elendarn/supermarkov_chain

Code and data for my project on Markov Chain Monte Carlo (MCMC) simulations applied to analyze the behavior of different customer types and their impact on traffic and congestion levels in supermarkets. The project aims to provide insights into the dynamics of customer behavior and its implications for supermarket operations.

Introduction

This repository contains the code and data for my project on Markov Chain Monte Carlo (MCMC) simulations applied to analyze the behavior of different customer types and their impact on traffic and congestion levels in supermarkets. The project aims to provide insights into the dynamics of customer behavior and its implications for supermarket operations.

Project Description

The project leverages MCMC simulations, a powerful computational technique, to model and analyze customer behavior patterns within the context of the DOODL Supermarket dataset. By understanding how different customer types behave, we can gain valuable insights into traffic flow, congestion levels, and optimize various supermarket operations.

The main objectives of this project are as follows:

1. Data Exploration: Perform a comprehensive exploration of the DOODL Supermarket dataset to gain an understanding of its structure, variables, and potential relationships.

2. Customer Segmentation: Segment customers into distinct types based on their behavior, such as shopping preferences, timing, and movement patterns within the supermarket.

3. Markov Chain Modeling: Develop Markov chain models to capture the probabilistic transitions between different supermarket areas and customer states. This enables us to simulate customer movements and evaluate the impact on traffic and congestion levels.

4. MCMC Simulations: Employ MCMC simulations to estimate the posterior distributions of model parameters, enabling us to analyze the uncertainty associated with the customer behavior models and their impact on supermarket traffic.

5. Traffic Analysis: Investigate the effects of different customer types on supermarket traffic and congestion levels, including peak hours, bottlenecks, and hotspots. This analysis provides actionable insights for supermarket managers to optimize layouts, staffing, and operational strategies.

Repository Structure

• data/: Contains the DOODL Supermarket dataset used for the analysis.
• notebooks/: Jupyter notebooks that showcase the step-by-step analysis and modeling process.
• src/: Source code files for data preprocessing, model implementation, and MCMC simulations.
• results/: Output files, visualizations, and summary reports generated during the analysis.
• README.md: Project overview and instructions for running the code.

Getting Started

To replicate and explore the analysis conducted in this project, follow these steps:

1. Clone the repository to your local machine using the command:

   git clone https://github.com/elendarn/supermarkov_chain.git
   

2. Install the required dependencies by running:

   pip install -r requirements.txt
   

3. Navigate to the notebooks/ directory and open the Jupyter notebooks to examine the step-by-step analysis.

4. Execute the code cells in the notebooks to perform data exploration, customer segmentation, Markov chain modeling, and MCMC simulations.

Conclusion

By applying Markov Chain Monte Carlo (MCMC) simulations to the DOODL Supermarket dataset, this project offers valuable insights into the impact of different customer types on traffic and congestion levels within supermarkets. The analysis provides supermarket managers and decision-makers with actionable information to optimize layouts, staffing, and operational strategies for enhanced customer experiences.

If you have any questions or suggestions, feel free to reach out or open an issue in the repository. Happy analyzing!