Synthetic Minority Over-Sampling Technique for Regression

Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting

In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, whi…

RCSMOTE: Range-Controlled Synthetic Minority Over-sampling Technique for handling the class imbalance problem

Predicting students admission with Logistic Regression, Decision Tree, SVM (SVC) and Random Forest

Trained and evaluated two supervised machine learning models using original and resampled data to identify 'healthy loan' and 'high risk loan' applicants from financial disclosures.

Multivariate Normal Distribution Based Over-Sampling for Numerical and Categorical Features

Football Positions: A Multi-class Classification Problem

Genre Identification task along with Text Analytics with Multi-Class and Imbalanced Learning on Gutenberg Corpus

Imbalanced data commonly exist in real world, especially in anomaly-detection tasks. Handling imbalanced data is important to the tasks, otherwise the predictions are biased towards the majority class. RandomOverSampler, SMOTE, and ADASYN are useful oversampling tools to fabricate data for minority classes and make the dataset balanced.

Credit Card Fraud detection based on anonymized data using multiple classification algorithms

The aim of this project is to determine which classification technique produces the best results when applied to the task of determining credit riskiness.

Evaluation of various ML models for text using NLP driven approach for imbalanced data while balancing data using over and under sampling technique.

Repo contains scripts to perform data analysis on structure data. It also provides a comparison of various ML algorithms at different stages of data preparation.

Predicts the qualified employee for promotion using Classification

Supervised Machine Learning and Credit Risk

Multivariate Normal Distribution based Oversampling

Use random forest, gradient boosting, neural network, with SMOTE-ENN and random over-sampling

Kaggle Competition: Predictions of West Nile Virus outbreaks in the City of Chicago.