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Summary : This article equips Data Analysts with a solid foundation of key Data Science terms, from A to Z. Introduction In the rapidly evolving field of Data Science, understanding key terminology is crucial for Data Analysts to communicate effectively, collaborate effectively, and drive data-driven projects.
Once the data is acquired, it is maintained by performing data cleaning, data warehousing, data staging, and data architecture. Data processing does the task of exploring the data, mining it, and analyzing it which can be finally used to generate the summary of the insights extracted from the data.
Several datamining and neural network techniques have been employed to gauge the severity of heart disease but the prediction of it is a different subject. Researchers often experiment with various algorithms like random forest, K-nearestneighbor, and logistic regression to find the best combination.
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