Machine Learning-Based Buyer Segmentation and Investment Profiling for Real Estate Market Intelligence
<b>Abstract:</b>Modern real estate platforms manage heterogeneous buyer populations ranging from first-time residential home buyers to institutional corporate entities and international high-net-worth investors. Traditional marketing strategies relying on broad demographic generalizations result in inefficient advertising spend and generic property recommendations.This research presents an end-to-end unsupervised machine learning framework utilizing K-Means Clustering and Agglomerative Hierarchical Clustering to identify latent buyer profiles across a dataset of 2,000 client records and 10,000 property transaction logs. By evaluating feature spaces using the Elbow Method and Silhouette Analysis, we identify four optimal buyer cohorts: Global Investors (C1), First-Time Buyers (C2), Corporate Buyers (C3), and Luxury Investors (C4).<b>Repository Contents:</b>Full Research Paper ManuscriptStreamlit Web Dashboard Source Code (<code>app.py</code>)Final Processed Dataset with ML Cluster Classifications (<code>segmented_clients_final.csv</code>)<b>Collaborating Organizations:</b> Parcl Co. Limited & Unified Mentor<b>Domain:</b> Financial Analytics & Real Estate Market Intelligence<b>References / Related Links:</b>https://real-estate-buyer-segmentation-i44gwexcmyefck5urs2rtd.streamlit.app/https://github.com/khushiisainii16/real-estate-buyer-segmentation/tree/main