Data Mining for Sales Prediction in Tourism Industry
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.
Modules
Admin
- Add Packages- Here the admin will add place details along with other package information.
- Add reviews-Admin can add reviews
- View Reviews- Admin can view the reviews
- Sales report- Graphical representation of the sales report is made available to the user
User
- View Packages-Here the user can view the packages added by the admin and can book the same
- Send Feedback – here the user can add feedback
Software Requirements:
- Windows 7 or higher.
- SQL 2008
- Visual studio 2010
Hardware Components:
- Processor – i3
- Hard Disk – 5 GB
- Memory – 1GB RAM
Advantages
- The system is useful for tourists as it helps them to search for more valuable places.
Application
This system can be used by tourists to decide their place preferences