Predict loan approval by using different variable selection methods

A collection of machine learning mini-projects.

Predict whether customers of a bank will subscribe to a term deposit and analyze customer behaviour based on the bank's historical telemarketing campaign records.

Machine Learning model for Credit Card Fraud Detection

Classification with imbalanced classes

Machine learning model for Credit Card fraud detection

Using SageMaker's linear classifier to detect fraud. Addressing class imbalance and setting target metrics for Precision and Recall

Class imbalance correction algorithm for multiple-instance data

The implementation of Synthetic Minority Oversampling based on stream Clustering (SMOClust)

Human or Robot? Predict if an online bid is made by a machine or a human.

Binary classification, with every feature as categoricals

Credit Card Fraud Detection using Machine Learning

Advanced NER Applications: Implementing KNN, Feed-Forward, and LSTM Models with Class Imbalance Reduction Techniques.

This repository contains the code, documentation, and datasets for a comprehensive exploration of machine learning techniques to address class imbalance. The project investigates the impact of various methods, like ADASYN, KMeansSMOTE, and Deep Learning Generator, on classification performance while effectively demonstrating benefits of pipelining.

Sampling-based class imbalance solutions for multiple-instance classification

PREDICTING A PULSAR STAR - A Classic Class Imbalance Problem

Evaluate Machine Learning Models with Yellowbrick

Most existing classification approaches assume the underlying training set is evenly distributed but many real-world classification problems have an imbalanced class distribution, such as rare disease identification, fraud detection, spam detection, churn prediction, electricity theft & pilferage etc.

A credit card fraud detection kernel