Overview
In this assignment, you are going to explore home loan data that is made available from the home mortgage disclosure act website. Specifically, you are going to use EDA to investigate what influences whether a loan is originated, or whether it is denied.
Instructions
ETL Tasks:
1. Import the hmdaActionTaken.csv file into Power BI. Use the Power Query Editor within Power BI to complete the ETL tasks.
2. Convert the values in the intro_rate_period column to a decimal data type.
3. Delete columns for which there are more than 50% missing values or errors, or only one distinct value. Be sure to make your decision based on the full dataset, not just the top 1,000 rows.
Exploratory Data Analysis Tasks:
4. Use Power BI to complete the EDA tasks below. Please use one or two pages on which to save all of your charts and text boxes. Please organize your charts and text boxes in a meaningful way.
5. We are interested in understanding what factors influence whether a loan is originated, or rejected. Use a filter on all pages to only keep observations for which the action taken is equal to either “Loan originated”, or “Application denied”.
6. Create a line chart that shows whether the total_units changes from 2018 to 2019 for each loan_purpose value. Insert a text box below the plot and briefly summarize two takeaways from the chart.
7. Create a chart that explores whether income varies between the values in the action_taken column. Insert a text box below the plot and briefly summarize what the plot shows about the relationship between income and whether a loan was originated. If you use a box and whisker plot, make sure to set the whiskers so that they are less than 1.5 times the Interquartile range. Comment on whether this plot is consistent with your expectations and why.
8. Create a chart that explores the frequency of occupancy_type between the values in the action_taken column. Insert a text box below the plot and briefly summarize how helpful occupancy_type is for determining whether an application is denied.
9. Create a plot that investigates how income is related to debt_to_income_ratio and action_taken_name. Specifically, put income on the x-axis, and do not summarize those values. Put debt_to_income_ratio on the y-axis, and summarize values using the average. Put action_taken_name in the Legend box. Use a filter on the visual to only show observations for which the income is less than 500,000 and greater than or equal to 0.
Insert a text box below the plot and comment on (1) whether there is an upward or downward relationship between income and debt_to_income_ratio, (2) why that relationship makes sense or not, and (3) how, if at all, that relationship appears to differ based on the action_taken_name values.
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