In this data science project, you will learn how to perform market basket analysis with the application of Apriori and FP growth algorithms based on the concept of association rule learning.

Analysis of historical customer data can highlight if a certain combination of products purchased makes an additional purchase more likely. This is called market basket analysis (also called as MBA). It is a widely used technique to identify the best possible mix of frequently bought products or services. This is also called product association analysis. The set of items a customer buys is referred to as an itemset, and market basket analysis seeks to find relationships between purchases. Market Basket Analysis creates If-Then scenario rules, for example, if item A is purchased then item B is likely to be purchased. The rules are probabilistic in nature or, in other words, they are derived from the frequencies of co-occurrence in the observations. Market Basket analysis is particularly useful for physical retail stores as it can help in planning floor space and product placement amongst many other benefits.

If you want to understand this entire project overflow, please refer the jupyter notebook file inside notebook folder.

1. streamlit
2. Machine learning
3. Apriori and Fpgrowth algorithms
4. Association rule learning

Clone the repository

https://github.com/entbappy/Customer-Market-Basket-on-E-Commerce.git

conda create -n basket python=3.7.10 -y

conda activate basket

pip install -r requirements.txt

Now run,

streamlit run app.py

Note: Before clicking on show Busket first of all click on Train Market Basket for generating models