MA5832 : Capstone Report - Apply Machine Learning Methodologies - IT Computer Science Assignment Help

Assignment Task :

Overview 

During this assessment you will produce a written report on analyses of a real-world problem using a neural network and an alternative machine learning method that have been presented in this course. For both techniques, you will investigate the properties of the methodologies by altering the machine learning parameters. Additionally, you will compare and contrast the two methodologies using the same data source. 

The purpose of the assignment is to enable you to: 

• apply machine learning methodologies; 

• undertake independent research investigating machine learning parameters;

• compare and contrast machine learning methodologies; 

• construct a written communication and interpretation of findings resulting from machine learning methodologies.

1 Capstone scenario 

Dataset 

The data, “AUS Data.xlsx”, used in the capstone is aggregated and collected from the Australian Bureau of Statistics (ABS).1 The data is available quarterly from June 1981 to September 2020. The data includes the response variable (unemployment rate) and 7 predictors:2 

• Y : unemployment rate measured in percentage 

• X1: Percentage change in Gross domestic product; 

• X2: Percentage change in the Government final consumption expenditure; • X3: Percentage change in final consumption expenditure of all industry sectors; • X4: Term of trade index (percentage) 

• X5: Consumer Price Index of all groups (CPI) ; 

• X6: Number of job vacancies measured in thousands; 

• X7: Estimated Resident Population measured in thousands. 

Further explanations about the variables can be found in the ABS website. 

2 Assessment Tasks 

1. Provide an overview of the Australian unemployment rate over the last 21 years (1999-2020) and some insights on factors driving the unemployment rate (provide relevant references when needed; maximum one A4 page). 

Data 

2. Prepare data appropriate for the proposed supervised machine learning methodolo gies such as: 

(a) implementing appropriate data wrangling procedures, e.g. missing values treat ment /transformation of variables. 

(b) provide and comment on descriptive statistics of the variables. 

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