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ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES FACULTY OF INFORMATICS INFORMATION SCIENCE DEPARTMENTALIZATION OF DATA MINING TECHNIQUES TO SUPPORT IMPORTERS AND EXPORTERS DELINQUENCY PREDICTION:
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Start by gathering the necessary data for the application of data mining.
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Clean and preprocess the data to ensure its quality and accuracy.
03
Choose the appropriate data mining technique for your specific application.
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Split the data into training and testing sets for model evaluation.
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Build and train your data mining model using the selected technique.
06
Evaluate the performance of the model using the testing set.
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Who needs application of data mining?

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Businesses looking to gain insights from large amounts of data.
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Researchers studying patterns and trends in various fields.
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Healthcare professionals analyzing patient data for diagnosis and treatment.
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Financial institutions detecting fraudulent activities in transactions.
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Government agencies monitoring and predicting social trends.
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Data mining is used for extracting useful information from large databases to discover patterns and relationships that can help improve decision-making in various fields such as business, healthcare, finance, etc.
Companies or individuals who want to use data mining techniques to analyze their data and gain insights may file an application for data mining.
The application for data mining typically requires details about the goal of the data mining project, the dataset to be analyzed, the methods and algorithms to be used, and the expected outcomes.
The purpose of data mining applications is to uncover hidden patterns and relationships in data that can be used to improve decision-making, increase efficiency, and gain competitive advantages.
The application for data mining must include details about the data source, data collection methods, data preprocessing steps, data mining algorithms, and the expected results.
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