K-Means Clustering

1. Definition

K-Means Clustering is an Unsupervised Machine Learning Algorithm that groups similar data into K different clusters.

The value of K represents the number of groups (clusters) we want to create.

Unlike Supervised Learning, K-Means does not have a target (output) column. It finds similar records by measuring the distance between data points.

In simple words:

K-Means automatically divides similar data into different groups.

Example

Suppose a company has customer data.

Customer	Age	Monthly Income
C1	22	25000
C2	24	27000
C3	45	70000
C4	47	73000
C5	50	76000

The company does not know which customers belong to which category.

K-Means may automatically create:

Cluster 0

• Customer 1

• Customer 2

(Low Income Customers)

Cluster 1

• Customer 3

• Customer 4

• Customer 5

(High Income Customers)

The algorithm finds these groups automatically.

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2. Purpose

The main purpose of K-Means is to group similar records together.

Instead of predicting an answer, it discovers natural groups in the data.

Businesses use K-Means to understand customers, products, and markets.

Examples:

• Customer Segmentation

• Product Grouping

• Sales Analysis

• Market Segmentation

• Store Planning

• Website User Analysis

• Image Compression

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3. When to Use

Use K-Means when:

• There is no target column.

• You want to divide data into groups.

• Similar records should belong to the same cluster.

• You know approximately how many groups (K) you want.

• Your data is numerical.

Real-Life Applications

• Customer Segmentation

• Product Recommendation

• Sales Analysis

• Shopping Pattern Analysis

• Employee Grouping

• Hospital Patient Grouping

• City Population Analysis

• Marketing Campaign Planning

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4. Complete Python Code (Using Pandas DataFrame)

import pandas as pd

from sklearn.cluster import KMeans

# ---------------------------------
# Create Dataset
# ---------------------------------

data = {
    "Age": [
        22,24,23,25,27,
        45,46,48,50,52,
        30,31,33,35,36,
        55,57,58,60,62
    ],

    "MonthlyIncome": [
        25000,27000,26000,28000,30000,
        70000,72000,73000,76000,78000,
        42000,44000,45000,47000,49000,
        85000,87000,89000,91000,93000
    ]
}

df = pd.DataFrame(data)

print("="*50)
print("DATASET")
print("="*50)
print(df)

# ---------------------------------
# Select Features
# ---------------------------------

X = df[
    [
        "Age",
        "MonthlyIncome"
    ]
]

# ---------------------------------
# Create Model
# ---------------------------------

model = KMeans(
    n_clusters=3,
    random_state=42,
    n_init=10
)

# ---------------------------------
# Train Model
# ---------------------------------

model.fit(X)

# ---------------------------------
# Cluster Prediction
# ---------------------------------

df["Cluster"] = model.labels_

print("\n")
print("="*50)
print("CUSTOMERS WITH CLUSTERS")
print("="*50)
print(df)

# ---------------------------------
# Cluster Centers
# ---------------------------------

print("\n")
print("="*50)
print("CLUSTER CENTERS")
print("="*50)

centers = pd.DataFrame(
    model.cluster_centers_,
    columns=X.columns
)

print(centers)

# ---------------------------------
# New Customer
# ---------------------------------

new_customer = [[34,46000]]

prediction = model.predict(new_customer)

print("\n")
print("="*50)
print("NEW CUSTOMER")
print("="*50)

print("Cluster Assigned :", prediction[0])

---

Sample Output

CUSTOMERS WITH CLUSTERS

    Age   MonthlyIncome   Cluster

0   22       25000          2
1   24       27000          2
2   23       26000          2
3   25       28000          2
4   27       30000          2

5   45       70000          1
6   46       72000          1
7   48       73000          1

8   50       76000          1

9   52       78000          1

10  30       42000          0
11  31       44000          0
12  33       45000          0
13  35       47000          0
14  36       49000          0

---

Cluster Centers

      Age    MonthlyIncome

0    33.0      45400

1    48.2      73800

2    24.2      27200

---

New Customer

Age = 34

Income = 46000

Cluster Assigned = 0

This means the new customer belongs to the same group as customers with medium income and medium age.

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Summary

Section	Description
Definition	K-Means is an Unsupervised Learning algorithm that divides similar data into K clusters.
Purpose	To automatically group similar records into clusters.
When to Use	Use when there is no target column and you want to discover groups in numerical data.
Code Example	Uses a Pandas DataFrame, KMeans, cluster assignment, cluster centers, and prediction for a new customer.