Data Analysis

You have joined Qantas as part of the data You have joined Qantas as part of the data analytics team. You are now required to conduct predictive analytics using R and design a
method for anticipating delays and cancellations.
Paper details:
You are required to predict flight delays/cancellations based
on the identified important variables, discuss the results of your analysis, interpret the findings to derive actionable
insights and make recommendations for Qantas to improve its performance. I have attached the kind of predictive analytics tools we have learned that our
professors expect us to use for this assignment below.
Report structure:
Executive summary
Discuss data analysis techniques
Everyone discuss their own data analysis
Discuss the results
Everyone discuss their own results
Interpret findings & list insights
Make recommendations to Qantas
Maybe connect findings from individual report
Predictive analytics tools expected to use for this:
Define train and test data
Simple linear regression
Multiple linear regression
Use best subset selection to select the variables for a multiple linear regression
Evaluate the predictive performance of a regression model using the validation set approach
Model accuracy
Construct a classification tree
Plot an interpret the output of a classification tree
Calculate the classification accuracy and confusion matrix of a classification tree
Fit a logistic regression
Make predictions using a logistic regression
Calculate the classification accuracy and confusion matrix of logistic regression
Also please use boxplots and mosaic charts for little bit of data exploration.
Tips for analysing the data
Here is advice from the lecturing team on developing models and recommendations:
1. It is important to emphasise that there is not a correct answer to the assignment. There are many different
models that can be put forward that can effectively address Qantas’s objectives. Thus, it is important that you
clearly identify the analysis methods and set out a systematic, comprehensive plan to resolve Qantas’s
problem.
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2. To ensure the rigour of the model development and subsequent analysis, apply the frameworks discussed in
class and the workshops. In this assignment, you will be primarily leveraging the materials from Weeks 4 and
5. We are not expecting the use of analytical methods beyond the scope of this course.
3. Remember that your conclusions should be well supported by the undertaken data exploration and created
visualisations. You should also outline any key assumptions in your data-driven conclusions and acknowledge
limitations.
4. If appropriate, connect findings or questions from your individual reports to your team report.
All of this should be done in R studio and attach the code at the end(code is not part of word count)analytics team. You are now required to conduct predictive analytics using R and design a method for anticipating delays and cancellations.