PAKDD 2006 Data Mining Competition Approach and Understanding of the Problem The problem that was given by PAKDD 2006 is a data mining problem in which data has to be classified between two types of subscribers (2G/3G). The problem is a classification problem. We are given the training set of data from PAKDD to train the classifier. We used Supervised learning in which first the classifier is trained using the training data and then it is used to classify the unknown or prediction data. We used Neural Network for this classification problem. Technical Details We used a Multilayer Feed Forward Neural Network to classify given data set. It uses backpropagation algorithm for learning. Our network has three layers namely 1) Input layer 2) Hidden Layer 3) Output Layer. Before feeding the data to Neural network, preprocessing step was performed. In the preprocessing, we eliminated all those attributes which were irrelevant for the classification. Thereby reducing the data dimension. We also encoded and normalized all the attributes so that they suit the requirements of Neural network. Classification Model In our model, we used 224 features out of given 249 features. Hence, the Neural network has 224 perceptrons in the Input layer. Hidden layer of model has 8 and Output layer has 1 perceptron. First, we performed the supervised training of Neural network using the training data. Network was trained for 10000 loops.