Implement Naïve Bayes Algorithm from scratch for classification of the flower types in the Iris dataset

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CPSC 552 – Data Mining – Assignment #1

 Problem #1: Implement Naïve Bayes Algorithm from scratch for classification of the flower types in the Iris dataset. As explained in the lecture, this dataset contains 150 data items with 4 measurements for each flower. There are three classes of flowers (setosa, versicolor, virginica). The dataset contains 50 rows for each flower type.

Try to program this yourself. If you need help, the solution is given on the next page.

Problem #2: Implement the Naïve Bayes for wheat seed dataset. This is a three class dataset with 210 observations with 7 features. The variable names are as follows:

Area.

Perimeter.

Compactness

Length of kernel.

Width of kernel.

Asymmetry coefficient.

Length of kernel groove.

Last column indicates the class (1, 2, 3).

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