Getting meaningful insights from hidden data and statistics through analytics that use algorithms to solve data-related business problems is what Machine Learning is all about. Increasingly accurate and adaptive, the ML models are updated as and when new data sets are introduced whereby predictions are more on point.
ML algorithms coupled with computing technologies, artificial intelligence, and business analytics can improve the overall business operations and solve business complexities.
Web traffic surges, traffic patterns issues, consumer behavior, stocks, and commodities are easily predicted by the ever-evolving ML model algorithms. As far as businesses go ML offers scalable solutions to all business complexities.
The benefits of machine learning are predicting customer behavior, product recommendations, improving market strategies, data entry assistance, financial analysis, medical prediction, and treatment, while also detecting network intrusions.
Predictive analytics employs current and historical statistics to predict outcomes in future like customer behavior and market changes etc. Predictive analysis includes ML, predictive modeling and data mining which are all statistical techniques.
A lot of organizations are now open to the idea of embracing predictive analytics which is known to support business decisions while enhancing operations.
Predictive modeling drives predictive analytics which includes a ML algorithm which are trained to interact with new data sets and values to deliver business outcomes. Predictive analytics and ML are not the same as it is perceived to be however they do overlap and go hand-in-hand as predictive models includes ML algorithm delivering exceptional business results.
Predictive models are of two types they are classification models and regression models which are made of algorithms. Classification models predict class membership while regression models predict a number.
The algorithms, defined as classifiers which identify which set of categories data belongs to, execute data mining techniques, statistical analysis while determining data trends and patterns.
Built-in algorithms in predictive analytics software solutions can be used to make predictive models. Decision trees, regression (linear and logistic), neural networks are some of the most used predictive models.
Other classifiers are time series algorithms, clustering algorithms, outlier detection algorithms, ensemble models, factor analysis, naïve Bayes, and support vector machines. An organization must choose the right classifiers and models to get the desired business outcomes.
Predictive analytics and machine learning are utilized in different industries such as banking and financial services, security, retail, etc where data is overflowing and is used for security, marketing, operations, risk, and fraud detection.
It must be understood that predictive analysis and ML is not a one size fit solution for business needs and challenges. Businesses must first assess the challenges and need to determine the right suitable solution to improve the business outcomes.
Businesses can gain a competitive advantage by reducing risk, costs by drawing insights about consumer behavior to create better products and services, customer loyalty programs and rewards thereby improving operational efficiencies.