Flight-Price-Prediction: Overview

• The objective of this project is to implement a regressor to predict the fares of flights on the basis of different features like source, destination , date etc.

• The model is exported and an instance of the same is implemented using Flask.

• The algorithm used for this project was Random Forest, which performed well after adequate feature engineering and hyperparameter tuning.

• The model attained adjusted R^2 score of 90.30%.

Code and References

Python Version: 3.7

Packages: numpy,pandas, matplotlib, sklearn, seaborn, Flask, pickle

Flask Implementation: https://towardsdatascience.com/productionize-a-machine-learning-model-with-flask-and-heroku-8201260503d2