Predictive Analysis Techniques and Applications in Various Industries
VerifiedAdded on 2023/03/31
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AI Summary
This report provides a comprehensive overview of predictive analysis, a crucial subset of progressive analytics used to forecast future trends based on historical data. It delves into various techniques, including data mining, data modeling, machine learning, statistics, and artificial intelligence, emphasizing their role in analyzing existing data to predict future outcomes. The report highlights the importance of data mining in selecting suitable data for prediction and the collaborative efforts required from business management, information technologists, and data scientists. It also examines how predictive analysis is applied across different sectors, such as customer relationship management (CRM), healthcare, fraud detection, and risk management, showcasing the use of business intelligence tools like Qlikview to gain insights and make informed decisions. Furthermore, the report details the predictive analysis process, from project definition and data collection to analysis, model development, deployment, and monitoring, ensuring that the models accurately reflect future occurrences. The application of these techniques enables organizations to proactively manage risks, identify opportunities, and adapt to changes, ultimately enhancing their decision-making capabilities and strategic planning.
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