Airline Passenger Satisfaction

: Airline Passenger Satisfaction**

The **Airline Passenger Satisfaction** project focuses on analyzing airline customer data to understand the factors that influence passenger satisfaction. The main goal of the project is to study passenger feedback and service-related features in order to determine whether a passenger is satisfied or dissatisfied with the airline services.

The dataset typically includes several variables such as passenger age, gender, travel class, flight distance, seat comfort, food and drink service, check-in service, baggage handling, in-flight entertainment, and other service quality indicators. By analyzing these features, the project aims to identify patterns and relationships that affect overall passenger satisfaction.

Using **data analysis and machine learning techniques**, the system can predict whether a passenger is likely to be satisfied based on their travel experience and service ratings. This helps airlines better understand customer needs and improve their services to increase customer loyalty and satisfaction.

The project demonstrates how **data-driven decision making** can help airlines improve service quality, enhance passenger experience, and remain competitive in the aviation industry.

**Key Features of the Project:**

* Analyzing airline passenger data.

* Identifying key factors affecting passenger satisfaction.

* Using machine learning models to predict satisfaction levels.

* Providing insights to help airlines improve their services.

**Technologies that can be used:**

* Python

* Pandas & NumPy for data analysis

* Matplotlib / Seaborn for data visualization

* Scikit-learn for machine learning models