This project uses transactional sales data and applies RFM (Recency, Frequency, Monetary) analysis along with K-Means clustering to uncover patterns in customer behavior. The insights can be used for customer segmentation, targeted marketing, and improving business strategies.
The dataset used in this project is from Kaggle:
🔗 Transaction Data by Vipin
It includes transaction details such as customer IDs, transaction amounts, and dates.
- Load and clean transactional data
- Calculate RFM metrics for each customer
- Normalize the data
- Use Elbow method to find optimal number of clusters
- Apply K-Means clustering
- Visualize customer segments
- Python
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn