It is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
Its history may date back to at least 650 BC. Some early examples include the Babylonians, who tried to predict short-term weather changes based on cloud appearances and halos: Weather Forecasting through the Ages, NASA.
Medicine also has a long history of needing to classify diseases. The Babylonian king Adad-apla-iddina decreed that medical records be collected to form the Diagnostic Handbook. Some predictions in this corpus list treatments based on the number of days the patient had been sick, and their pulse rate (Linda Miner et al., 2014). One of the first instances of bioinformatics!
Many organizations are turning to predictive analytics to increase their bottom line and competitive advantage. Why now?
Growing volumes and types of data, and more interest in using data to produce valuable insights.
Faster, cheaper computers.
Tougher economic conditions and a need for competitive differentiation.
Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities.
Common uses include:
Detecting fraud. Combining multiple analytics methods can improve pattern detection and prevent criminal behavior.
Optimizing marketing campaigns. Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities.
Improving operations. Many companies use predictive models to forecast inventory and manage resources.
Reducing risk. Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics.
How many businesses are actively using predictive analytics?
According to research from Dresner Advisory Services, about 23%, a figure essentially unchanged from the previous years.
Interest, however, exceeds implementation. The same research suggests that 90% of businesses “attach, at minimum, some importance to advanced and predictive analytics.”
Types of Data Analysis
4 Stages of Predictive Modelling
Descriptive analysis on the Data – 50% time
Data treatment (Missing value and outlier fixing) – 40% time
Data Modelling – 4% time
Estimation of performance – 6% time
What is Time Series Data? -Analysis comprising of methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values.
Usage: Non-stationary data, economic, weather, stock price & retail sales
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