Top Machine Learning Uses for Complex Business Problems

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Machine learning applications are fast growing into an industry within its own boundaries. Today, the domain taps the top talents in AI and data science from leading machine learning courses in Gurgaon and Bangalore. These experts are part of the game changing movement built around the latest AI technologies called supervised machine learning. Data science modeling now focuses on training labeled data to perform predictive analysis and deliver accurate insights based on machine data that is labeled for specific business purposes like business analytics, sales forecasting, healthcare analytics, financial health management, and so on. A majority of the businesses that hire data scientists from data science courses rely on supervised machine learning and deep learning applications to determine the impact of various factors on the operations and the possible outcomes.

If you are planning to build a machine learning project as part of your machine learning course in Gurgaon, here are some of the top use cases in the business process management.

Hiring and Recruitment Analytics

Some industries have a very high employee turnover rate. For example, hospitality, finance, and healthcare jobs see the maximum employee churn compared to others. This could range between 18 to 35 percent during a year. In IT and software development domains, the churn could be even higher.

For ages, human managers have been handling the critical operations related to organizational development. Organizational development starts at the very point when you start identifying people to fill for your job vacancies. In most cases, hiring and recruitment are considered to be the first touch point in the employee’s journey. Machine learning based on supervised algorithms could help reduce the time HR managers take to identify the most suitable candidates required to accomplish the job — and also retain them for a longer period. AI has been found to reduce bias in hiring processes, making it simpler for HR managers to write attractive job descriptions, skim through thousands of CVs in one shot, and manage the hiring needs of the organization during unforeseeable times resulting from talent war, attrition due to company’s loss, economic recession, or pandemic.

Healthcare research

Healthcare is one of the most complex ecosystems to have emerged since the 1950s. Today, it employs millions of healthcare professionals and supports the livelihood of millions of others who are associated with the industry directly or indirectly. These could be logistics and supply chain management, food and hospitality, drugs and genetics research, academics, and much more. One thing that binds all these together is the role of Artificial Intelligence and data science that has brought together the ease of providing service to the doorsteps of the patients – or in modern times, to their smartphones operating on fingertips and wearables. So much has happened in the last 5 years around the use of supervised machine learning for healthcare that it is now impossible to stay detached from the investments pouring into the healthcare industry. By 2025, 70% of the healthcare industry would be completely AI based and by 2030, all of the decision making (for doctors) and patient monitoring systems would be hosted on Cloud operated by intelligent virtual assistants trained using machine learning deep learning supervised models.

Flights booking

Airlines carriers are getting tech affluent with the rise of AI use cases in the various operations leading up to ticket booking and final boarding and descent. During the pandemic, we saw how all major airline carriers utilized the power of supervised machine learning algorithms to quickly change, modify and suspend certain routes due to the explosive rise in COVID 19 cases. The same technology also alerted frequent flyers and families against booking any airline tickets in the carriers that were flying to crisis zones or whose recent flying historical records showed a high number of cancelations. This saved millions of dollars for both airlines as well as passengers. It is easier to use supervised learning algorithms for flight booking systems. What’s the reason? There are two- firstly, data analysts can easily label the flight records and booking data which helps them build a supervised machine learning algorithms specific to the case study. Secondly, by using classification models and K-means clustering techniques, these machine learning models can be used to design a predictive intelligence system to identify the exact nature of flying trends — whether the flight will get canceled or reach its destination despite crisis situations, and so on.

Data science with ML is the future of all businesses in 2022.