Fulfilling Flight?
Analysis of factors driving customer satisfaction
This project involves conducting data analysis to identify and assess the key factors driving customer satisfaction in the aviation industry.
Client
RV College of Engineering
Services
Data Analysis
Industries
Aviation Industry
Date
September 2022
For this project, I began by gathering extensive data on diverse factors such as flight distance, delay (arrival/departure), ease-of-booking, check-in service, and baggage handling. Employing Python with libraries like numpy, pandas, sci-kit learn, matplotlib, and seaborn, I conducted a comprehensive data analysis to identify correlations and patterns influencing satisfaction levels. The focal point of the analysis was the implementation of a K-Nearest Neighbors (KNN) model to predict customer satisfaction.
After rigorous testing, the model achieved its highest accuracy of 93% when configured with k=5. This outcome indicates the effectiveness of the selected features in predicting and understanding customer satisfaction within the aviation context. In conclusion, the project highlights the significance of specific operational factors in shaping customer contentment and offers a reliable predictive model for enhancing service quality in the aviation industry.