Regression Models

The regression model is a powerful method that allows you to examine the relationship between two or more variables of interest. In other word, 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 analysis is a form of predictive modelling technique which investigates the relationship between a dependent (target) and independent variable (s) (predictor). This technique is used for forecasting, time series modelling and finding the causal effect relationship between the variables

Regression technique varies from Linear Regression to SVR and Random Forests Regression.

Look at this dataset. It's related to Co2 emissions from different cars. It includes Engine size, number of Cylinders, Fuel Consumption and Co2 emission from various automobile models. The question is, "Given this dataset, can we predict the Co2 emission of a car using other fields, such as EngineSize or Cylinders?" Let’s assume we have some historical data from different cars and assume that a car, such as in row 9, has not been manufactured yet, but we're interested in estimating its approximate Co2 emission, after production. Is it possible?

We can use regression methods to predict a continuous value, such as CO2 Emission, using some other variables. Indeed, Regression is the process of predicting a continuous value.

In regression, there are two types of variables: a dependent variable and one or more independent variables.

The dependent variable can be seen as the "state", "target" or "final goal" we study

and try to predict, and the independent variables, also known as explanatory variables, can be seen as the "causes" of those "states". The independent variables are shown conventionally by x, and the dependent variable is notated by y.

A regression model relates y, or the dependent variable, to a function of x, i.e., the independent variables. The key point in the regression is that our dependent value should be continuous, and cannot be a discreet value. However, the independent variable or variables can be measured on either a categorical or continuous measurement scale. So, what we want to do here is to use the historical data of some cars, using one or more of their features, and from that data, make a model.
We use regression to build such a regression/estimation model. Then the model is used to predict the expected Co2 emission for a new or unknown car. Basically, there are 2 types of regression models: simple regression and multiple regression.
Simple regression is when one independent variable is used to estimate a dependent variable. It can be either linear on non-linear. For example, predicting Co2emission using the variable of EngineSize. The linearity of regression is based on the nature of the relationship between independent and dependent variables.
When more than one independent variable is present, the process is called multiple linear regression. For example, predicting Co2emission using EngineSize and the number of Cylinders in any given car. Again, depending on the relation between dependent and independent variables, it can be either linear or non-linear regression.

Following Machine Learning Regression models:

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector for Regression (SVR)
• Decision Tree Regression
• Random Forest Regression