Predictive flight fare model
- This is our group's data analysis project using R/Python
- The dataset (sourced from Kaggle, extracted from the booking website Ease My Trip - one of the leading travel companies in India) includes flight information for economy and business class tickets from six famous airlines in India, collected over a period of 50 days (11/02 - 31/03/2022), consisting of 300,261 rows and 11 fields.
- Analysis Factors that impact the flight fare (especially the time interval between order_date and flight_date)
- Support customer to choose the most suitable time to book the tickets
- Leveraging Supervised Machine learning models
- What are the differences between Economic and Business flight fares?
- How does the Flight fare change if the customer booking day is just before the flight day?
- Does department time and department place affect the flight fare?...
- Data preprocessing
- Exploratory data analysis
- Identify buying patterns and underlying trends of pricing
- Feature engineering: Data encoding with Label Encoding and One-Hot Encoding
- Forecast ticket prices (Decision Tree, Random Forest, K-Neighbor Regressions)
