List of Machine Learning Algorithms for Business Operations

Is your business looking for modern ways to amplify business operations? 

Machine learning algorithms could be one of your best options. Whether you’re a small startup or a large enterprise, understanding and implementing the right machine learning algorithms can unlock new opportunities for growth and innovation. 

From making data-driven decisions to predicting future trends, machine learning algorithms have the potential to help your business gain a competitive edge. However, you have to be strategic with the machine learning algorithms you adopt. 

That’s why this article explores a comprehensive list of machine learning algorithms that are best for different business purposes. From classification techniques that streamline decision-making to regression models that help forecast future outcomes, this list includes machine learning algorithms for business operations. 

What is a Machine Learning Algorithm?

A machine learning (ML) algorithm is a set of rules, instructions, or processes that tell an ML system how to perform its specialised tasks. These tasks often involve discovering new data insights and patterns or predicting output values from given input data. While a machine learning system is built to help computers learn from data and improve their performance over time, machine learning algorithms are the techniques and processes that enable that ML system to learn. 

Based on these capabilities, machine learning algorithms are often involved in identifying trends and predicting future events before they occur. Furthermore, they can also provide personalised support, categorise data into different classes or groups, and find the best possible solution to a problem by iteratively improving a model. 

All these capabilities are very important for businesses, which, by nature, deal with a lot of data. Generally, they help them automate repetitive and time-consuming processes so that high-value employees can focus on more strategic operations. However, machine learning algorithms’ applications go further than this. In the following section, we’ll discuss ten different examples of machine learning algorithms and how they can benefit business operations. 

Top 10 Examples of Machine Learning Algorithms for Business Operations 

The following are some of the most prominent examples of machine learning algorithms that can be used to optimise and improve business operations: 

1. Logistic Regression Algorithms

Otherwise known as logit regression, logistic regression is a powerful supervised learning algorithm primarily used for binary classification tasks. This algorithm focuses on estimating distinct values like 0 or 1 as an indicator of the probability of an event occurring. That’s why it is useful for classification tasks, Such as determining whether something belongs here or there. 

Logistic regression algorithms use the logistic function, which outputs values between 0 and 1. This output option creates a decision boundary that classifies inputs into two distinct categories. This nature serves as the basis of the application of logistic regression algorithms in business operations. Let’s examine some relatable use cases: 

• Fraud Detection: Logistic regression algorithms assign probability scores to different input variables of a data point, such as email information. Using this score, the ML system can now decide if the score qualifies each data point as fraudulent or not. This way, logistic regression helps to classify it as either fraudulent or genuine.
  
  
• Customer Churn Prediction: It also helps businesses and customer success teams to predict whether a customer will churn (leave) or stay via a very similar process. You pop historical customer data into the algorithm and specifically highlight the variables and factors that will influence a customer’s decision to leave or stay. The algorithm works on it and then calculates the odds of churn for each variable. Finally, the algorithm classifies customers as “churned” or “not churned” based on the calculated variable.
  
  
• Medical Diagnosis: Logistic regression algorithms can also estimate the probabilities of a patient having a condition based on various patient characteristics and data. In fact, the algorithm has been used to provide patient risk stratification in an effort to reduce the uncertainties in COVID-19 diagnosis. 

2. Linear Regression Algorithms

A linear regression algorithm is a supervised machine learning technique for modelling a linear relationship between input variables (features) and continuous output variables (labels). After assuming and modelling this relationship, a linear regression algorithm can now predict and forecast values that fall within the continuous range. That’s why businesses leverage the relationship between variables for prediction and forecasting. 

Here’s how it works: 

After plotting the input variables against each other, the linear regression model finds the line of best fit. This line, known as the regression line, minimises the sum of squared differences between the predicted and actual values. By using this line, the model can now use the variables to predict new variables and forecast future outcomes. 

Wondering how machine learning development services apply this to business operations? 

Let’s take predictive analytics, for instance; linear regression algorithms help businesses forecast future opportunities and risks. For example, businesses use it for demand analysis, predicting the number of items consumers will likely purchase. 

Furthermore, it can also be used to optimise business processes. For example, a factory manager can model the impact of oven temperature on baked goods on the shelf. In the same vein, call centres can analyse the relationship between call wait times and the nature of complaints. 

3. K-Nearest Neighbors

The K-Nearest Neighbors algorithm is a simple supervised machine learning algorithm mainly used for predictive modelling tasks and classification problems. It compares a new data point to existing data points with known categories. The algorithm then assigns the new data point to the most common category, which is “K Nearest Neighbors.”

This algorithm classifies new variables based on their similarity to some already-grouped data points. 

However, it is worth noting that it is computationally expensive because the KNN algorithm always has to compute the distance between each new query point and every other point in the dataset. Also, it is important to normalise the variables in a KNN algorithm to ensure that all data points are on the same scale. 

Nonetheless, KNN can be very useful in business operations like customer segmentation, where customers are grouped based on their similarities. For example, the KNN algorithm can be used to classify customers who are likely to purchase a particular product. And it does this using a very simple and understandable approach. It simply looks at the “K” as the most similar customers in your existing customer base. If most of those neighbours (customers) purchase that particular product, the algorithm will predict that the new customer will also purchase the new product. 

Pretty simple! 

