|University||Singapore University of Social Science (SUSS)|
|Subject||ANL252: Python for Data Analytics|
Answer all questions in this section.
This credit facility dataset to be analyzed comprises records of customers’
demographics, amount owed, repayment history/status etc.
The data dictionary of this dataset is depicted in Appendix 1.
List the categorical and numeric variables in this dataset.
Conduct four (4) data pre-processing tasks for the analysis of the data, explaining results obtained.
Articulate five (5) relevant insights of the data, with supporting visualization for each insight.
Perform linear regression modelling to predict the variable, B1, explaining the approach taken, including any further data pre-processing.
State the linear regression equation and explain key insights from the results obtained in Question 4.
Organization of Code The submitted Jupyter notebook will be accessed based on the following:-
• Readability, Consistency and Efficiency
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