Machine learning in business problems finds its place in all aspects of IT, from social media to complex financial applications. Machine learning can be used to improve the customer experience, better manage and predict the outcomes of complex data, and even transform the way different businesses operate.
Applying Machine Learning in Business Problems
By using data to detect patterns and various anomalies, a Data Scientist or a Machine Learning expert can help an organization predict future outcomes and improving its operations. There are many examples in almost every industry. In this article, I’ll give you some examples of how we can use machine learning in business problems.
Application of Machine Learning to the Health of Patients
One of the biggest machine learning in business problems in treating patients is that medications often affect people differently. Some drugs can cause terrible side effects for one patient while being an effective treatment for a different patient. A patient may have additional medical conditions that can cause a reaction to treatment. Age and gender can also have an impact on how well a drug works. Too often, doctors have to resort to trial and error to find the right treatment.
One solution to selecting the most efficient treatment is to create a machine learning model based on classification and regression algorithms. The classification model is needed to predict the impact of the drug based on known test results and patient conditions. The regression model is then used to predict changes in the patient’s condition while taking a certain drug.
Building this model using data allows researchers to understand how a patient population historically responds to various drugs. As the model is built and trained, it will be able to determine the likelihood that a certain drug will be the most effective for a patient.
When the machine learning model is online, it will continue to evolve as more patient data is added. A solution can be designed to include a conversational interface using cognitive application programming interfaces (APIs). This way a doctor can interact with the model and ask a variety of questions to ensure that the right treatment is provided with fewer side effects. You can practice very good machine learning in business problems relating to healthcare from here.
Leverage IoT to Create more Predictable Results
Machine learning algorithms are an ideal approach for the Internet of Things (IoT). The first thing to understand about IoT data analysis is that it involves data sets generated by sensors. These sensors are cheap and sophisticated enough to support a seemingly endless variety of applications.
The data generated by the sensors contains a specific structure and is therefore ideal for applying machine learning techniques. While the data itself is not complex, there is often a huge amount of data produced. Using this sensor data, along with known failures, machine learning algorithms can create models to predict future mechanical problems.
The model would include data on the optimal indicators of a baseline of a well-run machine as well as data points that preceded a failure. As the model is trained, it will be able to determine the anomalies that will predict the potential for failure. You can practice machine learning in business problems relating to security from here.
Machine Learning in Business Problems for Handling IT Issues
IT operations have always been complicated due to the range of different network devices, servers, applications, storage systems, endpoints, etc. Each system has its own ways of managing its components. As new versions of the software are implemented, configuration updates may be required to keep the system operating as intended. This is the normal way that systems need to interact to maintain a steady state. Often times, a single error in an area can result in a massive outage, which can be difficult to determine the root cause of a problem – despite the fact that there is significant instrumentation in the data centre.
Applying machine learning in business problems for complex IT operations data enables organizations to proactively respond to potential IT issues. Traditionally, event correlation has been used to find patterns in performance data. There are times, however, when the correlation alone can be misleading. Therefore, to gain precision, data scientists are starting to bundle machine learning algorithms to identify event anomalies.
The benefit of the machine learning application is that it can create a model based on a complex set of data created in the data centre, including alerts, logs, and instruments or sensors. The machine learning algorithms create a model based on the data which is highly relevant. The model can understand the dependencies between the different elements that make up the environment.
The model can also help identify patterns of ideal performance measures and compare them to the current state of the environment. You can practice good machine learning in business problems relating to IT from here.
Machine Learning in Business Problems Against Frauds
Detecting Fraud is a cat-and-mouse game. Bad actors are increasingly savvy in fraud. With the increase in the use of online services, the potential for fraud has increased. Additionally, payment processors want to make sure customers have a frictionless transaction and don’t want to block legitimate payments.
Many companies find that the only approach that can help stop fraud is to use the software, which is based on machine learning algorithms. A machine learning model is trained to identify an anomaly before a fraud event is perpetrated. In essence, the model can identify an action associated with an intrusion or an unauthorized action and block the intruder before damage occurs. You can practice good machine learning in business problems against frauds from here.
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