K-Means Customer Segmentation in Python This project implements K-means clustering to segment customers based on their purchase history (annual income and spending score). It allows retailers to identify distinct customer groups with unique spending habits, which can be valuable for targeted marketing campaigns and improved customer service.

Project Structure:

kmeans.py: Python script containing the K-means clustering implementation. Technical Details:

Libraries: pandas, scikit-learn, seaborn (for visualization) Data Preparation: Cleans column names (e.g., 'Annual Income (k$)' to 'annual_income') Encodes categorical data ('gender') for numerical compatibility Selects relevant features ('annual_income', 'spending_score') Determining Optimal Number of Clusters: Employs the elbow method to analyze inertia for various k values Calculates silhouette score to evaluate clustering quality K-Means Clustering: Trains a KMeans model with the chosen k value (e.g., k=5) Assigns cluster labels to each customer data point based on spending habits Visualization: Creates a scatterplot using Seaborn to visualize customer segments Colors data points based on their assigned cluster Instructions:

Ensure you have the required libraries installed (pandas, scikit-learn, seaborn). Run the script: python kmeans.py (Optional) The script will generate inertia and silhouette score plots to help determine the optimal number of clusters. The script also creates a scatterplot visualizing the customer segments based on their assigned clusters.

Further Exploration:

Experiment with different numbers of clusters (k) and observe the impact on segmentation. Explore incorporating additional features that might influence purchase behavior. Use the customer segmentation insights to develop targeted marketing campaigns. I hope this README provides a clear overview of the project!