| Catalog Content |
The data mining course covers data preprocessing, exploratory data analysis, and an overview of data mining. The process begins with data cleaning, detection of missing and outlier values, data transformation, and visualization techniques. The structure of the data is understood through correlation and distribution analysis. Decision trees, logistic regression, and Naive Bayes are taught for classification; k-means and hierarchical methods are taught for clustering. Model performance is improved with market basket analysis, association rules, feature selection, and PCA. |
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