🛒 Customer Segmentation Using K-Means Clustering

📌 About

This repository contains my Skillcraft Technology ML Internship Task 2 project: Customer Segmentation Using K-Means Clustering.
The goal of this project is to segment mall customers into distinct groups based on Annual Income and Spending Score, enabling businesses to design personalized marketing strategies and improve customer engagement.

🔍 Key Topics

• Unsupervised Learning – K-Means clustering for customer segmentation
• Data Preprocessing – Feature scaling with StandardScaler
• Elbow Method – Determining the optimal number of clusters
• Cluster Visualization – Using Matplotlib and Seaborn for clear insights
• Business Applications – Targeted marketing, personalized offers, customer relationship management

⚙️ Tech Stack

• Python
• Pandas, NumPy
• Scikit-learn (KMeans, StandardScaler)
• Matplotlib, Seaborn
• Jupyter Notebook

📊 Insights

• Customers can be grouped into meaningful segments (e.g., high-income/high-spending, low-income/low-spending).
• These clusters help businesses understand customer behavior and tailor strategies accordingly.

🚀 How to Run

1. Clone the repository

git clone https://github.com

2. Navigate to the project folder

cd customer-segmentation

3. Install dependencies

pip install -r requirements.txt

4. Run the Jupyter Notebook

jupyter notebook

Open your task notebook to see the clustering analysis and visualizations.