5 Essential Machine Learning Algorithms Transforming Business Operations

Machine learning has redefined its status from being just a buzzword to becoming a transformative force in the business world. There has been a surge in the number of businesses across various industries using machine learning algorithms to streamline routine operations and lower costs while boosting business operations as per the report by Statista, the global machine learning market is projected to reach USD 79.29bn in 2024. This technological shift is not just about keeping up with trends but also about enjoying a competitive edge in this dynamic business landscape.

From predicting customer behavior to optimizing supply chains, machine learning algorithms stay at the forefront of innovation. They help businesses analyze a vast amount of data with utmost accuracy.

What is Machine Learning?

Machine learning is a kind of artificial intelligence involving the use of algorithms and statistical models to let computers learn and make decisions without explicit programming. It means making computers aware of managing a large amount of data, which the system uses to make predictions or take actions as per the identified trends. Machine learning is by most industries, from healthcare to education to finance to real estate, all use these technologies.

1. Regression

It is one of the machine learning algorithms used by various industries to predict the relationship between two variables, either independent or dependent, predicting numerical values based on historical data.

Regression models are useful to predict continuous variables based on independent predictor variables. This method is useful for evaluating outcomes where the response variable is in quantity, like prices or temperature. The input features in regression algorithms include time, size, or other quantifiable metrics.

Use Case

1.     Financial Sector: For doing financial calculations, a regression algorithm is used to predict future stock prices by tracking historical price data and market trends. This is beneficial for the companies and investors to make informed decisions regarding which stocks to buy and which ones to sell.
2.     Healthcare Industry: These models are also used within the healthcare department as well to predict patient outcomes based on clinical. The doctors use the outcome to create tailored treatment plans.
3.     Retail: Companies in the retail sector also use this machine learning algorithm to forecast sales and manage inventory by analyzing seasonal trends and consumer shopping preferences.
4.     Dataset Analysis: It is used by dataset analysts to establish the relationships between variables and an output 

2. Classification

The classification model consists of data sorting into predefined categories. It starts by learning from a dataset with known labels and then using the same to classify new observations. This can be binary and distinguishes between spam and non-spam emails, or multiclass, like identifying the type of fruit in an image.

Use Case

•         Healthcare: In healthcare, classification algorithms are used to deduct diseases by analyzing patient data against historical records. For example, machine learning models for businesses identify images of skin lesions as benign or malignant, aiding the experts in making accurate diagnoses.
•         Automotive: Classification machine learning algorithms are also used in other industries like the automotive industry. The algorithm helps classify objects detected by vehicle sensors and differentiate between pedestrians, other vehicles, and stationary objects. This improves drivers’ safety with better decision-making processes.
•         Customer Behavior Prediction: The classification model is used to predict customers’ behavior and their buying patterns. Based on their web store or app browsing patterns, marketers craft strategies accordingly.

3. Clustering

Clustering segments a set of objects in a way that objects within the same group look similar as compared to the ones in other groups. This machine learning algorithm is used for exploring data, identifying patterns, and dividing data into different groups even with no knowledge of group membership.

Additionally, businesses can get key insights about new users/customers through the clustering of data.  Clustering is used for customer segmentation, image compression, digits classification, visual search, etc.

Use Case

1.     Retail Sector: Retail companies are the ones dealing with huge data of customers. Thus, they use machine learning algorithms to analyze their shopping behavior, demographics, and preferences based on which they craft tailored marketing strategies.
2.     Telecom Sector: Clustering algorithms in the telecom domain analyze data from network traffic to segment users having similar usage patterns. This helps telecom companies in offering better services to their customers along with effectively utilizing the network resources.

4. Deep Learning

Deep learning is a subset of machine learning based on artificial neural networks with representation learning. Businesses use this to analyze data with a logical structure similar to the way humans conclude. Deep learning is used to process data with complex attributes like images, sound, and text.

As per the report, the estimated size of the US deep learning software market is expected to touch $80 million by 2025.

Use Case

1.     Automotive company- These companies use deep learning to develop autonomous driving technologies. These systems process and interpret huge volumes of sensor data. It ensures alertness to take the right and safe driving decisions quickly.
2.     Healthcare: When it’s about using advanced technologies, the healthcare sector is no exception to this. Deep learning algorithms are useful in medical imaging like MRIs, CT scans, etc.. It is used to detect subtle patterns in imaging data that humans might miss. These solutions help in offering improved healthcare services.

5. Dimensionality Reduction

Dimensionality reduction is a machine learning algorithm that helps in reducing the number of input variables or features to simplify data and get the most accurate results possible. Also, the data is kept informative enough for analysis. Dimensionality reduction is not just helpful in data preparation but also for visualizing high-dimensional data. 

Use Case

1.     eCommerce Platforms- eCommerce platforms use dimensionality reduction to improve product recommendation systems. With easy-to-read data, algorithms identify patterns and recommend products liked the most by the customers.
2.     Financial Organizations: Financial institutions also use machine learning algorithms to ease complex datasets of customer behaviors. It lets them identify key factors that influence risk.

Grow Your Business with Kodehash Machine Learning Expertise

Due to the diversity of top machine learning algorithms, sometimes it becomes difficult to choose the most appropriate option. Also, consider a data-related or problem-related approach while choosing the most suitable machine learning algorithm based on your project needs.

All the businesses who want to be a part of technology and drive significant business change, connect with Kodehash Technologies. Our experienced AI engineers are ready to help with choosing and deploying ML algorithms into your business.           

Connect with our ML engineers to leverage the complete potential of machine learning to achieve tangible business results.