Retail AI Use Case: Building a Demand Prediction Model

Overview

Explore retail AI demand prediction models, KPIs, and implementation strategies for effective business use cases in the retail industry.

Syllabus

• Introduction to Retail AI and Demand Prediction
Overview of AI in Retail
Importance of Demand Prediction in Retail
Case Studies of Successful AI Demand Prediction Models
• Understanding Key Performance Indicators (KPIs)
Definition and Examples of Retail KPIs
How AI Demand Prediction Influences KPIs
Measuring Success: KPIs Specific to Demand Prediction
• Data Collection and Preparation
Identifying Relevant Retail Data Sources
Techniques for Data Cleaning and Preprocessing
Feature Engineering for Demand Prediction
• Building Demand Prediction Models
Overview of Machine Learning Algorithms Used for Demand Prediction
Choosing the Right Model for Your Retail Use Case
Hands-on Lab: Developing a Basic Demand Prediction Model
• Evaluating Model Performance
Metrics for Evaluating Demand Prediction Models
Techniques for Optimizing Model Accuracy
Avoiding Overfitting and Underfitting
• Implementation Strategies
Integrating the Model into Retail Business Processes
Tools and Platforms for Deployment
Ensuring Scalability and Robustness
• Ethical and Practical Considerations
Handling Data Privacy and Security in Retail AI
Addressing Bias and Fairness in Demand Prediction
• Case Study Analysis and Project
In-depth Analysis of a Real-world Retail Demand Prediction Use Case
Final Project: Build and Present a Demand Prediction Model Tailored to a Specific Retail Scenario
• Review and Future Trends in Retail AI
Recap of Key Learning Points
Discussion on Emerging Trends in Retail AI
Preparing for Future Developments in AI Demand Prediction Models

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Subjects

Data Science