Synthetic Minority Over-sampling Technique

Data Mining of Caravan Insurance Data Set Using R

This script of code helped oversampling the data for getting a balanced data set

Identifying fraudulent credit card transactions in a highly imbalanced dataset by oversampling and using ensemble learning/neural network

Develop predictive models that can determine, given a particular compound, whether it is active (1) or not (0).

All digital lowpas delta-sigma modulator (+digital up-converter) tune to fmax = 9 MHz

Multi-level second-order (Silva Steensgaard Structure) delta-sigma modulator

Machine Learning Project on Imbalanced Data in R

Sampling Algorithms for Two-Class Imbalanced Data Sets in R

For a classification problem, when classes in the dependent variable are severely imbalanced (e.g. 90 yes, 10% no), training an efficient machine learning model becomes very difficult. However with SMOTE method, we can transform the data into a balaced form and train the model efficiently.

Predicting the appropriate star ratings for the text reviews of Amazon movies and TV shows using Natural Language Processing and methods like Multinomial Naive Bayes and Logistic Regression.

Dealing with class imbalance problem in machine learning. Synthetic oversampling(SMOTE, ADASYN).

NTI-Final-Assignment Use flask(python) and shiny dashboard (R) to build simple user interface to see how choosing classification model may affect prediction accuracy, using Customer Churn Dataset.

📝 ML Paper implementation of machine learning paper, ADASYN

Udacity capstone project | Credit card fraud prediction | Supervised Learning | Ensemble model | Data Sampling

SOUL: Scala Oversampling and Undersampling Library.

SOUL: Scala Oversampling and Undersampling Library. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021000868

Class imbalance correction algorithm for multiple-instance data