Data Science Algorithms

What are the different algorithms in data science?

There are mainly two types of learning methodologies in data science and all the other algorithms are based under these two categories only.

• Supervised Learning: These algorithms are defined as that class of machine learning methodologies where the user can train with the help of labelled data. For instance, the data can be historical data where the user wishes to predict whether a customer will take a loan or not. Supervised algorithms tend to train over the well-structured data after the preprocessing and feature characterization of this labelled data. It is further tested on a completely new data point for the prediction of a loan defaulter. The most popular supervised learning algorithms are the k-nearest neighbour algorithm, linear regression algorithm, logistic regression, decision tree, etc. 
• Unsupervised Learning: These algorithms are defined as that class of machine learning methodologies where the tasks are performed using the unlabelled data. Clustering is the most popular use case for unsupervised algorithms. It is defined as the process of grouping similar data points together without manual intervention. The most popular unsupervised learning algorithms are k-means, k-medoids, etc. 

Different algorithms in data science

Let us talk about a few data science algorithms below:

Linear Regression

This algorithm is a well-known supervised algorithm in the data science field. This algorithm aims at finding a hyperplane where the maximum number of points are lying on the hyperplane. For instance prediction of weather or prediction of rainfall, etc. This algorithm works on an assumption that the relation between the dependent and the independent variables is always linear. Also, there is very little or nearly no multicollinearity there. These algorithms are generally used to predict the values in a continuous quantity. 

The algorithms work on the equation below:

z= a0 + a1x

Where,

z= the dependent variable whose value the user wishes to predict

x= the independent variable with the help of which value of a dependent variable (z) will be predicted.

a0, a1= These both are the constant where a0 is the y-intercept whereas a1 is the slope.

Let us see the diagram below for the algorithm:

IMAGE

Logistic Regression

As we have discussed in the above algorithm, linear regression is used to represent the relationship between various continuous values. However, when we talk about logistic regression, this algorithm actually works on discrete values. This algorithm is most commonly used to solve binary classification problems. It can be like two possibilities for an event to happen, meaning either the event will take place (1) or it won’t occur (0).

Logistic Regression converts the predicted values into the values lying in the range from 0 to 1 using a nonlinear function basically called a logistic function.

The shape of a logistic function is an S-shaped curve. It is hence called a Sigmoid function which is defined by the following equation:

[ Related Article: Data Science Training in Canada ]

P(z) = e^(a0+a1z)/1 + e^(a0+a1z)

Where,

a0, a1= These both are the coefficients

The main aim of the algorithm is to find the values of these 2 coefficients.

Let us see the diagram below for the algorithm:

K-means Clustering

This algorithm is a type of unsupervised algorithm. Clustering is actually a process of grouping together similar data in the form of clusters. To measure this, the user can use the formula of Euclidean distance which is:

F(x) = e^(a0+a1x)/1 + e^(a0+a1x)

Below are the steps for K-means clustering:

1. The user first selects the value of k that is actually equal to the cluster number into which the user wishes to categorise his data.
2. Then he assigns the random values to each of the k clusters formed from the data.
3. Then the user can start searching for the data points closest to the centre of the cluster using the above-mentioned Euclidean distance formula.
4. The next step calculates the mean of these data points which are assigned to every cluster.
5. Again the user searches for the data points closest to the newly formed centers and they are then assigned to the closest clusters.
6. The above steps are repeated until there is not much change in the data points of the k clusters.

Let us see the diagram below for the algorithm:

IMAGE

Support Vector Machine

Support Vector Machine is a supervised algorithm that aims at finding an application for both regressions as well as classification problems. It is commonly used to classify the data points with the help of a hyperplane. The initial step of SVM is to plot all the data values as individual points in the form of an n-dimensional graph. We define ‘n’ as the number of features. The value of each feature is actually the value of a specific coordinate. The user can then get the hyperplane which will separate the two classes at their best for the classification purpose.

It is very important to find the correct hyperplane. The data points that help in separating hyperplanes and are closest to them are known as support vectors.

Let us see the diagram below for the algorithm:

IMAGE

Want to know more about Data science,visit here Data science Tutorial !

Neural Networks

These are also termed artificial neural networks.

