Predictive Analytics: Detailed explanation, techniques, and uses

by Precious Cyprain
4 mins read
Predictive analytics explained

What is Predictive Analytics? | Definition 

Predictive analytics is a type of advanced analytics that involves the use of historical data, statistics and modelling techniques to forecast future outcomes and performances. Predictive analytics helps to boost operating efficiency and reduce risk by allowing companies and investors to regulate how they use their resources and make better decisions to take advantage of possible future events and opportunities. 

Predictive analytics evaluates the patterns in current and historical data to determine if those patterns are probable to surface again and the possible effects at a high degree of certainty. This determination is made through a series of techniques including data mining, artificial intelligence (AI), statistics, machine learning and modelling 

Predictive analytics models are being used in various organizations for multiple purposes like developing investment portfolios, forecasting the weather, developing marketing strategies, forecasting sales, etc. 

How does Predictive Analytics work? 

Predictive analytics work using variables which can be measured and evaluated by predictive analytics software applications. These variables help to forecast the likely behaviour of machinery, individuals, or other entities. A predictive model that can assess future possibilities with a suitable level of reliability is used to combine multiple variables.  

To achieve this, predictive analytics software depends heavily on advanced algorithms and strategies like time series analysis, decision trees and logistics regression models. 

Predictive Data Analytics techniques 

  1. Neural Networks 

The neural network is a technique of predictive analytics that mimics the pattern-detecting system of the human brain in forecasting future values. This model is a biologically inspired data processing system that identifies complex connections buried in data. It uses artificial intelligence and pattern recognition to deal with complex data relationships. 

Neural networks are vastly used for patient diagnoses and image recognition. They compose of different layers such as the input layer that accepts data, the hidden layer that computes predictions and the output layer that provides the output.

       2. Decision Trees 

A decision tree is a predictive analytics technique that uses data mining algorithms based on machine learning to predict likely risks and advantages of undertaking certain choices.  

A decision tree places data into various sections based on specific variables like market capitalization. They are the simplest models of predictive analytics as they are quite straightforward to understand and examine. It solves all kinds of classification problems and is most useful for decision-making in a short time. 

      3. Regression model 

This predictive analytics model is used to determine the patterns in large sets of data, also when there is a linear relationship between the inputs. It works by deducing a formula which describes the relationship between the inputs seen in the dataset. It is most important when it comes to estimating a numerical number.  

        4 . Text analytics 

Text analytics is used when anticipating a numerical value. It helps in predicting text-based content like the theme of a document, social media post, email, etc and analyses words used in the supplied form.  

Predictive Analytics examples utilized in different industries 

1. Healthcare 

Predictive analytics in healthcare is used to detect and oversee the health of chronically ill patients and also trace the progression of certain diseases in patients. It is used to predict the probability of patients developing particular medical conditions, optimize result allocations, identify patients at increased risk of hospital readmission, manage supply chains, etc. 

2. Marketing  

Predictive analytics in marketing can be used to examine how consumers reacted to a service or product and use the information to determine if they will be enticed to purchase the new products. It can also be used for demand forecasting, lead qualifications, predicting media and messaging styles for certain customers, churn management, etc.  

3. Supply chain management 

Here, predictive analytics can be used to predict and manage pricing strategies and inventory levels. It helps to identify and deal with risks, optimize resources and improve decision-making.  

4. Fraud detection  

Predictive Analytics is used to analyze trends, transactions and patterns to detect any irregularities which will then be investigated for fraudulent activity. 

5. Human resources 

Human resources use predictive analytics to match prospective job applicants, increase employee engagement, reduce employee turnover, predict future workforce needs and skills requirements and analyze employees’ performance and skills to forecast their career progression as a guide to career development planning.  

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