Overview
The Travel Price Predictor is a sophisticated time-series forecasting system that analyzes historical pricing data, seasonal patterns, and market signals to predict optimal booking windows for flights and hotels.
Business Problem
Thrillers Travels needed to:
- Maximize revenue by suggesting optimal pricing
- Help customers find the best booking times
- Reduce inventory waste from poor timing
- Compete with larger OTAs using data-driven insights
Data Pipeline
We built a robust data pipeline processing over 10 million data points daily:
Data Sources
- Historical booking data (5 years)
- Competitor pricing (scraped hourly)
- Flight capacity data
- Seasonal events calendar
- Economic indicators
Feature Engineering
def create_features(df):
df['day_of_week'] = df['date'].dt.dayofweek
df['days_until_departure'] = (df['departure'] - df['date']).dt.days
df['is_holiday_season'] = df['date'].apply(check_holiday)
df['competitor_price_ratio'] = df['our_price'] / df['competitor_avg']
return df
Model Architecture
We employed an ensemble approach combining:
- Prophet: For capturing seasonality and trends
- XGBoost: For feature-based predictions
- LSTM: For sequential pattern recognition
The final prediction is a weighted average based on recent performance.
MLOps Pipeline
The entire system runs on AWS SageMaker with:
- Training: Weekly retraining on fresh data
- Inference: Real-time predictions via API
- Monitoring: CloudWatch dashboards for model drift
- A/B Testing: Continuous evaluation against baseline
Results
- 15% increase in revenue from dynamic pricing
- 23% improvement in booking conversion rates
- $50K+ saved annually from optimized inventory
- Real-time predictions in under 100ms
Key Insights
- Booking patterns vary significantly by destination type
- Competitor pricing is highly predictive within 48 hours
- Social media signals provide early demand indicators
- Weather forecasts impact last-minute bookings