divyansh1195 / flight-fare-prediction

End to end implementation and deployment of Machine Learning Airline Flight Fare Prediction using python, flask, gunicorn, scikit-Learn, etc. on Heroku web application platform.

Airline Flight Fare Prediction:

This repository consists of files required for end to end implementation and deployment of Machine Learning Flight Fare Prediction web application created with Flask and deployed on the Heroku platform.

Table of Contents

• App Link
• About the App
• Deployement on Heroku
• Technologies Used
• Bug / Feature Request

App Link

If you want to view the deployed model, click on the following link:
https://airlinefareprediction.herokuapp.com/

A glimpse of the web app:

• If you encounter this webapp as shown in the picture given below, it is occuring just because free dynos for this particular month provided by the Heroku platform have been completely used. You can access the webpage on 1st of the next month.

• Sorry for the inconvenience.

About the App

The Airline Flight Fare Prediction is a Flask web application to predict airline flight fares across the Indian cities. The dataset for the project is taken from Kaggle, and it is a time-stamped dataset so, while building the model, extensive pre-processing was done on the dataset especially on the date-time columns to finally come up with a ML model which could effectively predict airline fares across various Indian Cities. The dataset had many features which had to pre-processed and transformed into new parameters for a cleaner and simple web application layout to predict the fares. The various independent features in the dataset were:

Airline: The name of the airline.

Date_of_Journey: The date of the journey

Source: The source from which the service begins.

Destination: The destination where the service ends.

Route: The route taken by the flight to reach the destination.

Dep_Time: The time when the journey starts from the source.

Arrival_Time: Time of arrival at the destination.

Duration: Total duration of the flight.

Total_Stops: Total stops between the source and destination.

Additional_Info: Additional information about the flight

Price: The price of the ticket

The code is written in Python 3.6.10. If you don't have Python installed, you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:

pip install -r requirements.txt

Deployement on Heroku

Login or signup in order to create virtual app. You can either connect your github profile or download ctl to manually to deploy this project.

The next step would be to follow the instruction given in the Heroku Documentation to deploy a web app.

Technologies Used

Bug / Feature Request

If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result

Please do ⭐ the repository, if it helped you in anyway.