Predictive Visualization of Community Municipal Challenges

The system is an advanced web-based solution designed to simplify the process of reporting, analyzing, and predicting urban civic problems. It addresses recurring challenges such as road damage, drainage leakage, water overflow, garbage accumulation, electricity outages, and borewell malfunctions. Citizens can submit detailed complaints through an intuitive interface that supports descriptive text, geolocation data in the form of latitude and longitude, and image uploads. The backend is developed using the Flask framework with MySQL as the database, ensuring secure authentication and organized data storage. For administration, the platform provides a comprehensive dashboard to manage complaints, monitor resolution progress, and support informed decision-making. A machine learning model based on the Random Forest Regressor, trained on historical complaint records and optimized with RandomizedSearchCV, is integrated to predict the estimated time required to resolve reported issues. Spatial patterns are analyzed using K-Means clustering to detect areas with high complaint density. Visual insights are generated in the form of bar chart, heatmap, and cluster map using Matplotlib and Seaborn. Through its combination of real-time complaint collection, predictive modeling, and spatial analytics, the system improves transparency, speeds up municipal responses, and supports data-driven governance. Future developments may involve mobile application integration, real-time status tracking, and intelligent allocation of resources for issue resolution.