This R project involves loading data, performing min-max normalization, integrating categorical features, splitting data, and applying k-Nearest Neighbors algorithm with different k values. Accuracy is calculated, and confusion matrices are generated for evaluation.
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This R project involves loading data, performing min-max normalization, integrating categorical features, splitting data, and applying k-Nearest Neighbors algorithm with different k values. Accuracy is calculated, and confusion matrices are generated for evaluation.
Tanzim-prog/DataNormClassKNN
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This R project involves loading data, performing min-max normalization, integrating categorical features, splitting data, and applying k-Nearest Neighbors algorithm with different k values. Accuracy is calculated, and confusion matrices are generated for evaluation.
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