# Predicting Hotel Performance in Oman with AI-Driven Predictive Analytic

R. S. Al Jassim, Karan Jetly, Shqran Al Mansoory, Muna Al-Balushi, Hilal Al Maqbali

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R. S. Al Jassim, Karan Jetly, Shqran Al Mansoory, Muna Al-Balushi, Hilal Al Maqbali. Predicting Hotel Performance in Oman with AI-Driven Predictive Analytic. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.478-490, 10.1007/978-3-031-34107-6_38. hal-05498093

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# Predicting Hotel Performance in Oman
with AI-Driven Predictive Analytic

R. S. Al Jassim(✉), Karan Jetly, Shqran Al Mansoory, Muna Al-Balushi,
and Hilal Al Maqbali

University of Technology and Applied Sciences, Muscat, Oman
rashas.soh@cas.edu.om

**Abstract.** The primary objective of this study is to assess the performance of hotels in Oman by developing an AI based model using a new approach that we refer to as Linear Genetic Programming for Optimization Decision Tree (LGPDT). The LGPDT algorithm seeks to optimize decision trees, automatically select relevant input attributes, and adjust hyperparameters to improve prediction accuracy. The research findings demonstrate promise after testing the model with datasets from literature and the tourism sector. This approach has the potential to improve the assessment of hotel performance in Oman by providing accurate predictions of customer satisfaction, empowering managers to enhance their services and meet customers' demands more effectively.

**Keywords:** Genetic Programming · Decision Tree · Tourism

## 1 Introduction

Tourism is a key driver of economic and social development in many nations worldwide. The industry is closely tied to global geopolitics, societal trends, environmental factors, and technological advancements, all of which have a profound impact on the direction and growth of tourism. By supporting the development of infrastructure, cultural heritage, and the workforce, tourism plays a vital role in the economies of many nations. In recent years, there has been an increasing demand among tourists for personalized experiences, ecotourism, and the incorporation of innovative technologies, all of which have significant implications for the future of the industry.

Extensive research in the field of information technology has explored the tourism industry, utilizing both conventional and unconventional approaches to address various problems in this sector [13]. Artificial intelligence and machine learning techniques have been utilized to tackle diverse tourism related issues, such as William [2] developed a machine learning model to predict hotel occupancy rates, while Nan [15] used a deep learning approach to accurately forecast tourism demand. Furthermore, Rob [10] used deep learning to forecast monthly tourist arrival volume in Macau based on past factors, showing superior accuracy compared to other models. Similarly, Houria [9] presented a deep learning

© IFIP International Federation for Information Processing 2023

Published by Springer Nature Switzerland AG 2023

I. Maglogiannis et al. (Eds.): AIAI 2023, IFIP AICT 676, pp. 478–490, 2023.

https://doi.org/10.1007/978-3-031-34107-6_38
model for forecasting future tourism demand in Morocco. Additionally, Akin
[1] compared the accuracy of various models, including SARIMA, support vec-
tor machine (SVM), and neural network, for forecasting tourism demand using
monthly tourist arrival data from certain countries to Turkey [1]. The applica-
tion of machine learning and natural language processing has also been employed
to enhance the customer experience and gain a competitive advantage such as
Wooseok [5] employed these techniques to improve the customer experience,
identify areas for improvement, and gain a competitive edge.

Customer satisfaction is a critical factor in the tourism industry, impacting future demand. Extensive research, such as studies by Syjung et al. [3] and others [12], have examined factors affecting customer satisfaction and its implications for airline services. This study aligns with the importance of customer satisfaction in the tourism industry and aims to employ innovative approaches to assess hotel performance in Oman, focusing on service quality, perceived value, and customer satisfaction's impact on intention to revisit an ecotourism destination.

In this paper, we propose Linear Genetic Programming for Optimization Decision Tree (LGPDT), a novel approach for building accurate and interpretable predictive models. LGPDT is a machine learning algorithm specifically designed for optimizing decision trees to check hotel performance in Oman. The approach leverages the strengths of genetic programming [7,14] to enhance overall accuracy and is faster to implement compared to traditional techniques that require manual feature engineering. The algorithm automatically learns the relevant features for tree optimization, reducing overfitting and improving model accuracy. Benefits of this innovative method include automatic selection of relevant input attributes, tuning of decision trees through hyperparameter adjustment, and elimination of manual feature selection, saving time and improving performance.

