# Top 10 Data Science Algorithms Tech Enthusiasts Should Know

by Sayantani Sanyal

April 30, 2022

## Data science algorithms will help professionals perform their tasks effectively and efficiently

Data science has become the backbone of some of the most advanced enterprise applications in the world. The field has quickly become one of the most in-demand professions on the market, with multinationals and small businesses around the world looking for data scientists and skilled data professionals. Data science remains one of the most essential techniques in almost every situation. Data professionals are essential to maximizing the organization’s time, resources and work. But to excel in the field, aspiring professionals need to learn the various data science algorithms that will help them perform these analytical and predictive tasks with ease and efficiency. The best data science algorithms will help to perform complex data science tasks like prediction, classification, clustering, etc. In this article, we’ve listed the most popular data science algorithms that tech enthusiasts and data professionals should definitely know about in 2022.

**Linear regression**

Linear regression method is widely used to predict the value of the dependent variable using the values of the independent variables. This algorithm is mainly used to understand the linear relationship between the input variable and the output variable. It is represented as a linear equation that has one set of inputs and one predictive output.

**Logistic regression**

Logistic regression is used for binary classification of data points. It performs a categorical classification that results in the output belonging to one of two classes (1 or 0). The two most crucial parts of this algorithm are the hypothesis and the sigmoid curve.

**K-Means Clustering**

K-means clustering is a type of unsupervised machine learning algorithm. Clustering is basically dividing the data set into groups of similar data called clusters. K means grouping categorizes data items into k groups with similar data items.

**Principal component analysis**

PCA is essentially a technique for performing dimensionality reduction of datasets with the least effect on dataset variance. This means removing redundant features but keeping the most important ones.

**Decision tree**

Decision tree algorithm in machine learning is one of the most popular algorithms which is widely used for data science purposes. It is a supervised learning algorithm that is generally used to classify problems. It works well for ranking categorical and continuous dependent variables.

**naive bayes**

A naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of another feature. Even though these features are all related to each other, this classification algorithm would consider these properties independently when calculating the probability of a particular outcome.

**K.N.**

The KNN is another algorithm that can be applied to both classification and regression problems. The field of data science is widely used to solve classification problems. This easy and simple model can be used to store all available cases and rank all new cases by taking the majority vote of the k neighbors.

**Random Forests**

Random forests help overcome problems created by decision trees and help solve classification and regression problems. It works on the principle of ensemble learning, which believes that a large number of weak learners can work together to give very accurate predictions.

**Support vector machines**

SVM is a supervised algorithm that is used for data science classification problems. The algorithm tries to draw two lines between the data points with the largest margin between them. The user must plot the data items in n-dimensional space, where n is the number of input features. Based on this, the SVM algorithm separates the possible outputs by their class label.

**Artificial neural networks**

Neural networks are modeled after neurons in the human brain. It comprises several layers of neurons which are structured to transmit information from the input layer to the output layer. A simple neural network consisting of a single hidden layer is called a perceptron.

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