MSFT Opening Price

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][1] 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 of course) and here how my model looks like in AzureML. ![enter image description here][2] 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 also be published as a web service quickly to be consumed by a mobile app, website etc. Ref: https://mlninja.blogspot.com/2015/03/predictive-analytics-with-azureml.html Ref: http://archive.ics.uci.edu/ml/datasets/Dow+Jones+Index [1]: http://archive.ics.uci.edu/ml/datasets/Dow+Jones+Index [2]: http://4.bp.blogspot.com/-e_z-QhYV6LE/VQcFgYEDFbI/AAAAAAAAEJM/6HXeRncehdQ/s1600/MSFT.PNG