SEM310DS Logistic Regression Data Science Assessment Solution
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ASSESSMENT TASKS
Task 1 FORMATIVE TASK Logistic Regression in Principle
FORMATIVE TASK
Instruction: Produce a briefing document for the intern in your department that explains logistic regression and its potential uses in your organisation. The report must contain the following:
- An outline of what is understood by logistic regression, and how it is different from linear
- Identify and explain the characteristics of the Logistic Function, the Odds Ratio and the Logit function
- Discuss the potential uses for logistic regression within your organisation, and a judgment as to its overall value to the organisation
Task 2 SUMMATIVE TASK Logistic Regression in your Organisation
SUMMATIVE TASK
Instruction: Carry out an evaluation of a logistic regression undertaken within your organisation. Your report must contain the following:
- An outline of how Python was used to create the logistic regression, including calculating correctly the probability values of inputs belonging to classes using the Logistic function (LO 1, 3.1)
- Identify and explain how the Odds-ratio metrics were calculated accurately (LO 2,)
- A judgment as to the conclusions that can be drawn from the data, and the accuracy of these conclusions in making future decisions (LO 3, 3.2)
| Learning Outcomes:
To achieve this unit, the learner must be able to: |
Assessment Criteria:
Assessment of these learning outcomes will require a learner to demonstrate that they can: |
| 2. Be able to perform logistic regression calculations. | 2.1 Calculate correctly the probability values of inputs belonging to classes using the Logistic function.
2.2 Calculate correctly the Odds-ratio. 2.3 Calculate correctly relevant classification evaluation metrics for logistic regression model outputs. |
| 3. Be able to create logistic regression models. | 3.1 Use Python to build an accurate logistic regression model for datasets.
3.2 Use Python to evaluate the accuracy of the model built in 3.1. and analyse the results. |
