Data Science is all about analyzing the data, finding patterns, and predicting the future. If the pattern identified is accurate, the prediction is correct. So the struggle is always to do the right analysis. Though being skilled in Coding is essential for a data scientist, that’s not all. A data scientist needs to have skills in coding, statistics, and critical thinking. Our online course on __Data Science using Python__** **is a complete course from basics to the latest tools and techniques followed.

**Regression:**

Regression is a popular statistical technique used in Data Science for the prediction of unknown values in a data set based on the known features. It is used when there is a missing value in a data set. By analyzing the relationship between the dependent (target) and the independent (predictor) variable we can forecast the nature of the missing variable.

This statistical relationship between the known and unknown can be of different forms based on various factors like the type of predictors, outcomes, the function used to build the relation, etc. There are innumerable forms of regressions. Three main factors for deciding on the regression model is

- Number of independent variables
- The shape of the regression line
- Type of independent variable

**Let’s look at a few commonly used regression forms:**

__Linear Regression__: This is the most simple, popular regression form. Here there is only 1 dependent value and mostly only 1 independent value. The shape of the regression is linear (straight line).__Multiple Regression__: Quite similar to linear regression however the difference is that the independent variable is more than 1. Here since there are more independent values the result is expected to be more accurate.__Logistic Regression__: This regression is used to find out the probability of a class or event. Whether the result will be ‘Pass or Fail’ or ‘Yes or No’. it is widely used for classification problems. Logistic regression can be binary, ordinal, or multinomial.__Stepwise Regression__: This form of regression helps with high dimensional data sets. It is used when we are working with more than one independent variable. The selection of the independent variable is an automatic process. In each step, a variable is considered to be added or removed from the set based on specific criteria.__Ridge Regression__: In a data set where the independent variables are highly correlated (multicollinearity) ridge regression is used. Here the L2 regularization tool is used. Ridge regression uses a type of shrinkage called ‘ridge shrinkage’. It shrinks the value of the coefficients but not to zero. Unlike least square estimates here a degree of bias is added to the regression estimates to reduce the standard errors.__LASSO (Least Absolute Shrinkage & Selection Operator) Regression__: Here unlike Ridge regression, the value of the coefficient gets shrunk to zero. This regression uses the L1 regularization technique. LASSO regression provides a subset of predictors which is simple and sparse.__ElasticNet Regression__: This is a combination of Ridge and LASSO regression forms .i.e a hybrid of L1 and L2 regression methods. Though it inherits advantages from both the models it might also suffer from double shrinkage.

These are a few Regression models you learn and use as a Data Scientist. Data Science is a very interesting and in-demand job. Join our online course on **Data Science using Python** for the right understanding of the technology before you plunge into the career. Our course uses __ Python__ as the programming language as it the most suitabe for all advanced technologies. Our expert team of trainers can rightly handhold you with proper explanations, real-life examples, and practical assignments.

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