Content Inside :
SPSS data mining overview Various Data Mining Techniques Steps in the Data Mining Process CRISP-DM Examples of Data Mining Applications. Data mining application : Student academic success/Retention and graduation Identify high risk students Predict course demand and pattern Profile good transfer candidates Application success rates
Predict potential alumni donations. SPSS Data Mining Techniques Technique Method Types 1. Predictive 1. Neural Networks 2. Rule Induction 3. Linear & Logistic Regression 4. Sequence Detection. Selecting the Appropriate Modeling Technique. Model Building : Predictive or Descriptive Selecting data mining tools Transforming data if needed Generating samples (as necessary) for training, testing and validating the model Build, test and select models.
Tags : data mining tools, student academic success, rule induction, transfer candidates, logistic regression, application success, sequence detection, data mining techniques, select models, risk students, research content, model building, institutional research, success rates, clementine
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