NLP project. Developer an NLP model to classify abstract sentences into the role they play (e.g. objective, methods, results, etc) to enable researchers to skim through the literature (hence SkimLit ) and dive deeper when necessary. Feature engineering is used, hybrid model, different embedding forms(multi data models).

Computer vision project Developer of a model based on convolutional neural networks with Tensorflow that allows identifying between 101 different classes of food dishes, using EffcientNet (transfer-learning) and new mixed precision features of tensorflow. 75,750 images (750 per class) were used for the training set and 25,250 (250 per class) images for the test set, an accuracy of 0.80 was reached.

Developer of a predictive random forest regression model with Scikit-learn and Python to estimate the cost of sales of heavy machinery(bulldozer), based on a time series data. Exploratory data analysis ,data cleaning, feature engineering, model selection, evaluation metrics and feature importance.

Developer of a logistic regression model with Scikit-learn for binary classification of patients with heart diseases, based on previous medical records. EDA, model selection, feature importance, metrics evaluation (ROC curve and AUC score, confussion matrix, accuracy, recall and f1 with cross validation).