Machine Learning Dashboard

Overview:

The ML Dashboard is a web application where developers can store, edit, and share code templates for machine learning algorithms. It also allows starting programmers to download fully-described machine learning algorithms to include in their own projects or see them in action. The project requirements include creating a web application with a relational database to store templates, implementing backend and frontend code for adding and summarizing templates, using Matplotlib for visualizations, and allowing CRUD operations using SQL or NoSQL. The backend code is written in Python using the Flask micro-framework, with HTML, CSS, and JavaScript used for the dashboard's appearance. The website is data-driven and utilizes various tools, modules, and techniques for web development, database development, data analysis, and machine learning. The database structure consists of three main parts: user credentials, ML Dashboard view, and ML algorithm type information.

Features:

• Contribute: Allows users to add new code templates to the site.
• Records per page button: Allows users to scale the table view in the dashboard.
• Search: Provides keyword search functionality in the datatable.
• Clickable headers: Allows users to sort the datatable.
• Type of Estimator button: Redirects to a page showing a summary of each algorithm type.
• Edit button: Redirects to the edit template page.
• Delete button: Removes a code template from the database.
• Download button: Downloads the code template in a .py form.
• Downloads counter: Displays the number of downloads for each template.
• Pagination feature: Enables users to navigate through multiple pages in the datatable.
• Github Repository View: Provides a view of the Github repository with an option to change to another contributor.

Summary:

The ML Dashboard is a web application that allows developers to store, edit, and share machine learning code templates. It provides a user-friendly interface for managing the templates and offers features like search, sorting, pagination, and download functionality. The application is built using Python, Flask, and various Python libraries for web development, database management, data analysis, and machine learning. With its structured database and data-driven approach, the ML Dashboard provides a convenient platform for developers to access and utilize machine learning algorithms in their projects.