Machine Learning is a cutting-edge field that is critical to the industry’s success. If you’re a data science student, you may have wished to figure out how to select a specific algorithm for your research.
One of the most notable aspects of this revolution is the democratisation of computer tools and processes. The outcomes have been spectacular. Because there are so many ML algorithms available, it might be difficult for data science students to choose the best one for their projects.
- Linear Regression
In statistics and machine learning, linear regression is one of the most well-known and well-understood ML techniques. To comprehend linear regression, you don’t need to know anything about statistics or algebra. As a result, it’s the perfect algorithm for your data science project.
- Logistic Regression
Logistic regression is a statistical analysis approach for predicting a data value based on previous data set observations. A logistic regression model analyses the relationship between one or more existing independent variables to predict a dependent data variable. It is one of the most popular machine learning algorithms in data science projects in 2021.
- Decision Trees
For classification and regression, Decision Trees (DTs) are a non-parametric supervised machine learning approach. The goal is to learn simple decision rules from data attributes to develop a model that predicts the value of a target variable. A tree is an approximation to a piecewise constant. For data science students, it is one of the most popular ML methods.
- Naive Bayes
Naive Bayes models are a family of exceptionally quick and simple classification algorithms that are well-suited to very large datasets. They are highly useful as a quick-and-dirty baseline for a classification task since they are so fast and have so few configurable parameters. For any data science project, Naive Bayes is an excellent machine learning algorithm.
- Support-vector Machines
Support vector machines are supervised learning models with accompanying machine learning algorithms for classification and regression analysis in machine learning. The SVM algorithm is a classification method in which raw data is plotted as points in an n-dimensional space (where n is the number of features you have). Each feature’s value is subsequently linked to a specific coordinate, making data classification simple. Classifiers are lines that can be used to separate data and plot it on a graph.
- K-Nearest Neighbours
The k-nearest neighbours’ algorithm (k-NN) is a non-parametric classification approach invented by Evelyn Fix and Joseph Hodges in 1951 and later expanded by Thomas Cover in statistics.
It is employed in the categorization and regression of data. In both circumstances, the input is a data set with the k closest training samples. In 2021, it is one of the finest machine learning algorithms for data science projects.
- K-Means
It is a clustering problem-solving unsupervised learning algorithm. Data sets are divided into a certain number of clusters (let’s call it K) in such a way that all data points within each cluster are homogeneous and distinct from data in other clusters.
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