The objective of this study is to evaluate the effectiveness of a novel learning algorithm in predicting the performance of hotels in Oman. Specifically, the study examines the structure of the data source for hotels in Oman and examines its compatibility with the new learning algorithm. Additionally, the authors aim to provide valuable insights into the modeling of tourism demand in Oman and other regions facing similar challenges by exploring this approach.

The paper consists of six sections: problem statement and study objectives presented in Sect. 2, comprehensive literature review in Sect. 3, framework design and data preparation in Sect. 4, experiment results and comparing the proposed algorithm with other ML algorithms presented in Sect. 5, and conclusion with key findings and future research directions in Sect. 6.

## 2 Problem Statement and Objectives

Stakeholders in the tourism sector aim to enhance Oman's hotel industry and attract more tourists, but there is a need for an approach to evaluate hotel quality and tourism facilities. Issues such as poor hygiene standards and customer service can negatively impact the tourism industry, leading to negative perceptions and
decreased tourist numbers. To enhance Oman's tourism industry, identifying and addressing challenges in the hotel sector is crucial.

Availability of relevant tourism data, particularly publicly accessible sources for analysis, poses a key challenge in this study. Thus, the first research question aims to explore suitable data sources for collecting relevant information on hotel performance in Oman, considering the unavailability and lack of structure in tourism data. To address this, the authors propose collecting data from web-based sources to evaluate the quality of hotels and tourism facilities in Oman. The second research objective is to evaluate the proposed method's effectiveness on preexisting datasets and to assess its efficiency in accurately forecasting hotel performance in Oman. This evaluation will involve comparing the method's performance with other machine learning algorithms commonly used in the field. By achieving these objectives, this study aims to provide insights into the challenges of modeling tourism demand in Oman and to offer a novel approach to addressing these challenges.

## 3 Literature Review

Many scientific disciplines have successfully applied genetic programming (GP) to their respective fields, including finance and economics. GP has been used in the tourism sector for tasks like predicting tourist demand and simulating the design of unique tourist itineraries. Despite being less researched compared to other evolutionary algorithms, GP's popularity in intelligent tourism is increasing due to its capability for solving complex problems with vague solutions. Its automatic evolution of feasible solutions through evolutionary search makes it well suited for tackling complex tourism challenges [7, 8].

Marcos et al. [4] presented a novel two-step approach for predicting tourist arrivals to Spain's Balearic Islands using a genetic programming (GP) algorithm. The results showed that the GP algorithm outperformed traditional statistical methods like ARIMA and exponential smoothing in terms of accuracy and robustness, making it a promising solution for predicting tourist arrivals in the Balearic Islands and other tourist destinations. Similarly, the study by Mie et al. [11] introduced a multitasking GP algorithm for solving the stochastic team orientation problem with time windows (STOPTW) in tourism route planning. The proposed algorithm outperformed other state-of-the-art approaches in benchmark testing, demonstrating its potential for solving combinatorial optimization problems in the tourism industry.

The tourism industry is in need of further research on the full potential of GP as a tool for assessing hotel performance. Therefore, this study aims to explore the potential of using GP, a novel approach that has not been extensively studied in the existing literature, to evaluate hotel performance. Traditional metrics for hotel performance evaluation, such as occupancy rate, average daily rate, revenue per available room, and guest satisfaction scores, are limited in their ability to provide a comprehensive view. GP offers the potential to incorporate multiple variables and complex relationships, providing a more holistic view of
hotel performance. Additionally, GP allows for continuous refinement and optimization of the evaluation as new data becomes available, making it a valuable tool for hotel managers and researchers.

# 4 Framework Designed

The paper's proposed framework comprises three interconnected components for predicting hotel performance in Oman, as depicted in Fig. 1. These components will be elaborated further in subsequent subsections.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Overall Framework Designed</figcaption></figure>

## 4.1 Data Cleaning and Exploration Unit

The proposed framework relies heavily on the data extraction technique, which automatically scrapes web data from Agoda for hotels located in Suhar, Sahalah, and Muscat. This extracted unstructured data offers a wealth of insights into various aspects of our research question. As a result, 17 attributes were included in the dataset. To ensure the proper functioning of the new approach, the data must be structured. However, upon investigation, we found that the data is incomplete, contains missing values, and attributes are not in the correct format. This component plays a significant role in identifying and addressing data quality issues, thereby ensuring that the collected data aligns with the requirements of the new approach.

