Traditional Methods\n## Opportunities from Advanced Technologies and Big Data\n## Implications for Policymakers, Counsellors, and Individuals\n## Implications for Family Law and Judicial Systems","[{\"question\":\"How does the proposed approach forecast divorce cases in India?\",\"answer\":\"It uses machine learning algorithms trained on socio-demographic information, marriage history, and psychological factors gathered from multiple sources.\"},{\"question\":\"Which machine learning models are investigated and how are they evaluated?\",\"answer\":\"The study explores logistic regression, support vector machines, random forests, and gradient boosting. Performance is assessed using accuracy, precision, recall, and F1-score.\"},{\"question\":\"Why does the paper emphasize ethical and interpretability considerations?\",\"answer\":\"Model interpretability helps identify underlying predictors, while ethical concerns such as privacy, fairness, and transparency are addressed to enable responsible deployment in legal and counselling settings.\"}]","Revolutionizing Divorce Case Prediction in India - A Machine Learning Approach to Save Marriages and Enhance Decision Accuracy | PDF",1785806990,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"revolutionizing-divorce-case-prediction-in-india-a-machine-learning-approach-to-save-marriages-and-enhance-decision-accuracy","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/revolutionizing-divorce-case-prediction-in-india-a-machine-learning-approach-to-save-marriages-and-enhance-decision-accuracy/121812/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the proposed approach forecast divorce cases in India?","Question",{"text":75,"@type":76},"It uses machine learning algorithms trained on socio-demographic information, marriage history, and psychological factors gathered from multiple sources.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are investigated and how are they evaluated?",{"text":80,"@type":76},"The study explores logistic regression, support vector machines, random forests, and gradient boosting. Performance is assessed using accuracy, precision, recall, and F1-score.",{"name":82,"@type":73,"acceptedAnswer":83},"Why does the paper emphasize ethical and interpretability considerations?",{"text":84,"@type":76},"Model interpretability helps identify underlying predictors, while ethical concerns such as privacy, fairness, and transparency are addressed to enable responsible deployment in legal and counselling settings.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]