I'm a MSc computer science student in Instituto Superior Técnico de Lisboa, ending my thesis on Multivariate Time Series Forecasting and Causal Modelling using Dynamic Bayesian Networks.

My main goal is to become a professional Data Scientist, developing a diversity of solutions for data-driven real life problems, either using statistical data analysis, extracting insights and visualize them on dashboards or implementing a Machine Learning end-to-end solution.

Check my DS Portfolio

This project is a recommendation system for Real Estate companies based on insights extracted from exploratory data analysis, to answer to real estate common business questions:

1. Which houses should be bought and for what price?
2. Once its bought when it's the best time period to sell it and for what price?
3. To rise the housing selling price, the company should do a renovation. So what would be good renewal changes?

Build a report to answer business questions based on the data analysis and some empirical rules.

Project repo: https://github.com/MikeMadeira/HouseSales-RecommendationSystem

Domain: International Bank is an international bank that provides financial products such as loans, checking accounts, savings accounts, investment options, credit cards and etc.

Business model: Lend credit to customers and earn some profit from yield rates applied to credit loans.

Every quarter of the year, leaders meet to decide the company's goals for the next 3 months. At this last meeting, one of the decided goals is to create a marketing strategy to address customers who use credit cards.

1. Create a client segmentation based on clear selection criteria.
2. Make the segments accesible to business teams.
3. Teach business operation teams how to use the solution.
4. Transfer knowledge to Data Scientists on International Bank.
5. Point 2 or 3 actionable insights and recommend each respective business leveraging actions for each client segment.

(Working On) I've already done my first CRISP cycle and got some clustering results. Now I have to interpret them.

Project repo: https://github.com/MikeMadeira/Credit-Card-Customer-Segmentation