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How to develop churn prediction tool for mobile telecommunication using data mining evolutionary algorithm

I am trying to develop a tool for churn prediction for mobile telecommunication
My project is divided in four phases
phase 1)user interface which is divided in the following components
a)Start page
The start page essentially consists of two buttons
• Button for selecting the training data for the program
• Button for selecting the data on which the prediction has to be performed
The pressing of button 1 launches a file browser, which can then be used to select the text file which contains the training data. This training data is then used to generate rules using dmel algorithm.

b)File browser
The file browser is invoked whenever the user has to select a file. The file browser is a file explorer window that prompts the user to browse to a file and return its absolute path to the program.

c)Attribute selector
Once the file is selected, the program reads the contents of the file. The attributes are identified. A popup window is displayed which prompts the user to select the attributes that should be used for generating the rules. The attributes that are unchecked are discarded.

d)Window for setting importance factor
After selecting the attributes, the user is prompted with another window which displays a slider for each attribute selected in the previous step. The slider ranges from 0 to 1 in steps of 0.1. Once this step is complete, the selected attributes and their corresponding importance factors are then stored into attribute instances of the attribute class, each of which contains a variable named importance factor. Once this step is complete, the program returns to the starting page. Once the user is at the starting page again, he clicks button 2 to browse for the actual database file on which the prediction is to be done. For this the file browser is used again.

e)Output page
Once the user enters the database file on which prediction is to be done, the prediction algorithm is invoked. A set of rules are generated on the basis of the training data that has been previously selected and these rules are then applied on the database on which prediction is to be done. The target attribute value is then updated for every row in the database. The output page then provides options for displaying the set of rules as it is generated, and also for displaying the entire database, and also for displaying only the target attribute field.

this is how my phase 1 will work but the problem i m facing is with button 2 which is use to select data for prediction. I want to know from which data ll be used for prediction.Please explain me with an eg. it ll be a gr8 help to me to start a proj.

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