Understanding Regression Types and Techniques in Data Science

by Jones David

With the world now heading towards an industry primarily fuelled by data, the ambit of data science has become the prime focus for all enterprising industries. With ample organizations now providing data science courses, professionals are now expected to move beyond the traditional modes of data management and programming and are expected to master the entire spectrum of data science. To understand more about this topic we have a referral to learn a Data Science Online Course to enhance your skills.

Data is the new oil, and this adage has been supported by the rapid growth that the data industry has displayed over the past couple of years. Data generation in terms of quantity has been doubling every couple of years. Supported by the overwhelming popularity IoT and other connected ecosystems have had over the past few years, the ever-rising need for data has also brought in a host of trends. Here are some of the most recent trends in data science:  

Automated Data Science: It is an integral part of managing data that stems from cleaning up the big data generated. To tackle the problem and manage this more efficiently, there are automation tools on offer for data cleaning. Apart from this, there are also a lot of automated solutions on offer to tackle industry problems.  

Data privacy and security: A growing concern with the rise of the data-based industry has been security. In this regard, companies have slowly started shifting to more advanced forms of security like SOC 2 compliance. 

Natural Language Processing: Deep learning research has made NLP a necessity when it comes to data science. NLPs can be used to process the vast amounts of data generated and extract information from them. 

What is Data Science?

Gone are the days when a data scientist was expected to merely organize, compile, and analyze data. Modern data science requires the user to come on board with a wide range of high-level technical skills. Data science today refers to a cycle of broadly five major things:

  • Capture: Referring to data entry, acquisition, and extraction
  • Maintain: Referring to data cleansing, storage, architecture
  • Process: Involving data mining, modeling, and clustering
  • Analyze: Making analysis both predictive and qualitative
  • Communicate: Involving decision-making and reporting

What is Regression and how is it used in Data Science? 

Regression analysis involves using a machine-learning algorithm to assess how variables over a chart relate to a corresponding variable. Substantial use of such an analysis helps in building that can use datasets to correlate values and predict accurate results pertaining to the model at hand. 

In data science, regression analysis involves splitting an acquired dataset into two parts – a training data set used to generate a model to note the best approach on a graph that can be taken for the dataset and a testing dataset to use the values to test the viability of the model. This model thus created can be used to allocate values and predict results in relation to the testing dataset. 

If the accuracy rate of the model is not as per expectations, this model could then be revised or changed to fit into set parameters. One could also employ the use of polynomial regression analysis to achieve higher accuracy rates and better functioning of such models.

Regression Types and Techniques in Data Science

As regression analysis primarily employs using a statistical method to analyze and determine outcomes, there are also a lot of assumptions that need to be made. These assumptions depend on the type of model to be made and therefore create types of regression that a data scientist can employ while making such analysis. Here are some of the most popular types:

Linear Regression: This is one of the most common forms of regression types employed by data scientists. The name of the regression comes from the type of relationship that is established between the dependent and the independent variables. However, the type comes with its fair share of assumptions, including the absence of heteroscedasticity, multicollinearity, and autocorrelation.   

Polynomial Regression: In the event that the equation is nonlinear, polynomial regression is used to generate a relationship between the two variables, which usually is represented as a curve. However, because of the nonlinear nature of the model, it could also result in overfitting and hence bring about inconsistencies in results. 

Logistic Regression: This type of regression is used when the dependent variable consists of two categories or is binomial in nature. The concurrent independent variables can be either binary or continuous. This is termed a multinomial logistic regression. Here is an example of a logistic regression equation:

Ridge Regression: In the case of regression data, the model might encounter a bias owing to multicollinearity. This makes the resultant model too complicated and overfit. To address these problems, a bias degree is affixed to minimize variance in the calculations and reduce errors. This is done by using ridge regression. 

Lasso Regression: Lasso regression is used for both variable selection as well as regularisation since it reduces the number of selections as in the case of a ride selection. The full form of Lasso is the Least Absolute Shrinkage Selector Operator, which is an appropriate indicator of its function.

Ordinal Regression: Ordinal regression is usually used by industries for surveys and rankings to predict values pertaining to ranking. As the name suggests, the relationship between the two variables being linked should be ordinal in nature. For instance, surveys that try to rank experience on a scale of 1-10 or outcomes when it comes to football matches (Draw, Win, or Loss) can be represented by this model.

Poisson Regression: Poisson’s regression can only be used based on a couple of factors. Firstly, the dependent variable should exhibit a Poisson distribution i.e have a count data consistent with a time period. And secondly, the values should not be negative or non-whole numbers. This model can be used to process data related to processes like call volumes received.

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