Behind the Visualizations: The Data That Powers This Story

This project combines multiple datasets to explore how food delivery has grown, what people order, and how they feel about it.

Restaurant and Ratings Data

Source: Uber Eats USA Restaurants (Kaggle)

This dataset contains restaurant-level information including ratings, categories, pricing, and locations. It was used to analyze how restaurants differ across cities and cuisines.

Key Fields

  • name, category, price_range
  • score, ratings
  • city, location (lat/lng)

Used For

  • Geography visualization
  • Cuisine comparisons
  • Restaurant distribution insights

DoorDash Orders Dataset

Source: DoorDash Dataset (Kaggle)

This dataset captures operational data such as order timing, delivery duration, and platform activity. It helps reveal when and how people use food delivery.

Key Fields

  • created_at, delivery time
  • total_items, subtotal
  • dashers & order volume

Used For

  • Time-of-day ordering trends
  • Weekly usage patterns
  • Demand vs supply insights

Google Trends and Sentiment Data

Sources: Google Trends

Combines search interest data with sentiment analysis scores to understand both popularity and public opinion of food delivery platforms.

Key Fields

  • search interest over time
  • sentiment (positive/neutral/negative)
  • VADER & AFINN scores

Used For

  • Trend growth visualization
  • Platform sentiment comparison
  • Public perception analysis

Canadian DoorDash Dataset

Source: Food Delivery in Canada (Kaggle)

Focused on Canadian cities, this dataset was used to explore regional differences in cuisine preferences and ordering behavior.

Key Fields

  • city, restaurant category
  • ratings and pricing

Used For

  • Canada-focused map visualization
  • Cuisine popularity by city

Cleaned Combined Dataset

Source: Food Delivery Apps (Mendeley)

A cleaned and merged dataset combining multiple sources. This dataset standardizes fields like city, category, and ratings for consistent analysis across visualizations.

Key Fields

  • restaurant, city, category
  • star ratings, price range

Used For

  • Star rating visualization
  • Cross-dataset comparisons
  • Final unified analysis