Machine Learning for Consumer-Centric Safety in Automotive Commerce

Modern automotive commerce depends heavily on consumer safety measures which machine learning operates as a vital element to improve security measures and identify fraud attempts along with risk evaluations. Researchers use the Random Forest algorithm within this study to develop improved automotive transaction security by making use of predictive analytics and anomaly detection techniques. Accident records from the past along with details about vehicle upkeep and driver conduct and consumer buying actions allow the model to provide real-time safety scores which detect scheming listings. Random Forest feature importance analysis reveals the important variables that impact vehicle safety by showing past crash reports and both mileage irregularities and seller reliability status. The detection system using this method delivers better accuracy in predictions combined with improved interpretability and resistant performance when identifying dangerous vehicles and scams. Random Forest produces superior results than conventional rule-based approaches by delivering a scalable.

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