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🙋🏼‍♂️ Hey, hey, hey. This is Luis!

A passionate Full Stack Data Scientist and Data Content Creator 🚀


More about me

👋🏻 Hello, Hola, Ciao, Bonjour.

📚 My name is Luis and I am a Full Stack Data Scientist, born and raised in Argentina and currently based in London, UK. Having graduated with a Masters Degree in Data Science & Management, in addition to my Bachelors in Economics (both with the highest academic distinctions), I have solidified theoretical concepts related to Data Science field.

👨🏼‍💻 Furthermore, I have been working in the IT industry for the past 4 years, during which time I have gained considerable experience working globally with different stakeholders while defining the problem, collecting and querying data, developing end-to-end ML projects, data analysis and visualization.

🎥 On the other hand, I really enjoy teaching and connecting with people. For that reason I started my journey as Data Content Creator, reaching up to 15.000 followers on several platforms like TowardsDataScience, Youtube, Instagram. My portfolio is a representation of some projects I usually prepare to share with my online audience, guiding them from the very beginning of the project (finding diverse datasets), defining the scope of the problem, running some exploratory analysis and finally shaping ML models.


Project Index


Languages & Tools

mysql pandas postgresql python pytorch scikit_learn seaborn tensorflow


Reach out to me!


¿Hablas español? SIGUEME EN INSTAGRAM: www.instagram.com/luigi_analytics

¿Hablas español? SIGUEME EN YOUTUBE: www.youtube.com/@luigi_analytics

Luis Ezequiel Muñoz's Projects

coursera-deep-learning-specialization icon coursera-deep-learning-specialization

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks;

fraud-credit-card-project icon fraud-credit-card-project

ML models development to check how accurate they perform in detecting whether a transaction is a normal payment or a fraud.

knn-rock-mine-detection icon knn-rock-mine-detection

Developing K-Nearest Neighbors (KNN) model - Two approaches: GridSearchCV with no pipeline implementation and GridSearchCV with pipeline implementation

people-analytics-absenteeism-data-cleaning icon people-analytics-absenteeism-data-cleaning

The goal of this project is to apply certain Data Cleaning techniques to prepare the dataset, which will be further analyzed. Pandas and NumPy are the main libraries used in this project.

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