In the analysis of the tourism dataset, variables are created and modified using an analysis tool to ensure consistency and uniformity. For instance, a new variable called "city" is derived from the Location attribute to maintain consistency in city names. Hotel ranking is determined by averaging guest evaluations of various features. Variables such as "Great for Walkers", "Number of Restaurants", "Number of Reviews", and "Number of Attractions" are converted into categorical scales with values ranging from 1 to 5. Duplicate hotels are removed, missing values are replaced with zero, and unwanted variables are dropped to create a comprehensive and uniform dataset for analysis. The resulting dataset comprises 376 observations with 15 attributes, including wifi, parking, gym, iron, room service, hotel rank, location rating, cleanliness rating, service rating, value rating, star rating, number of reviews, number of attractions, number of restaurants, and a variable indicating walkability.
## 4.2 LGPDT Unit

LGPDT is an evolutionary method for developing accurate and intelligible prediction models. LGPDT encodes decision trees using a linear program, with separate genotype and phenotype structures. The genotype represents attributes and hyperparameters, while the phenotype is generated during evaluation. LGPDT aims to create high performing decision trees with minimal complexity by employing fitness functions during the evolutionary process. Benefits of LGPDT include reduced search space, faster convergence, efficient computation of fitness function, flexible encoding of decision trees, automatic feature selection. The LGPDT consists of two primary components: the LGPDT-Prep, which is a genetic programming data preparation unit, and the LGPDT engine. The LGPDT-Prep is a Python-based module designed for data preparation and operates in conjunction with the LGPDT engine. It scans the data file, retrieves feature names, and dynamically adapts to any dataset. The data is then split into training and testing sets, with the target variable selected based on user specifications. The purpose of the LGPDT-Prep unit is to provide high quality input data to the LGPDT engine, ensuring accurate and reliable outputs.

Selection, crossover, and mutation are the three basic operators in most evolutionary algorithms, and LGPDT is no exception. These operators are driven by the fitness function, which chooses which individuals to reward depending on their fitness. However, the way these operators are implemented in LGPDT can significantly impact the quality of the resulting models. Below, we'll discuss the innovative aspects of implementing these operators in further depth.

**Individuals Pool Representation.** In LGPDT, the population is comprised of chromosomes which contain a set of integer genes. The length of each chromosome varies and is composed of two primary segments. The first segment pertains to the attributes of the dataset, while the second segment represents the hyper parameters of the decision tree. These hyper parameters include the maximum depth of the tree and the minimum number of samples needed to split an internal node. The overall structure of the chromosomes is depicted in Fig. 2.

<figure><img src="image_5.png" /><figcaption>Fig. 2. Structure of Chromosomes in LGPDT.</figcaption></figure>

This method selects genes randomly, subject to two conditions for insertion into the chromosome. First, the number of genes is generated randomly based on the number of features in the dataset. The chromosome length must have at least three genes. The selected genes are represented as integer values that
correspond to the index of the selected attributes. These genes do not need to be
chosen in any particular order. For instance, if set F is composed of attributes
$F_i$ such that $F = F_1, F_2, ... F_n$, then the first part of the chromosome is com-
posed of genes representing any of the features $F_i$ in set F. Given a dataset
D with seven features, if the LGPDT algorithm selects three genes at random,
the resulting chromosome C may be represented by $F_2, F_3$, and $F_7$, where each
gene in the chromosome corresponds to a distinct attributes from the set F.
The second condition for adding a feature to the chromosome is that it must be
unique within the chromosome, implying that the genes within a chromosome
are distinct. Suppose chromosome C is composed of the features $F_1, F_2, ... F_7$,
and a new gene $F_i$ is added to it, then the intersection of C and F must be an
empty set, i.e., $C \cap F = \emptyset$. In other words, the genes within a chromosome are
mutually exclusive, and each feature in the dataset can only be represented once
within a chromosome. In the second segment of the chromosome, there are only
two genes available for depiction. These two genes are utilized to enhance the
hyperparameters of the decision tree, specifically the maximum depth value and
the minimum samples split value, respectively. Thus, these two genes are critical
for determining the efficiency of the decision trees. The minimum samples split
and maximum depth value are critical components in controlling the size and
depth of the decision tree, and they can have a significant impact on its perfor-
mance. Let the size of the dataset be denoted by N. The initial value of the min
samples split is set to the square root of N, i.e., min samples split= $\sqrt{N}$. This
value is then selected randomly to be one of the possible values in the interval
$[2, \sqrt{N}]$. On the other hand, the maximum depth value is also randomly chosen
from the range specified in the interval $[2, 30]$.

