Q Learning
The Future of Text Analytics
Using Predictive Analytics
Data Mining Techniques for Customer Relationship
Understanding the Predictive Analytics Lifecycle
Data Mining in Customer Service
Ethical Considerations in Predictive Analytics
The Importance of Predictive Analytics Today
Business Analytics Strategies 
2 Predictive Analytics Predictive analytics uses statistical techniques and machine
learning algorithms to forecast future outcomes based on historical data
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Practical Data Mining Association Rule
Learning A method for discovering interesting relations between variables in large databases
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The Future of Text Analytics analytics: Technology Description Impact on Text Analytics Machine
Learning Algorithms that improve automatically through experience
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Using Predictive Analytics analytics is a branch of advanced analytics that uses various statistical techniques, including predictive modeling, machine
learning, and data mining, to analyze historical data and make predictions about future outcomes
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Data Mining Techniques for Customer Relationship Below are some of the most prevalent techniques: Classification Clustering Association Rule
Learning Prediction Text Mining 2
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Understanding the Predictive Analytics Lifecycle Predictive analytics is a branch of advanced analytics that uses historical data, statistical algorithms, and machine
learning techniques to identify the likelihood of future outcomes based on historical data
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Data Mining in Customer Service Association Rule
Learning: This method uncovers relationships between different variables in large datasets
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Ethical Considerations in Predictive Analytics analytics, various frameworks and guidelines have been proposed: The Fairness, Accountability, and Transparency in Machine
Learning (FAT/ML) initiative: This initiative provides guidelines for developing fair and accountable machine learning systems
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The Importance of Predictive Analytics Today Predictive analytics is a branch of advanced analytics that uses various statistical techniques, including machine
learning, to analyze current and historical data to make predictions about future events
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Business Intelligence (K)
2010s: Integration of big data analytics and machine
learning into BI
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