Data Science and Programming Portfolio
A collection of statistical and machine learning projects I've worked on for academic, self-learning, and professional purposes.
Python 
Defining inkjet printing conditions of superconducting cuprate films through machine learning
- Performed inkjet printing experiments and advanced characterizations to generate a dataset to perform machine learning.
- Analyzed feature distribution and correlations, preprocessed the data and engineered features to develop machine learning models.
- Developed 4 machine learning models, a linear regression that served as a benchmark model and 3 decision tree-based regression models (Random Forest, AdaBoost and Gradient Boosting).
- Predicted the number of drops deposited and the total volume required for optimal inkjet printing deposition with accuracies of 0.98-0.99.
- Identified the most important features,drop and line spacing, as well as drop volume, and their influence in the optimization process.