Objective
The goal of this project was to carry out a retail shop clustering of the enterprise MasMóvil using the tracking of several quality parameters. In addition to this, we sought to enrich the data of each shop with external datasets.
As a result, a new data resource was formed with information from the environment of each shop such as the municipal population or the number of nearby competitors. From this new dataset, a model based on a novel dual unsupervised learning paradigm was developed.
This innovative paradigm is based on clustering static information on the one hand and dynamic information on the other, thus providing a profile of the average behavior of the branch and static features as well as its trend over time. The final result is an outstanding tool capable of supporting the company’s decision making in an effective and clear way, facilitating considerably the management and monitoring of its retail shops.
FINAL DEGREE PROJECT OF:
AITOR IZUZQUIZA GIMENO
Degree
Carlos III University of Madrid, Sep. 2018 – Jun. 2022
Degree in Computer Science and Engineering
Honors in 15 courses, final grade: 8.9/10.
Universidad Nacional de Educación a Distancia, Sep. 2020 – Jun. 2024
Bachelor’s Degree in Mathematics
Honors in 4 courses
Work Experience
MásMóvil – Back End Developer Sep. 2022 – Actual
Responsible for analyzing the technological and methodological needs of the group’s multiple departments, while designing efficient and effective solutions to the problems identified.
MásMóvil – Machine Learning Researcher Sep. 2021- May. 2022
Selected by the Machine Learning research group at Universidad Carlos III de Madrid to work on a project based on data provided by MasMovil Telecomunications.
Technical skills
Programming languages: C/C++, Rust, Golang, Python, R, Java, JavaScript, SQL
Technologies: Redis, PostgreSQL, Docker, Kubernetes, Bazel, VS Code, JetBrains IDEs, Jupyter Notebook, Git.