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Machine Learning · Mini Project
Flight Fare Prediction
Estimates Indian domestic airfares from airline, route and timing
1-2 weeks build
Overview
Uses the public Kaggle flight price dataset of Indian domestic routes to predict ticket fares from airline, source, destination, stops, departure time and days left before travel. Linear regression, random forest and XGBoost are compared, and a small Flask form lets anyone enter a trip and see an estimated fare.
What makes it stand up
- 01Feature engineering for days-to-departure, time of day and stops
- 02Three regressors compared on MAE, RMSE and R-squared
- 03Feature importance plots explaining what drives fares
- 04Clean exploratory analysis notebook with route-level charts
- 05Flask web form for instant fare estimates
Module breakdown
- 01
EDA
Fare distributions by airline, route and booking window.
- 02
Preprocessing
Encodes categorical fields and derives timing features.
- 03
Model training
Cross-validated linear, forest and XGBoost regressors.
- 04
Evaluation
Error metrics and residual analysis per airline.
- 05
Web app
Flask form that loads the saved model and returns a fare.
After this, you will be able to
- Explain how tree ensembles capture non-linear fare patterns
- Defend your choice of regression metric
- Demo a fare estimate for a route of the examiner's choosing
Technology stack
- Python
- Pandas
- scikit-learn
- XGBoost
- Flask
- Level
- Mini Project
- Domain
- Machine Learning
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support