An automated freight cost prediction system was developed for a major home improvement retailer to handle half a million requests daily. Key challenges included data quality, digital infrastructure, variable shipping price factors, and performance requirements. A machine learning model using XGBoost was chosen for its accuracy
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Project Scope and Technological ImplementationKey Challenges AddressedImplementation ApproachResults and Performance MetricsKey Lessons LearnedConclusionSort: