Welcome to the Supervised Machine Learning Regression repository!
This repository provides a collection of resources and examples for supervised machine learning regression techniques. Whether you are a beginner looking to understand regression algorithms or an experienced data scientist seeking practical implementations, this repository aims to be a valuable resource.
Supervised machine learning regression is a powerful technique used to predict continuous numeric values based on input features. This repository aims to provide a comprehensive understanding of regression algorithms, along with practical implementations and examples.
The repository currently includes implementations and examples for the following regression algorithms:
- Linear Regression
- Polynomial Regression
- Decision Tree Regression
- Support Vector Regression
- Gradient Boosting Regression
Each regression algorithm is implemented using popular machine learning libraries and accompanied by clear explanations and usage examples.
To use the regression algorithms provided in this repository, simply clone the repository or download the desired implementation files. Each regression algorithm has its own folder, containing the necessary code, documentation, and example datasets.
Feel free to explore the implementations, experiment with different datasets and hyperparameters, and integrate the algorithms into your own projects. The documentation and example scripts provided within each algorithm folder will guide you through the usage and customization process.
This repository is licensed under the MIT License. You are free to use, modify, and distribute the code in this repository for both commercial and non-commercial purposes. See the LICENSE file for more details.
Happy exploring and building predictive models with supervised machine learning regression!