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🛍️ Customer Segmentation using Machine Learning

Welcome to the Customer Segmentation project!

In this project, we use unsupervised machine learning techniques to divide customers into different groups (called clusters) based on their purchasing behavior.

The idea is to help a retailer better understand their customers — for example, who are the high spenders, which customers buy similar items, or who buys frequently but spends less.

📂 What This Project Does

The retailer has provided transaction-level data (i.e., each row is a customer's purchase).

Our job is to:

  • Analyze this data
  • Understand buying patterns
  • Build a clustering model that groups similar customers together

We use:

  • Data analysis to explore the data
  • K-Means clustering to create the customer groups

🧠 What is Unsupervised Learning?

This project uses unsupervised learning, which means:

  • We don’t have any labels (like "loyal customer" or "one-time buyer")
  • The model finds patterns and forms groups on its own, based only on the data

📊 What’s in the Notebook?

The Jupyter Notebook included in this project walks through the entire process step-by-step:

  1. Loading the data
  2. Exploring and cleaning the data
  3. Analyzing customer behavior
  4. Building a clustering model (K-Means)
  5. Visualizing the customer segments
  6. Interpreting the results

📁 Dataset

The input dataset is located in the Files folder.

It contains:

  • Customer IDs
  • Items purchased
  • Quantity
  • Price
  • Transaction date
  • And more...

🔧 Tools & Libraries Used

  • Python
  • Jupyter Notebook
  • Pandas
  • NumPy
  • Matplotlib / Seaborn
  • Scikit-learn (for K-Means)

📈 Outcome

At the end of the project, you'll be able to:

  • Understand how to segment customers using data
  • Create meaningful clusters
  • Use clustering results to improve business decisions (like marketing or product targeting)

🚀 Getting Started

To run this project on your machine:

  1. Clone the repo or download the files
  2. Open the Jupyter Notebook
  3. Run the cells step-by-step

💡 Who is this for?

This project is great for:

  • Beginners in machine learning
  • Anyone interested in customer analytics
  • Aspiring data analysts or data scientists

📬 Questions?

If you have any questions, feel free to reach out or open an issue.