Machine Learning
Case Studies

The Datasethas information about 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allow viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers.

The moduledata_analysis.pycontains the fucntions that bring the order and structure to the selected data.

The first part provides a general overview of the data, prints samples, and Null values. The second part prepares the data for the following analysis, extracting the relevant information.

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The Datasethas information about 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allow viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers.

The modulek_means.pycontains the functions that bring the order and structure to the selected data.

RFM method analyses customer value. The abbreviation stands for the attributes used in segmentation, namely recency, frequency, and monetary value. Frequencydetermines how often the purchase is made, recency defines the most recent purchase and, finally, monetary valuemeasures spend per customer.

In marketing terms, client segmentation splits business clients into groups that have common attributes based on behavioural, demographic, psychographic or geographic data. Customer segmentation enables companies to target specific groups, allowing effective allocation of resources, appropriate pricing, service, and product customisation, strategizing and innovation.

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