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Problem Statement

Flight ticket prices can be something hard to guess, today we might see a price, check out the price of the same flight tomorrow, and it will be a different story.

To solve this problem, we have been provided with prices of flight tickets for various airlines between the months of March and June of 2019 and between various cities, using which we aim to build a model which predicts the prices of the flights using various input features.

Flight-Fare-Prediction

  • A tool that estimates Flight Prices to help users look for best prices when booking flight tickets
  • Engineered Features from the Departure Time, Date of Journey, to quantify the data and make it more understandable.
  • Optimized multiple regression models to reach the best model.
  • Built a client-facing API using Flask
  • Dataset: [https://www.kaggle.com/nikhilmittal/flight-fare-prediction-mh]

Model Building

  • Tried different regression models and evaluated and compared the performance metrics of all the models.
  • Models/Algorithms used:
  • Linear Regression
  • Decision Tree Regressor
  • Gradient Boosting Regressor
  • Random Forest Regressor

lab-flask

Home page

To run flask application

python app.py

To access your flask application open new tab in and paste the url:

https://{your_url}.pwskills.app:5000/