amsharmaa

Conducted a comprehensive analysis of the health insurance sector, examining premium structures, claim settlement ratios, operational efficiency metrics and CAGR over a 5-year period to provide actionable insights for decision-making.  Conceptualized and developed two innovative Vehicle Total Addressable Market (TAM) analysis model (Sales based TAM and Penetration based TAM) to quantify market potential and identify growth opportunities in the automotive sector.  Improved financial data analysis by refining R code for outlier detection, increasing accuracy from 43% to 64% for a time sensitive data. Migrated the enhanced process from R to Python, ensuring scalability and ease of future upgrades

 Cleaned, transformed, and visualized datasets to derive actionable insights, supporting data-driven decision-making.  Analyzed website advertisement data using Logistic Regression, achieving a remarkable 93% classification accuracy. This analysis informed strategies for optimizing advertisement performance and helped to achieve maximum engagement.  Explored and evaluated alternative machine learning models, such as Naïve Bayes and Decision Trees, to benchmark performance, with Logistic Regression emerging as the most effective model for the given dataset.