TY  - BOOK
AU  - Yassmin Shawki  Ali Ahmed, 
AU  - Salwa Mahmoud Assar
AU  - Abd El-Tawab Ahmed Gira
TI  - Prediction of a travel package based on tourists’ personal data using data mining
U1  - 006.31 
PY  - 2025///
KW  - data mining
KW  - التنقيب عن البيانات
KW  - Data mining
KW  - Decision tree
KW  - Travel package recommendation
KW  - Tourism service personalization
KW  - Tourists’ personal data
KW  - Customer satisfaction improvement
KW  - تنقيب البيانات
KW  - شجرة القرار
N1  - Thesis (M.Sc)-Cairo University, 2025; Bibliography: pages 82-83.; Issues also as CD
N2  - In the era of big data and the evolution of digital technology, the need for advanced methods of data 
analysis has become essential to keep up with modern requirements and enhance competitiveness. 
Tourism companies face significant challenges in meeting the increasing and diverse needs of 
customers, as the decision-making process regarding the selection of tourism packages now requires 
greater accuracy and speed. 
With the substantial growth in the tourism industry and the expanding range of options available to 
travelers, companies encounter numerous challenges related to their ability to offer the most suitable 
tourism packages to customers quickly and accurately. The core issue lies in the difficulty of 
understanding individual customer needs due to the diversity and complexity of data. Additionally, 
the time consumed for each customer who needs guidance in selecting the appropriate package 
impacts company performance and customer trust. This may also result in ineffective decisions that 
influence customer satisfaction and lead to the loss of important marketing opportunities. Therefore, 
it has become essential to use advanced analytical tools to extract knowledge from large data sets and 
transform it into actionable and impactful decisions. 
In this research, data mining techniques, specifically the decision tree algorithm, were utilized to 
build a predictive model aimed at recommending the most suitable tourism package based on 
customers' personal data. The data included a set of critical attributes such as nationality, gender, age, 
travel purpose, budget, preferred destination, and package name. The data was collected and 
processed to ensure its quality and suitability for analysis. The model was then trained using the 
decision tree algorithm due to its ability to handle multidimensional data and provide clear and easy-
to-interpret results. 
The results showed that the proposed model achieved high accuracy in predicting the appropriate 
tourism packages. The model also demonstrated balanced performance when using both training and 
testing data, reflecting its effectiveness, generalization capability, and ability to make accurate 
predictions without overfitting. 
This study highlights the importance of relying on data mining techniques to improve decision-
making processes in the tourism sector. Companies can provide personalized recommendations that 
contribute to enhancing customer satisfaction and increasing their loyalty. Furthermore, this research 
represents an effective step toward improving the efficiency of marketing operations and achieving a 
sustainable competitive advantage in the growing tourism market. 
We used Python in Jupiter notebook, and extracted data using Structured Query Language (SQL) 
from Salesforce (Salesforce Service Cloud provides a fast, artificial intelligence (AI)-driven 
customer service and support experience to customers and enables businesses to scale their 
operations efficiently).; في عصر البيانات الضخمة والتكنولوجيا الرقمية، أصبحت الحاجة لاستخدام أساليب تحليل متقدمة أمرًا ضروريًا لتعزيز تنافسية شركات السياحة. تواجه هذه الشركات تحديات كبيرة في تقديم الحزم الأنسب للعملاء بسرعة ودقة، حيث يؤدي تنوع البيانات وتعقيدها إلى صعوبة فهم احتياجات العملاء بشكل فردي، مما يزيد من الوقت المستغرق لكل عميل ويؤثر سلبًا على أداء الشركة وثقة العملاء. أيضًا، قد يؤدي ذلك إلى قرارات غير فعالة وخسارة فرص تسويقية مهمة.
في هذا البحث، تم استخدام تقنيات التنقيب عن البيانات، وتحديدًا خوارزمية شجرة القرار، لبناء نموذج تنبؤي يُوصي بالحزمة السياحية الأنسب بناءً على البيانات الشخصية للعملاء، مثل الجنسية، النوع، العمر، الهدف من السفر، الميزانية، الوجهة، واسم الحزمة. بعد جمع البيانات ومعالجتها، تم تدريب النموذج باستخدام شجرة القرار بسبب قدرتها على التعامل مع البيانات المعقدة وتقديم نتائج واضحة.
أظهرت النتائج دقة عالية للنموذج في التنبؤ وتوازنًا في الأداء بين بيانات التدريب والاختبار، مما يعكس فعاليته وقدرته على التعميم دون الإفراط في التعلم. تؤكد هذه الدراسة أهمية الاعتماد على التنقيب عن البيانات لتحسين اتخاذ القرار وزيادة رضا العملاء وولائهم، كما تعزز كفاءة العمليات وتحقيق ميزة تنافسية مستدامة في سوق السياحة. تم تنفيذ العمل باستخدام لغة Python في بيئة  Jupiter Notebook، وتم استخراج البيانات عبر SQL من Salesforce.
ER  -