A Journey of Data Exploration for Flight Price Prediction: From Raw Data to Machine Learning Readiness

The data exploration journey for flight price prediction aims to reveal key factors influencing ticket prices through systematic exploratory data analysis (EDA), laying the foundation for subsequent machine learning modeling. This project covers core steps such as data preprocessing, feature engineering, and visualization analysis, using Python ecosystem tools (e.g., Pandas, NumPy) to process flight datasets, explore relationships between features like time, route, and airline and price, and provide decision support for aviation stakeholders.

Project Background

In an increasingly competitive aviation industry, accurate flight price prediction is of great value to airlines, OTA platforms, and passengers. As a key step in the data science process, EDA helps understand data distribution, discover patterns, identify anomalies, and provide a basis for modeling.

Dataset Composition

The dataset includes time-related features (Date_of_Journey, Dep_Time, etc.), route and airline features (Airline, Source, Destination, etc.), and the target variable Price.

Original Author and Source

• Original author/maintainer: drylikov
• Source platform: GitHub
• Original title: Flight_price_predicting_EDA
• Original link: https://github.com/drylikov/Flight_price_predicting_EDA
• Release time: 2026-06-15

Data Preprocessing Flow

1. Time feature engineering: Split Date_of_Journey into day/month, extract hour/minute from Dep_Time/Arrival_Time, and extract hours/minutes from Duration.
2. Missing value handling: Identify and handle missing values (strategies like deletion, imputation).
3. Categorical variable encoding: Convert categorical variables like Airline and Source into numerical form.

Tech Stack and Tools

• Python: Core programming language
• Pandas: Data processing library
• NumPy: Numerical computation
• Jupyter Notebook: Interactive development environment

Visualization Techniques

Use distribution plots, box plots, heatmaps, and time series plots to present data insights.

Key Analysis Insights

1. Price distribution: Right-skewed distribution, with most prices concentrated in the low-to-medium range and a few high-end prices significantly higher.
2. Airline differences: Full-service airlines (e.g., Air India) have higher prices, while low-cost carriers (e.g., IndiGo) are more competitive.
3. Seasonal patterns: Prices are higher during holidays/peak seasons, with more promotions in off-seasons.
4. Stopover and price: Direct flights have the highest prices; the more stopovers, the lower the price.
5. Departure time impact: Early morning/late night flights are cheaper, while prime-time flights have higher prices.

Feature Correlation

Analyze correlations between variables, identify features most relevant to price, and aid feature selection and business logic validation.

Practical Application Value

• Airlines: Optimize revenue management and dynamic pricing.
• OTA platforms: Provide price trend predictions for users.
• Passengers: Choose cost-effective travel plans.
• Analysts: Understand market dynamics and support investment decisions.

Core Conclusions

EDA is a key step before modeling; fully understanding the data avoids blind modeling. This project demonstrates the complete process from raw data to insights, providing a solid foundation for building flight price prediction models.

Subsequent Modeling Directions

1. Deepen feature engineering: Create features like weekend/holiday indicators and days until departure.
2. Model selection: Consider linear regression, random forests, XGBoost, neural networks, etc.
3. Hyperparameter tuning: Use grid/random search to optimize parameters, and cross-validation to ensure generalization ability.
4. Evaluation and deployment: Evaluate using metrics like RMSE and MAE, and plan deployment solutions to serve business scenarios.