Data Analyst | Data Scientist
CurrentI use clustering algorithms (unsupervised learning) to group public transportation customers based on their monthly behavior. These algorithms identify patterns in customer travel data, such as frequency of use, trends of decrease or increase in transportation usage.After grouping, I use time series algorithms to predict the behavior of customers in each group. These algorithms use historical travel data from customers in each group to estimate how many times each group will use the transportation in the next month.This information is used to create campaigns with the goal of increasing public transportation usage by customers.In addition to this work using unsupervised learning and time series, I also work with statistical analysis of transportation customers and the creation of dashboards. These dashboards are important for visualizing the results of campaigns conducted with customers, especially for identifying the ROI (return on investment) of the campaigns.I have also worked with WebScraping of bus ticket data from competing companies, for more assertive decision-making within the loyalty program.
