Businesses often serve a wide range of customers. To tailor their strategies effectively, they must understand different customer groups. This project uses unsupervised machine learning (K-Means clustering) to divide customers into distinct groups.
Features:
1)Data Cleaning and Preprocessing
2)Feature Scaling with StandardScaler
3)Optimal cluster selection using Elbow Method
4)K-Means Clustering
5)Visualizations (scatter plots)
Technologies Used:
1)Python
2)Pandas, NumPy
3)Scikit-learn
4)Matplotlib, Seaborn
5)Jupyter Notebook
Results:
*Clustered customers into k segments
*Identified patterns in spending habits, income
*Generated clear visualizations to understand customer groups