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*Assignment 2: Proposal for the Final Project **

**Overview: **

Assignment 2 serves as the groundwork for your final project in this course. You are tasked with crafting and presenting a proposal that outlines a solution to a real-world problem through the application of machine learning techniques discussed in class. This proposal should aim to innovate or improve upon existing solutions by integrating a limited proportion (no more than 30%) of external methods or technologies. If such external resources are utilized, your team must thoroughly explain their relevance and application within the context of your project.

The final projects should target real-life applications in the UAE context. The project could aim to contribute to the development of to offer inclusive services in healthcare, agricultural analytics, tourism and crowd management, or ICT advancements in the public sector. Alternatively, you can propose another project that is in line with .

**Data Sources: **

Ideally, if you can access data, you should use local data from UAE resources which will ensure the relevance and applicability of your work. In case of problems accessing data, you can use other data resources that contain relevant data for your project. You can use datasets from reputable sources such as , , or dedicated machine learning dataset repositories like . In case you use external resources, you should provide a short plan of how data could be collected in UAE context.

**Proposal Components: **

_Section 1: Problem Statement _

What is the problem/challenge?

Why is it important to solve this problem/challenge?

Is this problem new? Explain

Who will be the user of your product?

How is this problem relevant in the context of the UAE?

_Section 2: Dataset _

Describe the dataset used for solving the problem

How many features (columns) and instances (rows) are available in the dataset?

Provide information such as missing values, outliers, and so on.

_Section 3: Summary of Existing Solutions _

Detail the pre-processing steps typically applied to the data.

List the models that have been tested in previous studies.

Report on the performance outcomes of these models.

Limitations of existing solutions

At least 10 references should be included in this part

_Section 4: Project Implementation Plan _

For projects without prior implementations, enumerate the processes and machine learning techniques you intend to apply.

For projects improving upon existing solutions, outline the modifications, additional tests, or different approaches you plan to implement.

**Evaluation Criteria: **

Your proposal will be assessed based on the clarity, innovation, and feasibility of your problem and proposed solution.

**Submission Requirements: **

Your submission should consist of a 3-4 page document (in PDF format) summarizing your project proposal. This document must be prepared in accordance with the provided guidelines and submitted by the specified deadline. Your instructor will provide feedback on the proposal. It is important to incorporate that feedback in the final project, which will be based on this assignment.

**Key Areas of Focus: **

Proper formatting and presentation of the proposal

Answering all expected parts

Novelty and significance of the identified problem

Study of the existing solutions and their limitations to the identified problem

Benefit to and context of the UAE for your problem

Problem identification based on thorough research of UAE problems that can be solved via machine learning or deep learning

**Real-World Application: **

Clearly articulate the real-world problem your project addresses, particularly within the UAE context.

Discuss potential challenges in data collection, model implementation, or application.

Explain how your solution can make a tangible impact in the specified scenario.

**Final Note: **

The objective is to propose a project that not only showcases your understanding and application of machine learning techniques but also contributes meaningfully to solving practical problems.

Learning Outcomes Added

• : Explain why overfitting occurs and how it can be avoided.
• : Apply a range of common model performance metrics (e.g. classification accuracy, recall, precision).
• : Select appropriate classification methods in both supervised and unsupervised tasks.

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