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Analysis and Estimation of Popular Places in Online Tourism Based on Machine Learning Technology

Logistic Regression

0 University Institute of Lisbon , Lisbon , Portugal

0000 0003

Online Tourism Popular Places Machine Learning

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 Holidayiq.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 tourism sector due to climatic conditions and historical features [ 1-16 ], taking into account the requirements of environmental protection and preservation and enrichment of is heritage [ 17-25 ].

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 employment [ 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 important 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 India. 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.

The method that will implemented in this work is the classification tree.

To implement this course work, we chose the Python programming language. Python 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 ]. 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.

The following graph is also a chart, but it shows how feedback after visiting the theater affects the feedback of beaches, lakes, and we can see points from 0 to 10 here.

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 cluster it in the future.

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.

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. 4 5 Here we have to divide our data as 70% training and 30% testing.

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. 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 represented by a vector of numbers belongs to a particular class.

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 conclude that decision tree is the best method for this dataset with an accuracy of 44%.

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 building 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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