This repository provides a comprehensive analysis of the diabetes dataset, which is readily available in the sklearn library. The analysis employs the linear regression algorithm, leveraging the implementation provided by the sklearn library itself.
Linear regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. In this case, the algorithm is applied to the diabetes dataset to understand and predict the progression of diabetes based on various features.
The repository contains code and documentation demonstrating how to load the dataset, preprocess the data, train the linear regression model, and evaluate its performance. The goal is to gain insights into the dataset and use the trained model to make predictions or further analyze the relationship between the input features and the progression of diabetes.