**Selection Operator.** In genetic programming, new generations are created through parent selection and crossover operations. Parents are carefully chosen using a binary tournament selection operator, which randomly selects individuals from the population and selects the one with the best characteristics as the winner. This approach is simple and effective in maintaining diversity within the population while ensuring successful inheritance of genes [6].

**Double Segment Linear Crossover.** In the context of LGPDT, a new crossover operator is proposed due to the structure of the chromosome titled Double Segment Linear Crossover. As mentioned previously, the length of the first segment of the chromosome is not uniform. To address this, a random integer within the range of 0 to the length of the two parent individuals is randomly chosen as the crossover point. This ensures that the crossover point is within the boundaries of both parents. Subsequently, the remaining genes from the first parent after the crossover point are copied to one offspring, while the remaining genes from the second parent are copied to the second offspring. Duplicate genes are removed from the resulting offspring to ensure gene uniqueness. To maintain a minimum chromosome length of 2, we calculate the difference $\Delta$ between the length of the parent chromosome and the length of the offspring, i.e., $\Delta = |parent| - |offspring|$, and generate $\Delta$ new genes randomly from the set F to add to the offspring, i.e., $offspring = parent \cup g_1 \cup g_2 \cup ... \cup g_\Delta$, where
$g_i \in F$. This check is performed for the two offspring produced by the two parents (see Algorithm 1). To determine whether to transfer the maximum depth and minimum number of samples from parents to offspring, we set a probability threshold $p = 0.5$. If the probability is greater than $p$, offspring inherit the values from parents, otherwise, random values are generated.

<caption>**Algorithm 1.** Double Segment Linear Crossover</caption>

Require: $F = f_1, f_2, f_3, \dots, f_m$
1: point $\in [0, \min(|parent_1|, |parent_2|)]$
2: for $i \in [0, point]$ do
3:     (offspring$_1$, offspring$_2$) $\leftarrow$ (offspring$_2$, offspring$_1$)
4:     end for
5:     offspring$_1$ $\leftarrow$ $\{gene_i \in offspring_1 : gene_i \notin offspring_2\}$
6:     offspring$_2$ $\leftarrow$ $\{gene_i \in offspring_2 : gene_i \notin offspring_1\}$
7:     if $|offspring_1| < 2$ then
8:         $\Delta \leftarrow |parent_1| - |offspring_1|$
9:         for $i = 1$ to $\Delta$ do
10:             $gene_i \sim U(F)$
11:         while $gene_i \notin offspring_1$ do
12:             $gene_i \sim U(F)$
13:         end while
14:     offspring$_1$ = offspring$_1 \cup gene_i$
15:     end for
16: end if
17: if $|offspring_2| < 2$ then
18:     $\Delta \leftarrow |parent_2| - |offspring_2|$
19:     for $i = 1$ to $\Delta$ do
20:         $gene_i \sim U(F)$
21:     while $gene_i \notin offspring_2$ do
22:         $gene_i \sim U(F)$
23:     end while
24:     offspring$_2$ = offspring$_1 \cup gene_i$
25: end for
26: end if

**Mutation.** After performing crossover operation, the next genetic operator applied is mutation. In this process, a gene is randomly chosen and replaced by another gene based on specific criteria. A new gene is generated within the range of 0 to the number of features in the dataset. To avoid redundancy, it is ensured during the mutation process that the selected gene is not already present in the chromosome. Let $r$ be a random number between 0 and 1. If $r$ is less than or equal to 0.5, the new gene replaces a gene already in the chromosome.

