AI Based System for Predictive Analysis of Car Complaints

Abstract - Car owners find it hard to explain car issues accurately due to insufficient technical knowledge, which leads to delays in diagnosis and unnecessary repairs. This paper presents Adam Automotive, a predictive diagnostic system employing a rule-based approach to pinpoint potential car defects based on user complaints. Users explain issues in plain, common language, which is matched with a pre-existing fault database. The system does not necessitate complex machine learning methods, maintenance history, or external data; it classifies complaints, determines probable faults, and provides actionable recommendations. Simplifying problem identification and maximizing efficiency in service, this approach improves user experience and enables timely car maintenance. This paper presents the details of implementation, system architecture, and practical applications of this rule-based AI system in car diagnosis. Future directions will be towards expanding the fault database and optimizing keyword mapping to deal with varied user input.