The project, aimed at assisting Amazon toilet paper vendors in improving their product ratings, showcases a comprehensive analysis of the factors influencing customer satisfaction and purchasing decisions. By analyzing variables such as price, number of ratings, brand, sheet count, material, scent, and product description, this research provides valuable insights into consumer preferences within the highly competitive toilet paper market.

Key Skills:
• Data Analysis: Utilized statistical methods to understand the relationship between product features and customer ratings.
• Statistical Modeling: Conducted correlation analysis, linear regression, and ANOVA to identify significant predictors of customer satisfaction.
• Visualization: Employed SAS and Python for data visualization, creating bar charts, box plots, and regression plots to succinctly present the findings.
• Strategic Recommendations: Provided actionable insights for product optimization and marketing strategies, focusing on product features, pricing, and promotional tactics that align with consumer preferences.

The project underscores the importance of a data-driven approach to understanding consumer behavior and optimizing product offerings. By integrating analytical techniques with strategic thinking, the research offers valuable directions for enhancing customer satisfaction and competitive positioning in the toilet paper market.