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Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Validation of a Machine Learning model that prevents the diagnosis of diabetes in patients according to the Diabetes Simple Diagnosis dataset available on Kaggle.
This project investigated the behavior of a nonlinear harmonic oscillator solver and explained the observed loss of accuracy under certain conditions. It extended a linear harmonic oscillator solver to a nonlinear counterpart using the model 'Method of Manufactured Solutions'.
Code repository for the manuscript 'Validation of the performance of competing risks prediction models: a guide through modern methods' (published in BMJ)
Code repository for the manuscript: 'Assessing performance in prediction models with survival outcomes: practical guidance for Cox proportional hazards models' (published in Annals of Internal Medicine)
Jupyter notebook for IoT threat detection using ensemble machine learning. Features data preprocessing, model training (Logistic Regression, Decision Trees, Neural Networks, etc.), and ensemble techniques for enhanced accuracy.