Market Segmentation in Insurance Using Unsupervised Learning

Developed and implemented K-means clustering and Principal Component Analysis (PCA) algorithms to perform market segmentation on an insurance customer dataset.
Analyzed behavioral variables of customers, including Balance and Purchases, to identify distinct customer segments for targeted marketing strategies.
Engineered features and optimized model parameters to improve segmentation accuracy, enabling the insurance company to tailor their products and services to meet the specific needs of different customer clusters.
Utilized Python and data science libraries to preprocess data, execute algorithmic models, and visualize the segmentation results for stakeholder presentation.