**Fitness Function.** The fitness function ranks individuals in the LGPDT method and is used for parent selection in crossover and mutation. It promotes survival of the fittest individuals. In LGPDT, the goal of this function is to find the optimal combination of attributes and hyperparameters that results in the best
performing decision tree classifier. The fitness function operates on the linear representation of an individual, which consists of genes representing decision tree attributes and hyperparameters. This linear representation is utilized to construct a decision tree model, and its performance is evaluated using a specified metric. The fitness function then assigns a fitness score to the individual based on its model performance, reflecting its effectiveness in addressing the problem under consideration. High fitness individuals become parents for the next generation, undergo crossover and mutation, and repeat until optimal attributes and hyperparameters are found, resulting in the best performing decision tree classifier.

The AUC.Entropy function is the first fitness function that is being tested in LGPDT. It combines two factors, namely the AUC ROC score and entropy, to evaluate the performance of a decision tree. AUC is a commonly used metric in machine learning and measures the quality of a binary classification model. Advantages of this metric include insensitivity to imbalanced class distribution and a single scalar value representing overall model performance. Entropy, on the other hand, measures dataset impurity and aids in decision tree evaluation for identifying high purity splits, improving classification performance. Combining AUC and entropy provides a more balanced evaluation of the model's performance as it considers both aspects of the model's output. The ACU.Entropy function is a useful for developing high performing decision trees that are well suited for a range of classification tasks. An illustration of ACU.Entropy is given in Eq. 1.

$$Fitness(c_i) = 0.8 \times AUC + 0.2 \times \log_{10}(Entropy) \quad (1)$$

AUC.Entropy assigns a weight of 0.8 to AUC and 0.2 to entropy, with logarithmic transformation applied to entropy to make entropy values more manageable. It aims to holistically evaluate the model's performance, considering class separation (AUC) and subset purity (entropy). The resulting fitness value reflects the overall performance, with higher values indicating better fitness.

The second fitness function used in LGPDT, known as AUC-NRules, considers two factors for evaluation: the AUC ROC score and the number of rules generated by the tree. The number of rules is an indicator of the complexity of the decision tree, with simpler trees preferred for ease of interpretation. The AUC-NRules fitness function aims to provide a comprehensive evaluation of the model's performance, considering both class separation ability and complexity, with weights of 0.8 and 0.2, respectively. Taking the reciprocal of the logarithm of the number of rules helps to make the range of values smaller and easier to work with, as well as to amplify the effect of the number of rules on the fitness function. The chromosome $c_i$ is evaluated using the following equation:

$$Fitness(c_i) = 0.8 \times AUC + 0.2 \times \frac{1}{1 + \log_{10}(n_rules)} \quad (2)$$

The last fitness function in this study is the combination of all three factors (AUC ROC, the number of rules, and entropy) with different weights to evaluate the performance of the decision tree in a more comprehensive way. The weights assigned to the AUC, the number of rules, and entropy are 0.4, 0.2, and 0.5, respectively. The weights were chosen to assign greater importance to the AUC
ROC measure and the entropy measure, while assigning less importance to the number of rules (0.2). This suggests that the model's ability to separate classes and produce pure subsets is more important than the complexity of the decision tree (as measured by the number of rules). An illustration of this function is given in Eq. 3.

$$Fitness(c_i) = 0.4 \times AUC + 0.2 \times \frac{1}{1 + \log_{10}(n_{rules})} + 0.4 \times \frac{1}{1 + \log_{10}(entropy)} \quad (3)$$

All fitness functions will be evaluated to identify the most effective measure of model performance. The best performing fitness function will be determined through experiments and it will be selected for further study. Weights assigned to each factor range from 0 to 1, with the sum of all weights equaling 1. The specific weights for each factor were determined based on experimental results.

# 5 Experiment

In this section, LGPDT will be applied to predict hotel performance in Oman. Algorithm efficacy will be verified by testing on literature datasets [16], and fitness values of proposed fitness functions will be evaluated on the selected dataset. Findings will be used to establish an appropriate model for the Oman tourism dataset. Implications, study limitations, and future research directions will be discussed.

