This project aims to analyze customer satisfaction in the airline industry by examining various factors that influence passenger experiences. The analysis includes identifying key independent variables that affect customer satisfaction and utilizing machine learning models to predict satisfaction levels. The primary objective is to understand how different service aspects impact customer perceptions and to identify areas for improvement.
- Decision Tree: A tree-like model that splits the data into branches to make predictions. It is used for its simplicity and interpretability.
- Random Forest: An ensemble of decision trees that improves accuracy by reducing overfitting. It is known for its high performance in classification tasks.
- Logistic Regression: A statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. It is useful for binary classification problems.
The analysis revealed that the Random Forest algorithm was the most accurate model for predicting customer satisfaction, achieving a high accuracy rate. The model's performance indicates its robustness and reliability in handling diverse variables related to customer satisfaction.
- Clone the repository
- Load the dataset
- Run the provided R scripts to train and evaluate the models
Detailed results and visualizations can be found in the Visualization folder, showcasing the model performance and insights derived from the analysis.
The Random Forest model's high accuracy underscores its suitability for predicting customer satisfaction in airline services. Further improvements can be explored by incorporating additional features and more complex models.