Assignment 2 (Individual): Data Analysis Project (50%):
The retail bank that you work for recently ran a telephone marketing campaign to encourage customers to subscribe to a term deposit. Data was obtained from the marketing campaign, internal systems relating to the characteristics of the customer as well as whether or not they subscribed. The data was supplemented with economic indicators. The banks head of marketing would like to understand which factors are related to subscriptions so that future marketing campaigns can be more effectively targeted. The results of the analysis will also inform company training materials for customer service employees so that they can promote term deposits when appropriate. More information about term deposits is available here
This assignment consists of the analysis of the term deposit banking data as well writing a report based on the findings from the analysis. The analytics tasks that you need to complete are described below, along with the suggested structure of the report.
The term loan training and test datasets are available on CANVAS along with a data dictionary describing the data.
All analytics tasks should be carried out using R, and should be reproducible from the R code which must be submitted as an appendix.
The tasks you are required to complete for this assignment are described below:
- Carry out a descriptive analysis (summary statistics and visualisations) of the variables that you plan to use. Summary statistics should be produced for all variables you use, and you should produce at least four visualisations.
- Ensure the data is appropriately formatted and that any data quality issues have been addressed and documented.
- Produce appropriate measures of correlation / association.
- Using the bank_train dataset, build at least one multiple regression model to examine the relationships between the variables specified in your hypotheses. You can include additional variables to improve the model.
- Use the bank_test dataset to predict who will subscribe, and evaluate the model accuracy.
- The R code used should be included as an appendix.
The written report should cover the following areas (but is not limited to these):
- Introduction and background to the problem: consider the business problem or question to be solved through the analysis. Briefly review any literature that may help to inform your analysis. Specify the relationships that you expect to find in the data.
- Methodology: a description of the analytics tasks undertaken and reasons why these were used.
- Results and discussion: present the results from the visualisation of the data and from the predictive model. Consider how these findings relate to other research in the area.
- Conclusions: Consider the main implications of the project for both theory and practice. Consider any limitations of the project.
The maximum word count for assignment 2 is 2500 words (excluding tables, figures, references and appendices). Students will be penalized for exceeding the word count by more than 10%.Harvard referencing style should be used. Students are required to submit the assignment via CANVAS by 11:59pm 2th January 2022
The breakdown of marks for assignment 2 is as follows:
|Data exploration, visualisation, and measures of association||/20|
|Data format and quality||/5|
|Prediction and Accuracy||/5|
The assignment will be marked using the postgraduate conceptual marking scale as recommended by the University.
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