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Data Analysis using R

Data Analysis using R

  • 10th Nov, 2021
  • 16:18 PM

Introduction

Such notes are designed to allow individuals with basic statistical methodology training to work through examples demonstrating the use of R for a variety of data manipulation types, graphical presentation, and statistical analysis.
R is a programming language drawn up in 1993 by Ross Ihaka and Robert Gentlemen. R has a large collection of statistical and graphic methods. It includes algorithms for machine learning, linear regression, time series, and statistical inference, to name only a few. Most R libraries are written in R, but the C, C++ and FORTRAN codes are preferred for heavy computational tasks.

Description

The goal of this report is to identify potential improvements that Airlines should introduce in order to enhance the customer experience of AA customers leaving International Airport. The results are shown with bar plots to indicate customer complaints by airline, destination and form of complaint. Someone who regularly flies knows that airlines are struggling to offer a reliable, meaningful customer experience. The American Customer Satisfaction Index quantifies the impression through comprehensive interview and survey research

A histogram divides the x-axis into equally spaced boxes and then uses a bar's height to display the number of observations falling within each bin. The highest bar shows in the graph above that nearly 1,000 observations have a sapphires value between 0.25 and 0.75, which are the bar's left and right edges.
The sheer scale of the airline industry offers a justification to think about it: the height of its economic impact affects not only millions of people directly but also millions more indirectly. The International Air Transport Association, in a report from December 2016.

Analysis and visualization 

Data Visualization is an art of turning data into knowledge easily interpretable. If we have an overview of the dataset, and the variables, we have to define the interest variables. Framework knowledge and the correlation among variables help to select these variables. To keep it easy, we chose only three of those variables, id,v1, v2, v3. In the X-axis we have the surviving number, 0 representing the non-surviving passengers and 1 representing the surviving passengers. The Y -axis reflects passenger numbers. Here we see that more than 250 passengers did not survive.
Conclusion
The conclusions from the results review that the customer's satisfaction level is overall high are cleared. Of the 10 variables, one V1 variable is small. Focusing on yields as compared with rivals is suggested to the management.

Reference

R Tutorial. (n.d.). Retrieved May 01, 2020, from https://www.tutorialspoint.com/r/index.htm
R Introduction. (n.d.). Retrieved May 01, 2020, from http://www.r-tutor.com/r-introduction
Exploratory Data Analysis in R (introduction). (2018, August 01). Retrieved May 01, 2020, from https://www.r-bloggers.com/exploratory-data-analysis-in-r-introduction/
Vidhya, & Analytics Vidhya. (2019, June 24). A Complete Tutorial to learn R for Data Science from Scratch. Retrieved May 01, 2020, from https://www.analyticsvidhya.com/blog/2016/02/complete-tutorial-learn-data-science-scratch/
 

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