Base of Data Science (DS) and Machine Learning

Overview

This repository serves as a foundation for projects in Data Science and Machine Learning. It includes essential tools, libraries, and configurations to streamline development and experimentation. Key components include a requirements.txt file for dependencies and a Dockerfile for setting up a Jupyter-based environment.

Table of Contents

• Overview
• Features
• Installation
• Usage
• Contribution
• License

Features

• Pre-configured environment for Data Science and Machine Learning.
• Docker support for consistent and isolated development.
• Dependencies listed in requirements.txt for easy installation.
• Ready-to-use Jupyter Notebook interface.

Installation

Using Docker

1. Build the Docker image:

   docker build -t base-of-ds .

2. Run the container:

   docker run -p 8888:8888 -v $(pwd):/home/jupyter base-of-ds

3. Access the Jupyter Notebook interface at http://localhost:8888.

Without Docker

1. Clone the repository:

   git clone https://github.com/crissyro/base-of-DS.git

2. Navigate to the project directory:

   cd base-of-DS

3. Install dependencies:

   pip install -r requirements.txt

Usage

This repository is structured for flexibility in Data Science workflows. Use the Jupyter Notebook interface or scripts in the repository for experimentation and analysis.

Example

1. Launch Jupyter Notebook:

   jupyter notebook

2. Open and run notebooks in the notebooks/ directory.

Contribution

We welcome contributions to improve this repository. See CONTRIBUTING.md for guidelines.

License

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