Use python programming language to complete the task.

Refer to the rubrics for better mark allocations.

And include some explanations for me to understand more.

Thanks!

Supervised Machine Learning using Artificial Neural Network with Native \& Web/Mobile Interfaces - Supervised machine learning is a sub field of artificial intelligence, where presented with data, machines can learn from the data and do prediction for unseen data, without explicit programming. - There is more than one way to carry out machine learning, but one that is getting more popular nowadays is by using artificial neural network (ANN), which is chosen as the topic of our assignment. - Choose a relevant, practical problem that can be solved using Python programming language and ANN. Example of applications that fall under this category, are including (but not limited): - House price / crime rate prediction - Type of plant diseases based on images of leaves. - TWO applications are to be developed. The first application must be native, but the second application can be running on a web browser or a mobile phone/tablet (the group must collectively decide which to be implemented for the second application). - Native Application (COMPULSORY) - Graphical User Interface (GUI) application, which is running on the user's desktop/laptop. - Web Application, OR - Application which is running on and accessible via any web browser (eg: Google Chrome, Mozilla Firefox, Microsoft Internet Explorer, Apple Safari, etc) - Mobile Application - Application which is running on mobile phones or tablets operating system (eg: Android or iOS) - Below are related Python modules than can be used in this project. You are, of course, welcome to explore and use other Python modules beside the ones listed here (except PyQT which is - Below criteria are how the assignments will be assessed: - Functioning (Group) - Basic operation of the application are shown to work fine/as intended. For example, given a new sample, prediction is carried out with the correct class is chosen as the output. - Complexity (Group) - Built upon this basic operation (such as inference to current query), the applications provides more complete/additional features/operations. For example, an alarm can be automatically raised/sent to the appropriate party, if flood is predicted to happen above certain probability. - Usefulness (Group) - The application is useful, solving a relevant, practical problem. - User Interface (Group) - Both of the native and web/mobile applications provide user friendly, easy to use, and intuitive UI. For example, instead of outputting just number as output, the number is accompanied by graph