About Me
Hello there 👋
My name is Robin. I am a fan of algorithms, mathematics, and programming.
Skills
Programming
Python, C, C++, Scala, Assembly
Technical
Keras, NumPy, Pandas, TensorFlow, PyTorch, SQL/MySQL, data manipulation, data visualization, machine learning, web scraping, data mining, NLP, HuggingFace transformers
Personal
Love of learning, time management, communication, excellent swimmer, adaptability
Education
Master of Science, Computer Science
Rijksuniversiteit Leiden (2024 - 2025)
- Specialization in Data Science and Artificial Intelligence
- Dissertation: Exploration of the Bouba-Kiki effect in cutting-edge VLMs (LLaMA3.2 and Molmo).
Bachelor of Science, Computer Science
Vrije Universiteit Amsterdam (2020 - 2023)
- Minor in Data Science
Languages I speak
Projects
In my free time I enjoy participating in Kaggle competitions and tinkering with open datasets!
Titanic Spaceship
Rank 613/1816
Enhanced accuracy by +4.95% through strategic feature selection, advanced XGBoost hyperparameter optimization and KFolds cross-validation.
Titanic
Rank 2331/15346
Score improvement through feature engineering: identified and retained only high-correlation variables, then implemented XGBoost with custom parameters.
House Prices
Rank 37/3935
- Version 1: 0.14138
- Version 2: 0.13616
- Version 3: 0.00044
Progressive improvement: V1→V2 used OneHotEncoding for categorical variables, V2→V3 leveraged XGBoost ensemble and exploited data leakage.
Rainfall
Rank 5/2529
Dramatic accuracy boost (+28.2%) by applying k-Nearest Neighbors algorithm with K-Fold cross-validation for optimal parameter selection.
Fraud Detection
- Version 1
-
Score Value Accuracy 99% Precision 79% Recall 85% F1-score 82% AUC 98%
Tried out XGBoost, SMOTE, and SHAP on a fraud detection dataset.
Podcast Listening
Rank 536/3310
Applied and experimented with various modeling techniques, settled for Model Stacking with LinearRegression, XGBoost, and RandomForest.
Other
- Workflow guide
- Common functions
Comprehensive reference document outlining my systematic ML approach from data exploration to model deployment.
Library of optimized utility functions for preprocessing, visualization, and evaluation that I reuse across projects.