[Data Analysis]A data analyst team needs to segment customers based on customer spending behavior. Given one million rows of data like the…

The objective is to segment customers based on a continuous numerical variable, Amountspent. Binning, also known as discretization or bucketing, is the data preprocessing technique used to convert continuous data into a finite number of categorical intervals or "bins." By applying binning to the Amountspent column, the team can group customers into distinct segments such as 'low spenders,' 'medium spenders,' and 'high spenders.' This directly accomplishes the task of segmenting customers based on their spending behavior for further analysis.

A. Standardization: This rescales numerical data to have a mean of 0 and a standard deviation of 1, which is useful for certain machine learning algorithms but does not create customer segments.

B. Concatenate: This operation joins text strings or data structures (like tables) together; it is not used for grouping numerical values into categories.

D. Appending: This process adds new rows of data to an existing dataset, which is unrelated to segmenting the current customer data.

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1. Pandas Development Team. (2023). pandas.cut. In pandas 2.1.1 documentation. This official documentation describes the cut function

which is used for binning. It states

"Bin values into discrete intervals. Use cut when you need to segment and sort data values into bins." This directly aligns with the requirement to segment customers.

Source: https://pandas.pydata.org/docs/reference/api/pandas.cut.html

2. DeNero

J.

& Gonzalez

J. (2023). Data 100: Principles and Techniques of Data Science

Lecture 12: Feature Engineering. University of California

Berkeley. The course material explains feature engineering techniques. In the section on "Discretization (Binning)

" it describes the process of converting a quantitative feature into a set of binary features (one for each bin)

which is precisely the technique needed to create spending segments.

Source: https://ds100.org/su23/lecture/lec12/

3. Han

J.

Kamber

M.

& Pei

J. (2012). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. In Chapter 3

"Data Preprocessing

" Section 3.5.2

"Discretization and concept hierarchy generation for numeric data

" the authors detail binning as a primary method for data discretization. They explain that binning "is a top-down splitting

unsupervised discretization technique" used to partition a continuous-valued attribute into a series of intervals.

Source: Chapter 3

Section 3.5.2

pp. 113-114.