Generate Travel Synthetic Data in Python

Travel platform data has subtle but critical coherence requirements: check_out must be after check_in, arrival_at must be after departure_at, and cancellation_reason should only be populated for cancelled bookings, never for active or completed ones. Get any of these wrong and your analytics will look broken from the first query. Misata generates a five-table travel dataset where all of these invariants are enforced from the start, across users, hotels, flights, bookings, and reviews.

import misata

tables = misata.generate(
    "A travel booking platform with 5k users, hotels, and international flights",
    rows=5000,
    seed=42,
)
print(list(tables.keys()))   # ['users', 'hotels', 'flights', 'bookings', 'reviews']

# Cancellation reason is only set for cancelled bookings
cancelled = tables["bookings"][tables["bookings"]["status"] == "cancelled"]
active = tables["bookings"][tables["bookings"]["status"] != "cancelled"]
assert cancelled["cancellation_reason"].notna().all()
assert active["cancellation_reason"].isna().all()

What Misata generates

Five tables: users → bookings (linking users, hotels, and flights) → reviews. Hotel star ratings and prices are correlated; flight prices vary by seat class; users are distributed across loyalty tiers.

Tables and columns

TableKey columns
usersuser_id, name, email, country, loyalty_tier, joined_at
hotelshotel_id, name, city, country, stars, price_per_night, total_rooms
flightsflight_id, origin, destination, airline, departure_at, arrival_at, seat_class, price
bookingsbooking_id, user_id, hotel_id, flight_id, check_in, check_out, total_price, status, cancellation_reason
reviewsreview_id, booking_id, rating, title, body, reviewed_at

Realistic distributions

  • cancellation_reason is null for non-cancelled bookings: conditional null enforced consistently
  • check_out always after check_in; arrival_at always after departure_at
  • Hotel price_per_night is correlated with stars, 5-star hotels cost more than 2-star
  • Loyalty tier follows a realistic pyramid: most users are base tier, fewer are gold/platinum
  • Review ratings slightly right-skewed with a realistic 1-star tail

Quick start

import misata
import pandas as pd

tables = misata.generate(
    "Global travel platform with 3k users, hotels, and flights",
    rows=3000,
    seed=42,
)

# Hotel price by star rating
print(tables["hotels"].groupby("stars")["price_per_night"].describe())

# Booking status distribution
print(tables["bookings"]["status"].value_counts(normalize=True))

# Average review by loyalty tier
merged = tables["reviews"].merge(
    tables["bookings"][["booking_id", "user_id"]], on="booking_id"
).merge(
    tables["users"][["user_id", "loyalty_tier"]], on="user_id"
)
print(merged.groupby("loyalty_tier")["rating"].mean())

Common use cases

  • Hotel recommendation engine development: use booking history, star ratings, and review scores to build and evaluate hotel ranking models
  • Cancellation prediction models: train classifiers on bookings with status, total_price, loyalty_tier, and lead time to predict cancellation likelihood
  • Dynamic pricing prototype: test pricing algorithms against flights and hotels with realistic base price distributions
  • Booking flow QA: validate end-to-end booking, modification, and cancellation workflows against data with correct date semantics and status transitions
  • Customer support tooling: test support dashboards with realistic booking histories, cancellation reasons, and review text
  • Loyalty program analytics: analyse tier upgrade patterns and booking frequency across a realistic loyalty tier distribution

Advanced: seasonal booking narrative

tables = misata.generate(
    "European travel platform — summer beach holiday peak in July-August, "
    "winter ski bookings in December-January, autumn shoulder season",
    rows=5000,
    seed=42,
)

Advanced: locale-aware generation

# Southeast Asian travel platform — SEA destinations, regional airlines
tables = misata.generate("Southeast Asian travel platform with flights to Bangkok and Bali", rows=2000)

# European OTA — European cities, EU airlines, EUR pricing
tables = misata.generate("European travel agency with flights and hotels across EU cities", rows=3000)

Advanced: quality-guaranteed generation

tables = misata.generate(
    "Travel platform with 5k users",
    min_quality_score=85,
    smart_correlations=True,  # auto-adds hotel_stars↔total_price, loyalty_tier↔rating
    rows=5000,
    seed=42,
)

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