The process of analyzing data and identifying patterns to meet business goals is a general definition of data analytics. However, with the exponential growth of data flow into enterprises over the years, it has become increasingly complex to analyze data using traditional statistical methods alone.
Also, the vast amounts of data make it almost impossible for human analysts to spot patterns.
That hurdle, however, can now be crossed by introducing machine learning (ML), a subset of artificial intelligence (AI) in data analytics.
Machine learning techniques help automate the process of data analysis by building efficient algorithms (or models) that can unravel the hidden patterns and insights from data.
What is Data Analytics?
The analysis of data is a multi-step process that ultimately culminates in visualizing the data to draw insights from the results. Such analytics is today used across almost every industry — in banking, marketing, and stock markets, to name a few.
What is big data machine learning? Once big data — both structured and unstructured data — is collected by an enterprise, it must be analyzed for patterns and insights. This leads to better decision-making within an organization.
Compared to earlier days, this is also a scientific, evidence-based approach to doing business. Big data analysis enables interaction with data that was previously not possible with traditional enterprise business intelligence systems. Real-time big data analytics further enhances this interaction, allowing organizations to respond swiftly to emerging trends and insights.
Data analytics can be utilized by businesses in their marketing and sales activities to target leads, prospects, and customers with cross-selling and upselling offers.
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What is Artificial Intelligence
Artificial intelligence is a broad-based discipline that mimics human intelligence and can be applied to a wide range of areas, from automation to robotics.
AI aims at making a machine more "intelligent" by imparting to it the ability to learn from data.
AI is broadly classified into four different types:
Reactive Machines AI
This type of AI includes machines that operate solely based on the present data, considering only the current situation. Reactive AI machines cannot form inferences from the data to evaluate their future actions and can perform a narrower range of predefined tasks. Ex — Any Chess Engine, like Deep Blue
Limited Memory AI
Limited Memory AI can make informed and improved decisions by analyzing past data stored in its memory. Such an AI has a short-lived or temporary memory that can be used to store past experiences and hence evaluate future actions. Ex – Self-driving cars
Theory of Mind AI
The Theory of Mind AI is a more advanced type of Artificial Intelligence. This category of machines is speculated to play a significant role in psychology, focusing mainly on emotional intelligence, so that human beliefs and thoughts can be better understood.
Self-aware AI
In this type of AI, machines possess their own consciousness and can make decisions independently, much like human beings. But this is a very advanced stage of AI.
What is Machine Learning
Machine learning is a subset of AI with the narrow purpose of learning from information (data) as far as possible without explicit programming.
ML utilizes numerical and statistical approaches to encode learning in models. Machine learning for analytics is a new approach to designing algorithms that learn independently from data and adapt with minimal human intervention.
An example would be a model that understands the difference between a $10 temporary fluctuation and a $100 jump in the price of a company share at any given trading hour.
The ML algorithm is referred to as a model, and its aim, like in traditional data analytics, is to derive insights from data.
An example of machine learning (ML) in day-to-day life is the automatic segregation of spam emails in your 'Spam' folder in your email inbox.
Machine Learning Today
Is machine learning (ML) a new discipline? The answer is no. AI has been around for years, but it has only become commercially viable recently. That's because of advancements in technology, which have made computing faster and also removed the cost barrier to deployment.
Recent iterations of ML can apply complex mathematical calculations to data faster.
In ML, machines are trained to make computations through repeated usage. They are used to build and automate data analytics models and given tasks such as classification, clustering, and divergence detection.
The idea is to see if computers can learn from data. As ML models progress, they are monitored to check whether the machines are learning independently when exposed to new data.
There is a subset of ML, even called "Deep Learning," where such artificial programs, by working on vast amounts of data, uncover new patterns with the help of neural networks.
The neurons of the human brain inspire the concept of Neural Networks.
Deep learning technologies have proven to be highly successful in solving complex problems that traditional ML algorithms can take a long time to solve, often requiring extensive fine-tuning.
Another example of machine learning (ML) in action today is the recommendation engines of Netflix or Amazon Prime, which suggest movie recommendations to their viewers.
How Machine Learning Works in Data Analytics
In comparison to traditional data analytics, machine learning in data analytics is a distinct and entirely different process.
It automates the entire data analysis workflow to provide a more comprehensive set of insights.
Analytics tools like those running on ML are capable of performing the actual laborious work required for data analytics that was once the task of humans.
Mostly, in an unsupervised or semi-supervised manner. Yet, let's not forget that even in such machine-learning models, it is humans who ultimately extract the results of data analysis.
Starting with machine learning in data analytics, most algorithms are either classification-based, where machines classify data, or regression-based, where they forecast values.
Then, there are the two popular machine learning methods often deployed by enterprises: supervised and unsupervised algorithms.
