Linear-Regression-Modeling

In-depth exploration of linear regression models, including data cleaning, model building, and performance evaluation on various datasets.

Contents

• Introduction
• Data Cleaning
• Model Building
• Model Evaluation
• Future Work

Introduction

Linear regression is a fundamental algorithm in machine learning for predicting continuous outcomes. This repository showcases various aspects of linear regression, from data preparation to model evaluation.

Data Cleaning

Data cleaning is a critical step in the machine learning pipeline. In this section, I demonstrate techniques to preprocess and clean datasets to ensure high-quality inputs for the models.

Model Building

This section covers the implementation of linear regression models, highlighting different approaches and techniques used to build and refine the models.

Model Evaluation

Evaluating the performance of a model is crucial. Here, I use various metrics such as R-squared, mean squared error, and mean absolute error to assess the effectiveness of the linear regression models.

Future Work

I plan to expand this repository with more advanced techniques and applications related to linear regression, including regularization methods, multivariate linear regression, and model optimization.

Thank you for exploring my linear regression project. I hope you find it insightful and valuable!