Machine Learning or ML has been adopted by businesses at a rapid rate. It helps them personalize user experience through data learning and experience, resulting in better user journeys and higher satisfaction. But how? Machine Learning algorithms allow businesses to make their software smarter.
The computer programs automatically train from the data that is provided to them and the data on which they process. The mathematical models or different algorithms in machine learning keep on analyzing and gaining accuracy with greater exposure to the user data.
This allows the software to make more appropriate and accurate recommendations and suggestions and personalize user experience. And thus, they are being increasingly used worldwide by businesses for multiple purposes, doubling productivity and profits.
However, it is a bit confusing for the business to choose from the variety of algorithms in machine learning, the best ones that suit their project requirements.
This blog lists the best machine learning algorithms for businesses that allow them to optimize digital products using this technology.
Table of Contents
Machine Learning Algorithms For Business
With the rapid adoption of ML technology and its diverse nature of it, it is important for businesses to learn about the multiple ML algorithms to understand their benefits and incorporate them into the product accordingly.
The multiple machine learning algorithms are divided into three broad categories namely:
Supervised learning: algorithms here learn from the labeled data. They have input data and predefined outcome/output expectations.
Example: Biometric attendance
Unsupervised learning: there is no pre-labeled data or predefined output expectations here.
Reinforcement learning: This is more of a hit-and-trial method of learning wherein there is an interaction between an environment and the learning agent. The latter learns to make optimal decisions to minimize penalties and maximize rewards.
Now that we have briefly learned about the different types of ML algorithms, let’s look at which ones are best for businesses.
Regression, as a basic ML algorithm, analyzes the relationship between at least two variables, independent and dependent, predicting numerical values as per historical data. This algorithm simply helps to identify the relation between things, predicts numbers, by keeping historical data as a base, and highlights patterns over time. It is the forecasting of continuous outcomes such as house prices, stock prices, or sales.
Regression has more than 10 models, two of the most popular types are: simple linear regression and multiple linear regression. The former facilitates the identification of a relationship between two factors, whereas the latter attempts to figure out the impact of multiple factors on a specific thing.
For example, in a Kahwa business, simple liner regression would be helpful to identify the impact of temperature/weather on the sales of Kahwa. On the other hand, multiple factors such as temperature, pricing, flavors, regional tastes and preferences, etc., all can impact sales. This is what a multiple linear regression model would measure.
The relationship between the independent and dependent variables is assumed to be linear in the former case, whereas, for multiple linear regression models, the relationship is non-linear due to the involvement of multiple variables/factors.
Regression is more useful when data has been collected over time. Time series data facilitates regression to predict future outcomes and hence, it is increasingly used in retail, business processes optimization, recommendation systems, etc.
Some common use for ML regression models include:
Continuous outcomes forecasting such as property prices, stock prices, or sales
Retail sales or marketing campaigns’ success or failure forecast
Customer or user trends prediction like streaming services or e-commerce websites
Dataset analysis for establishing the relationships between variables and an output
Interest rates or stock price prediction factoring in multiple internal and external elements
Time series visualisations
As the name suggests, this method revolves around classifying or categorizing structured or unstructured data based on certain parameters or factors. It is the categorization of objects based on learned features.
By input analyses, the ML model learns the classification of fresh data and mapping of labels or targets to the data.
There are 3 key types of classification algorithms namely binary, multiclass, and multilabel.
Binary classification facilitates the categorization of data into 2 classes such as Yes /No, good/bad, high/low, etc.
Whereas multiclass classification allows the training of the model in a way that data can be classified into more than 2 classes. This type includes document classification, product categorization, and malware classification.
Multilabel classification refers to assigning multiple leads to different objects.
While the classification of data, the ML model makes the prediction with a probability between 0 to 1, indicating the surety behind the classification decision. Here, 0 means uncertainty whereas 1 indicates 100% probability. This range of probability brackets could be, however, modified depending on the business specifications and client requirements.
Classification is primarily used in areas of:
Customer behavior prediction; buying patterns, web store or app browsing patterns, etc.
Spam filtering; email text to a spam classification (or non-spam classification).
Document classification; a function that maps from a document to a category label
Defect and anomaly detection
Image and web text classification
Clustering is a machine learning method that allows the identification and grouping of data points together or in organized structures, representing large data sets. Additionally, new user/customer insights are also achieved through the clustering of data.
