K-Means Clustering for Customer Segmentation

This project utilizes K-Means clustering to segment customers based on their annual income and spending scores, helping to identify distinct customer groups.

Project Overview

• Dataset: Mall_Customers.csv, containing data on customer demographics and purchasing behavior.
• Goal: To apply K-Means clustering to group customers into meaningful segments based on their spending habits and annual income.

Code Breakdown

1. Import Libraries:

   • Essential Python libraries are imported, including numpy, pandas, matplotlib, and seaborn for data handling and visualization.
   • KMeans from sklearn.cluster is used for clustering.

2. Data Preparation:

   • The dataset is read using pd.read_csv(), and the shape and initial rows are inspected.
   • Feature extraction: The data is prepared by selecting the 'Annual Income' and 'Spending Score' columns for clustering (X).

3. Finding Optimal Number of Clusters:

   • The Elbow Method is used to find the ideal number of clusters. This method involves plotting the Within-Cluster Sum of Squares (WCSS) for different values of k (number of clusters) and identifying the "elbow point" where the WCSS starts to decrease more slowly.
   • The KMeans model is fitted iteratively from 1 to 10 clusters, and WCSS values are stored and visualized with matplotlib and seaborn.

4. Model Training:

   • The K-Means model is trained with n_clusters=5 (based on the elbow point) using the k-means++ initialization to optimize cluster placement.

5. Visualization:

   • Customer segments are plotted using a scatter plot, with each cluster represented by a unique color.
   • Cluster centroids are marked for reference.

Visuals

• Elbow Point Graph: Shows the optimal number of clusters using the WCSS plot.
• Customer Segmentation Plot: Visualizes the customer clusters in a scatter plot with distinct colors and labeled centroids.

This project provides insight into customer groupings, which can be leveraged for targeted marketing strategies and better business decision-making.