End to end flight price predictor

In this project I created a web application that can predict fare of a flight.

This app is currently live and can be found at: https://flight-fare-predictor-flask.herokuapp.com/

This project looks into various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting price of a flight. This repository was created to help clarify how to utilise flask and gunicorn to easily deploy a python machine learning model as a web app on Heroku. The trained model achieved accuracy of more than 95% on the test set and its weights have been saved in the very useful HDF5 format.

we are going to take following approch:

1. problem defination

Problem Build a system capable of predicting fare of a flight.

2. data

Original data came from the kaggle competition https://www.kaggle.com/nikhilmittal/flight-fare-prediction-mh/

3. Data preprocessing and EDA

4. Creating a model

I buid two models

1. XGboost
2. Random Forest

Deployment

For deploying my web application I used Flask framework and deployed it on heroku.