AI ENABLED GRIEVANCE REDRESSAL ANALYTICS PLATFORM

Abstract

This paper Presents an AI-Enabled Grievance Redressal Analytics System Platform that automates complaint registration, classification, prioritization, and management using Artificial Intelligence (AI) and Machine Learning (ML). The proposed system is designed to improve the efficiency of traditional grievance redressal by automatically predicting the Issue Type and Priority Level from complaint descriptions submitted by citizens. Complaint text is preprocessed using Natural Language Processing (NLP) techniques and TF-IDF (Term Frequency–Inverse Document Frequency) for feature extraction. A trained Scikit-learn model classifies complaints and assigns priority levels such as High, Medium, or Low. The application is developed using Python, Flask, SQLite, HTML, CSS, and JavaScript, providing a simple and userfriendly interface for complaint submission, tracking, and administration. A centralized dashboard enables administrators to monitor complaints, update their status, and analyze grievance records. By reducing manual effort, improving classification accuracy, and ensuring faster complaint resolution, the proposed system enhances transparency, operational efficiency, accountability, and citizen satisfaction in municipal grievance management.