Design and/or Evaluate a ‘Predictive Model - IT Assignment Help

Assignment Task

Scenario
This is your chance to design and/or evaluate a ‘predictive model’ of your own/choice for a real-world application. Application and data can be of your choice but also a wide range of recommended datasets for machine learning problems are available in UCI Machine Learning Repository and challenges, datasets and analytics contributions Kaggle 2 - www.kaggle.com/datasets, For this coursework your design/choice, and your approach to evaluate a machine learning solution (or a predictive model) is key - you can (but do not need to) implement a model, write code or collect data yourself. You should identify a real problem, need, frame a solution and come up with analytical analysis to evaluate your choice of a learning algorithm for your predictive model.

Your report (in a form of a discussion paper) should cover the following elements:

Discuss a machine learning problem given your chosen application; identify the problem, the requirements for a predictive model and its impact.
Describe and analysis a dataset and its characteristics; size, representation and attributes,
Discuss whether bivariate or multivariate analysis is most suitable for your predictive model.
Choose/apply (a) learning algorithm(s) and identify its/their categories; supervised, unsupervised, semi-supervised.
Analytically or experiemntally evaluate your choice of machine learning solution; its suitability, cost, and apply an error evaluation metric to justify your choice, e.g., classification accuracy of classification problems, MSE and/or R^2 (R squared) for regression models, etc.
Choose a learning algorithm which you think is less suitable for your predictive model and justify your “rejection” reasons.
Format
You will be assessed on you ability to express your design in a report of 2,000 words excluding references. This should be written in accordance with the word count policy assuming a ±10% tolerance, and the word count should be clearly indicated at the end of the report.
You should submit the report as a word-processed document. The report should include a bibliography relevant to student research and this should be presented in UWE Harvard style.

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