Introduction to predictive analytics

Predictive analytics is a part of data analytics used to build predictions. This can be achieved via employment of techniques involving historical data mining, data modeling, statistics, machine learning, and artificial intelligence. If used correctly, this will enable an organisation to interpret its historical data, to make predictions on the future. Before building a model, you need to clearly identify the questions you are trying to answer. Once you’ve done this, then you can choose from a variety of modeling techniques to procure a predictive outcome. Let us look at them one after the other. Regression models are used to show or predict the strength of a relationship between two variables, such as the linear regression model.

Classification models are a categorisation of data based on historical knowledge and algorithms that learns the correlation between the data and its labels, such as decision trees and random forests. Clustering models are a grouping of data with similar attributes, such as K-means or hierarchical. Time series models analyse historical data events to predict future events, such as the trend lines and seasonality analysis. One thing is a definite non-negotiable and must be in place is data accuracy and completeness. These are necessary because the effort to develop and implement predictive analysis models can be substantial. However, it can also be potentially dangerous, if incorrect decisions are going to be made from the analysis.