Here we are to discuss the top 5 Data Science algorithms you must know. Data Science is a relatively new term, but in reality, they are just an advancement of statistics and analysis. The Data Science domain combines statistics with programming and data visualization to extract and represent insights from data sets.

Data Science gave new energy to Artificial Intelligence because we can build accurate models only if we have error-free data. Machine Learning is a subset of Artificial Intelligence. Machine Learning is increasingly being adapted with Data Science to improve and enhance it. Check out the best Data Scientist Training to help you master the subject and get good hands-on experience in the domain.

Before we dive into all these algorithms, let’s understand the need for algorithms. Algorithms are instructions that could be used for solving problems and completing tasks. So Data Science Algorithms are algorithms that help in dealing with and solving different problems. Each problem or task could be broken down into several different types, which then could be easily solved using these algorithms.

There are many popular algorithms in the Data Science domain, and they are:

- Linear Regression
- Logistic Regression
- Support Vector Machines (SVM)
- Decision Tree
- K-means
- Dimensional Reduction Algorithms
- Gradient Boosting Algorithms
- Random Forest and many more.

Each of these algorithms solves different types of problems. Now let’s understand the top 5 algorithms in Data Science application.

**Linear Regression**

It is one of the most popular Data Science algorithms in usage. As we know, Data Science uses Data Visualization to display the insights extracted. So if in a graph of points, the Linear Regression algorithm tries to find the best fit straight line among all the points. This line will help predict or anticipate values. How to find a straight line, you may wonder. Linear Regression does this by finding out the least loss by squaring method. Loss is calculated by the difference in vertical distance calculated from the line. Linear Regression is of two types Simple Linear Regression which has only one independent variable, and Multiple Linear Regression, where multiple variables are defined.

**Logistic Regression**

Logistic Regression is also similar to Linear Regression. This algorithm is used in Data Science when the expected result is binary. That means the result could be either of the two values. Logistic Regression is used when the values lie within the range of two values. Logistic Regression has a special formula for an “S” shaped graph called a Sigmoid graph. This formula helps in squeezing out values to a particular range, finally helping in predicting the output for a problem or a task. For instance, for figuring out whether today it will rain or not?

**Support Vector Machines (SVM)**

This algorithm helps in solving classification-type problems. Using this algorithm, we can separate disparate data points using lines. This algorithm is used in facial recognition. This algorithm helps in minimizing classification errors using the pre-assembled regularization model. If you are a beginner in Data Science, check out this Data Science Tutorial.

**K means clustering**

It is also one of the most popular algorithms used in clustering problems. This algorithm solves clustering problems by clustering different data points into groups of similar points. If you have a dataset containing data points at different positions, then this algorithm helps in clustering homogeneous points together. Here “K” means the input of the algorithm. So, once you apply this algorithm to a problem, it selects the “k” number of centroids. Data points neighboring to a centroid combine to form a cluster. If new data points are added, they combine with the existing cluster to expand them.

**Dimensional Reduction Algorithm**

There are many problems for which there are too many variables to handle. When there are too many variables at hand, it becomes difficult to handle. Nowadays, the data is collected from multiple sources; it could be text, video, etc. When systems rely on more than one source then it becomes difficult to identify the most important variable that is capable of bringing in the best results. Sometimes datasets may contain thousands of data, and most of them would be unnecessary.

So, this algorithm helps in identifying the most important variables that will help in identifying the set of variables that impact predictions the most. Dimension Reduction Algorithms use various algorithms like Random Forest, Decision Tree to help identify these variables from a sea of those.

**Gradient Boosting Algorithm**

This algorithm is used in the place wherein a problem the algorithms used are weak. So in that scenario, we use Gradient Boosting Algorithm to create or build a more powerful algorithm that is more accurate and powerful. This works on the principle that it’s always better to use multiple algorithms in place of a single algorithm. That will help in increasing stability and robustness.

Some of the Gradient Boosting Algorithms are LightGBM, XGBoost, etc. These algorithms are known for their high accuracy and performance.