Recent Work
With a wealth of experience in transforming data into actionable insights, I bring a strategic approach to analytics that empowers decision-making and drives success. Through advanced data analysis and business intelligence, I deliver solutions that not only inform but also elevate business outcomes, enabling organizations to thrive in a data-driven world.
Developed a predictive model for employee performance using machine learning techniques in Python. Applied data processing with Pandas and built classification models, including an MLP neural network that achieved 95.8% accuracy. Conducted feature engineering to identify key drivers of performance, such as environment satisfaction and tenure, and optimized data preprocessing for effective model performance. This analysis provided actionable insights into factors influencing employee productivity.
Conducted a detailed sales analysis in Excel, uncovering key KPIs such as total sales, product performance, and location-based trends. Created an interactive dashboard to visualize peak sales periods, product category performance, and hourly sales patterns. Provided a comprehensive view of customer preferences, supporting data-driven decisions and more effective sales strategies.
Conducted an in-depth analysis of 8,000 Netflix titles using a Kaggle dataset, examining content trends and distribution over time. Used Python’s Pandas library for data extraction, transformation, and cleaning. Findings revealed that from 2008 to 2021, movies accounted for 97% of the catalog, with an 84% surge in TV show releases during the COVID-19 pandemic, highlighting the shift toward OTT platforms and changing consumer behavior.
Analyzed New York City crime data using Python for data cleaning and SQL for querying large datasets. Developed interactive visualizations in Tableau, featuring advanced elements like dual-axis and floating components. Identified key crime trends and patterns across categories, providing a clear, data-driven representation of crime insights.
Performed customer segmentation on a Kaggle dataset of 50,000+ records from an online retail platform. Used Python (Pandas, Scikit-learn) for RFM analysis and K-means clustering, alongside SQL for data preprocessing to ensure data quality. Created interactive Tableau dashboards to visualize four distinct customer segments, offering insights into purchasing behaviors and preferences.
Real Estate Market Analysis
Analyzed public real estate datasets for SimonCRE to uncover trends in property values, rental rates, and market demand across the US. Built predictive models to forecast property value changes based on economic indicators. Developed interactive Tableau dashboards to visualize market trends and property performance, supporting business operations. Utilized Snowflake for data warehousing and dbt for streamlined data pipelines, enhancing data integration and query performance.
