This model tries to predict the opening price for Microsoft(MSFT) based on a sample data from Dow Jones Index.
I started exploring AzureML(Azure Machine Learning) few weeks back and quickly fell in love with its simplicity and robustness. I grabbed the sample data of Dow Jones Index from UC Irvine Machine Learning Repository and applied the Linear Regression algorithm to create a prediction model to predict the future values of Microsoft stock's opening weekly price (so that I can be rich ofcourse) and here how my model looks like in AzureML. First I am removing the entire rows with missing values from the data. Then I am applying the filter for MSFT symbol in the first split and I am dividing the data to 80-20 ratio to train the actual model on 80% of the data with the help of Linear Regression algorithm. After that I am trying to predict price variable in Train Model and verifying it using 20% of remaining data. In the last, I am evaluating the model that how effective and reliable it is. At this point I need to seriously improve my model using other algorithms, removing/adding new variables etc because the "Coefficient of Determination" is nowhere closer to 1 and "Mean Absolute Error", "Root Mean Squared Error", "Relative Absolute Error" & "Relative Squared Error" values are very high. But that's how a prediction model (more or less) will eventually look like in AzureML. It can be published as a web service with few clicks. Ref: http://archive.ics.uci.edu/ml/datasets/Dow+Jones+Index