Template Instructions

Welcome,

This is the Code Institute student template for the bring your own data project option in Predictive Analytics. We have preinstalled all of the tools you need to get started. It's perfectly okay to use this template as the basis for your project submissions. Click the Use this template button above to get started.

You can safely delete the Template Instructions section of this README.md file and modify the remaining paragraphs for your own project. Please do read the Template Instructions at least once, though! It contains some important information about the IDE and the extensions we use.

How to use this repo

1. Use this template to create your GitHub project repo

2. Log into your cloud IDE with your GitHub account.

3. On your Dashboard, click on the New Workspace button

4. Paste in the URL you copied from GitHub earlier

5. Click Create

6. Wait for the workspace to open. This can take a few minutes.

7. Open a new terminal and pip3 install -r requirements.txt

8. Open the jupyter_notebooks directory, and click on the notebook you want to open.

9. Click the kernel button and choose Python Environments.

Note that the kernel says Python 3.8.18 as it inherits from the workspace, so it will be Python-3.8.18 as installed by our template. To confirm this, you can use ! python --version in a notebook code cell.

Cloud IDE Reminders

To log into the Heroku toolbelt CLI:

1. Log in to your Heroku account and go to Account Settings in the menu under your avatar.
2. Scroll down to the API Key and click Reveal
3. Copy the key
4. In the terminal, run heroku_config
5. Paste in your API key when asked

You can now use the heroku CLI program - try running heroku apps to confirm it works. This API key is unique and private to you so do not share it. If you accidentally make it public then you can create a new one with Regenerate API Key.

Dataset Content

• Describe your dataset. Choose a dataset of reasonable size to avoid exceeding the repository's maximum size and to have a shorter model training time. If you are doing an image recognition project, we suggest you consider using an image shape that is 100px × 100px or 50px × 50px, to ensure the model meets the performance requirement but is smaller than 100Mb for a smoother push to GitHub. A reasonably sized image set is ~5000 images, but you can choose ~10000 lines for numeric or textual data.

Business Requirements

• Describe your business requirements

Hypothesis and how to validate?

• List here your project hypothesis(es) and how you envision validating it (them)

The rationale to map the business requirements to the Data Visualizations and ML tasks

• List your business requirements and a rationale to map them to the Data Visualizations and ML tasks

ML Business Case

• In the previous bullet, you potentially visualized an ML task to answer a business requirement. You should frame the business case using the method we covered in the course

Dashboard Design

• List all dashboard pages and their content, either blocks of information or widgets, like buttons, checkboxes, images, or any other item that your dashboard library supports.
• Later, during the project development, you may revisit your dashboard plan to update a given feature (for example, at the beginning of the project you were confident you would use a given plot to display an insight but subsequently you used another plot type).

Unfixed Bugs

• You will need to mention unfixed bugs and why they were not fixed. This section should include shortcomings of the frameworks or technologies used. Although time can be a significant variable to consider, paucity of time and difficulty understanding implementation is not a valid reason to leave bugs unfixed.

Deployment

Heroku

• The App live link is: https://YOUR_APP_NAME.herokuapp.com/
• Set the runtime.txt Python version to a Heroku-20 stack currently supported version.
• The project was deployed to Heroku using the following steps.

1. Log in to Heroku and create an App
2. At the Deploy tab, select GitHub as the deployment method.
3. Select your repository name and click Search. Once it is found, click Connect.
4. Select the branch you want to deploy, then click Deploy Branch.
5. The deployment process should happen smoothly if all deployment files are fully functional. Click now the button Open App on the top of the page to access your App.
6. If the slug size is too large then add large files not required for the app to the .slugignore file.

Main Data Analysis and Machine Learning Libraries

• Here you should list the libraries you used in the project and provide an example(s) of how you used these libraries.

Credits

• In this section, you need to reference where you got your content, media and extra help from. It is common practice to use code from other repositories and tutorials, however, it is important to be very specific about these sources to avoid plagiarism.
• You can break the credits section up into Content and Media, depending on what you have included in your project.

Content

• The text for the Home page was taken from Wikipedia Article A
• Instructions on how to implement form validation on the Sign-Up page was taken from Specific YouTube Tutorial
• The icons in the footer were taken from Font Awesome

Media

• The photos used on the home and sign-up page are from This Open-Source site
• The images used for the gallery page were taken from this other open-source site

Acknowledgements (optional)

• Thank the people that provided support through this project.