# Analysis of Factors Influencing the Prices of Tourist Offers
## 1 Introduction  
The paper reports on the part of a research project under the ePredyktour project, which is an attempt to solve a problem of predicting trip prices on the basis of various factors. The presented research was carried out for data provided by two Polish tour operators in 2021. In Poland, expenditure on tourism and recreation in the consumption basket amounted to 8.2% (in 2018). Poles more often choose organized trips than trips on their own. They account for 1.6% of the consumption basket compared to 1% in the European Union. The COVID-19 pandemic had a heavy impact on the travel industry. In most countries, the contribution of tourism to GDP has decreased significantly; in Poland, this decrease amounted to about 2 percentage points in total GDP [1]. Therefore, in 2021, the COVID pandemic is the overarching factor influencing the tourism industry, and certainly has an impact on the conclusions drawn from the presented research. Nevertheless, the methods proposed in the paper are universal and can be repeated for data before and after the pandemic. The entire ePredyktour project consists of the following stages (presented in Figure 1):  
* Theoretical analysis and definition of various factors that may affect the price of the trip, finding the sources of these factors and performing initial normalization. This stage was made by specialists in the tourism industry. At this stage, it turned out that it is not possible to access all the databases that were theoretically considered. For example, various opinions posted on social media could not be used in this project due to a lack of authorization. The identified factors have been divided into three groups: micro (factors directly related to a given trip, such as hotel properties, transport type, type of meals, etc. ), mezzo (factors related to the vicinity of the holiday destination, such as weather, historic attraction, terrorist attacks risk, etc. ), and macro (global factors such as fuel prices, exchange rates, economic indicators, etc.) ** The raw data from the various databases were downloaded, transformed into predefined structures and saved on the Hadoop Distributed File System (HDFS) [2]. These data are available in the form of Data Frames via the API of the Apache Spark [3]. The tourism, IT and big data specialists were responsible for this stage. Data were collected from the beginning of 2021 and initially collections were created for two tour operators. Each trip was recorded almost daily to a new record. In this way, millions of records were created. * This stage is the subject of the paper and the authors were responsible for its implementation. Its goal was the analysis of factors in order to determine their impact on both the entry price and the change in the price of trips. The latter task was of particular importance, as the whole project aims to design a system that predicts changes in trip prices. Initially, the data were transformed into structures corresponding to the input of the machine learning algorithms and several algorithms were used to evaluate the influence of various factors on the changes of trip prices. Theses methods and the results achieved are the subject of the following sections of this paper. * The use of machine learning algorithms, which, based on the factors examined in Stage III, will predict the price of the trip currently offered by the tour operator. During the preparation of this paper, this stage was still in progress. As a result of Stage II, six datasets have been created: offers, trips, properties, nbp, opec, and tradingeconomics. When a client comes to a travel agency, he/she first chooses an offer, whose name in the case of stays is the same as the name of the hotel (eg. Hotel Sea), and, in the case of touring trips, it is related to the scope of the trip (e.g., the most beautiful towns of Provence). This information is included in the offers set. Then, the details of the trip are selected: day of departure and return, transport and food type, standard of the room, number of persons, etc.