Mall-Customers

An unsupervised machine learning to segment mall customers into distinct groups based on their demographics and purchasing behavior. The goal is to identify key customer profiles that can inform targeted marketing strategies.

Model: Unsupervised Learning Model

Dataset: Mall Customer Segmentation Data

Preprocessing

One-hot encoded categorical variables

Scaled numerical features using MinMaxScaler and StandardScaler

Applied PCA and t-SNE for visualization

Evaluation: KMeans

Silhouette Score: 0.591665871638838

Davies Bouldin Score: 0.4846581131074428

Calinsku Harabasz Score: 1420.9895912666316

Objective:

To group customers into meaningful segments using K-Means Clustering, and interpret those clusters to help the business:

Identify high-value customer groups

Develop personalized marketing strategies

Improve customer retention and satisfaction