https://github.com/pegah-ardehkhani/flight-price-eda-and-prediction

Analyze and Predict the Flight Price Using Machine Learning Models and Plotly Library
https://github.com/pegah-ardehkhani/flight-price-eda-and-prediction

data-science data-visualization exploratory-data-analysis feature-importance flight-price flight-price-prediction machine-learning machine-learning-algorithms plotly prediction regression regression-algorithms statsmodels

Last synced: 19 days ago
JSON representation

Analyze and Predict the Flight Price Using Machine Learning Models and Plotly Library

• Host: GitHub
• URL: https://github.com/pegah-ardehkhani/flight-price-eda-and-prediction
• Owner: Pegah-Ardehkhani
• License: mit
• Created: 2022-06-28T19:36:41.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-12-02T09:45:49.000Z (about 2 years ago)
• Last Synced: 2024-11-10T13:07:25.223Z (3 months ago)
• Topics: data-science, data-visualization, exploratory-data-analysis, feature-importance, flight-price, flight-price-prediction, machine-learning, machine-learning-algorithms, plotly, prediction, regression, regression-algorithms, statsmodels
• Homepage:
• Size: 3.49 MB
• Stars: 3
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Flight Price EDA and Prediction ✈ ![license](https://img.shields.io/github/license/Pegah-Ardehkhani/Flight-Price-EDA-and-Prediction.svg) 

> **`Note`**: Use [**google colab**](https://colab.research.google.com/drive/1cOAoQaJ-GNLSvTc6i5WU6WbrVWX-eXDT?usp=sharing) in order to view the code and interactive plotly graphs.



  



## Dataset 📔

[Kaggle link: Flight Price Data](https://www.kaggle.com/datasets/shubhambathwal/flight-price-prediction)

## Objectives 🏆

In this project, these questions will be answered:

* [x] Does price vary with Airlines?

* [x] How is the price affected when tickets are bought in just 1 or 2 days before departure?

* [x] Does ticket price change based on the departure time and arrival time?

* [x] How the price changes with change in Source and Destination?

* [x] How does the ticket price vary between Economy and Business class?

* [x] Which features have the most impact on predicting flight price?

* [x] Which model is the best for predicting flight price?

## Project's Table of Contents ✍️

  Click to expand!

  

1. Problem statement

2. Import Libraries and Data

3. Handling Missing Values

4. Data Analysis and Visualization

5. Outlier Detection

6. Check for Rare Categories

7. Categorical Variables Encoding

8. Dataset Splitting

9. Modeling and Parameter Optimization

10. Feature Importance

11. Results

## Project Overview 💼

Sample Visualization:



  





  



Model Evaluation: