GitHub

End-to-end Machine Learning project predicting water potability using Random Forest, XGBoost, KMeans clustering, and SMOTE, Python, Scikit-Learn, SQL

A full end-to-end machine learning project to predict whether water is safe to drink based on its chemical properties. The project covers SQL-based exploration, data cleaning, feature engineering, ...
GitHub

krixx646/Pigent_forex_pro

pigent_forex_pro/ ├── 📊 Core Pipeline │ ├── fetch_candles.py # Data fetching from OANDA │ ├── compute_features.py # Technical indicator calculation │ ├── label_data.py # Buy/Sell/Hold labeling │ ├── ...
IEEE

SMOTEHashBoost: Ensemble Algorithm for Imbalanced Dataset Pattern Classification

Abstract: Class imbalance occurs frequently in machine learning, particularly in binary classification tasks where the majority class has a significantly larger number of samples than the minority ...
IEEE

Optimizing Diabetes Diagnosis Using Machine Learning With SMOTE and Feature Selection

Abstract: This study explores the use of machine learning for diabetes categorization, aiming to enhance diagnostic accuracy. Three classification algorithms—Random Forest, Logistic Regression, and ...