10 Algorithms to Adopt for Your Big Data Initiatives

Big data and advanced analytics are the latest technologies that have revolutionized the world, as we know it. Such technologies have become exceedingly popular in every industry, and businesses are also reaping the benefits of it. The problem is that many organizations want to attain favourable results as well but are not sure where to start exactly.

Advanced analytics often involves the implementation of new ways of data transformation and analysis to decipher the previously unknown patterns and trends within their data. When this new-found information is then integrated with the business processes and operating norms, it presents the potential to transform your business to a whole new level.

The following are some of the algorithms to consider in case of big data initiatives.

  1. Classification and Regression Trees

Classification and regression trees are based on a decision to distinguish the data. Each of these decisions is dependent on questions, which are associated with one of the input variables. With each question and its guided response, the data moves closer to being classified in a specific way. The number of questions, answers and subsequent categorization of data present a tree-like structure. At the end of each question, there is a category. This is recognized as the leaf node of the classification tree.

These classification trees can turn out to be quite huge and intricate. One effective way of toning down the intricacy is through pruning the tree or otherwise intentionally eliminating levels of questioning to maintain the balance between abstraction and exact fit. A model that sits well with all specimens of the input values, both the ones that are known in training and the ones unknown is significant. To stop the overfitting of this model, it’s significant to maintain a perfect balance between abstraction and exact fit.

A form of classification and regression trees is acknowledged as random forests. Rather than constructing a single tree with many branches of logic, a random forest is a collection of many simple and tiny trees, all of which assess the specimens of data and determine a categorization.

After these simple trees finish the assessment of data, the procedure combines the individual outcomes to present a final prediction of the category depending on the combination of the smaller divisions. This is commonly known as an ensemble method. These random forests often work wonderfully at balancing exact fit and abstraction. It has been integrated efficiently in case of many businesses.

As opposed to logistic regression, which puts emphasis on a yes or no categorization, classification and regression trees can be adopted to determine multivalue categorizations. They are also much simpler to visualize and envision the definitive path that leads the algorithm to a particular categorization.

  1. Clustering Techniques

Clustering (also identified as segmentation) is a type of independent learning algorithm where a data set is put together into unique and diversified clusters.

For instance, we have customer data spread across multiple rows. With the help of this clustering technique, we can categorize the customers into distinctive clusters or sections, depending on the variables. When it comes to the customer data, the variables can be demographic information or purchasing behaviour.

Clustering is an unsupervised algorithm because the analyst isn’t quite familiar with the output. The analysts don’t produce the output information, but let the algorithm itself to determine the output. Hence (similar to any other modelling exercise), there is no accurate and concrete solution to this algorithm, in fact, the best solution is dependent on business usability.

  1. Linear Regression

Linear regression is an algorithm based on statistical modeling and attempts to model the connection between a dependent variable and an explanatory variable by including the observed data points on a linear equation.

A linear regression is adopted when there is a significant association between the variables. If no connection is apparent between the variables, fitting a linear regression model to the data will not present any useful model.

  1. ANOVA

The ANOVA (one-way analysis of variance) test is applied to decipher if the mean of more than 2 groups of a data set is considerably different from one another.

For instance, a campaign of ‘buy one get one free’ is carried out on 5 groups of consisting 100 consumers. Each group is separated according to their demographic traits. Now the next step would be to monitor how the five different groups react to the campaign. This would ultimately prompt the store to devise the appropriate campaign according to the different demographic group, boost the frequency of response and decrease the expenses of a campaign.

The main aspect of this technique lies in the evaluation of whether all the groups are belong to one large population or they belong to an entirely different population with different traits.

  1. Principal Component Analysis

The objective of dimension reduction techniques is to minimize the data set from higher to a lower dimension without losing the feature of information that is communicated through the dataset.

PCA depends on weighing the data from the perspective of a principal component. A PCA analysis includes rotating the axis of every variable to highest Eigenvalue/ Eigenvector pair and deciphering the principal components, i.e. the highest variance axis or in simple words the direction that clearly traces the data. Principal components tend to be orthogonal.

  1. K-Nearest Neighbours

K-nearest neighbour is another classification algorithm. It is identified as a “lazy learner” because the training period of the process is also restricted. The learning process comprises of the training set of data being saved. As new specimens are assessed, the space between each data point in the set is evaluated, and a decision is made as to which division the new specimen of data falls into depending on the closeness to the training instances.

This algorithm can be expansive considering the scope and size of the training set. As every new instance has to be equated to all specimens of the training data set and a distance measured, this process can take up many computing resources each time its underway.

K-nearest neighbours are adopted more often because it’s comparatively easier to adopt, easy to train, and easy to decipher the results. It is often utilized in search applications when you are trying to look for similar items.

  1. AdaBoost & Gradient Boosting

These are boosting algorithms adopted when an enormous amount of data have to be managed to make predictions by maintaining high accuracy. It is a form of ensemble learning algorithm that presents the predictive power of several base estimators to accelerate the robustness.

All in all, it brings together multiple average or weak predictors to a develop strong predictor. Such boosting algorithms are known to always stand out in data science competitions. These are one of the most preferred machine learning algorithms that are applied today. You can choose to apply them along with R Codes and Python to achieve the perfect outcomes.

  1. Neural Networks

Neural network (also acknowledged as Artificial Neural Network) is ideally based on the mechanism of the human nervous system and resembles how complicated information is derived and processed by the system. Much similar to humans, neural networks are known to learn by examples and are utilized on a specific application.

Neural networks are adopted to look for patterns in complex forms of data and also offer forecast and categorize the data points. Neural networks are generally arranged in layers. These layers are made up of a number of conjoined ‘nodes.’ Patterns are provided to the network through the ‘input layer,’ which conveys to one or more ‘hidden layers’ where the actual processing takes place. The hidden layers then connect with an ‘output layer.’

  1. K-Means

This is again an unsupervised algorithm, which resolves the issues concerned with clustering. Data sets are categorized into a specific number of clusters (assuming the number is K) in a certain way that all the data points within a cluster are homogenous, and heterogeneous from the data in other clusters.

The following ways K-means develops a cluster.

  • The K-means algorithm selects the k number of points, known as centroids, for each cluster
  • Every data point develops a cluster with the closest centroids that is k clusters.
  • It now makes way for new centroids, depending on the existing cluster members.
  • With these new centroids, the nearest distance for each data point is understood. This process is continued until the centroids don’t change.
  1. Hypothesis Testing

Hypothesis testing cannot be exactly called an algorithm, but any data researchers must have a clear idea about this. When you completely master this process, you can move on to many other intricate processes.

Hypothesis testing is the technique in which statistical tests are conducted using the data to monitor if a hypothesis is true or not. Depending on hypothetical testing, the researchers seek to either accept or reject the hypothesis. When an incident takes place, it can be a passing trend or coincidence. To see if the event is significant or happened by chance, hypothesis testing is crucial.

Author Bio:

Shirley Brown is a software developer working for a multinational organization. She had pursued her degree in engineering from the Australian National University. She has spent close half a decade in Silicon Valley. She has also been part of MyAssignmenthelp for the past three years as a homework help expert and provides math homework help to the students.

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