Welcome! This repository showcases my hands-on experience in data science, machine learning, and statistical modeling. The projects here span - Exploratory Data Analysis (EDA)
- Machine Learning & Deep Learning
- NLP & Recommendation Systems
- Predictive Maintenance
- Optimization & Simulation
- Time Series Forecasting
I’m a former theoretical physicist with 10+ years of research experience, now applying simulation and modeling expertise to data science challenges.
Based in Austria | Open to applied roles across DACH | Fluent in English (German: A2–B1)
- Goal: Predict the Remaining Useful Life (RUL) of jet engines using time-series sensor data.
- Methods: Regression, feature engineering, time-series modeling.
- Skills: Pandas, SciKit-Learn, visualization, model evaluation.
- Goal: Explore orbital dynamics using Hamiltonian mechanics.
- Methods: Lagrangian and Hamiltonian modeling, simulation.
- Skills: Physics, mathematical modeling, numerical simulation.
- Goal: Predict the presence of Polycystic Ovary Syndrome (PCOS) using clinical data.
- Methods: Classification models, EDA, data balancing.
- Skills: SciKit-Learn, medical data analysis, model evaluation.
- Goal: Build a song recommender system using Taylor Swift's discography.
- Methods: NLP (TF-IDF), cosine similarity, metadata analysis.
- Skills: NLP, data wrangling, recommendation systems.
Goal: Exploratory data analysis of Taylor Swift’s discography and streaming patterns.
- Skills: EDA, feature visualization, audio metadata analysis
- Outcome: Developed insights into popularity trends, sentiment, and musical evolution.
Feel free to explore each project folder for notebooks, data, and additional insights!