Display and analyze ROC curves in R and S+
You can find exercises and codes realized during this lecture
ROC-GLM for DataSHIELD
Clustering validation with ROC Curves
Data Science (Data preprocessing) along with machine learning where patients with digestive and kidney diseases are predicted using(kNN, Naïve Bayes , and Random Forest) classifiers in R Programming Language
Survival analysis in R for Public Health (Imperial College London through Coursera)
R | Classification Project
Evaluation of Binary Classifiers
R Shiny App to determine the factors that are most influential in patients’ survival of CHD. I created a Logistic Regression model in R using RStudio to predict the survival of CHD patients. Retrieved the data from the PHIS database using SQL & built tableau dashboards. The model predicted the survival of CHD with an AUC of over .90 and indicate…
Optimal cutpoints in R: determining and validating optimal cutpoints in binary classification
Built a logistic regression model and a classification tree model for predicting the final status of a loan based on various variables available. Confusion matrix and misclassification rate for each model for a test dataset. Variables that appear to be important for predicting outcome. Plotted and described the ROC curves and AUC for the four mo…
Naive Bayes, Confusion Matrix, and ROC Analysis were conducted using R to determine how different variables lead to a customer of a bank taking out a personal bank loan.
mewto is an R package that allows you to experiment with different thresholds for classification of prediction results in the case of binary classification problems and visualize various model evaluation metrics, confusion matrices and the ROC curve. It also allows you to calculate the optimal threshold based on a weighted evaluation criterion.
Hormone Therapy Decision Support System for Breast Cancer
A light and flexible R package to evaluate GWAS-based gene prioritization methods for complex traits.
Class to perform cross validation and draw ROC curves for Test and Training data
Complete package for all Data Science models using R. Starting form Preprocessing, Data Manipulation, Feature Engineering, Model Building, and Model Validation.
R Code and synthetic data accompanying the paper "Identifying patients using antidepressants for the treatment of depression"
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