CAB420 - Machine Learning & Socio-Economic Data from The 1990 US With LASSO Regression Model - Economics Assignment Help

Assignment Task

Further Instructions: 

1. Data required for this assessment is available on blackboard alongside this document in CAB420_Assessment_1A_Data.zip. Please refer to individual questions regarding which data to use for which question. 

2. Answers should be submitted via the TurnItIn submission system, linked to on Blackboard. In the event that TurnItIn is down, or you are unable to submit via TurnItIn, please email your responses to cab420query@qut.edu.au. 

3. For each question, a short written response (approximately 2-4 pages depending on the nature of the question, approach taken, and number of figures included) is expected. This response should explain and justify the approach taken to address the question (including, if relevant, why the approach was selected over other possible methods), and include results, relevant figures, and analysis. 

4. MATLAB or Python code, including live scripts or notebooks (or equivalent mate rials for other languages) may optionally be included as appendices. Figures and outputs/results that are critical to question answers should be included in the main question response, and not appear only in an appendix. Note that MATLAB Live Scipts, Python Notebooks, or similar materials will not on their own constitute a valid submission and a written response per question is expected as noted above. 

Problem 1. Regression. The data in Q1/communities.csv contains socio-economic data from the 1990 US census for various US communities, and the number of violent crimes per capita (in the column ViolentCrimesPerPop). The purpose of the data is to explore the link between the various socio-economic factors and crime. 

Given the provided data, you are to: 

• Split the data into training, validation and testing sets. 

• Train a linear regression model to predict the number of violent crimes per captia from the socio-economic data. 

• Train a LASSO regression model to predict the number of violent crimes per captia from the socio-economic data. 

• Train a Ridge regression model to predict the number of violent crimes per captia from the socio-economic data.

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