Retail — E-Commerce Customer Behavior Prediction Model Training
FreeThis DAG trains machine learning models to predict customer purchasing behaviors using historical data. It ensures optimal model performance through evaluation and real-time deployment, enhancing decision-making in retail.
Overview
The primary purpose of this DAG is to develop predictive models that analyze historical customer data to forecast purchasing behaviors. It ingests data from various sources, including transaction logs and customer profiles, through a well-defined data ingestion pipeline. The pipeline begins with the extraction of relevant features from the historical datasets, followed by data preprocessing steps such as normalization and encoding. The processed data is then fed into multiple machine learning al
The primary purpose of this DAG is to develop predictive models that analyze historical customer data to forecast purchasing behaviors. It ingests data from various sources, including transaction logs and customer profiles, through a well-defined data ingestion pipeline. The pipeline begins with the extraction of relevant features from the historical datasets, followed by data preprocessing steps such as normalization and encoding. The processed data is then fed into multiple machine learning algorithms for training, where models are evaluated based on key performance indicators (KPIs) such as model accuracy and training duration. The best-performing models are selected and deployed for real-time predictions, enabling retailers to make informed decisions based on anticipated customer behavior. Quality controls are embedded within the pipeline to monitor model performance continuously, and in case of failures, the DAG is designed to automatically restart the process while sending notifications to stakeholders. The outputs of this DAG include trained models, performance reports, and real-time prediction capabilities. The business value lies in improved customer targeting, enhanced inventory management, and increased sales through data-driven insights.
Part of the Governance & Compliance solution for the Retail industry.
Use cases
- Enhanced customer targeting through predictive analytics
- Improved inventory management based on buying patterns
- Increased sales through data-driven decision-making
- Reduced time-to-market for new predictive models
- Higher operational efficiency with automated processes
Technical Specifications
Inputs
- • Customer transaction logs
- • Customer demographic profiles
- • Historical sales data
- • Product catalog information
Outputs
- • Trained predictive models
- • Model performance evaluation reports
- • Real-time customer behavior predictions
Processing Steps
- 1. Extract features from historical customer data
- 2. Preprocess data for normalization and encoding
- 3. Train multiple machine learning models
- 4. Evaluate models based on accuracy and training time
- 5. Select and deploy the best-performing model
- 6. Monitor model performance and send notifications
Additional Information
DAG ID
WK-0385
Last Updated
2025-02-18
Downloads
61