This repository contains the implementation of a customer segmentation task using the K-means clustering algorithm. The project was completed as part of Task-2 during my Machine Learning internship at Prodigy InfoTech.
The objective of the task was to group customers of a retail store based on their purchase history. The K-means clustering algorithm was employed to achieve segmentation, providing insights into customer behavior and preferences.
- Understanding of the K-means clustering algorithm.
- Determining the optimal number of clusters using the elbow point method.
- Exploring industrial applications of K-means clustering in retail stores, banks, and insurance companies.
The segmentation was performed on customer data, focusing on their annual salary.
mallCustomerSegmentation.ipynb
: Jupyter Notebook containing the implementation of the K-means clustering algorithm.Mall_Customers.csv
: contains the dataset used for the analysis.README.md
: Project documentation.
- Open the
mallCustomerSegmentation.ipynb
notebook to view the implementation details. - Install the required dependencies using
pip install -r requirements.txt
(if applicable). - Run the notebook to perform the clustering analysis.
To explore the project in detail, visit the GitHub repository: Customer Segmentation with K-means Clustering
Special thanks to Prodigy InfoTech for providing the opportunity to work on this impactful project during my internship.
Feel free to connect with me on LinkedIn: www.linkedin.com/in/abdul-hannan-chougle-78840a252
Thank you for exploring this project!
#MachineLearning #DataScience #ProdigyInfoTech #CustomerSegmentation