Steps to Creating a README

I. Following Cisco at Data Analytics Essentials Course [1]

The following is a sample project README file that was created using a template. Select each section of the document for more information.

1. Project Title

The name of the project and a brief description of the main goal and purpose of the project.

2. Demo Link

If you have a working demo of the project, provide the link so that readers can see your project in action.

3. Table of Contents (optional)

If the project has sections, a table of contents can be generated from the section titles. In this raw text example, the section titles are proceeded by the ## characters.

4. Business Understanding

Your description of the goals and purpose for the project. In this section, you should explain a little about what you wanted to accomplish, why you selected this analysis project, and any challenges that you faced while working through the project.

5. Data Understanding

Explain your data sources and why you chose the particular data sets that you did. You can also discuss additional enhancements or analysis that you want to perform in the future.

6. Screenshots of Visualizations/Results

Examples of the process or outputs. In analytics, these can be images of visualizations.

7. Technologies

List all of the technologies you used to complete the project, to show your proficiency with these tools.

8. Setup

Instructions on how to set up or install the environment and any dependencies that are required. This section can also include usage instructions for readers who want to duplicate your results.

9. Approach

For analytics projects, this section describes the process and the types of analysis utilized to obtain the data, prepare and clean it, and to test the outcomes.

10. Status

The current status of the project. In most cases, you will be putting information into the repository while you are working on the project, so the status will be “in process” and, later, “complete/released”. If there are multiple versions of your project, you can indicate the version here, as well.

11. Creadit

List of individuals or organizations that contributed or provided assistance to you while you created the project. You can also list sites that provided inspiration or feedback.

II. Following freeCodeCamp at “How to Write a Good README File for Your GitHub Project” article [2]

1. Project’s Title

This is the name of the project. It describes the whole project in one sentence, and helps people understand what the main goal and aim of the project is.

2. Project Description

This is an important component of your project that many new developers often overlook.

Your description is an extremely important aspect of your project. A well-crafted description allows you to show off your work to other developers as well as potential employers.

The quality of a README description often differentiates a good project from a bad project. A good one takes advantage of the opportunity to explain and showcase:

• What your application does,
• Why you used the technologies you used,
• Some of the challenges you faced and features you hope to implement in the future.

3. Table of Contents (Optional)

If your README is very long, you might want to add a table of contents to make it easy for users to navigate to different sections easily. It will make it easier for readers to move around the project with ease.

4. How to Install and Run the Project

If you are working on a project that a user needs to install or run locally in a machine like a “POS”, you should include the steps required to install your project and also the required dependencies if any.

Provide a step-by-step description of how to get the development environment set and running.

5. How to Use the Project

Provide instructions and examples so users/contributors can use the project. This will make it easy for them in case they encounter a problem – they will always have a place to reference what is expected.

You can also make use of visual aids by including materials like screenshots to show examples of the running project and also the structure and design principles used in your project.

Also if your project will require authentication like passwords or usernames, this is a good section to include the credentials.

6. Include Credits

If you worked on the project as a team or an organization, list your collaborators/team members. You should also include links to their GitHub profiles and social media too.

Also, if you followed tutorials or referenced a certain material that might help the user to build that particular project, include links to those here as well.

This is just a way to show your appreciation and also to help others get a first hand copy of the project.

7. Add a License

For most README files, this is usually considered the last part. It lets other developers know what they can and cannot do with your project.

We have different types of licenses depending on the kind of project you are working on. Depending on the one you will choose it will determine the contributions your project gets.

The most common one is the GPL License which allows other to make modification to your code and use it for commercial purposes. If you need help choosing a license, use check out this link: https://choosealicense.com/

Up to this point what we have covered are the minimum requirements for a good README. But you might also want to consider adding the following sections to make it more eye catching and interactive.

8. Badges (Additional README Sections)

Badges aren’t necessary, but using them is a simple way of letting other developers know that you know what you’re doing.

Having this section can also be helpful to help link to important tools and also show some simple stats about your project like the number of forks, contributors, open issues etc…

Below is a screenshot from one of my projects that shows how you can make use of badges:

The good thing about this section is that it automatically updates it self.

Don’t know where to get them from? Check out the badges hosted by shields.io. They have a ton of badges to help you get started. You may not understand what they all represent now, but you will in time.

9. How to Contribute to the Project

This mostly will be useful if you are developing an open-source project that you will need other developers to contribute to. You will want to add guidelines to let them know how they can contribute to your project.

Also it is important to make sure that the licence you choose for an open-source projects is correct to avoid future conflicts. And adding contribution guidelines will play a big role.

Some of the most common guidelines include the Contributor Covenant and the Contributing guide. Thes docs will give you the help you need when setting rules for your project.

10. Include Tests

Go the extra mile and write tests for your application. Then provide code examples and how to run them.

This will help show that you are certain and confident that your project will work without any challenges, which will give other people confidence in it, too

III. Following LLMA (using the promt “profesional reame.md markdown”)[3]

Here is a sample README.md file for a machine learning project, written in a professional tone and using Markdown formatting:

Machine Learning Project: [Project Name]

Overview

This project aims to develop a machine learning model to [briefly describe the problem or goal of the project]. The model will be trained on [dataset/source] and evaluated using [evaluation metrics].

Background

[ Briefly describe the context and motivation behind the project ]

Dataset

• Source: [Dataset source]
• Description: [Briefly describe the dataset, including its size, features, and target variable]
• Preprocessing: [Describe any preprocessing steps taken, such as data cleaning, feature scaling, or encoding]

Methodology

• Model Selection: [Describe the machine learning algorithm(s) used, including any hyperparameter tuning]
• Training: [Describe the training process, including any data splitting, batch size, or number of epochs]
• Evaluation: [Describe the evaluation metrics used, including any validation or testing procedures]

Results

• Model Performance: [Report the model’s performance on the testing data, including any relevant metrics]
• Comparison: [Compare the model’s performance to any baseline or other models]

Code Organization

• data/: Contains the dataset and any preprocessing scripts
• models/: Contains the machine learning model code and any hyperparameter tuning scripts
• evaluation/: Contains the evaluation metrics and any testing procedures
• utils/: Contains any utility functions or scripts used throughout the project

Requirements

• Python: [Version]
• Libraries: [List any required libraries, including their versions]
• Hardware: [List any specific hardware requirements, such as GPU or RAM]

Usage

1. Clone the repository: git clone https://github.com/[username]/[project-name].git
2. Install the required libraries: pip install -r requirements.txt
3. Preprocess the data: python data/preprocess.py
4. Train the model: python models/train.py
5. Evaluate the model: python evaluation/evaluate.py

Contributing

• Issues: [Describe the process for reporting issues or bugs]
• Pull Requests: [Describe the process for submitting pull requests or contributing code]

License

[Insert license information, such as MIT or Apache 2.0]

Acknowledgments

• [List any contributors, collaborators, or acknowledgments]

References

• [List any relevant references or citations]

Note: This is just a sample README.md file, and you should customize it to fit your specific project’s needs.

References

[1] Cisco, “Data Analytics Essentials”. Available at: https://www.netacad.com/courses/data-analytics-presentation

[2] freeCodeCamp, “How to Write a Good README File for Your GitHub Project”. Available at: https://www.freecodecamp.org/news/how-to-write-a-good-readme-file/

[3] Llama 3.1 70b. Available at: https://deepinfra.com/