  Introduction: An important chain of supermarkets in the western zone of the
capital of Chile, needs to obtain key information to make decisions, this
information is available in the databases but needs to be processed due to the
complexity and quantity of information which becomes difficult to visualiz,.
Method: For this purpose, an algorithm was developed using artificial neural
networks applying Kohonen's SOM method. To carry it out, certain key procedures
must be followed to develop it, such as data mining that will be responsible
for filtering and then use only the relevant data for market basket analysis.
After filtering the information, the data must be prepared. After data
preparation, we prepared the Python programming environment to adapt it to the
sample data, then proceed to train the SOM with its parameters set after test
results. Result: the result of the SOM obtains the relationship between the
products that were most purchased by positioning them topologically close, to
form promotions, packs and bundles for the retail manager to take into
consideration, because these relationships were obtained as a result of the SOM
training with the real transactions of the clients. Conclusion: Based on this,
recommendations on frequent shopping baskets have been made to the supermarket
chain that provided the data used in the research
