=Paper=
{{Paper
|id=Vol-2631/paper34
|storemode=property
|title=Analysis and Estimation of Popular Places in Online Tourism Based on Machine Learning Technology
|pdfUrl=https://ceur-ws.org/Vol-2631/paper34.pdf
|volume=Vol-2631
|authors=Yurii Tverdokhlib,Vasyl Andrunyk,Liliya Chyrun,Lyubomyr Chyrun,Nataliya Antonyuk,Ivan Dyyak,Oleh Naum,Dmytro Uhryn,Vitor Basto-Fernandes
|dblpUrl=https://dblp.org/rec/conf/momlet/TverdokhlibACCA20
}}
==Analysis and Estimation of Popular Places in Online Tourism Based on Machine Learning Technology==
https://ceur-ws.org/Vol-2631/paper34.pdf
Analysis and Estimation of Popular Places in Online
Tourism Based on Machine Learning Technology
Yurii Tverdokhlib1, Vasyl Andrunyk2[0000-0003-0697-7384], Liliya Chyrun3[0000-0003-4040-
7588]
, Lyubomyr Chyrun4[0000-0002-9448-1751], Nataliya Antonyuk5[0000-0002-6297-0737], Ivan
Dyyak6[0000-0001-5841-2604], Oleh Naum7[0000-0001-8700-6998], Dmytro Uhryn8[0000-0003-4858-4511],
Vitor Basto-Fernandes9[0000-0003-4269-5114]
1-3Lviv Polytechnic National University, Lviv, Ukraine
4-6Ivan Franko National University of Lviv, Lviv, Ukraine
5University of Opole, Opole, Poland
7Drohobych Ivan Franko State Pedagogical University, Drohobych, Ukraine
8Chernivtsi Philosophical and Legal Lyceum, Chernivtsi, Ukraine
9University Institute of Lisbon, Lisbon, Portugal
yurii.tverdokhlib.sa.2017@lpnu.ua1, vasyl.a.andrunyk@lpnu.ua2,
Lyubomyr.Chyrun@lnu.edu.ua4, nantonyk@yahoo.com5,
ivan.dyyak@lnu.edu.ua6, oleh.naum@gmail.com7, ugrund38@gmail.com8
Abstract. This article discusses and compares some machine-learning regres-
sion methods for developing a prognostic model that predicts the daily number
of visitors in different areas (tourist places) of India. Visitor reviews from holi-
dayiq.com are used as data. The main features of the selected data set are de-
scribed.
Keywords: Online Tourism, Popular Places, Machine Learning.
1 Introduction
The article is based on a set of data consisting of specific data obtained from user
reviews posted on Holidayiq.com about different types of attractions in India [1-2].
This dataset is completed with feedback on appointments published by 249 Holiday-
iq.com reviewers by March 2020.
This paper discusses and compares some machine-learning regression methods for
developing a prognostic model that predicts the daily number of visitors in different
areas (tourist places) of India.
Implementation of strategic projects will allow for appropriate restructuring of the
tourism industry in relation to the socio-economic life of the state [3-9]. It is focus on
population, government, management and business structures and a comprehensive
approach to ensuring the effective use of benefits and opportunities of domestic tour-
ism sector due to climatic conditions and historical features [1-16], taking into ac-
count the requirements of environmental protection and preservation and enrichment
of is heritage [17-25].
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 Main Part
Tourism in India is important for the country's economy and growing rapidly [1]. The
World Travel and Tourism Council estimated that tourism in 2018 generated ($ 240
billion) or 9.2% of India's GDP and supported 42.673 million jobs, 8.1% of total em-
ployment [1]. It is projected that by 2028 this sector will grow from 6.9% to 32.05
GEL [1]. Tourism is one of the largest earners in foreign currency. The importance of
tourism as a tool for economic development and employment, especially in remote
and backward areas, has well recognized around the world. The benefits of tourism
can be increased either by increasing the number of tourists or by increasing the
length of stay of tourists in the country. Data on national length of stay are very im-
portant and useful for the purposeful promotion of tourism in the outgoing markets.
