I am a data analytics professional with expertise in data analysis, modeling/prediction, and machine learning techniques. With a Master’s degree in Data Analytics and a strong academic background, I bring a solid foundation to my work. I have experience in programming languages such as Python, R, SQL, Java, and C++, and I am skilled in analytical tools like Tableau, Power BI, and Excel.
Throughout my career, I have successfully utilized data-driven insights to drive business growth, reduce operational costs, and improve customer experience. I have a proven track record of delivering impactful results through efficient data cleaning, transformation, and predictive modeling techniques. My experience includes working as a Data Analytics Intern, where I contributed to revenue growth, optimized marketing campaigns, and improved forecasting accuracy.
I am excited about my role as a Predictive Analysis Graduate Teaching Assistant at Northeastern University, where I can share my passion for data analytics and empower students to excel in the field. With my expertise in Python, machine learning, and data modeling, I am dedicated to fostering critical thinking and helping students develop valuable skills.
Population survey Insights
Data-driven analysis of the Current Population Survey dataset. Predictive modeling achieved 85% accuracy in forecasting net family income. Visualizations highlighted influential variables, revealing demographic and socioeconomic insights.
Unveiling CKD: Risk Factors, Predictive Modeling, and Personalized Interventions.
In-depth analysis of chronic kidney disease (CKD) risk factors, predictive modeling, and personalized interventions. Unveiled key insights, developed accurate logistic regression model, and proposed targeted strategies for CKD prevention and management
House sales prediction with ML
Advanced House Sales Forecasting and Analysis. Leveraged predictive models to forecast prices, identify winning strategies for market share growth, and analyzed influential attributes for gross sales in King County’s housing market. Achieved 10% increase in market share.
Risk Analysis for Car Insurance: Predictive Modeling and Recommendations
Data-driven car insurance risk analysis. Identify influential variables like credit score. Evaluate models for accuracy. Tailored premiums, coverage, and incentives for informed decision-making and fair pricing.