What is Clustering?
Clustering is a method where we group similar data points together.In this project, we use clustering to group mall customers based on how much they earn and how much they spend.This helps us understand different types of shoppers.
STEP 1: Problem Scoping
We begin by deciding what we want to achieve.Our aim is to find different customer groups so the mall can plan better marketing strategies.We ask simple questions like:
• Who spends more?
• Who spends less?
• Do customers form natural groups?
This gives the project a clear purpose.
STEP 2: Data Acquisition
We collect the mall customers dataset, which includes details like:
• Annual Income
• Spending Score
• Age and Gender (if present)
This data becomes the base from which we discover patterns.
STEP 3: Data Exploration
We carefully examine the data to understand what is happening.We count, compare, and make small graphs to see the patterns clearly.
We notice:
• High income doesn’t always mean high spending
• Some customers spend a lot even with average income
• Clear groups begin to appear when we plot the data
• The dataset is clean with no major missing values
This step helps us understand the story hidden inside the numbers.
STEP 4: Modeling
We choose the K-Means clustering algorithm for our project.This method groups customers based on similarity.We also select the important features like Annual Income and Spending Score.This step prepares the model for learning.