https://github.com/Udacity-MachineLearning-Internship/Titanic-Survival-Model

Applying Titanic Survival Model with decision trees in python
https://github.com/Udacity-MachineLearning-Internship/Titanic-Survival-Model

decision-trees machine-learning sckit-learn

Last synced: 6 months ago
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Applying Titanic Survival Model with decision trees in python

• Host: GitHub
• URL: https://github.com/Udacity-MachineLearning-Internship/Titanic-Survival-Model
• Owner: BaraSedih11
• Created: 2024-05-15T07:21:04.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-17T03:35:41.000Z (over 1 year ago)
• Last Synced: 2024-05-17T04:32:57.446Z (over 1 year ago)
• Topics: decision-trees, machine-learning, sckit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 37.1 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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![Titanic_Survival_Model](https://github.com/BaraSedih11/Titanic-Survival-Model/assets/98843912/4bfa2692-43e2-4ef0-b8a4-63f3a9fe524c)

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This repository contains an implementation of Titanic Survival Model with decision trees in python.

## Overview

In this repository, we demonstrate how to perform decision trees and data manipulation using Python to predict a Titanic survival model. We utilize libraries such as NumPy, pandas, and scikit-learn to implement and visualize 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/Titanic-Survival-Model.git

```

2. Navigate to the repository directory:

```bash

cd Titanic

```

3. Open and run the Jupyter Notebook `titanic_survival_exploration.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.