Apply the different machine learning learned in class

Reference no: EM133709504 , Length: word count:2000

Artificial Intelligence and Machine Learning

Assessment - Machine Learning/AI for a Business Problem

Task
Develop a real-world Machine Learning or AI project plan/proposal based on the learnings from the subject.

Assessment Description

This assessment seeks to simulate a real-world classification task (binary or multi-class) that you may have to undertake in the future. Therefore, the assignment is non-prescriptive and requires you to pose a relevant, small, creative and significant problem to solve that could result in benefits to the organisation of choice.

In this assessment, you need to consider an organisation in an industry of your choice and articulate the steps this organisation needs to take to enable Machine Learning and/or AI for data-driven decision making. You are required to analyse a sample data set to demonstrate expected AI/ML outcomes.

You must work on a classification task (binary or multi-class), not a regression, forecasting, or reinforcement learning task. You must use the Orange Data Mining software for all analysis.

You need to be familiar with the organisation and industry (e.g., where you have worked or are working, a future start-up company), NOT an organisation such as Amazon/Boeing/Qantas etc.

Well-reasoned use of Generative AI is encouraged. However, generic and irrelevant content will be heavily penalised in the marking.

The report should address:
Why ML would help this organisation given its current operations?
What Machine Learning techniques you would recommend?
An example of the predictive model using sample data.
Deployment considerations for the model.
The benefits for the organisation are clearly articulated with estimates of expected revenue/profits or Return on Investment (ROI).

Assessment Instructions

Timeline. The following timeline will help you stay on track with this assessment.
By Week 9 identify a company and industry you are familiar with that would benefit from Machine Learning/AI. Note:
The application needs to be based on a classification task (not regression, forecasting, or some other prediction task).
Focus on a single, well-defined (small) application.

Sample datasets maybe sourced from:

an organisation. if you work there
public repositories
Open government data

By Week 12 draft some preliminary points pertaining to the report in class. You are encouraged to consider the current mode of operation, possible inefficiencies, available data and how this data may be used to provide efficiencies based on the concepts and techniques covered in the subject. Think of yourself as a consultant or a founder.

Your lecturer will advise on the appropriateness of your choice and proposed methodology regarding the requirements for the assessment.

Implementation. The Orange workflow file must be submitted to the file submission box. No marks will be awarded for the assessment unless both the report and the Orange workflow file have been submitted.

Report. Based on the template provided, write a 2000-word (maximum) report that summarises the analysis, as well as providing suggestions for further analysis. You may use Generative AI (eg: chatGPT) to enhance your research. The Orange workflow file must also be submitted via the file submission box.

PART B: Video Quiz

- Record yourself doing the video quiz using zoom screen share. Instructions are available on the assessment page.

- There are four sections: (1) business problem identification, (2) data collection, (3) machine learning implementation, and (4) improvements. For each section, the student will have 2-3 minutes to answer all the questions within the section. The total allocated time for the video quiz is 15 minutes.

There are few points regarding the assessment

1. Choose a company in an industry of their choice that would benefit from a Machine Learning application.

2. Define a business problem that can be solved using Machine Learning - Classification in the chosen company (binary or multi-class) (not a regression, forecasting, or reinforcement learning task).

3. Find a sample dataset suitable to solve the business problem defined.

4. Apply the different Machine Learning learned in class and justify their recommended ML techniques,

5. Highlight the benefits of this ML project for the organisation (The benefit could be financial, such as Return on Investment (ROI) or societal benefits).

6. The assessment must be done using Orange Data Mining software for all analyses.

7. Assessment 3 is a written report (2000 words, Based on the template provided).

Reference no: EM133709504

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