4  6  8  9  0  3  6 

The above numbers can be an easy task for the human eye to read. This happens because a human brain contains millions of neurons that can perform any complex calculations to identify a particular visual in no time. However, this is not an easy task for machines to perform. 

There comes neural network algorithms that help in solving this problem. They do this by training the bot or the machine with a huge number of instances. With the help of this, the machine learns from the provided data in recognizing the digits automatically.

Hence, the user can conclude on Neural Networks by saying that they are those Data Science algorithms that tend to do the work making the machine identify the patterns in a very easy way, mostly similar to how a human brain does.

Let us see the diagram below for the algorithm:

IMAGE

Decision Trees

These algorithms help the user to solve both predictions as well as classification problems. That way it gets easier for the user to understand the data better and also achieve better accuracy of the predicted value. The decision tree has nodes and links where nodes represent a feature of each attribute and each link represents the decision. It also has a leaf node that holds a label for the class label which is the outcome.

The only drawback that comes with decision trees has undergone the issue of overfitting. Overfitting refers to the noise created by the untrained data in an algorithm that adversely affects the performance.

Let us see the diagram below for the algorithm:

IMAGE

Recurrent Neural Networks

(RNN), a recurrent neural network is an artificial neural network that takes in sequential data or we can say time-series data. This algorithm is used for both temporal as well as ordinal problems like a translation of different languages, natural language processing, image captioning, recognition of speech, etc. They get included in applications like Google Translate, Siri, voice search, shazam, etc. The main difference is in the memory they acquire information from. The inputs and outputs of traditional neural networks are nondependent on each other, however, it is not the case in RNN.  Here, the outputs depend on the prior elements based on a sequence.  Some future events are useful to determine the output of a sequence.

Let us see the diagram below for the algorithm:

IMAGE

 Top 30 frequently asked Data Science Interview Questions !

Apriori

This algorithm has this name as it takes in previous knowledge of some item set properties. The user can work out an approach such as a level-wise where ‘a+1’ itemsets will be found out by a-frequent itemsets. There is a very important apriori property that helps to reduce the space for search while performing the algorithm. The property states that:

‘All the subsets which are non-empty of a frequent itemset must be frequent. The main concept of this data science algorithm is the anti-monotonicity of support measure.’ 

The drawback with Apriori Algorithm is that it is slow. The time that is required to hold a number of candidate sets with many frequent itemsets has low minimum support for large itemsets. This means there is no efficient approach for a huge number of datasets in the algorithm.

Random Forests

As we have discussed the issue of overfitting in decision trees, random forest algorithms overcome this overfitting problem. This algorithm also aims to solve both regressions as well as classification problems. The principle followed by the random forest algorithm is Ensemble learning.

The methods involved in Ensemble learning state that there are a number of weak learners who can work and perform together in order to make high-accuracy predictions. In the same way, the Random Forest algorithm works. It contemplates a large number of decision tree predictions together and gives the final output. This is done by calculating the number of predictive votes of various decision trees. The prediction with the maximum votes will become the final prediction of the given model.

Let us see the diagram below for the algorithm:

IMAGE

Principal Component Analysis (PCA)

It is a form of an unsupervised algorithm that a user can use to reduce dimensionality in machine learning. It is a process that converts the observations of features that are correlated with linearly uncorrelated features. This is performed under the guidance of orthogonal transformation. It is actually based on mathematical ideas such as variance or covariance, eigenvalues, or eigenvectors. Here for this algorithm, the user can get the data, structure and standardise it, get the covariance and then calculate the eigenvalues and the eigenvectors. Hence the new features can be calculated by them dropping the unimportant features.

Let us see the diagram below for the algorithm:

IMAGE

Conclusion

In this article, we have talked about the basic introduction of data science and the algorithms affiliated with it. These Data Science algorithms help the data scientists to solve a lot of Data Science problems and make efficient strategies. Some of the algorithms that we have covered in this article are random forest, decision trees, k-means algorithm, principal component analysts, apriori algorithm, etc. When a user wishes to decide which algorithm is the best, there is no clear answer because every algorithm has its own pros. It always starts with an algorithm that is simple to use and will increase the complexity of the problem gradually.

Related blogs :

Big Data vs Data Science 

Data Science vs Business Analytics

Computer Science vs Data Science

Data Science Training in Singapore

Data science with R training