https://github.com/ahmad-ali-rafique/linear-regression-modeling
In-depth exploration of linear regression models, including data cleaning, model building, and performance evaluation on various datasets.
https://github.com/ahmad-ali-rafique/linear-regression-modeling
artificial-intelligence data dataanalytics linear-models linear-regression model multilinear-regression regression regression-models
Last synced: 3 months ago
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In-depth exploration of linear regression models, including data cleaning, model building, and performance evaluation on various datasets.
- Host: GitHub
- URL: https://github.com/ahmad-ali-rafique/linear-regression-modeling
- Owner: Ahmad-Ali-Rafique
- Created: 2024-05-19T17:32:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-09T06:32:36.000Z (12 months ago)
- Last Synced: 2025-01-16T04:21:55.089Z (4 months ago)
- Topics: artificial-intelligence, data, dataanalytics, linear-models, linear-regression, model, multilinear-regression, regression, regression-models
- Language: Jupyter Notebook
- Homepage:
- Size: 716 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Linear-Regression-Modeling
In-depth exploration of linear regression models, including data cleaning, model building, and performance evaluation on various datasets.
## Contents
- [Introduction](#introduction)
- [Data Cleaning](#data-cleaning)
- [Model Building](#model-building)
- [Model Evaluation](#model-evaluation)
- [Future Work](#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!