https://github.com/ebadshabbir/decision_tree_algorithm

Decision Tree Classifier for Social Network Ads A Python implementation of a Decision Tree Classifier to predict user purchasing behavior based on age and estimated salary. Includes feature scaling, model evaluation (confusion matrix and accuracy), and visualizations of decision boundaries for both training and test sets.
https://github.com/ebadshabbir/decision_tree_algorithm

decision-tree-classifier jupyter-notebook machine-learning matplotlib-pyplot numpy pandas python scikit-learn

Last synced: 2 months ago
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Decision Tree Classifier for Social Network Ads A Python implementation of a Decision Tree Classifier to predict user purchasing behavior based on age and estimated salary. Includes feature scaling, model evaluation (confusion matrix and accuracy), and visualizations of decision boundaries for both training and test sets.

• Host: GitHub
• URL: https://github.com/ebadshabbir/decision_tree_algorithm
• Owner: EbadShabbir
• Created: 2024-11-15T12:04:24.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-15T12:07:49.000Z (2 months ago)
• Last Synced: 2024-11-15T13:19:14.035Z (2 months ago)
• Topics: decision-tree-classifier, jupyter-notebook, machine-learning, matplotlib-pyplot, numpy, pandas, python, scikit-learn
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/ebadshabbir/decision-tree-algorithm
• Size: 59.6 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Decision Tree Classifier for Social Network Ads

This repository implements a **Decision Tree Classifier** to predict user behavior based on age and estimated salary using the **Social Network Ads** dataset. The results are visualized for both the training and test sets.

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## Table of Contents

- [About the Project](#about-the-project)

- [Technologies Used](#technologies-used)

- [Setup](#setup)

- [Code Overview](#code-overview)

- [Results](#results)

- [License](#license)

---

## About the Project

The objective is to classify users as potential buyers or not based on their age and estimated salary using a **Decision Tree Classifier**. The dataset is split into training and test sets, scaled, and then passed through the model. Visualizations are generated to interpret the classifier's decision boundaries.

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## Technologies Used

This project uses the following technologies and libraries:

- **Python**: Programming language

- **NumPy**: Numerical computations

- **Pandas**: Data manipulation and analysis

- **Matplotlib**: Data visualization

- **Scikit-learn**: Machine learning algorithms

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## Setup

### Prerequisites

Ensure Python is installed. Recommended version: **Python 3.8+**.

Install the required libraries:

```bash

pip install numpy pandas matplotlib scikit-learn