Aggregation-based features in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What are aggregation-based features in data science?

Aggregation-based features are new data columns created by summarizing or combining existing data points, like calculating averages, sums, or counts over groups of data.

beginner

Why do we use aggregation-based features in machine learning?

We use aggregation-based features to capture group-level patterns and trends that single data points might miss, helping models learn better from the data.

beginner

Example: What does the aggregation 'mean' represent when applied to a group of data?

The 'mean' is the average value of the data points in the group, showing the central tendency of that group.

intermediate

How can you create aggregation-based features using Python's pandas library?

You can use pandas' groupby() function to group data by one or more columns, then apply aggregation functions like mean(), sum(), count(), etc., to create new features.

intermediate

What is the difference between 'count' and 'nunique' aggregations?

'Count' gives the total number of non-null data points in a group, while 'nunique' counts how many unique values are in that group.

Which of the following is NOT an aggregation function?

A filter

B sum

C count

D mean

Filter is used to select data, not to aggregate or summarize it.

What does the 'groupby' function in pandas do?

A Sorts the data

B Splits data into groups based on column values

C Deletes duplicate rows

D Creates new columns

Groupby splits data into groups so you can apply aggregation functions on each group.

If you want to find the total sales per store, which aggregation would you use?

A mean

B nunique

C count

D sum

Sum adds all sales values to get the total per store.

What does the 'nunique' aggregation tell you?

A Average value

B Total sum of values

C Number of unique values in a group

D Maximum value

Nunique counts how many different unique values exist in the group.

Why might aggregation-based features improve a model's performance?

A They capture group-level patterns

B They add noise to the data

C They reduce the number of rows

D They remove missing values

Aggregation-based features help models learn from group trends and patterns.

Explain how you would create an aggregation-based feature to find the average purchase amount per customer using pandas.

Think about grouping data by customer and then calculating the average.

Describe the difference between 'count' and 'nunique' aggregations and give an example of when each might be useful.

Consider counting total purchases vs. counting unique products bought.