Reference no: EM133142455 , Length: word count: 5000 words
DS7003 Advanced Decision Making - Predictive Analytics and Machine Learning Assignment - University of East London
Aims and learning outcomes - This module aims to develop a deep understanding of ways of making decisions that are based strongly on data and information. Particular focus will be on mathematical decision-making models including some use of computer-based support. Various case studies will be examined.
Learning outcomes for the module are for students to:
1. Understand the design of decision-making models.
2. Understand the mathematical logic basis of decision-making.
3. Understand how to assign probabilities to uncertain events; assign utilities to possible consequences and make decisions that maximise expected utility.
4. Use of software-based decision-making tools.
5. Critically evaluate alternative decision models.
6. Conduct decision-making exercises.
7. Critically evaluate and analyse data.
8. Compose decision-making based reports.
Assessment - The assessment for this Module consists of one item: an analysis and review of decision-making using data set(s) of the student's choosing using machine learning in R for classification, regression or time series of 5,000 words equivalence.
Decision Making Project -
You will work on a project individually. Through your project you should demonstrate competence in using machine learning for classification, regression or time series using R.
Machine Learning: you should choose a dataset from the UCI Machine Learning Repository except those featured in Lantz (2019) as listed below which are disallowed. Where a student has identified a suitable dataset for machine learning from another source, they should get approval from the module leader. You should carry out and compare contrasting two methods (e.g. regression tree vs. ANN) of supervised learning or temporal forecasting in order to achieve a best possible result from the modelling.
The project report should provide the reader with a clear understanding of the background and theoretical positions underscoring the analysis, a justification for the form of analysis undertaken and techniques used, and an evaluation and presentation of the results. There should be a list of references set out according to accepted academic conventions.
Your report should include the following sections:
-a title;
-an abstract (and for joint projects, a summary of your contribution to the work);
-a introduction including an explanation of the background to the topic and review of relevant literature;
-a critical summary of your overall methodological approach;
-a description of your data sources and what the data consist of (exploratory analysis);
-a presentation of your ML analysis, outcomes and their evaluation or sensitivity analysis;
-a concluding discussion of the findings, a critical reflection/comparison of the techniques used;
-a list of references in Harvard style;
-an appendix of R scripts for key parts of your analysis.
Attachment:- Advanced Decision Making Assignment File.rar