Project: Food Delivery Data Analysis In R

Task: Conducted comprehensive data analysis on a food delivery dataset to uncover insights about cuisine preferences, price points, and operational metrics, with the goal of identifying market opportunities for a potential restaurant or food truck business.

Takeaways:

• Analyzed over 1,800 food delivery orders across multiple cuisine types, focusing on the six most popular cuisines (American, Chinese, Indian, Italian, Japanese, and Mexican)

• Discovered that Indian cuisine had the highest average customer rating (4.54/5) despite having higher average cost, suggesting consumers are willing to pay premium prices for perceived quality

• Identified optimal price points for different cuisines through histogram analysis, with most popular price ranges clustered between $11-$14 and secondary peaks at higher price points for premium offerings

• Determined through regression analysis that food preparation and delivery times had negligible impact on customer ratings (p-values of 0.834 and 0.737 respectively)

• Found significant difference in ordering patterns between weekdays and weekends, with weekends showing higher order volumes and different pricing dynamics

• Developed a predictive model for order costs with extremely high accuracy (Adjusted R-squared: 0.9719), identifying key factors including day of week, customer rating, and their interaction effects

• Created visualization suite including bar charts, histograms, pie charts, and scatterplots to effectively communicate findings to non-technical stakeholders

  Skills Used: R programming, multivariate statistical analysis, predictive modeling, data cleaning and transformation, regression analysis, data visualization with ggplot2, interaction effect analysis, outlier identification, market segmentation, pricing analysis, exploratory data analysis, data-driven business intelligence