Machine learning application: predictions and interpretations
The aim of this coursework is for you to apply your knowledge in Machine Learning and Predictive Analytics, to work creatively on a dataset of a real-world application; to define a learning problem, discuss data attributes, evaluate suitable learning algorithm(s) analytically or through your implementation, and to present your findings and conclusions. This will be expressed as a 2000-word report
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 1 (Most Popular Data Sets – hits since 2007), and challenges, datasets and analytics contributions Kaggle 2, or check course’s Blackboard page for further 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 experimentally 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.
Datasets can be found here: https://www.kaggle.com/datasets
Delivering a high-quality product at a reasonable price is not enough anymore.
That’s why we have developed 5 beneficial guarantees that will make your experience with our service enjoyable, easy, and safe.
You have to be 100% sure of the quality of your product to give a money-back guarantee. This describes us perfectly. Make sure that this guarantee is totally transparent.Read more
Each paper is composed from scratch, according to your instructions. It is then checked by our plagiarism-detection software. There is no gap where plagiarism could squeeze in.Read more
Thanks to our free revisions, there is no way for you to be unsatisfied. We will work on your paper until you are completely happy with the result.Read more
Your email is safe, as we store it according to international data protection rules. Your bank details are secure, as we use only reliable payment systems.Read more
By sending us your money, you buy the service we provide. Check out our terms and conditions if you prefer business talks to be laid out in official language.Read more