The choice of “k,” i.e., the number of neighbours or customers to consider, greatly impacts the algorithm’s accuracy. Basically, a small k-value would be sensitive to noise. This means that outlier values or random fluctuations would greatly influence the algorithm. On the other hand, a large k value can smooth out the decision boundary, I.e., consider more neighbouring data points when making a decision. 

It has to be just right! 

4. Decision Tree Algorithms

Decision trees are another non-parametric supervised learning algorithm that can be used for both classification and regression algorithms. They typically improve business performance by identifying and considering the most important factors in a decision-making process and analysing them visually till you reach a rational conclusion. This rational and visual decision-making approach helps businesses picture all the possible outcomes and lines of action in a given decision-making scenario. 

Decision trees have a flowchart-like structure that approaches decision-making by breaking down a complex problem into simpler and more manageable parts. Technically speaking, decision trees start with a root node (the entire dataset under consideration) before splitting into branches based on different criteria. These branches lead to internal nodes (decision points) and finally end at lead nodes (outcomes).

So, let’s say a business’s design team is trying to choose between two different brand designs — let’s call them designs A & B. A decision tree algorithm for this decision might involve multiple questions like: 

• Does this design align with the brand’s core values?
   
• Which design resonates more with the target audience?
  
  
• Can designs A or B be modified to get the desired value? 

These questions and more are some examples of the common questions a decision tree would ask to help the business make the best decision. 

That’s why many businesses find decision tree algorithms particularly beneficial for analysing complex issues, especially those where one decision leads to multiple possibilities. They can also play a vital role in helping businesses with strategic planning and seeing where fresh ideas might take a company. More direct applications can be found in marketing operations to help executives decide on how to spend valuable resources. 

In general, decision tree algorithms can be instrumental in helping businesses make appropriate decisions in various aspects of their operations. 

5. Naive Bayes Algorithms

The Naive Bayes algorithm is a straightforward machine learning algorithm that primarily helps with classification problems. It works based on the principles of the Bayes theorem. The principle is basically based on establishing predefined groups or segments and classifying new uninitiated data points into these different predefined groups based on the probability and features of the new data points. 

Here’s a simple logic to get the simple idea. Let’s say you spot a bird with some distinct and noticeable features flying across the sky. You might not be able to identify the bird’s name exactly solely based on these features. However, you might be able to make a calculated and close guess based on these features. 

The same thing applies in a business setting when you’re trying to make some predictions about your customer or a particular business case based solely on its features because there are numerous instances with the same features. However, you might be able to make some predictions based on these same features, and that’s what the Naive Bayes algorithm does. 

The Naive Bayes algorithm makes these classification predictions based on the principles of probability. 

One of the good things about the Naive Bayes Algorithm, especially for business decisions, is that it doesn’t require a huge amount of training data. Its implementation process is quite straightforward. The fact that it functions with several data points and predictors makes it highly scalable, and by extension, it is highly practical and usable in real-time predictions. 

For example, its probabilistic learning technique makes it highly usable for text classification, making it ideal for businesses to use it for document classification. In addition, it can also be used to analyse customer sentiment to make probabilistic predictions about a customer’s feelings—whether positive, neutral, or negative. 

Based on the same principles as the text classification process discussed earlier, the Naive Bayes algorithm can also be used for spam filtering, I.e., determining whether an email received is spam or not. The probabilistic detection system can also benefit healthcare operations by predicting a patient’s risk level for certain diseases. 

6. Dimensionality Reduction Algorithms

A Dimensionality reduction algorithm is an unsupervised learning algorithm often used to solve several key problems associated with high-dimensional data, such as datasets containing a large number of features or columns. 

These algorithms solve the “curse of dimensionality,” which arises when a large number of features expands the volume of the data space. A reduction in computational efficiency often accompanies this scenario as processing times slow down, making it difficult to visualise and interpret these datasets. ML models trained on high-dimensional data are also often prone to overfitting, a situation in which the model performs excellently with the training data but poorly with the actual data. 

So, how do dimensionality reduction algorithms address these challenges? 

Dimensionality reduction algorithms reduce the number of features or variables within the dataset while retaining and focusing on only the essential information required to make relevant analyses. These algorithms do this using two major methods, namely: 

• Feature Selection: This approach literally selects only a few relevant feature subsets from the original complete set of features. 
  
  
• Feature Extraction: With feature extraction, the algorithm creates a new set of features by combining the original features in the dataset. This is a lot like condensing the features into a smaller number. 

So, how are these processes relevant to business operations? 

Well, for obvious cases, machine learning consulting services use dimensionality reduction algorithms to make machine learning models perform better since they’re now operating on fewer features and now require lesser computational power. Furthermore, these algorithms help businesses identify the most relevant customer features they need to focus on when making business decisions instead of being crowded out by irrelevant parameters. 

Overall, dimensionality reduction algorithms are valuable to organisations looking to harness their data effectively and make informed decisions based on clearer insights. 

Amplify Your Business Processes With Excellent Machine Learning Algorithms

The machine learning algorithms on this list have some awesome peculiarities that could drive your business operations to the next level. However, it takes seasoned machine learning consulting firms to recognise the right algorithm for your business or a specific business use case. 

Debut Infotech’s machine learning development company take care of this challenge by developing custom machine learning algorithms for your business’s operational peculiarities. With Debut Infotech Pvt Ltd, you get to enjoy different AI and ML models whether your business is in healthcare, business & finance, education, manufacturing, or any other industry. 

Reach out today! 

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