Like how ratings are crucial in a hotel booking process, they are equally important in the airline industry. The primary reason for such ratings is the expectation of service for the amount paid (since being the most expensive way of transport). A rating can boost a newbie up or pull a giant down. Again, it is all due to the service. Identifying the reasons and where to correct them is an example of a business model. The key objectives of this problem would be (a)To identify if changes are necessary to implement based on customer feedback. (b)To measure the modifications and minimize overall dissatisfaction. (c)Develop corresponding visualizations. And (d)To identify the satisfaction of a customer based on their feedback by fitting the appropriate model. In real-time, this study can be used for reviewing the performance of the airline’s front-end operations. With machine learning techniques, the user can find the best fit model. Furthermore, the financial data, it can also be used to alter the existing model to bring better expenditure versus profit per seat. The final model should also be able to predict satisfaction based on ratings and analyse the ways to minimize dissatisfaction.