https://github.com/datalopes1/flight_pricing
Project to predict flight tickets prices using XGBoost
https://github.com/datalopes1/flight_pricing
machine-learning python regression xgboost xgboost-regression
Last synced: about 1 month ago
JSON representation
Project to predict flight tickets prices using XGBoost
- Host: GitHub
- URL: https://github.com/datalopes1/flight_pricing
- Owner: datalopes1
- Created: 2024-08-25T21:56:35.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-27T15:10:36.000Z (almost 2 years ago)
- Last Synced: 2025-03-28T13:14:26.099Z (about 1 year ago)
- Topics: machine-learning, python, regression, xgboost, xgboost-regression
- Language: Jupyter Notebook
- Homepage: https://sites.google.com/view/portfolioandreluizls1
- Size: 27.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
README
# Flight Price Prediction with XGBoost 🛫
### Content
- [Introduction](#Introduction)
- [Project goals](#project-goals)
- [Data](#data)
- [Methods and models](#methods-and-models)
- [Results](#results)
- [Tools of work](#tools-of-work)
- [Contact me](#contact)
## Introduction
Dataset contains information about flight booking options from the website Easemytrip for flight travel between India's top 6 metro cities. There are 300261 datapoints and 11 features in the cleaned dataset.
## Project goals
Our goals is to (1) answer the business questions proposed and (2) to build a machine learning model to predict flight prices.
#### Business questions
- Does price vary with Airlines?
- How is the price affected when tickets are bought in just 1 or 2 days before departure?
- Does ticket price change based on the departure time and arrival time?
- How the price changes with change in Source and Destination?
- How does the ticket price vary between Economy and Business class?
## Data
|Column|Description|
|---|---|
|``airline``|Airline company|
|``flight``|Flight code|
|``source_city``|Source city|
|``departure_time``|Departure time|
|``stops``|Number of stops|
|``arrival_time``|Arrival time|
|``destination_city``|Destination city|
|``class``|Ticket class|
|``duration``|Flight duration|
|``days_left``|Days between booking and travel|
|``price``|Ticket price (target)|
The dataset is available in [Kaggle](https://www.kaggle.com/datasets/shubhambathwal/flight-price-prediction) and was uploaded by Shubham Bathwal.
## Methods and models
#### Pre-processing
- Scikit-learn, Optuna, Category Encoders, Feature Engine.
#### Models
- XGBoost.
#### Metrics
- Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, R2 Score.
## Results
### Answering the business questions
- Price varies with class and airline, especially with class. In the case of airlines, there is a significant difference when we consider Vistara and Air India;
- Kolkata and Chennai are the most expensive destinations;
- For arrival time, evening and morning have the highest prices, and for departure, morning and night do;
- Buying tickets in advance is the best way to get good prices.
### Machine learning model
#### Metrics
|Metric|Result|
|---|---|
|**MSE**|6855205.6699|
|**RMSE**|2618.2448|
|**MAE**|1328.5562|
|**MAPE**|0.0962|
|**R2 Score**|0.9868|
#### Cross-validation results
|Metric|Result|
|---|---|
|**General Mean MAE**|1377.5180|
|**MAE Standard Deviation**|10.8035|
#### Visualizations
## Tools of work
## Contact
- **E-mail:** andreluizlcons@gmail.com
- **LinkedIn:** https://www.linkedin.com/in/andreluizls1/
- **Phone** +55 88 999934237