Predicting Customer Value at Scale with Databricks, PySpark, and MLflow скачать в хорошем качестве

Predicting Customer Value at Scale with Databricks, PySpark, and MLflow

🚀 RetailPulse Intelligence: Transforming 1M Rows into Actionable Insights In this video, I present my end-to-end data engineering and predictive analytics project developed for the Codebasics Databricks 14-Day Challenge. Using the Medallion Architecture (Bronze, Silver, Gold), I processed over 1 million records to build a system that identifies high-value customer segments with an 88.95% prediction accuracy. Key Highlights: ✅ Scalable Engineering: Handled 1M+ rows using PySpark in the Databricks Lakehouse. ✅ Advanced Feature Engineering: Target Encoding for high-cardinality states and custom metadata stripping for Spark VectorAssembler compatibility. ✅ Governance: Full experiment tracking with MLflow. ✅ Business Intelligence: Live Databricks SQL Dashboard visualizing "Hidden Champions" and prediction confidence levels. #Databricks #DataEngineering #MachineLearning #PySpark #MLflow #RetailAnalytics #Codebasics #DataScience #UnityCatalog