University Dublin Business School (DBS)
Subject B9FT105: Data Analytics and Machine Learning

Assignment Details:

Consider a real-world, relational dataset. This dataset must have at least 2 categorical and 2 continuous variables.

Question 1

  • Describe the dataset using appropriate plots/curves/charts,
  • Consider one of the continuous attributes, and compute central and variational measures.
  • For a particular variable of the dataset, use Chebyshev’s rule, and propose a one-sigma interval. Based on your proposed interval, specify the outliers if any.
  • Explain how the box-plot technique can be used to detect outliers. Apply this technique for one attribute of the dataset.

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Question 2

  1. Select four variables of the dataset, and propose an appropriate probability model to quantify the uncertainty of each variable.
  2. For each model in part (a), estimate the parameters of the model.
  3. Express the way in which each model can be used for predictive analytics, then find the prediction for each attribute.

Question 3

From your dataset, specify your input and output variables, then:

  • Suggest an appropriate GLM to model output to input
  • Split the dataset into 80% as a trainset and 20% test set, then model the trainset by fitting your proposed GLM.
  • Specify the significant variables on the output variable at the level of 𝛼=0.05 and explore the related hypotheses test. Estimate the parameters of your model.
  • Predict the output of the test dataset using the trained model. And provide the functional form of the optimal predictive model.
  • Propose the appropriate measure of performance to evaluate the model and compute it for your derived model.
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