CS5812-Predictive Data Analysis Report - IT Computer Science Assignment Help

Assignment Task

Task 

Main Objective of The Assessment
The aim of this assignment is to generate value and insight from the processing of heterogeneous data. This will be achieved by implementing several analytic methods/techniques/algorithms, evaluating them and comparing the effectiveness of the adopted approaches. The development and implementation of the data analysis project will be supported by team-based effort andweekly tutor meetings.

Description of The Assessment
The final report should be an original and individual submission, but it will be underpinned by a group effort and by effective sharing of data and partial results. It is important that individual contributions shared among the group are clearly defined (see “Authorship Contribution” below for further details). These contributions should be agreed upfront in a designated meeting.
The management of the data during (and after the project) can be described in a formal Data Management Plan (DMP). A template is available on Blackboard Learn. The DMP should be discussed with the group in a designated meeting.

The following parts can be developed by shared group effort:

•  Data collection
•  Data preparation and cleaning
•  Exploratory data analysis

Each member of the group is expected to implement and apply at least two methods/approaches including:

•  One machine learning method for prediction (regression or classification)
•  One deep learning method for prediction (regression or classification)

For both of these two methods/approaches, each member of the group should produce their own results and distribute/exchange their results among all the other members. Finally, each member is expected to independently compare, discuss and evaluate these shared results.

The report should provide an "Authorship Contribution" statement (ACS). This statement should clarify how data generation and/or analyses made by other members of the group has contributed to the project.

Authorship Contribution: Authorship should meet all 2 of the following conditions:
1. Authors make substantial contributions to conception and design, and/or acquisition of data, and/or analysis and interpretation of data and/or evaluation, and/or visualisation;
2. Authors participate in discussing the results critically for important intellectual content;

Learning Outcomes And Marking Criteria
LO1: Implement appropriate analytic methods/techniques/algorithms for generating value and insight from the processing of heterogeneous data using statistical learning.
LO2: Critically reflect on analytic methods/techniques/algorithms, their ability to deliver accurate predictions of various kinds and the value and limitations of prediction.
LO3: Apply appropriate deep learning methods to real-world problem solving, justify the approach adopted, and critically evaluate its effectiveness.

The coursework will be marked according to the following criteria:
a) Identifying a data analytics problem and formulating a relevant research question and plan [LO2]
b) Preparing, integrating and exploring multiple data sets suitable to answer the research question [LO1]
c) Implementing and executing a complete and coherent data analysis [LO1, LO3]
d) Critically reflecting on the results of the data analysis (accuracy, limitations & interpretation) [LO2, LO3]

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