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Model-agnostic-feature-selection
Model-agnostic-feature-selection PublicDevelopment of a feature selection scheme that is robust across all the datasets and regardless of the ML model used for classification
Jupyter Notebook 1
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MNIST-project
MNIST-project PublicMulticlass classification problem. 90.04% accuracy with regularized SVC. No feature selection applied.
Jupyter Notebook
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Brain-Chain-LSPD-2020
Brain-Chain-LSPD-2020 PublicUniversity project concerning the development of software to call APIs, and create a `.csv` file to store the history of users' searches. This repo also includes testing made with unittest. Bachelo…
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kickstarter_projects
kickstarter_projects PublicComparison of ML algorithms for a binary classification task
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