Data Science Algorithms are Important to Learn.

Being a data scientist, one should properly understand the importance of AI and machine learning (ML) algorithms. Knowing data science algorithms through and through is deemed to be one of the most important skills in data science. These algorithms are the important slices in tasks like prediction, classification, and clustering from the data set in concern.

Here is a List of Ten Data Science Algorithms: 

Linear Regression 

The linear regression method is used for the value prediction of the dependent variable by using the values of the independent variable. It is suitable for predicting the value of a continuous quantity.

Logistic Regression 

Standing in stark contradiction to linear regression, logistic regression is used in finding the most common application in solving binary classification problems. This means that when there are only two possibilities of an event, either the event will occur or not occur. In logistic regression, the predicted values are converted into values that only lie between the range of 0 and 1. A non-linear transform function is used, which is known as a logistic function.

Decision Trees 

Decision tree algorithms specialize in solving both classification and prediction problems, delivering accurate and glitch-free data. The nodes of the decision tree are representative of features and attributes, the links represent a decision and the leaf nodes hold the class labels i.e. the outcomes.

ID3 Algorithm 

The ID3 algorithm uses entropy and information gain as the decision metric.

Cart Algorithm 

The cart algorithm uses the Ginni index as the decision metric.

Naïve Bayes 

The Naïve Bayes data science algorithm is used when there is a need to calculate the probability of the occurrence of an event. Naïve Bayes algorithm operates on the assumption that each feature is independent.

KNN 

K-nearest neighbors (KNN) is the data science algorithm that employs classification and regression problems. KNN algorithms search entire datasets to identify the k – most similar data sets and the nearest neighbors of that data point.

Support Vector Machine Algorithm 

Support vector machine (SVM) is counted in the category of supervised machine learning algorithms and finds application in classification and regression problems. Its most common usage is in the classification of problems using a hyperplane.

K-means clustering 

K-means clustering is an algorithm type that falls under unsupervised machine learning. Here, clustering means dividing all the datasets into groups of similar data items, which are known as clusters.

The PCA Algorithm 

The principal component analysis algorithm is a technique that is used for performing dimensionality reduction of the datasets. This will have the least effect on the variance in the datasets. This means the proper filtration of the redundant features while keeping the important ones unharmed.