Supervised ML algorithms provide class labels for each sample of data in the training set. In an unsupervised machine-learning algorithm, though, no class labels are provided for the training samples.
These are the two most popular methods of machine learning.
Additionally, we employ a semi-supervised method that combines a small amount of labeled data with a large amount of unlabeled data during training.
Supervised learning algorithms:
- Training is imparted on input-output pair examples, utilizing labels like an input where the resultant output (target variable) is also known
- Data points are labeled here
- The "learning" algorithm then gets a set of inputs along with the compatible correct outputs
- This helps the algorithm learn by matching its own actual production with the correct set of outputs to find mistakes
- When given additional unlabeled data, it utilizes methods such as regression, Regression, prediction, and gradient boosting to predict the corresponding label values
- Usually used to predict future events based on historical data
- The term supervised is used because the data used to train the model already contains the correct answers mapped with every data record, like a teacher supervising the learning of a student
Unsupervised learning algorithm:
- As compared to its cousin, here the data used for training has no output labels mapped; there's no "right" output to match the result with
- Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses.
- Human intervention is almost nil or minimal.l
- It is left to the algorithm to figure out things for the most part, and to model the underlying structure or distribution in the data to gain a deeper understanding of the data.
- The "answer" to the problem is not fed into the machine
- Used primarily on unstructured data to find some patterns within
- In marketing, such an unsupervised model can be used, for example, to segment customers. Also used to identify data outliers.
- The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or groupings in data
Semi-supervised learning:
- The disadvantage of any supervised learning algorithm is that the dataset must be hand-labeled, either by a human or by accumulating historical data. This is a very costly process, especially when dealing with large volumes of data
- The most basic disadvantage of any unsupervised learning is that its application spectrum is limited
- To overcome this, a new concept known as "Semi-Supervised Learning" is typically trained on a combination of small amounts of labeled data and large amounts of unlabeled data.
- The basic process first involves clustering similar data and using the labeled data to label the remaining unlabeled data.
- Inelasticity is again the machine that finds out the cause behind the result. It will attempt to determine which factor is associated with which outcome.
As you may have realized by now, machine learning in data analytics involves the use of techniques such as clustering, elasticity, and natural language.
In clustering, it is for the machine to identify commonalities between different datasets to understand how certain things are alike.
Natural language, of course, as we have explained before, is for ease of use for everyday business users and not coders or analysts.
One does not need to know coding language to perform deep analysis. Machines can make queries of your data in any human language.
As we said earlier in this guide, machine learning involves building automated models for data analytics. This means machines are tasked with classification, clustering, and anomaly detection.
In some algorithms, without relying on programming, the algorithms decide the output based on the detection of any change in a pattern.
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Top 10 Machine Learning Techniques You Should Be Aware of
Here are a few Machine Learning Techniques or methods you must be aware of as a data scientist.
Clustering
- Distribution-based clustering
- Centroid-based clustering
- Connectivity-based Clustering
- Density-based Clustering
Linear Regression
Logistic Regression
Decision-tree
- Categorical Variable Decision Tree
- Continuous Variable Decision Tree
While there is a range of machine learning algorithms available, let's examine some basic and popular ones.
Clustering
This falls under the category of unsupervised ML. Here, the aim is to group (cluster) people, objects, trends, and other entities that exhibit similar characteristics. The model does not require output information while in training.
Here, the goal is to identify distinct patterns in the data and create clusters that exhibit minimal variation within themselves. However, there should be a high variation between the clusters so that each cluster can be identified separately. An example would be developing an algorithm that groups customers who have always bought red T-shirts into one cluster and then testing other products with this group to understand what catches their attention.
Simply put, clustering is the recognition of similarities. One must understand that deep learning does not always require labels to find similarities. When no labels are provided to learn from, it uses machine learning to learn on its own, which is known as unsupervised learning. This retains the potential of producing highly accurate models. Examples of clustering can be customer churn.
There are mainly two types of clustering approaches — Hard Clustering and Soft Clustering.
In Hard Clustering, a data point (or sample) can belong to only one cluster, out of all the predefined clusters. However, in Soft Clustering, the output is provided as the likelihood (probability) of a data point belonging to each of the predefined clusters. A data point is placed under that cluster, indicating the maximum probability of the data point being contained within it.
Let's have a look at the different clustering techniques:
Distribution-based clustering
Here, the data points are classified in the same cluster if they belong to the same distribution. The most popular choice for this purpose is the Normal (or Gaussian) Distribution. The Gaussian Mixture Model (GMM), a well-known clustering algorithm, falls under this category. GMM models the data with a fixed number of Gaussian distributions. Through repeated iterations, it attempts to find the optimal set of hyperparameters that minimizes the error in clustering the data points. It uses a statistical algorithm called Expectation-Maximization for this purpose.
