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Regression Analysis

Regression Analysis with Python

What is Regression Analysis?

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. For example, relationship between rash driving and number of road accidents by a driver is best studied through regression.

Why do we use Regression Analysis?

It indicates the significant relationships between dependent variable and independent variable. And It indicates the strength of impact of multiple independent variables on a dependent variable. Regression analysis also allows us to compare the effects of variables measured on different scales, such as the effect of price changes and the number of promotional activities. 

What are the types of Regressions?

    • Linear Regression
    • Logistic Regression
    • Polynomial Regression
    • Stepwise Regression
    • Ridge Regression
    • Lasso Regression
    • ElasticNet Regression

In this workshop you will learn a few different regression algorithms used in machine learning, including decision tree, support vector machine and Gaussian process. The application programming interface using Pandas and Scikit-Learn will be gone through as well as a brief introduction to the mathematical concepts behind the algorithms to provide a deeper understanding of the nature of artificial intelligence.