PRODIGY_ML_02

KMeans Clustering for Mall Customers

This project demonstrates the application of KMeans clustering on the "mall_customers.csv" dataset to segment customers based on their annual income and spending score.

Overview

The primary goal of this project is to explore and understand customer segmentation using unsupervised learning techniques, specifically KMeans clustering. The dataset used contains information about customers' annual income and spending scores on a scale of 1 to 100.

Dataset

The dataset used in this project is "mall_customers.csv." It contains the following columns:

• CustomerID: Unique identifier for each customer
• Gender: Gender of the customer
• Age: Age of the customer
• Annual Income (k$): Annual income of the customer
• Spending Score (1-100): Score based on customer behavior and purchasing data

Approach

The main steps in this project include:

1. Data Preprocessing: Exploring the dataset,encoding categorical variables.
2. KMeans Clustering: Using the KMeans algorithm to group customers based on their annual income and spending score.
3. Visualization: Visualizing the clusters to understand the distinct customer segments.

Files

• mall_customers.csv: The dataset used for clustering.
• KMeans_Clustering.ipynb: Jupyter Notebook containing the Python code for KMeans clustering.
• README.md: This file, providing an overview of the project.

Usage

To run the code:

1. Clone this repository to your local machine.
2. Ensure you have Python installed along with libraries such as pandas, numpy, matplotlib, seaborn, and scikit-learn.
3. Open and run the KMeans_Clustering.ipynb notebook using Jupyter or any compatible environment.

Results

The output of this project will include visualizations showing the clusters formed based on annual income and spending score. It will help in identifying distinct customer segments that can be used for targeted marketing strategies.

Acknowledgments

The project is for educational purposes and was created as part of @Prodigy_Infotech Internship

Feel free to explore, modify, or expand upon this project!

If you have any questions, suggestions, or feedback, please feel free to email me at [email protected]