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Annual Income Predication

Chaudry, Muhammad Tanveer (2014) Annual Income Predication. Diploma thesis, Dublin, National College of Ireland.

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Abstract

The main objective of this project was to predict the annual salary of the local residence of San Francisco Bay area, the dataset which is used for predication was collected through surveys from local residence in different part of the city shopping malls.

The reason to predict and analyse this information was to give local residence of the area better facilities. Better education, health and transport system.

This is also a good source of information about the locals, what kind of occupation and education most of the people in this area have.

The problem with information was while collecting the data from locals, even though the survey form was designed in very simple way, still people did not reply many part of the questioner which was really problematic to achieve the results. With the help of different technical tools like R and weka, I overcame this problem and successfully predict the annual salary of the locals.

There are many graphical visualisation methods used about the dataset and showed the relationship between the variable which were used to gather information through these surveys. I found that most of the variable to predict the annual income were well related and over all survey design was very good.

Within this research I found there is very high correlation between most of variable and the annual salary, but particularly I found the education, age and occupation are very highly related to any person’s annual income. The least correlated variable I found is what language is spoken most often in your home. But overall most of the variables have big influence of annual income predication.

Item Type: Thesis (Diploma)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions: School of Computing > Higher Diploma in Science in Data Analytics
Depositing User: CAOIMHE NI MHAICIN
Date Deposited: 16 Dec 2014 15:38
Last Modified: 16 Dec 2014 15:38
URI: http://trap.ncirl.ie/id/eprint/1897

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