This project aims to predict diabetes progression using machine learning regression models. It utilizes the diabetes dataset from the Scikit-learn library and explores various regression algorithms to predict the progression of diabetes based on different features.
Key features of the project include:
- Exploratory Data Analysis (EDA) to understand the dataset and identify important features.
- Selection of relevant features for modeling.
- Training and evaluation of multiple regression models, including Linear Regression, Ridge Regression, Lasso Regression, Decision Tree, and Random Forest.
- Hyperparameter tuning for optimizing model performance.
- Selection of the best performing model for diabetes progression prediction.
Before running the project, make sure you have the following installed:
- Python (>=3.6)
- Jupyter Notebook (for running the provided notebook)
- Clone the repository:
git clone https://github.com/J3lly-Been/diabetes-progression-prediction.git- Navigate to the project directory:
cd diabetes-progression-prediction- Install the required dependencies:
pip install -r requirements.txtTo use the project:
- Open the provided Jupyter Notebook (
diabetes_progression_prediction.ipynb) in Jupyter Notebook or JupyterLab. - Follow the instructions and execute the code cells sequentially to perform data analysis, train models, and evaluate model performance.
- Analyze the results and select the best performing model for diabetes progression prediction.
The project directory contains the following files:
diabetes_progression_prediction.ipynb: Jupyter Notebook containing the project code and documentation.requirements.txt: List of required Python packages for installation.
Contributions are welcome! If you have any ideas, suggestions, or improvements, feel free to open an issue or create a pull request.
Distributed under the MIT License. See LICENSE for more information.