Featured Works
The following projects are hosted in my Medium page. Click on them to acces the full content.
[Project] Traditional Machine Learning Methods for Predictive Maintenance
Evaluated and compared four Machine Learning models (Random Forest, Support Vector Machine, Logistic Regression, Decision Trees) on three datasets for predictive maintenance project. Utilized evaluation metrics including accuracy, Kappa, F1 score, and customized cost analysis.
Keywords: Python, confusion matrix, principal component analysis
[Project] Sentiment Analysis in Drug Reviews using LSTM
Applied Natural Language Processing techniques, such as Tokenization, Stemming, and Lemmatization, to build a robust model capable of properly predict sentiment polarity in medication reviews. Used the unsupervised learning clustering algorithm to improve the final discussion.
Keywords: Python, LSTM, tokenization, clustering
[Article] Molecular Docking: Optimization by Genetic Algorithms Over Decades
Reviewed four articles focused on the use of Genetic Algorithms applied to Molecular Docking. Chose the articles in a manner to be possible to analyse the evolution of the applied technique over the last three decades. Compared the works regarding the precision of the results, availability of computer power, and sizes of datasets.
Keywords: Python, stochastic algorithm, score function, drug design, ligand-protein
[Project] Optimizing Cargo Bike Selection for Delivery Services
Suggested decision making through simulation and optimization techniques. Generated a town map using the Python library Pickle, containing customers and warehouse location. Ran simulations varying parameters like parcels to deliver per day and bike range. The optimizations of the routes were made using the Floyd-Warshall algorithm.
Keywords: Python, heuristic, optimization, simulation, logistic network, integer programming