A K-means clustering algorithm to group customers of a retail store based on their purchase history.
This repository contains a machine learning task (Task 02) implemented in both Jupyter Notebook (ML_Task02.ipynb) and Python script (ML_Task02.py). The task involves [briefly describe what the task involves, e.g., customer segmentation using K-means clustering].
Crafted with love by Sam Naveenkumar .V
Include the description of your project here, describing the task and objectives of ML_Task02.ipynb and ML_Task02.py.
- ML_Task02.ipynb: Jupyter Notebook file containing the implementation of the task with explanations, code, and visualizations.
- ML_Task02.py: Python script version of the task for ease of deployment and automation.
The dataset used in this project is Mall_Customers.csv. This dataset contains information about customers including their ID, age, gender, annual income, and spending score.
Include instructions on how to set up and run your project locally.
Describe how to use the project, including instructions for running ML_Task02.ipynb and ML_Task02.py. Provide examples or screenshots if applicable.
Explain how others can contribute to the project. Provide guidelines for pull requests and setting up a development environment.
This project is licensed under the MIT License. See the LICENSE file for more details.
Provide contact information if someone has questions or wants to reach out regarding the project.
Feel free to customize the sections and content according to your project's specific details and requirements. This template now includes a dedicated section for describing the dataset used (Mall_Customers.csv). If you have additional details or notes about the dataset, you can expand on this section further.