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Coursework-1
Source: studres.cs.st-andrews.ac.uk
Article summary:
1. This article outlines the requirements for Coursework Assignment 1 in the Knowledge Discovery & Data Mining course.
2. The task is to predict a numerical attribute from other attributes using data from the US Used Cars dataset.
3. Students are required to explore the structure of the data, split into training and test sets, perform data cleaning if necessary, select and train models, and evaluate performance on the test data using an appropriate measure.
Article analysis:
The article is generally reliable and trustworthy as it provides clear instructions on what is expected of students in terms of completing their coursework assignment. It also provides detailed information on the dataset that will be used for this assignment, including links to where it can be accessed and a starter notebook showing how to access it in Python.
The article does not appear to have any biases or one-sided reporting as it presents all relevant information objectively without favouring any particular point of view or opinion. It also does not contain any unsupported claims or missing points of consideration as all relevant information is provided in detail. Furthermore, there is no promotional content or partiality present in the article as it simply outlines what is expected from students for this assignment without promoting any particular approach or method.
The article does note possible risks associated with working with large datasets by providing smaller subsets of the original dataset which can be used instead if needed. Additionally, both sides of an argument are presented equally by providing both R and Python as options for completing this assignment, while also allowing students to discuss other languages with lecturers if they have a strong reason for wanting to use something else.
In conclusion, this article appears to be reliable and trustworthy as it provides clear instructions on what is expected from students while also noting potential risks associated with working with large datasets and presenting both sides of an argument equally.