A pain and anxiety level detection machine learning model using SVM model

Detecting Abnormal Markets - Early Warning Systems

Used CDC dataset for heart attack detection in patients. Balanced the dataset using SMOTE and Borderline SMOTE and used feature selection and machine learning to create different models and compared them based on metrics such as F-1 score, ROC AUC, MCC, and accuracy.

This project employs advanced SMOTE variants to effectively work on imbalanced classification challenges in machine learning datasets.

demonstrate different models such as Variational Autoencoders and GANs in a variety of datasets, including tabular, text and image data, including the generation of synthetic data for comparison of their effectiveness in all models for each kind of dataset

Deep Learning models aimed at improving performance on imbalanced time-series clinical data. The project explores data augmentation techniques and model optimization to enhance classification results in challenging imbalanced datasets.

Leveraging and comparing various ML techniques to forecast credit card defaults [Imbalanced data]

Develop a model to predict which retail customers will respond to a marketing campaign. Logistic Regression shows the best performance.