The dataset is sources from Kaggle. The dataset was found from a different user but I will apply it to this ficticious case.
The dataset contains 27 167 images in total, 17 678 of them are photos of male faces and 9 489 are of female faces.
Business Requirements
The business requirement is
Hypothesis and how to validate?
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 a ML task to answer a business requirement. You should frame the business case using the method we covered in the course
Dashboard Design
Summary page, to give an overview of the entire project, presenting the business requirements and how the application shows it.
Problemsolvning techniques, grasping the idea behind the project and bringing displaying the information clearly.
Hypothesis, showing the questions the project intends to answer and how they are validated.
ML Pipeline, to describe how the project pipeline works.
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 big variable to consider, paucity of time and difficulty understanding implementation is not a valid reason to leave bugs unfixed.
The project was deployed to Heroku using the following steps.
Log in to Heroku and create an App
At the Deploy tab, select GitHub as the deployment method.
Select your repository name and click Search. Once it is found, click Connect.
Select the branch you want to deploy, then click Deploy Branch.
The deployment process should happen smoothly in case all deployment files are fully functional. Click now the button Open App on the top of the page to access your App.
Main Data Analysis and Machine Learning Libraries
Here you should list the libraries you used in the project and provide example(s) on 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)
In case you would like to thank the people that provided support through this project.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent