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This project predicts the likelihood for heart failure. The project takes place in three parts: exploratory data analysis (EDA) and data preparation, the creation of three initial binary classification models including logistic regression, random forests, and a neural network. Then, the hyperparameters of the neural net were optimized using Bayesian Optimization.

Python 1.86% Jupyter Notebook 96.78% PureBasic 1.36%
bayesian-optimization data-science feature-engineering machine-learning neural-network python random-forest

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