Flight Price Prediction using Machine Learning, Flask, Docker, Azure Cloud
Flight Fare Prediction - a Classic Time Series Project
- Flight fare prediction is a classical problem of time series forecasting that finds trends in past observations to outline the future
- Many popular flight booking websites today, including Google Flights, showcase important insights on:
- Current fair status: high, low or fair
- Past fare trends, upcoming future trends and
- Helps decide the right time to book a flight ticket.
Project Overview
I built a Flight Fare Prediction App, that takes travel details as input, like: the departure date, arrival date, departure city, arrival city, stoppages, airline carrier; to predict flight ticket price. With this understanding, users may have a better idea of what the cost would be on their upcoming travel
The dataset can be found here
The steps involved in my Project
Data Ingestion using Python
Model Training using Random Forest Regressor
Model Deployment using Flask Web Application
Containerization using Docker.
Deploying Conatinersed model to Azure Cloud
Project - Plan of Action
High Level Project Flow
Below are some of the screenshots for Flask Application
Flight Price Prediction Web App
Flight Price Prediction Web App
Built Docker image of the Source Code and Pushed Image to Docker Hub
Pushed Docker image to Container Registry and Deployed the Web App Server in Azure
You can find the Project code here