What is meant by Predictive Analytics?
Predictive Analytics is a sub-branch of Advanced Analytics, the key area of Data Science that uses Statistical concepts to predict an event's maximum likelihood. Mathematics and Statistics are the building blocks of Predictive Analytics. It looks into historical data, finds trends and patterns using statistics, and builds a strategy to apply new data to compute and predict the best possible outcomes.
It is based not only on statistical terms but also on data mining, which provides Knowledge Discovery and Data-Driven features related to prediction. You may also learn more about cognitive analytics from this insight.
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
Predictive vs Prescriptive Analytics
Prescriptive analytics is the next step in predictive analytics. The branch of Data Analytics uses Predictive Modelling to suggest actions to achieve the optimal outcome. It relies on Optimization and Rule-based Techniques for Decision-Making. For example, Forecasting the load on the energy grid over the next 24 hours is predictive analytics, while operating the power plant based on the forecast of energy load is predictive analytics.
Predictive analytics
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Models specific features of a business.
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Predictions of likely outcomes
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Determine when it will occur.
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The results are not actionable; they only point out the need for a decision to be made.
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Tends to prioritize one function over others when optimizing it
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The majority of the time, based on hypotheses using predetermined scenarios with limited options
Prescriptive Analytics
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models the whole company
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It Is entirely data-driven
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Makes specific business recommendations
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Reflects on interdependencies
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Fixed rules do not constrain it.
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It brings about real, measurable advantages
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supports hypothetical situations
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It takes into account all the inputs, variables, and outputs
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It uses calibrated, verified models that accurately depict how the business is run.
How does Predictive Analytics work?
It incorporates and performs the Knowledge Data Discovery steps and forecasts the maximum likelihood outcome as the prediction. It combines data mining and machine learning to produce 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 to reach Prediction -
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Access and Explore Data- The necessary and relevant data acquisition is made from various data sources like sensors, databases, data lakes, etc.
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Pre-processing and Aggregating Data—The data is removed from messy data and transformed into the required format, along with the selected features extracted.
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Predictive Models Development: Statistical and Computational approaches are used to create the models and their experiments, optimizing the parameters used while training and monitoring their performance.
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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 Predictive Building and behaves how prescriptive implements.
Predictive Modeling helps to improve the claim process and detect fraud and provider payment abuse. Source: Insurance Analytics, Fraud Detection
What are the best techniques for predictive analytics?
It 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 that uses algorithms from data mining to predict the possible risks and rewards of pursuing several different actions. It is a visual chart resembling an upside-down tree, illustrating a decision's potential outcome. When deployed for analytics, it can solve all classification problems and answer tricky questions.
Text Analytics
Text Analytics is used when an organization is willing to predict a numerical value. It is based on Statistical, Machine Learning, and linguistic techniques. It helps predict the topics of a document and examines words used in the given form.
Neural Networks
Benefits of Enabling Predictive Analytics
Tools are more reliable. Predictive analysis tools increase the efficiency of prediction. Instead of performing predictive analysis manually, predictive analysis tools are made more accessible. A few benefits of predictive analysis tools are the following:
Time-Saving
Performing predictive analysis manually is time-consuming. Whereas tools will take less time. Just need to perform a few actions, and accordingly, the model will predict the result.
Essay to use
Although not all users may be familiar with the different techniques' models and algorithms, such users can use the tools to predict the business's future and make changes in the business.
Reduce error chances
Predictive analysis manually increases the chance of error at the analysis time, whereas everything is predefined in the tools, which decreases the error chances. One can only be aware of some techniques, but as all predictive analysis techniques are predefined with tools, the use of tools is easier for the user.
Predictive maintenance is critical in diverse application areas, such as manufacturing industry, information, and technology. Source: Predictive Maintenance Services.
Why it Matters?
The role of analytics in data science is mainly to leverage various new opportunities.
