Airline Fare Prediction System
ML system predicting airline ticket prices using ensemble models trained on 50,000+ fare records. Includes booking lead time analysis and interactive Streamlit interface.
01 — Problem
The Challenge
Airline ticket pricing is dynamic and opaque. Prices fluctuate based on route, time, booking lead time, airline, and seat availability. Travelers lack tools to objectively assess whether a current fare is reasonable or likely to change — leading to suboptimal purchase timing decisions.
For frequent travelers and budget-conscious individuals, even a 15-20% improvement in fare prediction accuracy translates to meaningful savings. The problem is well-defined enough for ML application with available public datasets.
02 — Approach
How I Approached It
Trained ensemble ML models (RandomForest and GradientBoosting) on a dataset of 50,000+ historical fare records. Engineered features including booking lead time, route characteristics, day-of-week, and airline class. Built a Streamlit interface for interactive fare prediction with confidence intervals.
Architecture
- 01Data ingestion and cleaning pipeline using Pandas
- 02Feature engineering: lead time buckets, route encoding, temporal features
- 03Model training: RandomForest and GradientBoosting with cross-validation
- 04Ensemble combination: weighted average of model predictions
- 05Streamlit web interface for interactive prediction and visualization
03 — Technology
Technology Choices and Why
RandomForest
Handles non-linear fare relationships well; robust to outliers in pricing data
GradientBoosting
Captures complex feature interactions; outperforms single models on tabular pricing data
Streamlit
Rapid prototyping of interactive ML interfaces without frontend engineering overhead
Pandas
Efficient manipulation of 50K+ row dataset; rich API for feature engineering operations
04 — Challenges
Obstacles and Solutions
High variance in fare data
Applied log transformation to price target variable; improved RMSE by approximately 23% compared to raw price prediction
Feature leakage risk
Careful temporal split for train/test; validated that no future-date features were used in training set construction
Model interpretability
Added SHAP value analysis to explain top features driving individual predictions; useful for communicating model behavior
05 — Results
Outcomes
- —Prediction accuracy within 15% of actual fare for 78% of test cases
- —Trained on 50,000+ fare records across 6 major Indian routes
- —Booking lead time identified as highest-impact feature for fare variance
- —Interactive Streamlit UI with confidence interval display
06 — Learnings
What I Learned
- —Target variable transformation (log-price) often matters more than model selection for skewed financial data
- —Feature engineering contributes more to performance than hyperparameter tuning in most tabular ML problems
- —Model interpretability is a product requirement, not just a nice-to-have
Skills Used
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