Machine Learning and Predictive Analytics for business


Artificial Intelligence is successfully transforming businesses across the globe. Firms, enterprises, and start-ups are racing to adopt AI technologies in their businesses. AI-driven technologies have blessed organizations with enhanced computing and data analysis, cloud-based services, and much more. Machine learning and Predictive Analytics are mostly implemented by businesses to cater to their needs and maximize their ROI. Although both Machine learning and Predictive analytics help organization ineffectual data processing and used sometimes interchangeably, they are two different terms that have greater potential in transforming businesses.

Machine learning, an AI technology where algorithms process data without predetermined rules. This allows machine learning models to learn automatically without the need for explicit programming. Accomplished by feeding ML models with data in the form of observations and real-world interactions, it is used by organizations in image and speech recognition, detection of online frauds, email spam and malware filtering, product recommendations, and so on. Machine learning technology is of two types: Supervised and Unsupervised. Supervised ML also called Assisted ML  requires an operator to feed predetermined patterns, behaviors, and inputs to help ML model to learn more precisely which enables the operator to gain control over the model. Whereas unsupervised learning enables machines to learn those patterns and streams without training.

Predictive Analytics, refer to the analysis of past data as well as existing data to find trends and patterns. It is an advanced form of analytics, which automates forecasting with accuracy so that businesses can focus on their crucial tasks. It is more static and less adaptive than ML models. Thus any change in these models must be manually done by data scientists. It is mostly used by banks and other Fintech industries for customer targeting, risk assessment, churn prevention, quality improvement, sales forecasting, and financial modeling.

We cannot say which of the two is better as its applications in business is different. While ML is used to measure employee satisfaction, predictive analytics is suited for marketing campaigns optimization, empowering brands to identify suitable markets for their products, enhancing the ROI of their marketing campaigns, etc. ML can help businesses in locating security risks and possible threats, playing a significant role in cybersecurity. Apart from their difference, these two AI-driven technologies pose great opportunities for businesses to gain a competitive edge in the market place.


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