Comparison of PCA, Linear Regression and Logistic Regression method in terms of accuracy and error rate for breast cancer dataset
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Updated
Mar 25, 2020 - MATLAB
Comparison of PCA, Linear Regression and Logistic Regression method in terms of accuracy and error rate for breast cancer dataset
Breast Cancer Wisconsin (Diagnostic) Data Set: Predict whether the cancer is benign or malignant.
Testing 21 models (machine learning ensemble) on the WDBC data set
Contains courses in specializations of coursera on deep learning
This is a collection of the first 12 assignments on Udemy's Web Development Bootcamp by Colt Steele. This page was created using Bootstrap 4.
Complete and Reproducible baseline for tabular breast-cancer diagnosis on the WDBC dataset. The project includes tidy EDA, feature prep, stratified train/test splits, and side-by-side benchmarking of Logistic Regression, SVC, Decision Tree, and Random Forest—delivering ~98% test accuracy with clear visuals and comparisons.(Research/educational use)
Read World of Warcraft DBC files in C#, with strongly-typed classes generated at compile time
This repository contains all the projects completed as part of the Machine Learning course.
Update of WDBC.zip (Breast cancer disease classification using fuzzy-ID3 algorithm with FUZZYDBD method: automatic fuzzy database definition)
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