Predictive Analytics Tools and Techniques

What is Predictive Analytics?

Predictive Analytics is the sub-branch of Advanced Analytics, the key area of Data Science that uses Statistical concepts to output an event’s maximum likelihood. Mathematics and Statistics are the building blocks of Predictive Analytics. It looks into the historical data and finds the trends & patterns from it by using the statistics and build the strategy to apply on new data to compute and predict the best possible outcomes. It is based not only on statistical terms but also on data mining that provides Knowledge Discovery and Data-Driven features related to the prediction. You may also learn more about Cognitive Analytics in this insight.


What are the Types of Predictive Analytics?

Based on the computation strategy, Predictive Analytics broadly classified into below given two classes –

Statistical/Mathematical Modelling

This approach of modeling uses the Equation-Based models that describe the whole phenomenon under consideration. Example: Time-Series Regression and Linear Regression.

Computational Modelling

This modeling relies on the models that are not easy to explain in the form of the equation and often requires the simulation of the built functions for making the prediction called ‘Black-Box Prediction Modelling’ because the model structure does not provide the insights into factors that can map models Inputs to outputs. Example: Neural Networks.

Are you confused with Predictive and Prescriptive Analytics?

Prescriptive Analytics is the next step to Predictive analytics. Prescriptive analytics is the branch of Data Analytics that uses Predictive Modelling to suggest the actions to be taken for getting the optimal outcome. It relies on Optimization and Rule-based Techniques for Decision-Making. E.g., Forecasting the load on the energy grid over the next 24 hours is predictive analytics, while how to operate the power-plant based on the forecast of energy load is Prescriptive Analytics.


Predictive Analytics help you determine what will happen—how well a product will sell, who is likely to buy it, which marketing to use for the greatest impact.
Source – The Future Of Data Analytics


How Predictive Analytics works?

It incorporates and performs the Knowledge Data Discovery steps and forecasts the maximum likelihood outcome as the prediction. It combines Data mining, Machine Learning to result in qualitative and quantitative predictions for the future. Each one of these is as important as others for estimating the outcome.

These are four necessary steps to be taken for reaching Prediction –

Access and Explore Data

The necessary and relevant data acquisition is made from various data sources like sensors, databases, data lakes, etc.

Pre-processing and Aggregating Data

Here, the data results in the removal of messy data and transformed into the required format, along with the selected features extracted.

Predictive Models Development

Statistical and Computational approaches are used to create the models and their experiments to optimize parameters used while training and monitoring the performance.

Integration with Real-Time Systems

Now, the integration of Predictive and Prescriptive analytics comes into the picture to generalize the Intelligent system that functionally works on the Predictive Building and behaves in the way as prescriptive implements.


Learn more about ” Insurance Analytics, Fraud Detection.”


What are the Predictive Analytics Techniques?

Predictive analytics combines various data analysis techniques; likewise, data mining, machine learning, and many more. Listed below are the methods involved in analytics:

Decision Trees

A decision tree is an analytics technique based on Machine Learning, uses algorithms from data mining to predict the possible risks and rewards of pursuing several different actions. It is a visual chart that resembles an upside-down tree, which illustrates a decision’s potential outcome. It can solve all types of classification problems and answer the tricky questions when deployed for analytics.

Text Analytics

Text Analytics comes into use when an organization willing to predict a numerical value. It is based on Statistical, Machine Learning, and linguistics techniques. It helps to predict the topics of a document, and also it examines words used in the given form.

Neural Networks

Benefits of Enabling Predictive Analytics

Predictive Analytics emerges the Insights from the data. It uses them to identify risks and patterns that help in making decisions in most areas like Business, Finance, Public Safety, Healthcare, and many others. Most of the Machine Learning and Artificial Intelligence applications are using Predictive Analytics as the core tool. You would also love to read more about the IoT Analytics platform in this insight. There are numerous benefits of Analytics as it is based on modeling to predict future results. Few of them are considered here with a precise category –

  • Helps in analyzing overall Business.
  • Analyze the End Product.
  • Regarding End-User

Why Predictive Analytics Matters?

Role of Analytics in Data Science is leveraging various new opportunities, mainly –

  • Decision-Making and Intelligence
  • Fraud Detection
  • Optimizing the Market Campaigns
  • Optimize operations and results
  • Risks Analysis and reduction

Where to Use Predictive Analytics?

  • Segmentation
  • Customer Retention
  • Dynamic Pricing
  • B2B analytics
  • Customer Lifetime Values

Click to explore Predictive Maitainence Services and Solutions with Industrial IoT.


What are the Best Practices of Predictive Analytics?

Advanced Analytics has become such a common word to be used by applying it in many fields and creating some specific domains –


Predictive Analytics Tools

Predictive Analytics Software Tools have advanced analytical capabilities like Text Analysis, Real-Time Analysis, Statistical Analysis, Data Mining, Machine Learning modeling and Optimization, and many more to add.

Libraries for Statistical Modeling and Analysis

  • Scikit-learn
  • Pandas
  • Stats model
  • NLTK (Natural Language Processing Tool Kit)
  • GraphLab
  • Neural Designer

Open-Source Analytical Tools

  • SAP Business Objects
  • IBM SPSS
  • Halo Business Intelligence
  • Daiku-DSS
  • Weka
  • R-Studio(R-Programming used)- most demanding Statistical tools for Machine Learning
  • Apache Mahout (easy integration with Hadoop)
  • RapidMiner Studio
  • H2O.ai
  • Knime Analytics
  • Statistica
  • Orange
  • Anaconda

A Holistic Approach

Predictive Analytics uses many techniques from Artificial Intelligence, Data mining but analyzes real-time data for future predictions and understanding predictive analytics. You should review each of the following steps for better understanding: