Welcome to my central repository for all professional projects completed through CodeCademy. This space documents my transition from foundational data analysis to becoming a Machine Learning Specialist, combining the intensive structure of Bootcamps with the specialized tracks of the Career Path.
My journey is defined by a "Mastery over Calendar" approach. Each project here isn't just a requirement; it's an opportunity to apply Production-Ready Python, Rigorous Statistics, and Advanced ML Architectures to solve real-world problems.
This portfolio is divided into two main strategic areas:
Focus: Intensive foundational projects and end-to-end data science workflows.
- Key Highlight: Predicting House Prices - A deep dive into Linear Regression and forensic EDA.
- Key Highlight: King County Housing Price Prediction - Regularized Linear Models (Lasso/Ridge).
- Key Highlight: Spotify Vibe Clustering - Categorize music tracks into distinct vibes or clusters.
- Key Highlight: NN Clasification of Handwritten Letters - Neural network that clasify handwritten letters (EMNIST dataset).
Focus: Specialized assignments from the Data Scientist: Machine Learning Specialist career path.
- Ongoing Study: Supervised Learning, Unsupervised Learning, and Deep Learning implementations.
- Note: First specialized project arriving in Late January 2026.
By integrating both Bootcamp and Platform curricula, I am developing a high-level command of:
- Advanced ML Models: Implementation of Random Forests, Gradient Boosting, and Neural Networks.
- Statistical Backbone: Deep understanding of p-values, confidence intervals, and Bayesian logic applied to ML.
- Feature Engineering: Advanced techniques for data transformation and dimensionality reduction.
- Model Optimization: Hyperparameter tuning (GridSearch/RandomSearch) and rigorous cross-validation.
I am an advocate for transparent learning. I believe in showing the process, the iterations, and the continuous improvements of every model I build.
"Good data science is about asking the right questions; great data science is about finding the most honest answers."