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Diabetes Progression Prediction

Table of Contents

About the Project

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.

Getting Started

Prerequisites

Before running the project, make sure you have the following installed:

  • Python (>=3.6)
  • Jupyter Notebook (for running the provided notebook)

Installation

  1. Clone the repository:
git clone https://github.com/J3lly-Been/diabetes-progression-prediction.git
  1. Navigate to the project directory:
cd diabetes-progression-prediction
  1. Install the required dependencies:
pip install -r requirements.txt

Usage

To use the project:

  1. Open the provided Jupyter Notebook (diabetes_progression_prediction.ipynb) in Jupyter Notebook or JupyterLab.
  2. Follow the instructions and execute the code cells sequentially to perform data analysis, train models, and evaluate model performance.
  3. Analyze the results and select the best performing model for diabetes progression prediction.

Project Structure

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.

Contributing

Contributions are welcome! If you have any ideas, suggestions, or improvements, feel free to open an issue or create a pull request.

License

Distributed under the MIT License. See LICENSE for more information.