A comprehensive customer segmentation project for a retailer using machine learning clustering techniques (K-means and AGNES) to identify distinct customer groups based on various features.
This project analyzes customer data to segment customers into meaningful groups using unsupervised machine learning techniques. The analysis helps businesses understand their customer base and develop targeted marketing strategies.
- Perform exploratory data analysis on customer demographics and purchasing behavior using K-means and AGNES
- Compare the outputs of both to identify the appropriate model for the given goals
- Apply clustering algorithms to segment customers into distinct groups
- Visualize customer segments using various plotting techniques
- Provide actionable insights for marketing and business strategy
├── Dataset/
│ └── marketing_campaign (1).xlsx
├── Notebook/
│ └── notebook.ipynb
├── requirements.txt
└── README.md
The initial dataset can be found on Kaggle by accessing this link and is publicly available under CC0 license: https://www.kaggle.com/datasets/ahsan81/superstore-marketing-campaign-dataset
Can be found on the author's website