Predicting flight ticket prices using machine learning models trained on airline datasets.
Flight prices fluctuate due to multiple factors such as airline, travel class, stops, seasonality, and demand.
This project applies data science and machine learning techniques to:
- Analyze flight booking and price trends
- Identify key drivers of flight pricing
- Build predictive models to estimate flight prices
Goal: Help travelers, airlines, and agencies with better price predictions and smarter booking strategies.
- Source: Publicly available flight price datasets
- Target Variable:
Price(continuous variable – flight ticket cost)
- Programming: Python (Jupyter Notebook)
- Libraries:
- Data Analysis → pandas, numpy
- Visualization → matplotlib, seaborn
- Machine Learning → scikit-learn, xgboost, lightgbm
- Model Evaluation → R², MAE, MSE, RMSE
-
Exploratory Data Analysis (EDA)
- Distribution of prices and other features
- Impact of airline, stops, duration, and time on price
- Outlier analysis & correlations
-
Data Preprocessing
- Handling missing values
- Feature engineering (e.g., extracting day/month from dates)
- Encoding categorical variables (One-Hot/Label encoding)
- Scaling numerical features
-
Modeling
- Linear Regression
- Random Forest Regressor
- XGBoost and LightGBM
-
Model Evaluation
- Train-test split & cross-validation
- Metrics: R², MAE, MSE, RMSE
- Comparison of models
-
Insights & Business Value
- Identifying which airlines, routes, and timings affect price most
- Recommendations for cost-efficient bookings
- Flight price distributions
- Boxplots of price by airline, class, stops
- Correlation heatmaps
- Actual vs Predicted price plots
- Deploy as a Flask/Django web app
- Integrate with real-time flight APIs
- Use deep learning models for comparison
- Hyperparameter optimization with Optuna
Ari R.
Data Scientist
🔗 GitHub | 📧 [email protected]
✨ Developed with passion for Data Science & Machine Learning. "# Flight Price Prediction" "# FlightPrice_Prediction"