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Decision-Making and Intelligence
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Optimizing the Market Campaigns
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Optimize operations and results
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Risk Analysis and Reduction
Where to use Predictive Analytics?
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Segmentation
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Customer Retention
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Dynamic Pricing
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B2B analytics
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Customer Lifetime Values
Business use cases 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 -
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Financial Analytics
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Risk Analytics
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Semantic and Sentiment Analytics
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Workforce Analytics
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Healthcare Diagnosis
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Customer Analytics
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Fraud Detection
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Equipment Maintenance
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Virtual Assistants
Enterprises reducing the need of manual decision making, make operational analytics actionable in real-time. Improve cross-department collaboration. Source: Enterprise Operational Analytics Services
What are the best tools for Predictive Analytics
Software Tools have advanced analytical capabilities, such as Text Analysis, Real-Time Analysis, Statistical Analysis, Data Mining, Machine Learning modelling and Optimization, and many more.
Libraries for Statistical Modeling and Analysis
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Stats model
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NLTK (Natural Language Processing Tool Kit)
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GraphLab
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Neural Designer
Open-Source Analytical Tools
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IBM SPSS
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Halo Business Intelligence
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Daiku-DSS
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Weka
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R-Studio(R-Programming used)- the most demanding Statistical tool for Machine Learning
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Apache Mahout (easy integration with Hadoop)
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RapidMiner Studio
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H2O.ai
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Knime AnalyticsStatistica
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Anaconda
Many tools are available in the market for predictive analysis. Some of them are discussed below-
IBM SPSS Statistics
It is one of the most reliable and most used predictive analysis tools. It has been around for a long time and offers a robust list of capabilities, including the Statistical Package for Social Sciences(SPSS) modeller. IBM SPSS's cost pricing is also easily understandable. The platform is complicated for the first-time user, so the user needs to be experienced. A few key features are data preprocessing, advanced statistics, extract, forecasting, decision trees, neural networks, direct marketing, etc.
SAS Advance Analytics
SAS is another useful analytics tool. It offers different incredible predictive analysis tools. These analytics tools could be used according to the purpose. A few key features are descriptive analysis, predictive modelling, code-based model development, dynamic group processing, and many more.
SAP Predictive Analytics
SAP Solution is a good predictive analytics product. It is a product of SAP Software. The company offers different options for features like SAS Advanced Analytics tools. It provides advanced machine learning and security features for analytics models. A few key features are Python API, data security, compliance features, automated data preprocessing, predictive modelling, native PySpark modelling, and many more. It supports datasets with more than 10,000 columns.
H2O
It is one of the best open-source tools. It also provides the data mining feature that puts this tool on the top analytics tools list. Its high performance, affordability, and flexibility make it a renowned predictive analytics tool for users. H2O is used for expert data science and citizen data scientists. A few key features are automatic feature engineering, machine learning interpretability, natural language processing, automatic scoring pipelines, time series analysis, and many more.
Oracle DataScience
Oracle has also entered the data analysis market. They purchased a renowned startup company, DataScience, and expanded the tool DataScience. The DataScience tool is mainly used by organizations that use the Oracle database and cloud service.
QResearch
This tool is mainly developed for market research analysis. The QResearch tool provides all the required facilities for better market analysis. It is a highly automated tool that requires less time to perform analysis. This tool can be integrated with R, a Microsoft Office product.
The best practices of Predictive Analytics
The best practices for predictive analytics are listed below:
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Validate the database set
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Clearly define objectives
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Identify high-quality data streams for its interface
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Discover the best visual representation
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Monitor the deployment and Performance
A Holistic Approach
It uses many techniques from Artificial Intelligence and data mining but analyzes real-time data for future predictions and understanding of predictive analytics. You should review each of the following steps for better understanding:
- Discover more about Predictive Analytics For Food Safety And Its Solution
- Read more about Automating AI Predictive Model For the Media And Entertainment Industry
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