“Predictive analytics is a form of advanced analytics that uses historical data, statistical modeling, data mining techniques, and machine learning to forecast outcomes”
Predictive analytics is an umbrella term for the use of quantitative methods and expert knowledge to derive meaning from data and answer essential questions related to business, meteorological conditions, healthcare, and many more areas. Predictive analytics can be used in conjunction with prescriptive analytics to drive knowledgeable insights.
Today's businesses are overflowing with data such as transactional databases, equipment log files, pictures, video, sensors, and other sources. Data scientists employ deep learning and machine learning algorithms to detect patterns and make predictions about future events to obtain insights from the provided dataset.
Predictive Analytics has existed for a long, but its usage has witnessed an upsurge of late. This is due to:
- Increasing volumes and varieties of data, as well as a growing interest in leveraging data to generate useful insights.
- Fast and less expensive computers.
- Availability of software that is easier to use.
- Gaining competitive differentiation as a result of tougher economic conditions.
Predictive analytics is no longer only the domain of mathematicians and statisticians, thanks to the increasing availability of interactive and user-friendly software. These technologies are also being used by business analysts and other business professionals. However, its implementation requires some basic knowledge of R Programming and/or Python Programming, and other programming languages.
How many types of models are there under Predictive Modeling?
Predictive models are divided into two categories. Class membership is predicted by Classification Models. For example, you might try to predict whether someone would attrition, whether he will reply to a solicitation, whether it is a good or bad credit risk, and so on. Such models are usually in the form of a 0 or 1, with 1 denoting the required event. Regression Models help in forecasting, such as the amount of income a client will make over the following year or the number of months until a machine component fails.
Where can we use Predictive Analytics?
- Machine learning and quantitative technologies are used in financial services to predict credit risk and detect fraud.
- In health care, predictive analytics is used to detect and manage the care of chronically ill patients.
- Predictive analytics is used by Human Resource teams to find and hire individuals, determine labor markets, and anticipate an employee’s performance level.
- Predictive analytics can be utilized in marketing campaigns and cross-sell strategies throughout the customer lifecycle.
- To generate product recommendations, anticipate sales, evaluate markets, and manage seasonal inventory, retailers employ predictive analytics.
- Predictive analytics is used by businesses to improve inventory management, allowing them to fulfil demand while reducing stock levels.
