Applied_ML

Applied Machine Learning Workshop

Practical Implementation of Machine Learning Techniques for Research

Table of Contents

1. Quick Start
2. Workshop Contents
3. Prerequisites
4. Setup
5. Installation
6. How to Use This Workshop
7. Resources
8. License
9. Authors
10. Acknowledgments

Quick Start

For immediate access to the workshop materials with a pre-configured environment, use the Google Colab link below: Open in Google Colab

Workshop Contents

• DSI_Applied_ML.ipynb: Jupyter notebook with the workshop code and instructions.
• Data_with_Depression.csv: Dataset file used for analysis and model training.
• geojson-fl-counties-fips.json: GeoJSON file for mapping FIPS codes to counties in Florida.

Prerequisites

• Basic knowledge of Python programming.
• Understanding of machine learning concepts.

Setup

To run this workshop on your local machine, clone the repository and install the necessary dependencies.

git clone https://github.com/matheusmaldaner/WorkshopArchive.git
cd WorkshopArchive/Applied_ML

Alternatively, you can use Google Colab to access the workshop without any local setup: Open in Google Colab

Installation

Ensure that you have the following Python libraries installed if running locally:

• pandas
• numpy
• scikit-learn
• matplotlib

You can install these using pip:

pip install pandas numpy scikit-learn matplotlib

How to Use This Workshop

1. If using Google Colab, simply click on the link provided above to access the notebook.
2. If running locally, launch Jupyter Notebook or JupyterLab in the cloned repository's directory.
3. Open the DSI_Applied_ML.ipynb notebook.
4. Follow the instructions within the notebook to complete the workshop.

Resources

For further learning and to expand on the concepts covered in this workshop, the following resources are recommended:

• Open in Google Colab - Direct link to the workshop notebook.
• Scikit-learn Documentation - A comprehensive guide to machine learning in Python with scikit-learn.
• Pandas Documentation - Useful for understanding data manipulation and analysis with pandas.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Authors

Hunor Vajda - Github

Matheus Kunzler Maldaner - Github

Acknowledgments

• Data Science and Informatics for hosting the workshop.

Thank you