This paper uses a set of user data (feedback) on different types of attractions in In-
dia. The dataset contains 1743 data points collected from Holidayiq.com [2].
The main features of the selected data set:
1. User ID;
2. Number of inspections of stadiums, sports complexes;
3. Number of reviews of religious institutions;
4. Number of reviews about the beach, lake, river, etc .;
5. The number of reviews about theaters, exhibitions;
6. The number of reviews about shopping centers, shopping malls;
7. Number of reviews about parks, picnic areas, etc.
Fig. 1. Data set.
The method that will implemented in this work is the classification tree.
To implement this course work, we chose the Python programming language. Py-
thon is an easy-to-use yet full-fledged programming language that provides much
more tools for structuring and supporting large applications [26-32].
The large selection of libraries is one of the main reasons that Python is the most
popular programming language used for ML [33-45]. A library is a module or group
of modules published from various sources, such as PyPi, that contains a pre-written
piece of code that allows users to achieve certain functionality or perform various
actions [46-51]. Python libraries provide basic-level elements, so you do not need to
encode them from the beginning. ML requires constant data processing, and Python
libraries allow you to access, process, and transform data [52-61].
Fig. 2. PyCharm interface
First, we need to connect libraries to use their capabilities in the future
After downloading the data, reading our data set, and building graphs. Next, we want
to show you a chart that visualizes data according to user feedback about picnics and
parks. As we see here, we have 2 local highs and 4 local lows. The same can be said
for the next 2 graphs. These graphs determine whether to work with this data and
allow you to determine whether to normalize it or not.
Fig. 3. Bar chart of reviews of parks and picnic areas.
Fig. 4. Bar chart of feedback on stadiums.
Fig. 5. Bar chart of reviews by religious places.
The following graph is also a chart, but it shows how feedback after visiting the thea-
ter affects the feedback of beaches, lakes, and we can see points from 0 to 10 here.
Fig. 6. Bar chart of reviews on blowing beaches, lakes, rivers.
The following graph is a scatter graph that shows us the distribution of attribute data
relative to the data distribution of another attribute. We need this data in order to clus-
ter it in the future.
Fig. 7. Schedule of scattering visits to theaters in relation to shopping centers
Fig. 8. Schedule of scattering visits to parks in relation to shopping centers.
3 Assigning Target and Feature Variables
Feature selection is one of the core concept in order to affect the performance of the
model. In the piece of code shown above, we have assigned the feature and target
variables.
4 Splitting the Dataset into Training Set and Testing Set
We generally split the data we have into training and testing sets so that our model
learns on this data. we use the test data to test how accurate our model is.
Here we have to divide our data as 70% training and 30% testing.
5 Accuracy for the Training Data
6 Accuracy of the Testing Data
7 Data Visualization
The entropy for each node in the decision tree is calculated and shown in the Fig. 9.
Fig. 9. Schedule of scattering visits to parks in relation to shopping centers
8 Perceptron
In machine learning, the perceptron is an algorithm for the controlled study of binary
classifiers. A binary classifier is a function that can determine whether an input repre-
sented by a vector of numbers belongs to a particular class.
9 Logistic Regression
10 Results
Decision tree: 44%. Perceptron: 10%. Logistic regression: 8%. Note: Since the dataset
is small, we are getting low accuracy. Conclusion from the above results we can con-
clude that decision tree is the best method for this dataset with an accuracy of 44%.
11 Conclusions
This article discusses and compares some machine-learning regression methods for
developing a prognostic model that predicts the daily number of visitors in different
areas (tourist places) of India. Visitor reviews from holidayiq.com are used as data.
The main features of the selected data set are described. Next, methods and means of
implementation are described. To implement this course work, we chose the Python
programming language. After downloading the data, reading our data set, and build-
ing graphs. This article demonstrates 6 graphs, namely 4 bar charts and two scatter
plots. Three machine-learning methods are used. The first was the decision tree,
which showed the best result of 44%. The second is the 10% perceptron, and the third
is the logistic regression method. Because the sample of my dataset is small, that is
why we got such accuracy of algorithms. In general, we can say that the algorithms
are not very successful in their task, but this is because the data sample is too small.
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