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Customer Segmentation Using K Means Clustering

The document summarizes a project that uses K-means clustering to segment customers using basic customer data from a supermarket membership card database. The project aims to understand customer types to help the marketing team develop targeted strategies. K-means clustering will be used to group customers into distinct clusters based on attributes like customer ID, age, gender, income and spending score. Required software includes Scikit-learn, Seaborn, NumPy, Pandas, and Matplotlib.

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MANNAM SARAN
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164 views7 pages

Customer Segmentation Using K Means Clustering

The document summarizes a project that uses K-means clustering to segment customers using basic customer data from a supermarket membership card database. The project aims to understand customer types to help the marketing team develop targeted strategies. K-means clustering will be used to group customers into distinct clusters based on attributes like customer ID, age, gender, income and spending score. Required software includes Scikit-learn, Seaborn, NumPy, Pandas, and Matplotlib.

Uploaded by

MANNAM SARAN
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PPTX, PDF, TXT or read online on Scribd
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PROJECT GUIDE :

M.Sandhya, Assistant Professor

CUSTOMER
SEGMENTATION USING TEAM MEMBERS :
18211A1290[Naveen]
K MEANS CLUSTERING 18211A1276[Abbas]
ABSTRACT
Customer Segmentation is the subdivision
of a market into discrete customer groups
that share similar characteristics. Customer
Segmentation can be a powerful means to
identify unsatisfied customer needs. Using
the above data companies can then
outperform the competition by developing
uniquely appealing products and services.

2
EXISTING SYSTEM

The most common ways in which businesses segment their customer base are:
1.Demographic information, such as gender, age, familial and marital status, income,
education, and occupation.
2.Geographical information, which differs depending on the scope of the company.
For localized businesses, this info might pertain to specific towns or counties. For
larger companies, it might mean a customer’s city, state, or even country of residence.
3.Psychographics, such as social class, lifestyle, and personality traits.
4.Behavioral data, such as spending and consumption habits, product/service usage,
and desired benefits.

3
PROPOSED SYSTEM

• You are owing a supermarket mall and through


membership cards, you have some basic data about your
customers like Customer ID, age, gender, annual income
and spending score.
• You want to understand the customers like who are the
target customers so that the sense can be given to
marketing team and plan the strategy accordingly.

4
Scikit-learn

Seaborn

NumPy

Pandas
SOFTWARE
REQUIREMENTS
Matplotlib

5
K MEANS CLUSTERING
ALGORITHM

1.Specify number of clusters K.


2.Initialize centroids by first shuffling the dataset and
then randomly selecting K data points for the
centroids without replacement.
3.Keep iterating until there is no change to the
centroids. i.e assignment of data points to clusters
isn’t changing.

6
CONCLUSION
K means clustering is one of the most popular
clustering algorithms and usually the first thing
practitioners apply when solving clustering
tasks to get an idea of the structure of the
dataset. The goal of K means is to group data
points into distinct non-overlapping subgroups.
One of the major application of K means
clustering is segmentation of customers to get a
better understanding of them which in turn
could be used to increase the revenue of the
company.

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