Analysing the content of an E-commerce database that contains list of purchases. Based on the analysis, I develop a model that allows to anticipate the purchases that will be made by a new customer, during the following year from its first purchase.
Performs an exploratory analysis on a dataset containing information about shop customers. Check that the assumptions K-means makes are fulfilled. Apply K-means clustering algorithm in order to segment customers.
Unsupervised learning techniques applied on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.
By means of this project I am trying to create a value-based customer segmentation model using RFM(Recency, Frequency, Monetary) analysis in python using pandas, numpy and matplotlib
In this project I apply unsupervised learning techniques and principal components analysis on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.
Capstone project for the Udacity MLND on the prediction of customer acquisition based on regional demographics and customer attributes. With the help of supervised and unsupervised learning techniques.