Centroid-based clustering
It is basically a partition-based Clustering technique where the number of clusters should be known beforehand. The K-means algorithm, one of the most popular clustering algorithms, falls under this category. Here, K stands for the number of clusters. In this algorithm, K data points are randomly chosen from the dataset, which are assumed to be the centroids. Those K centroids are initially taken as the K clusters. Using those K clusters, the rest of the data points are classified under the cluster to which they lie the closest.
Obviously, we need a distance function to measure the closeness of the data points from the chosen clusters. Therefore, the choice of distance function becomes crucial here. The algorithm proceeds over several iterations (which can be set beforehand). In each iteration, when a new data point is added to a cluster, the cluster mean gets updated accordingly.
The above image shows a dummy dataset and the results obtained after K-Means clustering. Other variations of K-Means include the K-Medoids algorithm, K-Means++, and Weighted K-Means, among others.
Connectivity-based Clustering
This algorithm is one type of Hierarchical Clustering, where data points that are more "similar" to one another should be clustered in the same group. The main idea of the Connectivity-based model is similar to the Centroid-based model, but they differ in the way the distance metric is computed. Apart from the popular distance functions, such as Euclidean, Manhattan, and Cosine, this type of clustering employs a concept called "linkage", which is another way of defining the distance between two clusters. There are three types of Linkage algorithms: single, Complete, and Average.
The Single Linkage technique merges two clusters if the minimum distance, computed over all possible pairs of points in these two clusters, lies below a pre-specified distance threshold.
The Complete Linkage technique merges two clusters if the maximum distance, computed over all possible pairs of points in these two clusters, lies below a pre-specified distance threshold.
The Average Linkage technique merges two clusters if the average distance, computed over all possible pairs of points in these two clusters, lies below a pre-specified distance threshold.
Density-based Clustering
In this clustering model, the data space is searched for areas of varying density, and data points belonging to similar densities are grouped. There are many advantages to this technique, one of which is preventing the formation of strip-like clusters that occur when clusters are grouped based on a distance threshold, even though they are actually different. This is known as the chaining effect. DBSCAN and OPTICS are two of the most popular algorithms that fall under this category.
The complexity of DBSCAN is relatively low, although it proves to be efficient in many cases. The image above is the result of applying the DBSCAN algorithm to a dummy dataset. DBCAN identifies the clusters with reasonable accuracy, and also some noisy points in the dataset that are not part of any cluster.
Linear Regression
This type of modeling is best suited for finding correlations between variables in data analysis. It is also the most popular machine learning algorithm because of its ease of use. This machine-learning algorithm involves fitting the dataset to a linear equation that combines a specific set of input variables (x) with the solution of the predicted output for that set of inputs (y). A particular coefficient in the form of a scalar value is assigned to each input variable by the equation.
Linear regression modeling is based on regression capabilities that change depending on the number of independent variables and the type of relationship between the independent and dependent variables. There are two types of linear regression models: simple and multiple linear.
The first is a type of regression analysis that involves finding a linear relationship between a single independent (input) variable and a single dependent (output) variable. The second involves two or more independent variables and one dependent variable.
The following graph represents a simple linear regression fit between the input variable (x) and the output variable (y).
Logistic Regression
Linear regression algorithms inherently look for correlations between continuous variables. Regression is used for classifying categorical data. It is yet another technique borrowed from the field of regression. Regression is used to solve binary classification problems where there are two classes. Regression can be referred to as a Linear Regression model. Still, the former uses a complex cost function, known as the 'Sigmoid function' or 'logistic function', instead of a linear function. The sigmoid function plots any real value into an alternate value in the range 0 to 1. In machine learning, the sigmoid function (represented by the S-shaped curve) is used to map projections to probability. By using regression, you can make simple predictions to forecast the probability that an observation belongs to one of two possible classes. An example would be to review historical records of a bank customer to determine whether they may or may not default on their loan repayments.
Multi-class classification, a Regression form of regression achieved through logistic regression using a one-vs-rest scheme. In the regression method, while working with one class at a time, that class is denoted by 1, and the remaining courses are denoted by 0; their results are then combined to obtain the final fit.
Decision-tree
The Decision-tree model falls under the category of supervised learning. However, unlike other supervised learning algorithms, this particular algorithm can also be used to solve regression and classification problems. It is primarily used to help make decisions about any process.
This model is basically a rule-based approach where a tree-like structure is created. Learning starts from the top of the tree (i.e., the root node). Each node basically consists of a question, to which the answer is positive or negative. The questions at different levels are related to the other attributes in the dataset. Based on the answers at various levels of the tree, the algorithm determines the output that corresponds to the input sample.
