Linear Regression Optimization Repository

This repository is intended to optimize the functioning of the linear regression model. The goal is to enhance the performance and accuracy of linear regression by exploring various optimization techniques and strategies.

Purpose

The primary objectives of this repository are to:

Model Optimization:

Document methods to improve the efficiency and effectiveness of linear regression models.

Feature Selection:

Explore techniques for selecting the most relevant features that contribute to model performance.

Parameter Tuning:

Analyze the impact of hyperparameters on the model's performance and fine-tune them for optimal results.

Methodology

Data Preparation: Preprocess datasets, including cleaning, normalization, and splitting into training and testing sets.

Model Implementation:

Implement linear regression models using libraries such as Scikit-learn.

Performance Evaluation:

Assess model performance using metrics like R-squared, mean squared error, and residual analysis.

Visualization:

Create visualizations to illustrate the effects of optimizations on the regression model.

Technologies Used

Python:

Primary programming language for implementing linear regression optimization.

Scikit-learn:

Library used for building and evaluating linear regression models.

Pandas and NumPy:

For data manipulation and preprocessing.

Matplotlib and Seaborn:

For data visualization.

Future Developments

As I continue to optimize linear regression models, I plan to incorporate more advanced techniques, such as regularization methods (Lasso and Ridge regression) and explore multiple linear regression for improved predictions.