The School Network Planner is a tool designed to enhance internet connectivity in schools, ensuring a smooth and uninterrupted learning experience for both students and teachers. In today’s digital age, stable and fast internet is vital for effective learning, especially with the increasing reliance on online education and digital resources. The planner uses data-driven insights to provide personalized network solutions tailored to the specific needs of each school. By collecting basic information such as the school name, village location, the number of classrooms, and the total number of students, the tool accesses a database to analyze the network requirements. It then recommends the most suitable Wi-Fi speed and bandwidth to ensure optimal connectivity for the entire school. The system considers factors such as peak internet usage times and high-demand areas like computer labs and staff rooms. This project specifically focuses on schools in the Sargodha region of Punjab, Pakistan. We used Python to gather and process the data, ensuring that the recommendations are based on accurate and up-to-date information about the region’s schools. This allows the planner to offer precise and effective solutions for each school’s unique setup. The planner also offers valuable advice on managing class schedules to prevent network congestion. It recommends distributing high-bandwidth activities, such as online lessons, across different time slots to ensure smooth internet use without interruptions. Additionally, it provides tips on prioritizing internet usage for classrooms that require more connectivity, helping schools efficiently manage their network resources. In conclusion, the School Network Planner empowers school administrators in the Sargodha region to optimize their internet systems, ensuring a reliable and cost-effective network for enhanced learning outcomes.

Network Optimizer is an AI-powered solution designed to enhance digital connectivity by optimizing network resources in real-time. It addresses common challenges in network utilization, such as weak signal areas, underutilized infrastructure, and weather-related disruptions. Using AI and real-time data analysis, the system identifies connectivity gaps, predicts potential signal issues, and provides actionable recommendations for telecom providers and policymakers. The interactive dashboard, built with Streamlit, offers visual insights on network performance, helping stakeholders improve service reliability and optimize infrastructure investments. Future enhancements include predictive ML models, AI-driven user support, and 5G network optimization, making Network Optimizer a cutting-edge tool for digital connectivity.

The AI Flood Prediction Bot collects real-time flood data from the internet using Python, focusing on cities in Pakistan affected by past floods. It analyzes historical flood trends, rainfall patterns, river water levels, and weather conditions to predict potential flood risks. By using DeepSeek as the base model, the bot provides alerts and risk assessments, helping authorities and residents take preventive measures. This system enhances disaster preparedness, helps reduce damages, and supports effective decision-making in flood-prone areas by offering accurate and data-driven flood predictions.