Practical Implementation of Airfare Price Prediction Model: A Comparative Study of Linear Regression and Random Forest

This project builds an airfare price prediction model based on machine learning, comparing the performance of linear regression and random forest algorithms. It aims to provide data support for travelers' decisions on when to purchase tickets, while exploring feasible paths for airfare prediction.

Complexity of Airline Pricing

The airline industry uses a dynamic pricing mechanism (revenue management), where ticket prices for the same flight vary significantly across different times and seats. This is driven by multi-dimensional factors such as demand forecasting and competitive dynamics.

Traveler Needs and Business Objectives

Travelers face the dilemma of booking in advance or waiting; prediction tools can enhance the scientific nature of decision-making. The core problem is predicting ticket prices given flight features, with business values including:

• Traveler side: Saving travel costs
• Platform side: Optimizing OTA recommendation strategies
• Airline side: Assisting revenue management

This project focuses on a comparative study of two classic algorithms.

Dataset and Feature Engineering

The inferred feature system includes dimensions such as route, time, airline, and cabin class. Feature engineering strategies include:

• Time features: Extract boolean features, days until holidays, periodic encoding
• Categorical features: One-Hot encoding (low cardinality), Target Encoding (high cardinality)
• Numerical features: Standardization/normalization, binning

Model Comparison

Linear Regression

• Form: Fare = β₀ + β₁×Distance + ... + ε
• Advantages: Strong interpretability, efficient computation, baseline value
• Limitations: Linear assumption, sensitivity to outliers

Random Forest

• Mechanism: Bootstrap sampling + random feature selection + ensemble prediction
• Advantages: Non-linear modeling, anti-overfitting, feature importance evaluation
• Limitations: Weak interpretability, high computational cost

Evaluation System

Core metrics: MSE, RMSE, MAE, R²

Dimension	Linear Regression	Random Forest
Prediction Accuracy	Baseline level	Usually higher
Training Speed	Fast	Slow
Interpretability	High	Medium (feature importance)
Non-linear Capture	Weak	Strong
Overfitting Risk	Low	Medium (needs tuning)
Outlier Sensitivity	High	Low

Key Business Findings

• Time Factor: There is an optimal booking window; prices rise during holidays/peak seasons
• Route Factor: Distance is positively correlated with fares but not strictly linear; prices are lower in highly competitive routes
• Airline Factor: Full-service airlines have higher pricing than low-cost carriers

Engineering Implementation Key Points

• Data pipeline: Raw data → Cleaning → Feature engineering → Train/test split → Model training → Evaluation → Deployment
• Model tuning: Linear regression regularization, random forest hyperparameter adjustment
• Cross-validation: K-fold or time-series cross-validation

Traveler-side Applications

• Price alerts: Push notifications when prices are below predicted values
• Booking advice: Recommend immediate purchase or waiting based on trends

Enterprise-side Applications

• Travel management: Bulk booking during price troughs
• OTA platforms: Optimize search ranking and develop dynamic pricing strategies

Technical Challenges

1. Data Acquisition Difficulty: Requires web scraping or commercial data, which carries risks
2. Complex Dynamic Pricing: Airlines adjust prices in real time
3. Limited Feature Dimensions: Lack of key features like real-time inventory

Improvement Directions

• Data: Collaborate to obtain desensitized data, use public datasets
• Dynamic Pricing: Introduce real-time data streams, online learning
• Features: Construct composite features, integrate external data

Model Upgrade Paths

• Gradient Boosting Trees (XGBoost/LightGBM)
• Deep Learning (LSTM/Transformer)
• Reinforcement Learning (sequence decision problems)

This project demonstrates the typical paradigm of machine learning applications:

1. Progressive Modeling: From linear regression baseline to random forest non-linear model
2. Comparative Thinking: Understand algorithm pros and cons to guide selection
3. Business Integration: Models serve real-world problems

For beginners, this is an excellent practice project to cultivate data-driven thinking, which is particularly valuable in a business environment.