passenger-satisfaction

Here are 6 public repositories matching this topic...

yogeshwaran-shanmuganathan / Airline-Passenger-Satisfaction

Determining the important factors that influences the customer or passenger satisfaction of an airlines using CRISP-DM methodology in Python and RapidMiner.

python random-forest exploratory-data-analysis jupyter-notebook naive-bayes-classifier logistic-regression decision-tree-classifier customer-satisfaction factors knn-classification rapidminer xgboost-algorithm crisp-dm airline-passengers adaboost-classifier passenger-satisfaction airline-passenger-satisfaction

Updated Sep 1, 2023
Jupyter Notebook

oguzhansan / Passenger-Satisfaction-Forecasting-Project

Star

The Project of Passenger Satisfaction Forecasting is a user-friendly web application designed to empower companies to assess and understand their satisfaction impact.

data-science machine-learning airlines predicition passenger-satisfaction

Updated Apr 1, 2024
Jupyter Notebook

Shubham-Dalvii / Airline-Passenger-Satisfaction-Dashboard

Star

A Power BI dashboard that visualizes passenger satisfaction across demographics, flight classes, delays, and service quality.

data-visualization data-analysis power-query dax powerbi customer-experience passenger-satisfaction airline-data-analysis

Updated Jul 22, 2025

EsraCesur4 / Airline-Passenger-Satisfaction-Data-Analysis

Star

Bu proje, uçak yolcularının memnuniyetini etkileyen faktörleri incelemek ve memnuniyet düzeyini tahmin etmeye yönelik bir veri analizi çalışmasıdır.

data-analysis passenger-satisfaction

Updated Jul 7, 2025
Jupyter Notebook

robinetnoticeable8551 / Airline-Passenger-Satisfaction

Star

Predict airline passenger satisfaction with 96% accuracy using a tuned XGBoost model, deployed in an interactive Streamlit app.

python data-science machine-learning scikit-learn eda xgboost classification lightgbm decision-tree-classifier customer-satisfaction factors knn-classification xgboost-algorithm streamlit adaboost-classifier passenger-satisfaction airline-passenger-satisfaction

Updated Oct 14, 2026
Jupyter Notebook

girishshenoy16 / airline-passenger-satisfaction-analytics

Star

Executive survey intelligence platform analyzing 129,880 airline passenger responses with Python statistical engine and interactive Plotly.js dashboard. Features Chi-Square testing, Cramér's V effect sizes, confidence intervals, and automated executive insights.

Updated Aug 13, 2026
Python

Add this topic to your repo

To associate your repository with the passenger-satisfaction topic, visit your repo's landing page and select "manage topics."

Learn more