🧠 Customer Segmentation using Machine Learning (K-Means Clustering)

This project focuses on segmenting customers based on their purchasing behavior using the K-Means clustering algorithm. The dataset used is a real-world online retail dataset, and the project walks through data cleaning, feature engineering, clustering, and visualization.

🚀 What This Project Does

• Cleans and preprocesses transactional data
• Aggregates customer-level features: Frequency, Quantity, Total Spend
• Applies standard scaling to normalize data
• Uses the Elbow Method to find the optimal number of clusters
• Performs K-Means clustering to group customers
• Visualizes the customer segments using PCA (Principal Component Analysis)

📊 Tech Stack

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn
• Google Colab / Jupyter Notebook

📂 Files

• Customer_Segmentation.ipynb: The main notebook containing the complete pipeline
• Online Retail.xlsx: The dataset used is included as Online Retail.xlsx
• README.md: You're reading it 😊

📸 Sample Outputs

• Elbow Curve to choose k
• PCA scatterplot showing customer clusters

🧠 What I Learned

• Data cleaning and preprocessing
• Unsupervised machine learning with K-Means
• Dimensionality reduction using PCA
• How to interpret and present clustering results

📬 Contact

Built during my AIML internship at Dlithe.
Feel free to reach out or connect on LinkedIn (www.linkedin.com/in/fragan-d-souza-64626a29b)