Regression Analysis ?

To establish the possible relationship among different variables, various modes of statistical approaches are implemented, known as regression analysis. In order to understand how the variation in an independent variable can impact the dependent variable, regression analysis is specially molded out.

Basically, regression analysis sets up an equation to explain the significant relationship between one or more predictors and response variables and also to estimate current observations. The regression outcomes lead to the identification of the direction, size, and analytical significance of the relationship between predictor and response where the dependent variable could be numerical or discrete in nature.

 Regression models (both linear and non-linear) are used for predicting a real value, like salary for example. If your independent variable is time, then you are forecasting future values, otherwise your model is predicting present but unknown values. Regression techniques vary from Linear Regression to SVR and Random Forests Regression.

In this part, you will understand and learn how to implement the following Machine Learning Regression models:

1. Simple & Multiple Linear Regression

2. Polynomial Regression

3. Support Vector for Regression (SVR)

4. Decision Tree Classification

5. Random Forest Classification

In this section also we will directly focus on Practical approach rather than theoretical approach. For all the regression models we need to understand first the template for training model and prediction output.

Process & Steps :

START 🔰 Importing libraries 🔰 Importing Datasets 🔰 <<DATA PROCESSING>> 🔰 Model Training 🔰 Value Prediction 🔰 Plotting Graph 🔰 END

In in study of regression models, we are not going to cover the << DATA PROCESSING>> as it was already covered in previous section. Those who want to visit Data Processing section can visit @ Data Processing .

 Simple Linear Regression

The goal of simple (univariate) linear regression is to model the relationship between a single feature (explanatory variable, x) and a continuous-valued target (response variable, y). The equation of a linear model with one explanatory variable is defined as follows:  

Y = mX + C

From the equation it is clear that this is a Linear line plot on a graph with constant C & slope m

There are lots of book or online link where you can easily find theory , here I am directly switching to Practical applications and little bit explanations :

Understanding Datasets