NisrineBennor/Langage_R_Data_Visualisation_Machine_Learning

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amirR01/Predicting-Breast-Cancer-Survival-Project

Predicting breast cancer survival using machine learning models

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shraddha-sanil/diabetic-retinopathy-detection-methodological-framework

This DR detection methodology has six steps: preprocessing, segmentation of blood vessels, segmentation of OD, detection of MAs and hemorrhages, feature extraction and classification. For segmentation of blood vessels BCDU-Net is used. For OD segmentation, U-Net model is used. MAs and hemorrhages are extracted using Otsu thresholding technique. Both clinical and non-clinical features are extracted and fed to SVM classifier.

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Vitor-Sallenave/Data-Mining-Cardiac-Arrhythmia

From the database about cardiac arrhythmias and the studies on pre-processing, the repository aims to present and dicsuss the results obtained using the Decision Tree model J48 and the SVM Linear model to classify the data.

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tetaniarizki/Marketplace-Review-From-Google-PlayStore

This repository is an analysis of the classification of sentiment reviews from users of the marketplace application, where the word weighting methods used are TFIDF and Word2Vec. Meanwhile, the classification method used is Support Vector Machine (SVM). There are two kernels used in this analysis, namely the kernel Linear and the kernel Radial Basis Function (RBF).

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