I grabbed the sample data of Down Jones Index from UC Irvine Machine Learning Repository and applied the Linear Regression algorithm to create a prediction model to estimate the future values of Microsoft stock's opening weekly price (so that I can be rich) 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 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.
I have published this experiment/source to AzureML gallery and can be accessed here:
https://gallery.azureml.net/Details/3f4d92649bfa4fa3bf4b0c93a3635227
My next step would be to grab data from SharePoint lists and apply some prediction algos on it.
