ML4J
ML4J aims to provide a simple yet powerful interface for performing machine learning tasks in Java without the overhead of heavy external dependencies. It is built to be easily understood, making it an excellent tool for learning the internal workings of ML algorithms.
Features:
DataFrame: A flexible structure for handling tabular data, supporting CSV reading, column manipulation, and splitting.
Preprocessing:
OutlierHandler: Remove outliers from your data.
Scaler: Scale features using Robust Scaler.
Splitter: Train/Test split functionality.
Models:
Regression: Linear Regression, Logistic Regression, K-Nearest Neighbours Regression.
Classification: K-Nearest Neighbours Classification, Support Vector Machine (SVM), Decision Tree.
Github link: https://github.com/aditya0589/ML4J
Features:
DataFrame: A flexible structure for handling tabular data, supporting CSV reading, column manipulation, and splitting.
Preprocessing:
OutlierHandler: Remove outliers from your data.
Scaler: Scale features using Robust Scaler.
Splitter: Train/Test split functionality.
Models:
Regression: Linear Regression, Logistic Regression, K-Nearest Neighbours Regression.
Classification: K-Nearest Neighbours Classification, Support Vector Machine (SVM), Decision Tree.
Github link: https://github.com/aditya0589/ML4J