https://github.com/udacity-machinelearning-internship/decision-trees-in-sklearn

Applying Decision Trees in SKLearn with pandas and numpy
https://github.com/udacity-machinelearning-internship/decision-trees-in-sklearn

decision-trees machine-learning sklearn

Last synced: 3 months ago
JSON representation

Applying Decision Trees in SKLearn with pandas and numpy

• Host: GitHub
• URL: https://github.com/udacity-machinelearning-internship/decision-trees-in-sklearn
• Owner: Udacity-MachineLearning-Internship
• Created: 2024-05-15T07:08:52.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-17T03:38:34.000Z (about 1 year ago)
• Last Synced: 2025-01-21T08:24:10.252Z (5 months ago)
• Topics: decision-trees, machine-learning, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 10.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        


![Decision_Trees_in_SKLearn](https://github.com/BaraSedih11/Decision-Trees-in-SKLearn/assets/98843912/dc0a34bc-66ff-45e4-92cd-5f92108a06f3)

   ![GitHub repo size](https://img.shields.io/github/repo-size/BaraSedih11/Decision-Trees-in-SKLearn) ![GitHub repo file count (file type)](https://img.shields.io/github/directory-file-count/BaraSedih11/Decision-Trees-in-SKLearn) [![Python Version](https://img.shields.io/badge/python-3.8-blue)](https://www.python.org/downloads/release/python-380/)

[![Pip Version](https://img.shields.io/badge/pip-21.0-orange)](https://pypi.org/project/pip/21.0/)

 ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/BaraSedih11/Decision-Trees-in-SKLearn/main)

[![Version](https://img.shields.io/badge/version-v1.0.0-blue)](https://github.com/BaraSedih11/Decision-Trees-in-SKLearn/releases/tag/v1.0.0)

[![Contributors](https://img.shields.io/github/contributors/BaraSedih11/Decision-Trees-in-SKLearn)](https://github.com/BaraSedih11/Decision-Trees-in-SKLearn/graphs/contributors)

![GitHub pull requests](https://img.shields.io/github/issues-pr-raw/BaraSedih11/Decision-Trees-in-SKLearn)



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:

```bash

pip install numpy pandas scikit-learn 

```

## Usage

1. Clone this repository to your local machine:

```bash

git clone https://github.com/BaraSedih11/Decision-Trees-in-SKLearn.git

```

2. Navigate to the repository directory:

```bash

cd DecisionTreesinSKLearn

```

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

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

## Acknowledgements

- [scikit-learn](https://scikit-learn.org/): The scikit-learn library for machine learning in Python.

- [NumPy](https://numpy.org/): The NumPy library for numerical computing in Python.

- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.