## 5.1 Comparative Analysis of LGPDT on Different Datasets

In this research experiment, the Heart Disease, Iris, Pima, and Wine datasets were utilized [16]. The Heart Disease dataset comprises 2 classes, 270 instances, and 13 attributes. In contrast, the Iris dataset consists of 150 instances, 4 attributes, and 3 classes. The Pima dataset is characterized by 8 attributes, 768 instances, and 2 classes. Lastly, the Wine dataset encompasses a total of 178 instances, 14 attributes, and 3 classes.

Table 1 summarizes an empirical study that examined the impact of three fitness functions on multiple datasets. The table presents the results obtained from the experiment, including the number of rules (#Rule), maximum depth (MDepth), selected attributes (SA), minimum sample splits (MSS), and accuracy scores, for various fitness functions and datasets. Accuracy was assessed using hold out testing. We noticed that the AUC_NRule shows that the number of rules obtained from the decision tree and the accuracy score have a negative relationship. A decision tree with fewer rules might not be able to handle the complexity of the dataset, which could make the results less accurate. On the other hand, adding too many rules can result in overfitting, causing the model to fit noise instead of the underlying pattern in the data. This can lead to poor performance on new, unseen data. This explains why this measure was given so little weight in AUC_RE and other fitness function. In the case where entropy is incorporated as a measure within the fitness function, the objective is to minimize entropy
<table><caption>**Table 1.** Comparison of Fitness Functions on Different Datasets</caption><thead><tr><th>Fitness</th><th>Dataset</th><th>#Rules</th><th>MDepth</th><th>MSS</th><th>SA</th><th>Accuracy</th></tr></thead><tbody><tr><td rowspan="4">ACU_Entropy</td><td>Heart Disease</td><td>27</td><td>10</td><td>6</td><td>10</td><td>0.8033</td></tr><tr><td>Iris</td><td>14</td><td>8</td><td>10</td><td>4</td><td>0.96</td></tr><tr><td>Pima</td><td>64</td><td>30</td><td>13</td><td>6</td><td>0.785714</td></tr><tr><td>Wine</td><td>13</td><td>26</td><td>4</td><td>10</td><td>0.9166</td></tr><tr><td rowspan="4">AUC_NRules</td><td>Heart Disease</td><td>18</td><td>24</td><td>12</td><td>11</td><td>0.7541</td></tr><tr><td>Iris</td><td>10</td><td>9</td><td>4</td><td>4</td><td>0.96</td></tr><tr><td>Pima</td><td>40</td><td>16</td><td>26</td><td>8</td><td>0.7597</td></tr><tr><td>Wine</td><td>12</td><td>24</td><td>11</td><td>9</td><td>0.9166</td></tr><tr><td rowspan="4">AUC_RE</td><td>Heart Disease</td><td>20</td><td>12</td><td>10</td><td>13</td><td>0.7541</td></tr><tr><td>Iris</td><td>12</td><td>16</td><td>5</td><td>4</td><td>0.9866</td></tr><tr><td>Pima</td><td>13</td><td>4</td><td>18</td><td>8</td><td>0.7403</td></tr><tr><td>Wine</td><td>14</td><td>16</td><td>8</td><td>11</td><td>0.9166</td></tr></tbody></table>

for each split in the decision tree. This may result in the generation of complex decision trees with numerous splits and branches, leading to an increased number of rules. Despite this, AUC_Entropy outperforms AUC_NRules in terms of accuracy, even when generating a larger number of rules. Furthermore, it was observed that AUC_RE achieves a favorable trade-off between the number of rules and accuracy, as it minimizes entropy while also considering the number of rules generated. As such, it is advisable to thoroughly evaluate the results obtained from all three functions and select the most appropriate one based on the specific characteristics of the dataset.

The effectiveness of LGPDT assessed through a comparative analysis with another method, specifically C4.5 models. Four distinct datasets were used in this analysis, and a 10 fold cross validation approach was employed for each dataset. Accuracy was used as the evaluation metric to determine the effectiveness of the LGPDT and C4.5 models. Table 2 indicates that LGPDT outperforms C4.5 on all four datasets. LGPDT shows higher mean accuracy (80.86%) with lower standard deviation (3.9%) compared to C4.5 (mean accuracy of 74.5% with standard deviation of 8.2%) on the heart disease dataset. These findings support the use of LGPDT for machine learning tasks, as it demonstrates higher accuracy and consistency in predictions compared to C4.5.

