REGRESSION AND LINEAR REGRESSION-AN EASY APPROACH

Regression can be defined as a statistical method of attempting to estimate the relationship between independent variables (X) and dependent variables (Y). Linear regression is a subset of regression analysis wherein the parameters(variables) have a linear correlation. Linear Regression models are used when we need to predict a continuous-valued, non-discrete numeric quantity like price, age, etc. It can be divided into 2 broad categories for our assessment. More variants are beyond the scope of this article.  

• Simple Linear Regression– Let us assume that you have to predict the body weight of an individual based on only ONE criterion. Let that criterion be his/her height. So in this case, you are predicting the value for the label (dependent variable) based on only one feature (independent variable). The linear regression model leveraged in this case is a simple linear regression model. In this model, there is only one predictor variable(height) that is used to predict just one outcome (bodyweight). The equation can be represented in the slope-intercept format, i.e.

Where,

• Y= dependent variable,
• X= independent variable,
• m= slope/gradient of the line, and
• C= The y-intercept.
• Multiple Linear Regression– In this tutorial, we shall be discussing how to predict automobile prices based on numerous factors like make, body style, no. of cylinders, etc. In this example, multiple predictors were used to determine one output in the form of automobile prices. This is an example of a multiple linear regression model. In this model, there is more than one predictor/ independent variable but only one outcome/ dependent variable.  The equation can be expanded as:

Where,

• Y= dependent variable
• m,n…..z= Coefficients
• x1,x2….xn= Independent variables
• c=Y-intercept

Now that your basic concepts about linear regression have been cleared, let us get on with the tutorial on Azure ML Studio

Are you a complete beginner to the broad spectrum of Machine Learning? Are you torn between R, Python, GNU Octave, and all the other computer programming languages and frameworks available for Machine Learning? Do you just not ‘get’ coding?

Don’t worry, you are in the right spot! Machine Learning can be a tough nut to crack, especially if one doesn’t have prior programming experience. For instance, ML aficionados who use Python are familiar with the basic data types, function definitions and calls, popular modules and libraries like NumPy and Pandas, and fundamental operations on cleaning and visualizing data, to name a few of the many pre-requisites. Machine Learning without programming appears to be a far-fetched dream. However, Microsoft Azure’s ML studio transmogrified this widely-coveted desire into concrete reality. This article seeks to cognize ML neophytes about Azure ML Studio and provides a short tutorial on building, training, and testing a basic ML model using Azure’s ML Studio.

Microsoft Azure’s ML Studio is a Graphical User Interface that leverages a user-friendly drag-and-drop UI to build, train and deploy resilient machine learning models at scale. It is a no-code interface that depicts a dynamic pipeline through smaller visual workflows. ML Studio streamlines the entire process from preprocessing to validation and visualization. It automates the project and reduces the demand for an intrinsic infrastructure by provisioning a robust foundation. ML Studio drastically reduces the complexity associated with ML workflows and its inherent simplicity renders it perfect for ML rookies.

We shall now attempt to build, train and test a simple machine learning model that predicts the approximate price of automobiles factored by their make, engine, built, etc on Azure ML Studio. Since price is a continuous-valued output, we shall be using a linear regression model. But first, we shall delve into the basics of regression.