Analysis of Number of National Students by Youth Age For 2016 Graduate of Public University

Fatin Asyikin(WIE160059),Nurul Nazira (WIE160061),Nurul Najwa(Wie160060)
14 May 2018

Introduction

This presentation reports the ShinyApp Project of the Introduction To Data Science offered by University Of Malaya on Coursera.
The goal of this project is to analysis a data set that Classification and categorisation based on number of Public University Graduates,Sex and Youth Age
This Shiny app is built entirely in R, and the predictions are made on the dataset provided by Coursera.
The dataset is from a data.gov.my or MAMPU (http://www.data.gov.my/data/ms_MY/dataset).

It is quite common for some values to be missing from datasets. This typically means that a piece of information was simply not collected. There are several options for handling missing data that will be covered below

For some columns, there are values that can be identified as obviously incorrect. This may be a 'gender' column where someone has entered a number, or an 'age' column where someone has entered a value well over 100.

More of a subcategory of 'correcting erroneous values', this type of data cleansing is so common it is worth special mention. In many cases where data is collected from users directly- spelling mistakes, language differences or other factors.

The dataset starts with a default input that display results automatically.
To try this dataset, user will need to input a phrase, and then press upload button.
The dataset will produce ouput dataset that contain type university,sex,age range ant total graduate student.
Scroll down for more output!

Advantages	Disvantages
-access to inaccessible subject	-can be viewed as too subjective
-can contain spontaneous data	-may effect the situation and thus validity of findings
-relatively low cost