df = pd.read_excel('data/Online Retail.xlsx')ĭf.dropna(axis=0, subset=, inplace=True)ĭf = df.astype('str') NOTE: I downloaded the data file from here and stored it in a subdirectory named data. Lastly, we’ll convert the InvoiceNo column to a string. Now, lets read in the data and then drop any rows that don’t have an invoice number. Then, import your libraries: import pandas as pdįrom equent_patterns import aprioriįrom equent_patterns import association_rules To get started, you’ll need to have pandas and MLxtend installed: pip install pandas mlxtend This data-set contains enough data to be useful in understanding market basket analysis but isn’t too large that we can’t use MLxtend (because we can’t unstack the data, which is required to use MLxtend ). Market Basket Analysis with MLxtendįor this example, we’ll use the data set found here. I prefer the MLxtend library myself, but recently there’s been some memory issues using pandas and large datasets with MLxtend, so there have been times that I’ve needed to roll my own.īelow, I provide an example of using MLxtend as well as an example of how to roll your own analysis.

You can use a pre-built library like MLxtend or you can build your own algorithm. There are a few approaches that you can take for this type of analysis. Market Basket Analysis with Python and Pandas In the remainder of this article, I show you how to do this type of analysis using python and pandas. if you want to learn more about Market Basket Analysis, here’s some additional reading.

If you have a large amount of transactional data, you should be able to run a market basket analysis with ease. Market Basket Analysis requires a large amount of transaction data to work well. With these rules, you can then build our recommendation engines for your website, store and salespeople to use when selling products to customers. If widget L, then recommend widget X, Y and R.If widget A, then recommend widget B, C and F.You can think of this type of analysis as generating the following ‘rules’: For example, if you sell widgets and want to be able to recommend similar products and/or products that are purchased together, you can perform this type of analysis to be able to understand what products should be recommended when a user views a widget. In the simplest of terms, market basket analysis looks at retail sales data and determines what products are purchased together. If you aren’t sure what market basket analysis is, I’ve provided a quick overview below. If you’ve ever worked with retail data, you’ll most likely have run across the need to perform some market basket analysis (also called Cross-Sell recommendations).

Market basket analysis in python