## 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 the maximum likelihood of an event to happen. 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 not only based on statistical terms but also a combination of 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.

### Types of Predictive Analytics

Based on the computation strategy, Predictive Analytics broadly classified into 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. Prescriptive Analytics relies on the Optimization and Rule-based Techniques for Decision-Making. E.g – Forecasting the load on energy grid over the next 24 hours is the predictive analytics while how to operate the power-plant based on the forecast of energy load is Prescriptive Analytics.

## How Analytics works?

Predictive Analytics is incorporating and performing 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 prediction for the future. Each one of these is as important as other for estimating the outcome.

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

Access and Explore Data

Steps where the necessary and relevant data acquisition done from various data sources like sensors, databases, data lakes, etc.

Pre-processing and Aggregating Data

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

Predictive Models Development

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

Integration with Real-Time Systems

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

## Benefits of Enabling Predictive Analytics

Predictive Analytics emerges the Insights from the data and uses them in identifying risks and patterns that help in taking decisions in most of the areas like the 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 IoT Analytics platform in this insight. There are numerous benefits of Analytics as it is based on the modeling to predict future results, few of them are considered here with a precise category –

• Regarding 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

## Predictive Analytics Best Practices

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

• Supply-Chain Analytics
• Financial Analytics
• Customer Analytics
• Risk Analytics
• Semantic and Sentiment Analytics
• Workforce Analytics

## 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

• IBM SPSS
• 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

You may also love to explore about Predictive Maitainence Services With Industrial IoT in this blog.

## A Holistic Approach

Predictive Analytics uses many techniques from Artificial Intelligence, Data mining, etc. to analyze real-time, data for future predictions. To understand more about predictive analytics, you should review each of the following steps for better understanding:

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