Trends and Patterns within life expectancy data: Technical Report

Kaewhin, Punnavit (2021) Trends and Patterns within life expectancy data: Technical Report. Undergraduate thesis, Dublin, National College of Ireland.

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Abstract

This project purposes is to study the dataset provided by the World Health Organisation on the life expectancy of Countries around the world. With hope of learning something interesting from the said dataset and implement a datamining technique on it for prediction. The dataset would be studied using the program SPSS and R studio to analyse for specific result.

The dataset will be studied under the datamining technique of KDD or Knowledge Discovery in Databases. Which will include processes such as selection, pre processing, transforming, datamining, and interpretation/evaluation. But first of a set of general testing was carried out in SPSS, test for things such as normality before going ahead with the actual prediction. The main scope of this project was to find out an appropriate method to be able to forecast a satisfying result. The results achieve should be interpreted carefully and with reservation, as due to the nature of the data there is not enough sufficient data for an “accurate” forecasting.

Overall Summary of the normality test as expected, was that the data was not normally distributed. This is due to the nature of the data set and how it was collected and sorted. This is because the data was collected overtime by WHO and then analyse and sort by them first before being put out by the public, hence the average life expectancy figure for each year. Thus, the data cannot be random and normally distributed, ensuring that a non-parametric test is needed.

Lastly it needs to be stated again that the key method used is for forecasting is called Arima modelling, and it usually works better when there is more sufficient data to work with (more than 30). But in this case, there is only about 14/15 per country. Hence the warning about the result mentioned earlier.

Item Type:	Thesis (Undergraduate)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions:	School of Computing > Bachelor of Science (Honours) in Technology Management
Depositing User:	Clara Chan
Date Deposited:	17 Sep 2021 15:03
Last Modified:	17 Sep 2021 15:03
URI:	https://norma.ncirl.ie/id/eprint/5060

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