A set of observations on a number of silhouettes is related to different type of vehicles, using a set of features extracted from the silhouette. Each vehicle may be viewed from one of many different angles. One dataset (vehicles.xls) is available and has 846 observations/samples. There are 19 variables/features, all numerical and one nominal defining the class of the objects. In this clustering part I used the first 18 attributes to your calculations.

Exchange rate prediction is one of the challenging applications of modern time series forecasting and very important for the success of many businesses and financial institutions. As like economic time series exchange rate has trend cycle and irregularity. Classical time series analysis does not perform well on finance-related time series. Hence, the idea of applying Neural Networks (NN) to forecast exchange rate has been considered as an alternative solution. In addition, recent research has been directed to Support Vector Machine (SVM) which has emerged as a new and powerful technique for learning from data and in particular for solving classification and regression problems with better performance. The main advantage of SVM is its ability to minimize structural risk as opposed to empirical risk minimization as employed by the NN system. In this forecasting part I used an MLP-NN and a SVM-based regression (SVR) model to predict the next step-ahead exchange rate of GBP/EUR. Daily data (exchangeGBP.xls) have been collected from January 2010 until December 2011 (500 data).