Taipy  is an innovative low-code package (python) to create complete applications.

Taipy is composed of two main independent components: Taipy Core and Taipy GUI. You can use either component independently. However, as you will see, they are incredibly efficient when combined.

What is Taipy GUI? 

The Graphical User Interface of Taipy allows anyone with basic knowledge of Python to create a beautiful and interactive interface. It is a simple and intuitive way to create a GUI. No need to know how to design web pages with CSS or HTML. Taipy uses an augmented Markdown syntax to create your desired Web page.

What is Taipy Core? 

A simple yet powerful pipeline orchestration package.
Some of the key features:

To learn more about Taipy, visit the website below: https://www.taipy.io/

What is Market Basket Analysis? 

Market basket analysis (MBA) is a data mining technique that retailers use to increase sales by better understanding customer purchasing patterns. It involves analyzing large data sets, such as purchase history, to reveal product groupings, as well as products that are likely to be purchased together.

MBA is based on the idea that customers who buy one item are more likely to buy another item, and that this information can be used to predict what products customers are likely to buy in the future. By analyzing past purchase data, retailers can identify which products are commonly purchased together, and use this information to create targeted marketing campaigns and promotions.

MBA can also be used to identify product groupings that are likely to appeal to certain types of customers. This information can be used to improve the layout of stores and to create more effective product displays.

MBA is a powerful tool that can help retailers increase sales and improve customer satisfaction. By understanding how customers purchase products, retailers can better tailor their marketing campaigns and product offerings to meet the needs of their customers.

Here is an example of how market basket analysis can be used:

A retailer might use MBA to identify that customers who buy diapers are also likely to buy baby wipes. The retailer could then use this information to offer a discount on baby wipes when diapers are purchased.

Another example of how MBA can be used is to identify that customers who buy milk are also likely to buy bread. The retailer could then use this information to place milk and bread next to each other in the store.

What is a recommender system?

A recommender system is a system that uses machine learning to predict what users might like. It does this by analyzing the user's past behavior, such as what they have purchased or watched, and then finding patterns in that behavior. The system then uses these patterns to predict what the user might like in the future.

Recommender systems are used by many different companies, such as Netflix, Amazon, and Spotify. They are also used by many different websites, such as news websites and social media websites.

Recommender systems can be very helpful for users. They can help users find new things that they might like, and they can also help users find things that they might not have found otherwise.

However, recommender systems can also be harmful. They can be used to manipulate users, and they can also be used to track users.

It is important to be aware of the benefits and risks of recommender systems. It is also important to be aware of how recommender systems work.

How can we combine Market basket analysis with recommender system? 

Market basket analysis and recommender systems can be combined in a number of ways. One way is to use market basket analysis to identify groups of products that are frequently purchased together. This information can then be used to create a recommender system that recommends products that are frequently purchased together to new customers.

Another way to combine market basket analysis and recommender systems is to use market basket analysis to identify products that are purchased by similar customers. This information can then be used to create a recommender system that recommends products that are purchased by similar customers to new customers.

Market basket analysis and recommender systems can also be combined to create a more personalized recommender system. This can be done by using market basket analysis to identify products that are purchased by individual customers. This information can then be used to create a recommender system that recommends products that are purchased by individual customers to new customers.

Combining market basket analysis and recommender systems can be a powerful way to improve the accuracy of recommender systems and to increase the number of products that are recommended to customers.

What is the goal of this project? 

In this project the student will develop a market basket analysis recommender system using python and  will implement it using Taipy python platform.  The outcome will be a video demo, a complete application that can be used for demo or implemented in practice. 

Objectives/deliverables, team responsibilities

The team can develop in any software platform such as: 

Team Skills

N/A