i10brook/census_income

Instructions Use the attached "Adult" data set (http://arcchive.ics.uci.edu/ml/datasets/Census+Income) of census data collected to predict income for the following steps. The basic idea is to use the apply() function (Chapter 9) to clean the data, and the split-apply-combine pattern (Chapter 10) to analyze it. 1. Similar to last week, replace '-' with spaces, where appropriate, using the apply() function. 2. Determine how to deal with missing values (if any) and use apply() to make the changes.i 3. Use apply() with Use Defined Functions (UDFs) to analyze missing values, similar to page 178 (if appropriate). 4. Use the grouping and aggregation methods in Chapter 10 to analyze data vs. income in several different ways. FOR EXAMPLE: education vs. income, job vs. income, job & education vs. income... etc. (This is NOT an exhaustive list. I expect you to do more). Remember to document your steps and reasoning using markdown cells.

Census_Income

Use the attached "Adult" data set (http://arcchive.ics.uci.edu/ml/datasets/Census+Income) of census data collected to predict income for the following steps.

The basic idea is to use the apply() function (Chapter 9) to clean the data, and the split-apply-combine pattern (Chapter 10) to analyze it.

1. Similar to last week, replace '-' with spaces, where appropriate, using the apply() function.

2. Determine how to deal with missing values (if any) and use apply() to make the changes.i

3. Use apply() with Use Defined Functions (UDFs) to analyze missing values, similar to page 178 (if appropriate).

4. Use the grouping and aggregation methods in Chapter 10 to analyze data vs. income in several different ways.

FOR EXAMPLE: education vs. income, job vs. income, job & education vs. income... etc. (This is NOT an exhaustive list. I expect you to do more).

Remember to document your steps and reasoning using markdown cells.