$**$Use react for frontend and backend with spring boot and rest all detials mention below$***$

$**$code fully functional program which run smoothly$**$

$**$i want full runnihg code with all functionality given below$***$

Optimizing In$-$Store Layout for Enhanced Customer Experience

Problem Statement:

While much focus has been given to online retail optimization, there is a unique opportunity to leverage data science to optimize the physical layout of Walmart stores. The goal is to design an intelligent system that continuously adapts the in$-$store layout based on customer behavior, product demand, and seasonal trends to enhance the customer shopping experience.

Full Description:

In physical retail, the layout of a store can significantly impact sales and customer satisfaction. Traditionally, store layouts are designed based on general retail principles and occasionally adjusted based on periodic reviews. However, these static layouts fail to account for the dynamic nature of customer preferences and shopping patterns.

Objective:

Develop a dynamic in$-$store layout optimization system that utilizes real$-$time data to continually adjust product placements, aisle arrangements, and promotional displays to improve customer satisfaction and increase sales.

Key Features:

Customer Behavior Analysis:

Collect data on customer movement within the store using IoT sensors and cameras.

Analyze patterns to identify high$-$traffic areas, dwell times, and frequently visited sections.

Product Demand Forecasting:

Use historical sales data to forecast demand for different products.

Adjust shelf space and positioning based on predicted demand, seasonal trends, and promotional events.

Personalized Layout Adjustments:

Implement personalized recommendations for returning customers using their past purchase history.

Display personalized promotions and product suggestions on digital displays as customers navigate the store.

Real$-$time Layout Adjustment:

Use robotics and modular shelving units that can be reconfigured in real$-$time based on data insights.

Ensure that the most popular and essential items are always easily accessible.

Feedback Loop:

Collect customer feedback on the layout and shopping experience through mobile apps and in$-$store kiosks.

Continuously refine the layout optimization algorithms based on customer feedback.

Tech Stack:

Data Collection:

IoT Sensors: For tracking customer movements $($e$.$g$.,$ RFID, Bluetooth beacons$).$

Cameras and Computer Vision: For analyzing customer behavior and movement patterns.

Data Processing and Storage:

Apache Kafka: For real$-$time data streaming and processing.

Hadoop$/$Spark: For large$-$scale data processing and analysis.

AWS S$3/$Google Cloud Storage: For scalable data storage.

Machine Learning and AI:

TensorFlow$/$PyTorch: For developing and training machine learning models.

Scikit$-$learn: For classical machine learning algorithms.

Apache Mahout: For large$-$scale machine learning.

Backend:

Python: For data analysis and backend development.

Flask$/$Django: For building RESTful APIs.

Node.js: For real$-$time updates and server$-$side scripting.

Frontend:

React.js: For developing interactive user interfaces.

D$3.$js: For visualizing data insights and customer behavior patterns.

Bootstrap: For responsive and mobile$-$friendly designs.

Database:

PostgreSQL: For relational data storage.

MongoDB: For storing unstructured data and customer feedback.

ElasticSearch: For fast search and retrieval of data.

DevOps:

Docker: For containerization and deployment of applications.

Kubernetes: For orchestration and management of containerized applications.

Jenkins: For continuous integration and deployment.

Security:

OAuth$2$: For secure authentication.

SSL$/$TLS: For secure data transmission.

By leveraging this tech stack, Walmart can create a smart, data$-$driven in$-$store layout optimization system that enhances the shopping experience, boosts sales, and keeps customers coming back.