Data Mining for Sales Prediction in Tourism Industry

Download Document/Synopsis
Although there are many forecasting models for determining sales in tourism industry, data mining techniques have been considered the best technique for forecasting sales in tourism industry. Data mining is defined as the process of finding out useful patterns, correlations, and rules, which are not known previously, by filtering through a large amount of data stored in some repositories(database).
In this system two data sets are considered in prediction of sales in tourism industry.
The two data sets are –

  • Orders count data
  • Sentiment analysis of comments

Here the orders table is scanned to find out how many times a particular package is been preferred by the user. The counting is then done to find out the preferences of the users from the orders table.
Second is the sentiment analysis where the words entered in the comments by the users are being scanned to find out whether the word is positive or negative. Accordingly the scores are assigned. After the scores are assigned they are added up to find out the rating.
Thus the two data sets used here are orders table and the comments table in the process of predicting sales in tourism industry.



Advantages
  • The system is useful for tourists as it helps them to search for more valuable places.

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