This project aims to perform segmentation analysis on a dataset to better understand customer groups and optimize marketing strategies.
Customer segmentation analysis involves dividing a customer base into groups that share similar characteristics such as behavior, demographics, or purchasing habits.
By segmenting customers, businesses can identify distinct groups and personalize their marketing efforts, leading to improved customer satisfaction and increased sales.
This project utilizes a public dataset to perform customer segmentation using K-means clustering.
The dataset contains 541.9k samples of the online purchase history of 2.4k customers.
Acknowledgements: This dataset has been referred from UCI ML Repository: https://archive.ics.uci.edu/ml/datasets/online+retail
- Data Preprocessing: Exploratory Data Analysis (EDA), handling missing values, feature scaling.
- Customer Segmentation: Applying Unsupervised Machine Learning Techinque (K-Means) to cluster customers based on relevant features.
- Visualization: Creating visual representations to illustrate the segmented customer groups.
- Insights and Recommendations: Deriving actionable insights from the segmentation results and suggesting targeted strategies.
Notebook: Access here
- Programming Language: Python
- Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
- Tools: Jupyter Notebook
Contributions are welcome! Feel free to submit issues or pull requests.
For any inquiries or collaboration opportunities, you can reach out to https://www.linkedin.com/in/andre-kuster/
This project is licensed under the MIT License.