flight-price-prediction

SDAIA Bootcamp project 2 - web scraping/linear regression.

This project aims to predict ticket prices for upcoming flights to help customers in selecting the optimum time for travel and the cheapest flight to the desired destination. A random forest regression model is applied to forecast the flight prices based on data scraped from Kayak.

Table of Contents

• Proposal
• MVP
• Scraping
• Analysis and Results
• Presentation
• Mobile App
• Authors

Project Proposal

The project proposal can be found here.

Project MVP

The project MVP can be found here.

Scraping

The Kayak Scraper Notebook can be found here.

Here's a demo of the scraper in action (played at 2x speed):

The scraped data can be found here.

In total, the data consists of 55,363 rows and 7 columns.

Analysis and Results

The project notebook can be found here.

Selected features are:

• Source (4 Sources were selected for this project)
• Destination (4 Destinations were selected for this project)
• Total Stops
• Average Price per Airline
• Duration
• Price (Target)

Correlation of features:

Experimenting with different models:

The final selected model is the random forest regression model with:

Metric	Score
MAE	61.87
MSE	40409.87
RMSE	201.02

Therefore, the final model is able to predict flight ticket prices within around ≈ $61.87.

The final model can be found here.

Presentation

The presentation can be found here.

Mobile App

We've also developed an app on Android that finds the average estimated prices for a selected route and month based on our scraped data.

Below, a demo of the mobile app is shown:

Authors

• Meshal Alamr

• Norah Alkhalifah