.

Essential Machine Learning Algorithms That Data Analysts Need To Know

AI is taking over. In 2021, we’re going to see machine learning taking on a bigger role in data analysis as algorithms become capable of reducing error and producing more accurate models within themselves. These algorithms cover iterative processes, decision trees as well as multi-dimensional splitting of datasets. Machine learning is enabling data analysts to have new and greater insights, affecting everything from marketing departments to the way we learn. Here are the essential machine learning algorithms for 2021.

 

1) Linear Regression

 

“A linear regression algorithm is a highly accurate predictive model and has been used in statistical analysis for years,” says Bruce Endres, an AI expert at Write My X and Britstudent. “Machine learning is adapting linear regression, and works by taking a series of dependent and independent variables to make accurate predictions about the relationship between these variables.”

Whatever your object of analysis, linear regression algorithms can provide valued information about them based on a series of inputs. Skilled engineers can build linear regression models that remove closely correlated variables that skew results as well as removing unrelated variables - noise in the data. As a machine learning algorithm employs linear regression, it will itself identify noise and correlated variables and so it can grow more accurate over time.

 

2) Decision Tree

 

Decision trees are a popular ML algorithm, used for classifying and categorizing problems so they can be approached more efficiently. Decision trees divide a set into any number of categories based on selected variables and can lead to sophisticated differentiations between possibilities in a set.

An ML decision tree divides sets up, penetrating deeper into an analysis by categorising the variables according to functionality. Visually, this creates a “tree” - many branches stemming from one single route - and it’s an informative process that can lead decision making.

 

3) Support Vector Machine (SVM)

 

Support vector machines are used to categorise data and can be deeply revealing when there are a wide range of variables at play. Raw data points are plotted on a graph in a multidimensional space where the number of dimensions, ‘n’, is consistent with the number of features of the data. These raw data points are then easily classified based on their position in the multidimensional graph.

Lines can then be drawn through these graphs to pool data points into subsets. Data classified in this way is much more approachable for analysts looking to understand and infer.

 

4) K-means Clustering

 

K-means clustering is a way of taking divergent datasets and finding classification for the data held therein. Through K-means, data sets are classified into clusters which contain homogenous data-points.

ML algorithms employ k-means clustering by splitting the data into a certain number of points for each cluster. The data is then re-analysed, forming new clusters with closer values. This process takes place iteratively and produces accurate insights into meaningful groupings.

 

5) Apriori

 

Apriori algorithms are most regularly found in market analysis, where they are used to reveal product combinations that frequently occur in databases. The algorithm takes two data points, let’s call them A and B, and then identifies positive and negative correlations between these two products.

“One application of apriori algorithms is to enable sales departments to identify links between products which typically appeal to consumers,” says Teresa Govan, a tech writer at 1day2write and Custom Coursework. “By identifying these correlations, sales teams can better target their marketing material.”

 

6) Random Forest

 

This ensemble learning technique - meaning multiple algorithms work on top of each other - takes multiple decision trees from the dataset and randomly assigns variable subsets to each stage of the decision tree. This randomization process brute forces new insights as decision trees are produced and then reiterated or discarded depending on their value.

The random forest algorithm reduces the risk of error within a single decision tree by generating multiple trees and discarding the ones deemed most faulty. Although the computational power required for random tree algorithms is greater, the outcome is a more reliable model.

 

Signing Off

 

Machine learning algorithms are becoming incredibly powerful, providing ever increasing accuracy. The strength of these predictive models allows us to make strong decisions about the future of our business. Although robots aren’t here to take our jobs, they’re sure going to be making them easier in 2021.

 

George J. Newton is a technology writer at Coursework Writing Services and PhD Kingdom. He has a background in data analysis and is fascinated by the rapid developments in machine learning technology. He also writes for Essay Help.

Views: 2552

Tags: Data, Machine, analysis, dsc_ml, dsc_tagged, learning

Comment

You need to be a member of Data Science Central to add comments!

Join Data Science Central

© 2021   TechTarget, Inc.   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service