PREDICTIVE ANALYTICS
Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques, and machine learning.
Predictive analytics is often associated with big data and data science.
Today, companies today are inundated with data from log files to images and video, and all of this data resides in disparate data repositories across an organization. To gain insights from this data, data scientists use deep learning and machine learning algorithms to find patterns and make predictions about future events. Some of these statistical techniques include logistic and linear regression models, neural networks, and decision trees. Some of these modeling techniques use initial predictive learnings to make additional predictive insights.
Types of predictive modelling:
- Classification models : These models categorize data based on historical data, describing relationships within a given dataset
- Clustering models : Clustering models fall under unsupervised learning. The group data is based on similar attributes.
- Time series models : Time series models use various data inputs at a specific time-frequency, such as daily, weekly, monthly, et cetera.
Predictive analytics industries use cases:
- Banking : Financial services use machine learning and quantitative tools to predict credit risk and detect fraud.
- Healthcare : Predictive analytics in health care is used to detect and manage the care of chronically ill patients, as well as to track specific infections such as sepsis.
- Human Resources : HR teams use predictive analytics and employee survey metrics to match prospective job applicants, reduce employee turnover and increase employee engagement.
- Marketing & Sales : Predictive analytics enables companies to be more proactive in the way that they engage with their clients across the customer lifecycle.
- Supply chain : Businesses commonly use predictive analytics to manage product inventory and set pricing strategies.
Benefits of predictive modelling :
- Security
- Risk reduction.
- Operational efficiency.
- Improved decision making.
Drawbacks of predictive modelling:
- Model calibration.
- Model validation.
- Model sensitivity.