Airline Fare Prediction System

Best Airline Fare Prediction System – A Complete Machine Learning Web App Using Flask

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Airline Fare Prediction System

This project is a full working airline fare prediction web app made using Python and Flask. It uses a machine learning model trained on real flight data from Kaggle, and it’s deployed online using Heroku. Basically, the app lets you enter flight details and it tells you how much the ticket might cost between major Indian cities.The backend runs on a model built with cleaned-up flight data and uses Random Forest Regressor from Scikit-Learn. The model is connected to a Flask web app where you can put in info like date, airline, etc., and it gives you a price right away through a simple and clean-looking interface.

Project Overview

The system is built to serve as a prediction engine for airline fares using essential flight details. The model processes categorical and timestamped features and makes fare estimates using a trained regression model.

This web application is created using Flask, styled using HTML and CSS, and structured to accept specific travel inputs such as departure, arrival, stops, and airline. The app is hosted on Heroku and contains all necessary files for training, testing, and deployment.

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Developer & Project Info

Project Name Airline Fare Prediction
Language Used Python 3.6, HTML, CSS
Framework Flask
Machine Learning Library Scikit-Learn
Type Web Application

Available Features

  • Predict flight fare based on input like airline, journey date, source, destination, and more.
  • Handles categorical data and datetime fields automatically in the backend.
  • Integrated with a trained machine learning model file for real-time fare prediction.
  • Lightweight front-end interface created using Flask templating.
  • Model training and preprocessing code included in Jupyter notebook format.

Dataset Details

The dataset used in this project was collected from Kaggle and includes key parameters affecting airfares. After processing, the following features were used:

  • Airline – Name of the carrier.
  • Source – Departure city.
  • Destination – Arrival city.
  • Date_of_Journey – Journey date.
  • Dep_Time – Time of departure.
  • Arrival_Time – Time of arrival.
  • Duration – Total flight duration.
  • Total_Stops – Number of stops.
  • Additional_Info – Extra flight details.
  • Price – Target variable for fare prediction.

During model building, features were engineered from datetime fields and categorical variables were converted using encoding techniques to ensure compatibility with the machine learning algorithm.

Installation Instructions

Navigate into the directory:

cd Flight-Fare-Prediction

Install dependencies:

pip install -r requirements.txt

Run the Flask app locally:

python app.py

Make sure your Python version matches 3.6 as used in this project. All the required dependencies are listed in the requirements.txt file.

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