OmBaval/Airline-Customer-Satisfaction
This project employs a dataset of 103,904 entries with 25 features. Utilizing the XGBoost classifier,The workflow involves data fetching, feature selection, preprocessing, correlation analysis, best feature selection, data rescaling, train-test split, and target balancing. Predicts whether a customer will experience satisfaction with a flight.
No commits in the last 6 months.
Stars
—
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 01, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/OmBaval/Airline-Customer-Satisfaction"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
wzhe06/Ad-papers
Papers on Computational Advertising
seanZhang414/openadserver
Open Source Ad Serving Platform with ML-Powered CTR Prediction | Self-hosted alternative to...
abhinav-bhardwaj/Walmart-Sales-Time-Series-Forecasting-Using-Machine-Learning
Time Series Forecasting of Walmart Sales Data using Deep Learning and Machine Learning
juancaalcaraz/automatizacion-de-reportes-de-ventas
Sistema para generar reportes (en Excel y PDF) automáticamente y crear predicciones del total de...
karthickai/Linear-Regression
Machine Learning - Linear Regression_Ecommerce_Prediction