Customer Segmentation Using K-Means Clustering

📌Project Overview This project applies K-Means Clustering to segment customers based on their Annual Income and Spending Score.

📂 Dataset

• Source:Mall Customer Segmentation Data(kaggle)
• https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python
• Columns: CustomerID, Gender, Age, Annual Income, Spending Score

->Steps:

1. Load and clean the dataset.
2. Use the Elbow Method to determine the best number of clusters.
3. Apply K-Means Clustering to segment customers.
4. Visualize clusters with scatter plots.

Results:

• Identified 5 distinct customer segments.
• Provided business insights for better targeting.

📁 Files:

• Mall_Customers.csv - Dataset
• customer_segmentation.ipynb - Jupyter Notebook with code

🛠️Tools Used:

• Python (Pandas, Matplotlib, Seaborn, Scikit-learn)

Business Insights:

• High-spending customers can be targeted for premium services.
• Low-spending high-income customers need better engagement strategies.