Title: Regression Algorithms Comparison on Diabetes Dataset
Welcome to the Regression Algorithms Comparison on Diabetes Dataset GitHub repository! This project focuses on exploring and comparing various regression algorithms using the Diabetes dataset. The Diabetes dataset contains ten baseline variables and a quantitative measure of disease progression one year after baseline. By applying different regression algorithms to this dataset, we aim to understand their performance and identify the most suitable regression model for predicting diabetes progression based on medical features.
Regression analysis is a vital tool in machine learning for predicting continuous values. This repository provides an opportunity to explore several regression algorithms' implementations and understand how they perform on the Diabetes dataset. By comparing different regression models, we aim to gain insights into their strengths and limitations in handling medical data for predicting disease progression.
The Diabetes dataset contains ten baseline variables, including age, gender, body mass index (BMI), and blood serum measurements. The target variable is a quantitative measure of disease progression one year after baseline. This dataset provides a real-world regression problem, allowing us to evaluate regression algorithms' accuracy in predicting diabetes progression based on medical attributes.
The repository includes implementations of the following regression algorithms:
- Linear Regression
- Ridge Regression
- Lasso Regression
- Decision Tree Regression
- Random Forest Regression
- Gradient Boosting Regression
Each algorithm is thoroughly documented, and code examples showcase their application to the Diabetes dataset.
To get started with this project:
- Clone the repository to your local machine.
git clone https://github.com/gmdeorozco/Diabetes-Regression-Algorithms-Comparison.git-
Install the required Python libraries, such as scikit-learn, pandas, and matplotlib.
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Run the Jupyter notebooks or Python scripts to observe the regression models' performance on the Diabetes dataset.
Feel free to use the code in this repository to experiment with different regression algorithms or adapt it for your own regression projects. We encourage you to contribute to the repository by adding new regression algorithms, experimenting with hyperparameters, or exploring other regression-related tasks.
Contributions to this project are welcome! If you have any improvements, new regression algorithms, or datasets to add, please submit a pull request. Let's collaboratively enhance this resource for the machine learning community.
This project is licensed under the MIT License.
For any questions or inquiries, please contact ernesto.orozco.coulson@gmail.com. We would be happy to assist you in any way possible. Happy regression modeling on the Diabetes dataset!