It is a very popular algorithm, mainly due to its simplicity. The benefit of this algorithm is that for some input samples, it can predict the output quickly, without even traversing a significant portion of the tree. But that depends entirely on the dataset.
Depending on the kind of target variables, decision trees come in two types:
Categorical Variable Decision Tree
In this type of Decision Tree, the output is the category (or class) to which the test sample belongs. This type of tree is known as a Classification Tree. Example: Deciding whether a customer will default on a loan.
Continuous Variable Decision Tree
In this type of Decision Tree, the output is a real number corresponding to a test sample. This type of tree is called a Regression Tree. An example of this would be trying to determine whether to invest in a specific company's shares or not. What is also required for that is all possible variables.
Some techniques, often referred to as ensemble methods, construct multiple decision trees. Ensemble learning involves combining the decisions of various weak learners (or models) to produce a single strong learner. In most cases, a single Decision Tree alone is not sufficient to provide good accuracy. Therefore, the general practice is to use multiple Decision Trees to develop a single robust algorithm.
What is Machine Learning Used for in Data Analytics
In one line, to analyze big data in a speedier and more in-depth manner. Here are some of its uses:
Deciphering patterns
Machine learning data analytics can help decode trends in certain businesses or sectors. It can help identify diseases in the initial stage among patients, for example. Or unearth the buying patterns of consumers in a specific geography. Machine learning can help accurately interpret consumer patterns and behaviors. The media and entertainment industry utilizes machine learning and data analytics to understand their audiences' preferences and send out targeted content.
Understanding customer behavior and segmentation
User modeling is a significant area of focus in machine learning and data analytics. Businesses can use it to explore customer behavior. It can mine data to capture the client's mind and enable intelligent decisions.
Customer segmentation can help you in many ways: it enables a business to develop focused strategies to retain its top-paying customers. Or, to re-engage those clients who haven't made a purchase in a while. It is also used to provide a heightened customer experience.
Help in decision-making
Using time series analysis, machine learning in data analysis can aid an enterprise's decision-making framework by aggregating and analyzing data.
Machine learning-based modeling techniques can provide reliable insights into a consumer's persona, helping to predict their behavior. It can help businesses make insightful marketing decisions.
Who is Using Machine Learning in Data Analytics?
Needless to say, almost every field or industry that relies on data is using or can use data analytics, and consequently, deploy machine learning. From financial institutions to governments, from the medical field to retail, including e-commerce, you can find machine learning being deployed across various industries.
Healthcare
Machine learning can be applied in disease diagnosis, medical research, and therapy planning. It can be utilized in the prognosis of cancer, for example. It can be used to analyze data from wearable devices and sensors, and to identify potential hurdles that may arise during a patient's medical treatment.
Financial Institutions
Machine learning-based models can be a valuable asset for financial institutions, including stock markets, banks, and credit card companies. Today, it's used for two main reasons: to get insights from economic data and to prevent financial fraud. Machine learning can help FIs to track customer spending patterns or to perform stock or currency market analysis.
Retail
This is where machine learning was first deployed. E-commerce Sites, for example, use machine learning in data analytics to recommend items you might like based on your previous purchase history. It is machine learning again that helps analyze the vast amounts of customer-related data, including likes and dislikes, previous purchases, and so on, to personalize the shopping experience or implement a marketing campaign.
Machine learning can also be used to enhance customer engagement while they browse online catalogs, thereby increasing engagement and positively impacting conversion rates.
Then, of course, there are recommender systems used to increase sales by offering highly personalized recommendations. These also help speed up searches, making it easy for customers to access the content they are interested in.
Machine learning for data analytics helps your businesses to:
- Reduce time spent on manual data exploration
- Automates complicated analytics tasks
- Identify trends early
Machine learning analysis enables e-commerce, healthcare, retail, and finance companies to solve significant problems more effectively than before.
Best practices for using machine learning techniques for data analysis:
- Clean and preprocess data: Eliminate noise and manage missing values
- Select the right algorithm: Match the method to your problem type
- Consistently test models: Use metrics such as recall, accuracy, and precision.
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Challenges and Opportunities in Machine Learning
Here are a few challenges and opportunities in machine learning. The most significant barrier for machine learning-based data analytics is the mindset of enterprises. If, eventually, your business decides to proceed with machine learning data analytics, what is required first and foremost is a change in management fundamentals.
While its potential gains do hold appeal, companies that plan to invest in such machine learning-based advanced analytics solutions must ask themselves this one fundamental question: Do we really need it?
Many organizations can benefit from using traditional data analytics without relying on complicated ML applications. In many cases, traditional data analysis is sufficient to accomplish the task. You can generate reports of what's happened in the past, or of what's happening today.
If your business has vast repositories of big data, and making sense of it is beyond the scope of your team of human analysts. Deploying machine learning in analytics is a better option.
