Analysis and classification using machine learning algorithms on the UCI Default of Credit Card Clients Dataset.

Official implementation for "Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images" https://arxiv.org/abs/2112.08810

Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification). 🌻

Binary classification of lumpy skin disease (imbalanced dataset) using ML algorithms in addition to oversampling/undersampling techniques.

Predecir el abandono de futuros clientes

Generative based data augmentation for ACPs

Data from a website that provides job reviews. The website wants to analyze texts and the corresponding rating that is provided by the user about startups. Based on the texts, try to verify if it corresponds to the score provided by the reviewer. the task helps the website to rank user's reviews or ratings

Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.

A machine learning project to predict Customers/Clients into correct segment to provide promotional information or for product advertising.

Cerebral stroke, a critical condition, demands vigilant analysis. Machine learning models, coupled with resampling techniques like SMOTEENN, enhance stroke prediction accuracy by addressing imbalanced datasets.

Spam messages detection model

Detect potential frauds so that customers are not wrongly charged for items that they did not purchase.

Analysis of public opinion sentiment toward BPOM's performance in supervising skincare products using SVM algorithms, as well as ADASYN and PSO methods

An assessment of logistic regression model performance in predicting customer loan worthiness using loan size, interest rate, borrower income, debt-to-income ratio, number of credit accounts, derogatory marks, and total debt as predictive factors.

Building a machine learning model to check bank frauds

"Data Analytics Challenge" course at the Catholic University of Eichstätt-Ingolstadt

Classification of Fraudulent Transactions in Mobile Based Payments

Project for predicting strokes from healthcare data for INDE 577 (Spr. 23) at Rice University