Data Mining using R: Analysis and Modeling with R Programming

This assignment presents a solution for a data mining task using the R programming language. The solution involves cleaning the data by removing missing values, wrangling the data, and selecting relevant predictors. The assignment focuses on two main tasks: The first task involves selecting predictors like age, income, alcohol consumption, gambling habits, and gender to model their impact. The second task explores the correlation between flying etiquette and predictors like children, violation of rules (smoking, electronic devices), gender, and elimination of data. The solution includes creating a data frame for these predictors and then building a model based on the selected predictors. The overall goal is to analyze and understand the relationships within the data to predict outcomes or behaviors.

Running Head: DATA MINING USING R 1
DATA MINING USING R
NAME OF STUDENT
UNSTITUTION OF AFFILIATION

DATA MINING USING R 2
Summary
The aim of the assignment is to analyse the data properly, clean the data to remove all
the missing data, wrangle the data and to come up with a model from the dataset. Before
coming up with a model one needs to choose predictors from the dataset. In task 2 I chose the
predictors and assigned them the following names: Age, Income, Alcohol, Gamble and
Gender. The reason I chose the following predictors is that I feel that betting is correlated to
these predictors. We might find that maybe certain gender intensively bet compared to the
other, one's income determines if the person bet or not, find whether those who bet engage in
drinking alcohol or maybe there is an age where everyone bet. After choosing the following
predictors, I assigned them to a data frame and then modelled it.
Task 1 also has a similar approach. The data is to be cleaned by eliminating rows with
missing data, wrangle the data, train the data, select predictors and then model it. I think that
flying etiquette might be correlated by the predictors I assigned i.e. children, violation,
Smoked, Gender and Elimination. Maybe the age of the passenger's child determines how the
passenger behaves, and whether the behaviour of the passengers is related to violation of
rules like smoking in the airplane and carrying electronic devices and also whether the gender
of a person determines the behaviour of the passenger. I assigned all the predictors to a data
frame to be modelled.