Land acquisition is one of the most critical and time-sensitive phases of infrastructure development. Delays in acquiring land significantly effect the execution of national and state-level projects. The causes of land acquisition delays are multifaceted, including prolonged administrative approvals, legal disputes, delayed compensation disbursement, incomplete documentation, pending notifications, land ownership conflicts, rehabilitation and resettlement challenges, and inter-departmental coord
of the Study: Develop an AI-powered Predictive Analytics System that can identifying land acquisition projects that are at risk of delay by analyzing historical and real-time project data.
Land acquisition is one of the most critical and time-sensitive phases of infrastructure development Delays in acquiring land significantly effect the execution of national and state-level projects
of the Study: Develop an AI-powered Predictive Analytics System that can identifying land acquisition projects that are at risk of delay by analyzing historical and real-time project data. The proposed solution should utilize machine learning algorithms to study patterns from completed and ongoing land acquisition cases, considering parameters such as project type, land area, number of affected families, compensation status, approval timelines, legal disputes, possession status, rehabilitation progress, stakeholder responsiveness, and historical performance. The system should generate a risk score for each project and predict the probability of delays at different stages of the land acquisition lifecycle. It should also identify the key contributing factors responsible for the predicted