<div> <div> <span>Design and Implementation of a Hybrid Economic Expert System for MSMEs in Nigeria</span> </div> </div>

The complexity of business environments in developing economies such as Nigeria has intensified demand for intelligent decisionsupport systems capable of interpreting dynamic economic conditions and assisting Micro, Small, and Medium Enterprises (MSMEs) in strategic planning. This study presents the design and implementation of a Hybrid Economic Expert System that integrates rule-based reasoning with machine learning analytics to support decision-making in taxation, compliance, funding, pricing, forecasting, and policy evaluation. The system was designed against three stated objectives: at least 85% accuracy in economic forecasting within a simulated environment, coverage of at least five distinct economic decision scenarios, and a user satisfaction rate of at least 80% in usability testing. Development followed a hybrid of Object-Oriented Analysis and Design Methodology (OOADM) and Agile methodology, decomposing the system into input, inference, output, and user interface components. The inference engine combines deterministic rule-based models for policy and tax computation with predictive machine learning models for trend forecasting. The system was implemented using Django, ReactJS, and SQLite, employing the Gemini and Hugging Face APIs for natural-language explanation of analytical outputs. A functional prototype covering nine advisory modules was completed and deployed for local testing. Scope of results reported. This report documents the design, architecture, and implementation of the prototype. The formal evaluation against the stated accuracy and satisfaction objectives has not yet been conducted; the test plan and instruments are specified in Chapter Four, and results will be reported on completion. No performance figures are claimed in advance of that evaluation.