Earticle

초록

영어

Retention of customers is vital for the long-term profitability of a business in the travel and tourism sector. In this sector churn, which is the continuous loss of clients, is an overwhelming hurdle. The major reason for this is that it is more time and cost-efficient to retain existing customers who spend money on acquiring new ones. There is a lot of value in being able to effectively predict customer churn. This knowledge will enable businesses to take a more proactive stance and offer personalized services in an effort to save and enhance the loyalty of the customer. The business problem here is to analyze and predict patterns of customer churn using various advanced algorithms like Categorical Boosting (CatBoost), Decision Tree (DT) and Knearest neighbors (KNN) techniques. Feature engineering and normalization are some of the many data preprocessing steps taken prior to training the machine learning (ML) model. Popular metrics such as accuracy, misclassification rate, precision, sensitivity, specificity, and F1 scores are used to examine the performance of each ML model.

목차

Abstract
I. INTRODUCTION
II. LITERATURE REVIEW
III. METHODOLOGY
A. Dataset Description:
B. ML Models
IV. SIMULATION AND RESULT
V. CONCLUSION
VI. FUTURE WORK
VII. REFERENCES

저자

• Muhammad Farrukh Khan [ Department of Computer Science, NASTP Institute of Information Technology, Lahore, 58810, Pakistan ]
• Shah Zain Haider [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
• Hassan Faisal [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
• Muntaha Liaqat [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
• Tayyab Nawaz [ School of Computer Science National College of Business Administration and Economics, Lahore 54000, Pakistan ]
• Shahan Yamin Siddiqui [ Department of Computer Science, NASTP Institute of Information Technology, Lahore, 58810, Pakistan. ]

참고문헌