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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

Table Key columns
users user_id, name, email, country, loyalty_tier, joined_at
hotels hotel_id, name, city, country, stars, price_per_night, total_rooms
flights flight_id, origin, destination, airline, departure_at, arrival_at, seat_class, price
bookings booking_id, user_id, hotel_id, flight_id, check_in, check_out, total_price, status, cancellation_reason
reviews review_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,
)