This project explores factors affecting airfare pricing in the Indian domestic flight market.
Using a real-world dataset of airline ticket prices, we apply data cleaning, exploratory data analysis (EDA), statistical methods, and SQL queries to uncover pricing trends and generate actionable insights for both customers and airlines.
What drives flight fare variation in India?
We investigate how features such as airline, number of stops, duration, route, and departure/arrival times impact ticket pricing.
- Time & duration conversions
- Missing value handling
- Distributions
- Outlier detection
- Visual trends
- T-tests and ANOVA for price differences
- Correlation analysis between duration and price
- Chi-square tests for categorical associations
- Aggregated pricing trends
- Identifying anomalies
- Price trends across airlines, routes, and stops
- Tools: Seaborn, Plotly, Power BI
- ✅ Cleaned CSV dataset
- ✅ Jupyter Notebook with analysis
- ✅ SQL scripts
- ✅ Visualization dashboard
- ✅ Summary report
Indian Airlines Ticket Price Dataset from Kaggle
🔗 View Dataset