https://github.com/daim-nickel-penny/fly-deals-backend

Using ML for predicting the future prices for various airlines to get a forecasted price comparison.
https://github.com/daim-nickel-penny/fly-deals-backend

machine-learning python sklearn

Last synced: 4 months ago
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Using ML for predicting the future prices for various airlines to get a forecasted price comparison.

• Host: GitHub
• URL: https://github.com/daim-nickel-penny/fly-deals-backend
• Owner: Daim-Nickel-Penny
• License: lgpl-2.1
• Created: 2021-03-25T13:10:43.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2021-08-03T18:41:15.000Z (almost 4 years ago)
• Last Synced: 2025-01-08T10:44:20.794Z (5 months ago)
• Topics: machine-learning, python, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.7 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Flight Fare Predictor

[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)

## Introduction

Flight ticket costs, as we all know, may be difficult to predict.

As a result, we attempted to forecast the pricing for future trip dates.

To do so, we used web scraped data from my journey to make the forecast.

## Steps done-

- 1. EDA for extracting the possible features, trends in data and detecting outliers.

- 2. Feature Extraction

- 3. Feature Selection using FFS and Ensemble Learning

- 4. Test

- 5. Prediction

## Algorithms for Model Building Used

| Algorithm | Obtained R2 Score |

| ------ | ------ |

| Linear Regression | 0.21 |

| Support Vector Regressor | 0.10] |

| XGBoost (Tree based) | 0.48 |

| Random Forest | 0.61 |

```sh

For getting more insights- view the PPT

```