Assessment Task 1 – Case Study (Report) – Individual
DUE DATE: Friday, by 8:00 pm (Melbourne time)
PERCENTAGE OF FINAL GRADE: 40%
WORD COUNT: Maximum number of words: 2000 words
This task provides you with opportunities to learn supervised machine learning and Python skills (GLO1 & ULO1) and apply your digital literacy to research and develop a machine learning solution (GLO3, GLO5, and ULO2). By completing this task, you will gain knowledge and skills in selecting and applying one or more appropriate supervised machine learning algorithm(s) to develop and evaluate a machine learning solution and present and interpret the outcomes to business clients.
VicCrashAnalytics is a fictitious data consulting firm that provides analytics services to governments and other organizations in Australia. The Assignment 1 project involves a consulting contract for the Victorian government’s Department of Transport (DOT). The client wants to understand the factors that contribute to black spots (also known as accident hotspots). This information will be used to develop effective education campaigns, propose legislative reforms, and potentially design and implement other interventions. You have been provided with a dataset containing information about blackspots, the demographics of the surrounding road segments, and their characteristics. Specifically, the client’s objective is to gain insights from the provided data and predict the risk of blackspots.
The dataset provided:
You are required to explore this dataset and develop and test a machine learning model(s) using Python. You are also required to report findings to Mr. Michael Howards, Transport Analytics Manager, VicCrashAnalytics.
Challenge: You have also been provided with a second dataset without labels: Blackspot_Competition.csv You are invited to deploy the model and apply it on this second dataset. The model with the best performance will win a small prize!
The dataset used in this assignment has been developed by Asel Mendis through integrating crash data from Department of Transport and demographics data from the Australian Bureau of Statistics (ABS). The dataset then has undergone further pre-processing and resampling by the unit team specifically for the purpose of learning. Therefore, it is important to note that the dataset may not fully represent real-world scenarios. It is essential that your insights and conclusions are justified based on the provided dataset. Your ability to effectively process, analyse, and model the data and interpret the outcome will be evaluated as part of the assessment.
You are required to:
Part 1. Business Report
1. Business understandings and the business problem to address.
2. Data understanding, data cleansing and preparation, exploratory data analysis and visualization, and insights gained.
3. The machine learning approach undertaken.
4. The model and performance metrics.
5. Discussion of the pros and cons of the model.
6. Business solution and recommendations (based on the model).
Part 2. Python notebook
Optional Part 3: If you participate in the Challenge then submit a VicCrash_Competion_PredLabels.csv file with your predicted labels.
A set of toolkits was prepared by experienced Deakin students to help you learn the generic skills required in the Business & Law professions: https://d2l.deakin.edu.au/d2l/home/93063
You will find the following tool kits to be useful:
This task allows you to demonstrate your achievement towards the Unit Learning Outcomes (ULOs) which have been aligned to the Deakin Graduate Learning Outcomes (GLOs). Deakin GLOs describe the knowledge and capabilities graduates acquire and can demonstrate on completion of their course. This assessment task is important in determining your achievement of the ULOs. If you do not demonstrate achievement of the ULOs you will not be successful in this unit. You are advised to familiarise yourself with these ULOs and GLOs as they will inform you on what you are expected to demonstrate for the successful completion of this unit.
The learning outcomes that are aligned with this assessment task are:
Unit Learning Outcomes (ULOs) Graduate Learning Outcomes (GLOs)
ULO1 Analyse and frame business challenges using machine learning GLO1: Disciplinespecific knowledge and capabilities
and capabilitiesconcepts, techniques, and the machine learning model
ULO2 Select and apply appropriate machine learning techniques to GLO3: Digital literacy
solve business problems and evaluate the machine learning GLO5: Problem-solving
You must submit your assignment in the Assignment Dropbox on the unit CloudDeakin site on or before the
due date. The submission must include two files:
Submitting a hard copy of this assignment is not required. You must keep a backup copy of every assignment you submit until the marked assignment has been returned to you. If one of your assignments is misplaced, you will need to submit your backup copy.
Any work you submit may be checked by electronic or other means to detect collusion and/or plagiarism and authenticate work.
When you submit an assignment through your CloudDeakin unit site, you will receive an email to your Deakin email address confirming that it has been submitted. You should check that you can see your assignment in the Submissions view of the Assignment Dropbox folder after uploading and check for, and keep, the email receipt for the submission.
Marking and feedback
The marking rubric indicates the assessment criteria for this task. It is available in the CloudDeakin unit site in the Assessment folder, under Assessment Resources. Criteria act as a boundary around the task and help specify what assessors are looking for in your submission. The criteria are drawn from the ULOs and align with the GLOs. You should familiarise yourself with the assessment criteria before completing and submitting this task.
Students who submit their work by the due date will receive their marks and feedback on CloudDeakin 15 working days after the submission date.
Extensions can only be granted for exceptional and/or unavoidable circumstances outside of your control. Requests for extensions must be made by noon on the submission date using the online Extension Request form under the Assessment tab on the unit CloudDeakin site. All requests for extensions should be supported by appropriate evidence (e.g., a medical certificate in the case of ill health).Order Now