DURGESH716/Filght-Fare-Prediction-WebApp
Flights Fare Prediction Webapplication deployed using Flask framework using python, implemented using random forests algorithm
The application preprocesses flight data through feature engineering and handles categorical variables via one-hot encoding before training the Random Forest model. It exposes predictions through a Flask REST API with an HTML frontend for real-time fare estimates based on airline, route, and temporal features. The model pipeline integrates scikit-learn for training and joblib for model serialization, enabling quick inference without retraining.
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Aug 13, 2022
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