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Oral Session A-3 : AI Applications

Tour and Travel Customer Churn Predictions

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    ICNGC 2025 The 11th International Conference on Next Generation Computing 2025 (2025.12)바로가기
  • 페이지
    pp.201-204
  • 저자
    Muhammad Farrukh Khan, Shah Zain Haider, Hassan Faisal, Muntaha Liaqat, Tayyab Nawaz, Shahan Yamin Siddiqui
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A478494

원문정보

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

키워드

Categorical Boosting (CatBoost) Decision Tree (DT) Machine Learning (ML) K-nearest neighbors (KNN).

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

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

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