Machine learning model for Credit Card fraud detection

Spam detection in SMS messages with BERT model and Machine Learning algorithms

Predict the enzyme class of a given FASTA sequence using deep learning methods including CNNs, LSTM, BiLSTM, GRU, and attention models along with a host of other ML methods.

Electricity Fraud Detection in Smart Grids

A compilation of codes for SMA, DC, ADS

Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods

A model that recommends University based on details of an applicant.

Credit Card fraud detection

This project is a part of the research on PolyCystic Ovary Syndrome Diagnosis using patient history datasets through statistical feature selection and multiple machine learning strategies. The aim of this project was to identify the best possible features that strongly classifies PCOS in patients of different age and conditions.

Gear detection using OpenCv and Machine Learning

Repository for "Data Mining - Advanced Topics and Applications" projects exam.

Obstructive Sleep Apnea classification with help of numerical data set which having the physical body characteristics with the help of machine learing

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

This project develops an activity recognition model for a mobile fitness app using statistical analysis and machine learning. By processing smartphone sensor data, it extracts features to train models that accurately recognize user activities.

This is a program to predict the possible risk of default on credit card use.

Course Project for CS273A: Machine Learning at UCI

Bank bankruptcy predictions on FDIC bank failure data using tensorflow, keras, sklearn, ensemble, and imblearn libraries.

Using Random undersampling, Tomeklinks, Random oversampling, SMOTE, SMOTE+Tomeklinks and ADASYN from inbuilt imbalanced learn library and found how many records added or discarded.

A minority oversampling method for imbalance data set