https://github.com/jelhamm/regression-algorithms

"This repository serves as a comprehensive resource for understanding and applying Regression techniques in achine learning and statistical modeling."
https://github.com/jelhamm/regression-algorithms

achine-learning builder-pattern decision-tree decision-tree-classifier design-pattern factory-pattern linear-regression python random-forest random-forest-classifier regression regression-models regular-expression statistical-models

Last synced: 11 months ago
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"This repository serves as a comprehensive resource for understanding and applying Regression techniques in achine learning and statistical modeling."

• Host: GitHub
• URL: https://github.com/jelhamm/regression-algorithms
• Owner: jElhamm
• License: mit
• Created: 2024-01-09T18:54:35.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-03-31T18:08:05.000Z (about 2 years ago)
• Last Synced: 2024-03-31T19:23:34.423Z (about 2 years ago)
• Topics: achine-learning, builder-pattern, decision-tree, decision-tree-classifier, design-pattern, factory-pattern, linear-regression, python, random-forest, random-forest-classifier, regression, regression-models, regular-expression, statistical-models
• Language: Python
• Homepage:
• Size: 1.09 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Support: Support Vector Regression/CustomSVR.py

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README

          
# Regression Algorithms

   This repository contains implementations of various [Regression Algorithms](https://en.wikipedia.org/wiki/Regression_analysis) in [Python](https://www.python.org/).

## Overview

   *Regression Algorithms* are a set of machine learning techniques used to model the relationship between a dependent variable 

   and one or more independent variables. The goal is to predict a continuous numerical value based on the input features. 

   Regression algorithms include linear regression, polynomial regression, decision tree regression, support vector regression, and others.

   These algorithms analyze the training data to learn patterns and relationships that can be used to make predictions on new, unseen data.

   The choice of regression algorithm depends on the specific problem and the nature of the data being analyzed.

## Algorithms Implemented:

   * [Random Forest](Random%20Forest)

   * [Decision Tree](Decision%20Tree)

   * [Ridge Regression](Ridge%20Regression)

   * [Linear Regression](Linear%20Regression)

   * [Logistic Regression](Logistic%20Regression)

   * [Polynomial Regression](Polynomial%20Regression)

   * [Support Vector Regression](Support%20Vector%20Regression)

## Usage

   Each algorithm implementation is in a separate Python script.

   You can simply run the scripts to see the algorithms in action.

## Algorithms Summary

   * *Decision Tree*: A tree-like model where an internal node represents a feature,

   the branch represents a decision rule, and each leaf node represents the outcome.

  

   * *Linear Regression*: A linear approach to modeling the relationship between

   a dependent variable and one or more independent variables using a linear equation.

   * *Logistic Regression*: A regression model where the outcome is binary,

   predicting the probability of occurrence of a categorical dependent variable.

   * *Polynomial Regression*: Fits a polynomial function to the data,

   allowing for non-linear relationships between the independent and dependent variables.

   * *Random Forest*: An ensemble learning method that constructs multiple decision trees

   during training and outputs the mode of the classes as the prediction.

   * *Ridge Regression*: A regularization technique used to prevent overfitting by adding a penalty term to the cost function.

   * *Support Vector Regression*: Uses support vector machines to perform regression,

   finding the hyperplane that best fits the data with a specified margin of tolerance.

## References

 

   * [Regression Analysis in Machine learning](https://www.javatpoint.com/regression-analysis-in-machine-learning)

   * [10 popular regression algorithms in Machine Learning](https://www.onlinemanipal.com/blogs/popular-regression-algorithms-in-machine-learning)

   * [What Is Regression? Definition, Calculation, and Example](https://www.investopedia.com/terms/r/regression.asp#:~:text=A%20regression%20is%20a%20statistical,more%20of%20the%20explanatory%20variables.)

## License

   This repository is licensed under the MIT License.

   See the [LICENSE](./LICENSE) file for more details.