Predictive Analytics and its Importance
Data is critical to business. Not only does it help a business to know how it has been performing over years, but it also helps organizations to become more proactive and forward looking by predicting outcomes and unknown future events by analyzing its data to take proactive decisions as needed. So, what is the technology that works behind the scene to predict it for a business? It is “Predictive Analytics”, a branch of advanced data analytics that uses current and historical data, algorithms, modeling, relationship matrix and techniques like data mining, machine learning, and artificial intelligence to predict outcomes. With the business world seeing growth in data Volume, Velocity and Varieties, there is an increased interest in using this technology to produce valuable insights, going beyond what has happened so far to what will happen in the future.
Some of the most common uses:
• Cyber Security – It analyzes all types’ illegitimate actions and events on an IT network real-time to spot anomalies that may indicate threats and suspicious activities much before the actual attack happens.
• Internet of Things (IoT)– Cars are slowly getting connected through IoT sensors. It analyzes such diagnostic data to predict a breakdown or maintenance, and alert the driver.
• Consumer Sentiments– It is used to derive consumer sentiment to run effective promotional events by analyzing customer spending, buying behavior, social media activities, etc.
• Financial Industry - The banking industry leverages it to detect fraudulent transactions, identify credit risk, forecast sales,and predict the best portfolio to maximize return.
• Healthcare - The healthcare industry is using it to identify treatments to provide the best care by analyzing health data of patients who are at risk of developing critical conditions.
One of the key steps in Predictive Analytics is Modeling that uses known data or train a model to generate predictions that represents a probability of the target variable.
The most widely used predictive modeling techniques are:
Regressionis a form of modelling technique which estimates the relationship between
dependent and independent variables and is driven by three metrics - number of independent variables,type of dependent variables and shape of regression line. For e.g. relationship between network events and security attack.
Linear Regression technique is widely used in which the dependent variable is continuous, independent variables can be continuous or discrete, and the nature of regression line is linear.
Other techniques are:
• Logistic Regression - Finds the probability of event and should be used when the dependent variable is binary in nature.
• Ridge Regression – It is used when the data is of multi collinearity.
• Lasso Regression– Capable of reducing the variability and improving the accuracy of linear regression models.
Decision Tree- It handles categorical features and is able to capture non-linearities and feature interactions. It partitions data into subsets based on categories and is like a tree with each branch representing a choice between a number of alternatives. Two types of decision trees are:
A. To classify, a Classification Tree is used: This predicts class membership as Yes or No. For e.g., whether security attack will happen or not, etc. C4.5 is used if the target variable has more than 2 classes.
B. To predict, a Regression Tree is used: This predicts a number in the form of response variable is numeric or continuous – for e.g., the predicted price of a product is based on seasonal demand.
Neural Network is a powerful modeling technique that represents complex relationships and performs intelligent tasks. It is similar to the workings of a human brain acquiring knowledge through learning. The true power of neural networks is its ability to represent both linear and non-linear relationships and to learn these relationships directly from the data being modeled. The most common neural network model is the Multi layer Perceptron (MLP) and the most common use of neural network is optical character recognition (OCR) application. Other uses include – machine diagnostics, medical diagnostics, voice recognition, financial forecasting, etc.
Some of the other popular techniques are:
Time Series Data Mining – It reduces time series datasets of even trillion observations to fewer dimensions by using data mining methods such as variable selection, clustering, etc.
Bayesian Analysis – It is a technique that employs probabilities to statistical problems. A prior probability distribution for a particular parameter is specified. To provide probability, the evidence is procured and combined with the application of Bayes’s theorem.
K-nearest neighbor (k-NN)– It is a type of lazy learning nonparametric method for classification and regression where the function is only approximated locally.
A successful Predictive Analytics consists of multiple steps – starts with a problem statement, then moves to data collection, data modeling, data analysis and finally, monitoring.
Predictive Analytics might have begun as marketing terms with the intent to attract attentions and excite business; however, technologists believe that it is not a one-size-fits-all solution to all our problems. Today, it is issued by organizations, law enforcement or various industries,etc. to take operational decisions in order to authorize actions and to reap advantages related to predictability, data-driven decisions, and business agility.
Knowledge is only potential power but decision is the real power of Predictive Analytics as prediction is the key to guiding these decisions. It has proven its worth and remains different from many technologies because it is revolutionary and its impact is readily apparent, visible and measurable.
Linear Regression technique is widely used in which the dependent variable is continuous, independent variables can be continuous or discrete, and the nature of regression line is linear.
Other techniques are:
• Logistic Regression - Finds the probability of event and should be used when the dependent variable is binary in nature.
• Ridge Regression – It is used when the data is of multi collinearity.
• Lasso Regression– Capable of reducing the variability and improving the accuracy of linear regression models.
Decision Tree- It handles categorical features and is able to capture non-linearities and feature interactions. It partitions data into subsets based on categories and is like a tree with each branch representing a choice between a number of alternatives. Two types of decision trees are:
A. To classify, a Classification Tree is used: This predicts class membership as Yes or No. For e.g., whether security attack will happen or not, etc. C4.5 is used if the target variable has more than 2 classes.
B. To predict, a Regression Tree is used: This predicts a number in the form of response variable is numeric or continuous – for e.g., the predicted price of a product is based on seasonal demand.
Neural Network is a powerful modeling technique that represents complex relationships and performs intelligent tasks. It is similar to the workings of a human brain acquiring knowledge through learning. The true power of neural networks is its ability to represent both linear and non-linear relationships and to learn these relationships directly from the data being modeled. The most common neural network model is the Multi layer Perceptron (MLP) and the most common use of neural network is optical character recognition (OCR) application. Other uses include – machine diagnostics, medical diagnostics, voice recognition, financial forecasting, etc.
Some of the other popular techniques are:
Time Series Data Mining – It reduces time series datasets of even trillion observations to fewer dimensions by using data mining methods such as variable selection, clustering, etc.
Bayesian Analysis – It is a technique that employs probabilities to statistical problems. A prior probability distribution for a particular parameter is specified. To provide probability, the evidence is procured and combined with the application of Bayes’s theorem.
K-nearest neighbor (k-NN)– It is a type of lazy learning nonparametric method for classification and regression where the function is only approximated locally.
A successful Predictive Analytics consists of multiple steps – starts with a problem statement, then moves to data collection, data modeling, data analysis and finally, monitoring.
Predictive Analytics might have begun as marketing terms with the intent to attract attentions and excite business; however, technologists believe that it is not a one-size-fits-all solution to all our problems. Today, it is issued by organizations, law enforcement or various industries,etc. to take operational decisions in order to authorize actions and to reap advantages related to predictability, data-driven decisions, and business agility.
Knowledge is only potential power but decision is the real power of Predictive Analytics as prediction is the key to guiding these decisions. It has proven its worth and remains different from many technologies because it is revolutionary and its impact is readily apparent, visible and measurable.
