This project aimed to segment customers into distinct groups based on their purchasing behavior, enabling more targeted marketing strategies. Using the K-Nearest Neighbors (KNN) algorithm, customer data was analyzed based on key attributes such as age, annual income, and spending score.

• Methodology:

  • Preprocessed and normalized the dataset to ensure fair distance calculations.

  • Applied the KNN algorithm to classify customers into clusters, leveraging Euclidean distance for similarity measurement.

  • Optimized the value of 'K' using elbow and silhouette methods for accurate segmentation.

• Results and Insights:

  • Identified customer groups such as high-income high spenders, budget-conscious buyers, and moderate spenders, providing actionable insights for marketing campaigns.

  • Highlighted spending patterns and demographic trends that can guide personalized promotions.

• Skills and Tools:

  • Machine Learning (KNN algorithm), Python (Pandas, Scikit-learn, Matplotlib, Seaborn), Data Preprocessing, and Visualization.

 Project GitHub Link: https://github.com/sm98code/Customer-Segmentation.git