Predictive analytics encompasses a variety of statistical techniques that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Predictive modeling solutions are a form of data-mining technology that works by analyzing data to find patterns and adjusting program actions accordingly.

Key Predictive Modeling Techniques

Below are some of the most common predictive modeling techniques:

• Regression Analysis

  Regression models are the mainstay of predictive analytics. They predict a continuous outcome variable (such as sales or price) based on one or more predictor variables. The goal is to establish a formula that helps predict the dependent variable based on the values of the independent variables. Linear regression and logistic regression are two common types.

• Decision Trees

  Decision trees are a non-parametric supervised learning method used for classification and regression. The model predicts the value of a target variable by learning simple decision rules inferred from prior data(training data). They are particularly useful when you need to make a series of decisions that lead to different outcomes.

• Random Forests

  Random forests are an ensemble learning method for classification, regression that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

• Neural Networks

  Neural networks are a series of algorithms that endeavor to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; you can generate the best possible results without redesigning the output criteria.

• Time Series Analysis

  Time series analysis involves analyzing time-ordered sequence data points to extract meaningful statistics and other characteristics of the data. Time series forecasting is widely used for non-stationary data, like economic, weather, stock price, and retail sales forecasting.

Application of Predictive Analytics

Applications of predictive analytics can be found across industries. From forecasting demand in retail, predicting failures in manufacturing equipment (predictive maintenance), to determining credit worthiness in finance and personalizing marketing strategies in business. This broad applicability is driving the widespread adoption of predictive analytics.