Implemented a robust data analytics initiative involving in-depth churn analysis and cluster analysis on extensive customer datasets. The churn analysis unearthed patterns and indicators of customer attrition, empowering the development of targeted retention strategies to proactively address customer turnover. Simultaneously, through cluster analysis, I delineated distinct customer segments based on shared characteristics and behaviors. This granular segmentation provided valuable insights into diverse customer profiles, enabling the tailoring of services and marketing strategies to specific needs. By deploying these analyses, the project not only contributed to a deeper understanding of customer dynamics but also laid the groundwork for data-driven decision-making, fostering a more responsive and customer-centric approach within the business.
Tools used :- Python, Pandas, NumPy, Matplotlib, Seaborn, K-Means clustering, SVM, Decision Tree, Random Forest