The clustering method is especially useful in businesses that need to segmentalize their customers out of large customer data. Hotels, mass media, publication companies, etc., could use clustering to send targeted campaigns or messages to their specific set of audiences.
Additionally, clustering in ML could be used for anomaly detection, image/visual segmentation, social network analysis, bettering marketing campaigns, and fraud detection.
Clustering is usually used for:
Clustering should be applied when the business is beginning with a large, unstructured dataset that is divided into an unknown number of classes. Here clustering would facilitate easy segmentation that would have been too labor-intensive if done manually.
For example, a retail company could use clustering to analyze an employee’s performance and analyze employees’ performance whose work is diversified to different supermarket chains with money.
A clustering model for anomaly detection could be created; anomaly being uncommon employee behavior. The clustering algorithm would identify the groups of employees with different behaviors from the majority.
4. Deep Learning
One of the main technologies behind AI/ML is deep learning. It works in a way similar to a human brain when it attempts to learn something. These algorithms replace a neural network with at least 3 layers that break problems into multiple data levels that facilitate problem-solving.
Deep learning models work with raw data and define the relevant factors and features of the data on their own. It is used across multiple industries such as healthcare, telecommunication and media industry, automobile, and fintech.
This field of AI facilitates text summarization, new image generation, speech-to-text conversation, sentiment analysis, movement recognition, etc., which helps a business get customer insights and enhance their experience through personalization.
In fact, the U.S deep learning and ML markets are expected to reach a value of$80 million by 2025.
Deep learning could be used in:
Automotive and Self-Driving Cars
Retail and voice assistants
Speech recognition and translation
Image and text classification
Natural Language Processing
Predictive and Real-Time Bidding (RTB) ads
Insurance fraud detection and prevention
5. Dimensionality Reduction
Dimensionality reduction is a technique used as a machine learning algorithm that allows reducing the number of input variables or features in order to simplify data and get the most accurate results possible. At the same time, the data is kept informative enough for analysis.
The idea behind this technique is to avoid overfitting the machine learning algorithm with features. This leads to poor algorithm performance, inaccurate predictions, and thus, business losses. Dimensionality reduction is done before modelling, as a data preparation step to enhance the efficiency of the algorithm. It is commonly undertaken as an intermediate step in more complex ML projects.
Noise reduction and high-dimensional data visualization are some of the common uses of dimensionality reduction algorithms.
Additionally, it is used for image compression,runtime optimization and complexity reduction of models.
Case study: A business XYZ produces as well as monitors multiple types of sensors.
This company has a lot of data to store and analyze. This is done through a prediction model that analyzes past data and predicts future outcomes based on that historical data.
Dimensionality reduction technique, in this case, is used prior to modeling to filter the sparse and large data from sensors. This practice reduces the number of input variables or features, keeping only those that are most relevant to the analysis. Without dimensionality reduction, the ML model would just show sub-optimal results due to massive amounts of unnecessary data.
After the data is received post filtering, regression or classification model is applied for prediction purposes based upon the project requirements.
Conclusion:How to choose the most suitable machine learning algorithms for your business?
Now that we have learned about the most popular machine learning algorithms, the next big step is to choose the most suitable ones for your business project. Choosing the wrong ones could lead to performance issues and sub-optimal or inappropriate results.
Thus, the following steps would help you choose the most business-appropriate machine learning algorithms for your project:
Understand and define your project problems and goals
Do data analysis by size, processing, and labeling/annotation required
Evaluate the speed and training timelines
Further evaluate the linearity and hence, the complexity of the data
Decide on the number of features as well as parameters
Due to the overlapping use cases of some machine learning algorithms and their general diversity of them, it could be a complex task to choose the most suitable ones for your business. However, if you move ahead step-wise in the selection process, it will help you make the right choices.
Furthermore, focus on the data and take a problem-related approach. Define your data input for its sufficiency, processing, and labeling, output for its goals, the field of study for its linearity or complexity, the project limitations such as time, training, and resource constraints, and lastly, the preferred features or variables.
This could be a bit overwhelming and that’s exactly where our AI/ML experts come to your rescue.
BigOh’s AI/ML professionals could help a business choose the most suitable ML algorithms for its projects and build a smart product for the optimization of resources and best results.
Get in touch with us today and have bright business predictions!