Using R and machine learning to build a classifier that can detect credit card fraudulent transactions.

Clustering validation with ROC Curves

MCC-F1 curve implementation in Python

News Articles Text Classification and Clustering using Machine Learning in Python. Also, KNN implementation from scratch using max heap.

Sinhala text extraction, preprocessing, and classification considering subject and domain.

Machine Learning analysis for an imbalanced dataset. Developed as final project for the course "Machine Learning and Intelligent Systems" at Eurecom, Sophia Antipolis

Sub-package of spatstat providing functions for exploratory and nonparametric data analysis

An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.

AUCC (Python Implementation)

ROC Tool is a tool that allows the user to visualize ROC curves and easily perform more advanced analysis on them. Specifically, the tool allows users to plot multiple ROC curves and their Regions of Interest, compute AUC and RRA, and plot multiple iso-PM curves.

Shiny App to help you generate adjusted ROC curves

Wolfram Language (aka Mathematica) paclet for Receiver Operation Characteristic (ROC) functions.

Knowledge Discovery in Dataset

Utility function to plot roc curves using k-fold cross-validation

Machine learning for credit card default. Precision-recalls are calculated due to imbalanced data. Confusion matrices and test statistics are compared with each other based on Logit over and under-sampling methods, decision tree, SVM, ensemble learning using Random Forest, Ada Boost and Gradient Boosting. Easy Ensemble AdaBoost classifier appear…

An analysis of the 2016 presidential election dataset to predict voter behavior

Slides for class presentation | A short course on SDT, ROC curves and diagnostic tests

The subject of this repository was to perform binary classification based on respondent's collected features (age, cholesterol level, fasting blood sugar, thallium stress test results, etc.).

Learn and practice alternative evaluation techniques for models on which more standard accuracy methods are not feasible