Regression Algorithms

This repository contains implementations of various Regression Algorithms in Python.

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
• Decision Tree
• Ridge Regression
• Linear Regression
• Logistic Regression
• Polynomial Regression
• Support Vector Regression

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
• 10 popular regression algorithms in Machine Learning
• What Is Regression? Definition, Calculation, and Example

License

This repository is licensed under the MIT License. See the LICENSE file for more details.