A machine learning project for predicting diabetes using data preprocessing, exploratory data analysis, and classification models.

Imbalanced data sets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class.

Machine Learning aplicado al mantenimiento predictivo. Se realizaron 2 modelos: 1 por medio de clasificación binaria que predice si una máquina fresadora estará en riesgo de fallar o no, y el 2 modelo a través de clasificación multiclase que predecirá el modo de falla

This repository has the code for implementation of Principal Component Analysis, Upsampling (SMOTE), Downsampling (Random Undersampler) and combined via SMOTETomek.

Predicting whether Insurance claims will accepted or rejected for online Travel Agencies

The Repository is created to cover undersampling and oversampling methods to deal imbalance problem.

Personal GitHub to host and shares my academic mini-projects related to my master degree.

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

Loan prediction using Random Forest, Decision tree, SMOTE and SMOTETOMEK techniques.

Proyecto final del curso de DataScience en CoderHouse que intenta ayudar a una entidad bancaria a la hora de decidir si emite una tarjeta de crédito al solicitante

Classification of an imbalanced dataset using SMOTE oversampling technique and ML Algorithms - KNN , XGBoost and Naive Bayes classifier

Churn prediction means detecting which customers are likely to leave a service or to cancel a subscription to a service.

Develop Machine Learning Models to Predict the UCI Bank Telemarketing Dataset

Interactive dashboard for predicting prediabetes risk using machine learning and SHAP interpretability. Built for clarity, modular benchmarking, and clinical transparency. Includes manual input prediction, threshold-based classification, SHAP visualizations, and model comparison across classifiers.

Machine Learning solutions for a fintech. Improving the success rate of term deposits

Identifying rare event.

Credit risk prediction workflow to identify likely loan defaulters using structured financial data, with emphasis on preprocessing quality, feature selection, and interpretable risk modeling.