<table><caption>**Table 2.** Comparison of Accuracy of LGPDT and C4.5.</caption><thead><tr><th>Dataset</th><th>C4.5 Accuracy</th><th>LGPDT Accuracy</th></tr></thead><tbody><tr><td>Heart Disease</td><td>74.5 ± 8.2%</td><td>80.86 ± 3.9%</td></tr><tr><td>Iris</td><td>94.1 ± 4.7%</td><td>96.6 ± 1.79%</td></tr><tr><td>Pima</td><td>73.9 ± 5.7%</td><td>76.04 ± 4.3%</td></tr><tr><td>Wine</td><td>90.9 ± 7.1%</td><td>92.97 ± 0.4%</td></tr></tbody></table>
## 5.2 Performance of LGPDT on Tourism Dataset

This section applies LGPDT to forecast hotel success in Oman, assessing its efficiency using proposed fitness functions and comparing outcomes to identify the most effective fitness function. Furthermore, the findings of this study will be compared with the outcomes of Weka.

Positive outcomes were seen when the LGPDT algorithm was applied to the tourism dataset, with the AUC_RE fitness function producing the highest accuracy at 73% along with a rule set of 69 rules. Through automated experimentation, we discovered that a maximum depth of 12, a minimum split of 18, and the selection of nine attributes provided the best results for LGPDT. These carefully chosen features constitute a subset that was found to be adequate for precise classification in the current experiment. These results show that LGPDT is a promising tool for tourism data classification because it can produce accurate and interpretable models with a low number of rules (Table 3).

<table><caption>Table 3. Comparison of Fitness Functions on a Tourism Dataset</caption><thead><tr><td>Fitness</td><td>Accuracy</td><td>#Rules</td><td>MDepth</td><td>MSS</td><td>#Attributes</td></tr></thead><tbody><tr><td>AUC_Entropy</td><td>0.7173 ± 0.02</td><td>73</td><td>14</td><td>16</td><td>6</td></tr><tr><td>AUC_NRules</td><td>0.7026 ± 0.04</td><td>78</td><td>12</td><td>15</td><td>9</td></tr><tr><td>AUC_RE</td><td>0.7306 ± 0.05</td><td>69</td><td>12</td><td>18</td><td>9</td></tr></tbody></table>

Furthermore, a comparison was made between the performance of LGPDT and Weka, utilizing the same dataset and the set of optimized hyperparameters. The results showed that LGPDT outperformed Weka, achieving an accuracy of 73%, compared to Weka's accuracy of 69%. These findings suggest that LGPDT is a more appropriate and effective method for the specific problem domain and dataset at hand. One possible explanation for the superior performance of LGPDT is its unique combination of genetic programming and decision tree learning, which may have enabled it to capture more complex relationships and patterns in the data. Moreover, LGPDT's ability to optimize hyperparameters during the training process could have contributed to its superior performance, as opposed to Weka, which may have relied on a fixed set of hyperparameters. These results highlight the significance of carefully selecting and optimizing the appropriate machine learning algorithm and hyperparameters for a given dataset and problem domain to achieve optimal performance.

## 6 Conclusion

Managers need to be able to accurately predict how well a hotel will do in order to serve their customers well. In this study, a new method was used to predict how well hotels in Suhar, Sahalah, and Muscat would do. The data for these
hotels was scraped from the Agoda website. The study aimed to address two research questions: 1) evaluating the proposed method's performance on collected datasets, and 2) assessing the method's accuracy in predicting hotel performance. The study evaluated LGPDT's performance on existing datasets and compared it to other methods. LGPDT outperformed existing methods, demonstrating its validity and reliability for hotel performance prediction. The study confirmed LGPDT's accuracy in predicting hotel performance, with an average accuracy rate of approximately 73%. In conclusion, this study demonstrated the effectiveness of LGPDT for predicting hotel performance, which has practical implications for managers in the hotel industry. Based on the findings of this study, future work could involve testing LGPDT on a larger and more diverse set of hotel datasets to further validate its effectiveness. Additionally, incorporating additional features such as social media data could potentially improve the accuracy of the predictions. Finally, investigating the impact of hotel performance prediction on business decisions and customer satisfaction could also be a valuable area for future research.

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