Skip to content

Latest commit

 

History

History
61 lines (38 loc) · 3.06 KB

README.md

File metadata and controls

61 lines (38 loc) · 3.06 KB
Raw

Decision_Trees_in_SKLearn

GitHub repo size GitHub repo file count (file type) Python Version Pip Version GitHub last commit (branch) Version Contributors GitHub pull requests

This repository contains an implementation of decision trees using sckit-learn.

Overview

Multiple linear regression is an extension of simple linear regression, where the relationship between a dependent variable and two or more independent variables is modeled. It assumes a linear relationship between the input variables (features) and the output variable (target), allowing for more complex modeling scenarios.

In this repository, we demonstrate how to perform multiple linear regression using Python. We utilize libraries such as NumPy, pandas, and scikit-learn to implement the decision trees model. Additionally, we provide a simple example along with explanations to help you understand how to apply decision trees to your own datasets.

Requirements

To run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:

  • NumPy
  • pandas
  • scikit-learn

You can install these libraries using pip:

pip install numpy pandas scikit-learn

Usage

  1. Clone this repository to your local machine:
git clone https://github.com/BaraSedih11/Decision-Trees-in-SKLearn.git
  1. Navigate to the repository directory:
cd DecisionTreesinSKLearn

  1. Open and run the Jupyter Notebook DecisionTreesinSKLearn.ipynb using Jupyter Notebook or JupyterLab.

  2. Follow along with the code and comments in the notebook to understand how decision trees is implemented using Python.

Acknowledgements

  • scikit-learn: The scikit-learn library for machine learning in Python.
  • NumPy: The NumPy library for numerical computing in Python.
  • pandas: The pandas library for data manipulation and analysis in Python.