In this project, demographic data of the general population and customers of a mail-order company in Germany was used to build the base core customer profile, using supervised and unsupervised machine learning techniques, using this information to optimize an adverstising campaign.
What if we could help governments and especially the population facing a disaster situation like severe storms, floods, earthquakes, and so on? This projects uses NLP techniques to classify messages related to disasters, which could be used to warn the right authorities and repond to those seeking for help.
A/B tests are an important tool to observe the effects of an event before they get released to a larger audience. Starbucks did exactly that. Our job was to use the data from this test to understand which clients were more responsive to a promotion, optimizing its strategy.
As Covid19 was changing our lives all over the world, I wanted to understand how the virus was affecting different cities in Brazil with all their social and cultural differences. At the end, a model was created to predict the total deaths in the cities that stood out in the analysis process.
It is clear that storytelling is one of the abilities that data scientists must have, so they can properly tell stories through data. How about we turn the tables and allow data to tell us some stories about data scientists and other Data Science professionals?
In this project, I use data from a Telecom company to understand possible reasons that could affect the customer churn of a company. Also, a model was created to indicate if clients are inclined to move their accounts to another company or not.
That was the question Santander recently asked to find out beforehand whether or not a customer was satisfied. In this project, I use the given data to create a predictive model capable of indicate if the customer is happy or not with the bank.