Customer referral is a crucial aspect of business growth and success, and the airline industry is no exception. Satisfied passengers who have had positive experiences with an airline are more likely to refer the airline to their friends, family, and colleagues. Identifying these potential advocates can help airlines improve customer satisfaction and loyalty and attract new customers.
In this project, we will use machine learning algorithms to predict whether a passenger will refer an airline to others. We will use a dataset that includes past passengers and their referral behavior, as well as various features such as age, gender, flight class, and route information.
Our first step will be to perform exploratory data analysis to gain insights into the data and identify any patterns or correlations. We will then preprocess the data by handling missing values, encoding categorical variables, and scaling numeric features.
We will then apply several machine learning algorithms, including logistic regression, random forest, and support vector machines, to predict the likelihood of a passenger becoming a referral. We will also perform feature engineering and selection to improve the performance of our models.
Finally, we will evaluate our models using metrics such as accuracy, precision, recall, and F1 score. We will also use techniques such as cross-validation and grid search to tune our hyperparameters and ensure our models generalize well to new data.