**Simple linear regression** is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables: One variable, denoted x, is regarded as the predictor, explanatory, or independent variable. The other variable, denoted y, is regarded as the response, outcome, or dependent variable.

Typically when we doing regression analysis, we consider about the correlation of coefficient of the input variables. Correlation analysis measures the extent to which two variables vary together, including the strength and direction of their relationship.

Linear correlation coefficient(also called Pearson product-moment correlation coefficient) measure of the strength and direction of a linear association between two random variables.

I used the Istanbul Stock Exchange dataset to demonstrate the steps in doing a simple linear regression prediction. Azure Machine Learning experiment has built (get the experiment from here) for building the regression model. Built-in Bayesian Linear Regression algorithm has been used for building the model.

The most interesting part is coming with python! 🙂

I’ve used a Jupyter Notebook and fetched the data to that workspace to visualize the dataset and to calculate the coefficient values between each variable. *Pearsonr* method in *scipy library *has used for that.

Refer the iPython notebook from Azure Notebook for the complete python script and the visualizations.

Do run the code by your own. You’ll get it for sure!