Multilayer Perceptron (MLP)-Based Machine Learning Framework for Identifying and Classifying Emerging Trends in Retail Analytics

This literature review is part a a larger Capstone Project that will be a case study on employing core data science methods and techniques learned in the University of Wisconsin MS Data Science program. Below is a draft of the project proposal and a outline of a suggested literature review. If possible I need sources no older than 5 years.

Project Idea Submission Format and Guidelines

1. Project Title/Working Title:

• Title: Multilayer Perceptron (MLP)-Based Machine Learning Framework for Identifying and Classifying Emerging Trends in Retail Analytics

2. Project Type:

• Type: Case Study. This project will analyze historical data from retail to identify patterns that could predict future trends.

3. Project Description:

• Description: This project aims to develop an MLP-based machine learning model to identify and classify emerging trends in the retail sector. The project will focus on utilizing historical sales data, customer interaction, and market conditions to forecast future trends and consumer behaviors.

4. Project Purpose/Rationale:

• Purpose: The business use case for this project involves leveraging machine learning to enhance decision-making in retail by identifying patterns and trends that are not immediately apparent through traditional analysis methods.

5. Relevant Data Science Concepts:

• Concepts: The project will utilize concepts such as neural networks, specifically Multilayer Perceptrons, supervised learning for classification and prediction, and feature engineering to handle time series data effectively.

6. Draft Project Objectives:

• Objectives:
  1. To develop a predictive model that can identify significant trends from historical retail data.
  2. To classify types of consumer behavior and product performance.
  3. To provide actionable insights that can guide inventory management, marketing strategies, and customer relationship management.

7. Project Data and Computational Requirements:

• Data Requirements: Although specific datasets have not yet been identified, the project will require access to extensive retail sales data, including transaction history, product information, and customer demographics. The computational needs will include processing power sufficient for training complex neural networks and handling large datasets.

8. Resources/Contacts that will be utilized for the project:

• Resources: The project will initially utilize publicly available datasets from sources like UCI Machine Learning Repository or Kaggle. Collaboration with a retail partner for access to proprietary data may be explored. Contacts in the retail industry and academic advisors specializing in machine learning will be engaged.

9. Summary/Additional Comments:

• Summary: The project’s success hinges on acquiring high-quality, relevant data and effectively applying machine learning techniques to uncover and predict trends. The outcome will contribute to a deeper understanding of market dynamics and customer preferences, potentially transforming strategic decision-making in the retail industry. The timeline for completion is set for the duration of a semester, with milestones for data collection, model development, and analysis.

Literature Review Structure

1. Define the Key Concepts: Start by defining the main concepts of your research. For a project focusing on machine learning in retail analytics, define what machine learning entails, especially focusing on Multilayer Perceptrons (MLP), and how these are applied in the retail industry.

2. Current Research and Theories: Summarize current research and theoretical approaches in the use of MLPs for trend identification and classification in retail. This could involve reviewing articles that discuss how MLPs have been used to analyze consumer behavior, predict sales, or optimize inventory management.

3. Methodological Approaches: Discuss different methodological approaches used by current research in the field. This could include case studies, experimental designs, or predictive analytics studies.

4. Gaps in the Research: Identify any gaps in the current literature. This might involve a lack of research on certain types of retail environments or perhaps insufficient studies on the long-term effectiveness of machine learning models in retail settings.

Potential Topics and Sources to Search:

• Machine Learning in Retail: Look for papers and articles that specifically discuss the application of machine learning techniques in the retail sector.
• Consumer Behavior Prediction: Research how machine learning is used to predict consumer behavior patterns and preferences.
• Inventory and Supply Chain Optimization: Explore studies that utilize MLPs to improve inventory management and supply chain efficiency.
• Sales Forecasting: Find sources that explain how MLPs can be employed to forecast sales more accurately.