Customer Segmentation using K-Means Clustering

Description

This project demonstrates how to segment customers based on their buying behavior using the K-Means clustering algorithm in Python. It guides you step-by-step from preparing the data to clustering it.

Acknowledgements

Dataset sourced from the UCI ML Repository: Online Retail Dataset.

Objective

1. Clean the dataset.
2. Build a clustering model to segment customers.
3. Fine-tune the model and compare metrics.

Steps

Data Cleaning

1. Handle Missing Values: Identify and manage missing data.
2. Create Attributes:
   • Monetary: Total amount spent by each customer.
   • Frequency: Number of purchases by each customer.
   • Recency: Days since the last purchase.
3. Merge Data: Combine necessary datasets.
4. Outlier Analysis: Identify and manage outliers in the data.

Model Building

1. K-Means Clustering: Apply the K-Means algorithm.
2. Elbow Curve: Determine the optimal number of clusters.
3. Visualization: Use boxplots to visualize clusters.

Usage

1. Data Cleaning: Follow the steps in the notebook to clean the data.
2. Model Training: Train the K-Means model and tune parameters.
3. Evaluation: Visualize and evaluate the clusters.

Conclusion

This project helps you understand customer segmentation using K-Means clustering, providing insights into customer behavior to enhance marketing strategies.