Increased total revenue by 18% through targeted marketing campaigns aimed At high – value custome r se gments identified via SQL -based customer segmentation. • Enabled real-time monitoring of sales performance through interactive Power BI Dashboards, leading to proactive decision-making that contributed to a 10% increase in sales efficiency. • Demonstrated the model’s efficacy in capturing policyholders risk profiles, resulting in a 26% reduction in average prediction discrepancy • Developed a comprehensive project using Python, Pandas, Scikit-Learn, and Seaborn for medical insurance price prediction And Improve 50% accuracy. • Utilized data analysis and visualization techniques to offer valuable insights for well-informed business decisions, leading to a 25% improvement in cost estimation accuracy. • Achieved a 20% reduction in pricing discrepancies. • Diminished data analysis duration by around 30% through the utilization of Pivot Tables and VLOOKUP, culminating in heightened efficiency for both report generation and analysis.
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