Diabetes Data Analysis with Linear Regression

This project aims to explore and analyze diabetes data using Linear Regression. The dataset used is the Diabetes Dataset, available in the scikit-learn library.

Objectives

• Load and explore the diabetes dataset.
• Split the data into training and testing sets.
• Train Linear Regression models with and without intercept.
• Evaluate the accuracy of the models on the training and testing sets.
• Compare the performance of the models using graphs.

Libraries Used

• scikit-learn: to load the dataset, train the Linear Regression models, and evaluate accuracy.
• matplotlib: to plot graphs showing the models' accuracy.

Installation

Ensure you have Python installed in your environment. You can install the necessary libraries using pip:

pip install scikit-learn matplotlib

Usage

The following code loads the dataset, splits the data into training and testing sets, trains Linear Regression models with and without intercept, evaluates accuracy, and plots the results.

Expected Results

This project is expected to provide a practical understanding of how Linear Regression can be applied to diabetes data analysis and demonstrate how intercept affects model performance.