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Split the data into training set and testing set

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Reference no: EM133220470

Scikit-Learn sklearn.datasets has the wine dataset in load_wine loader:

1. Show the classes of this data, the features of this data, and the number of the dimensions in this data, and how many samples this data has.

2. Split the data into training set and testing set, and implement the Bayesian classifier on this data and calculate the accuracy.

3. Split the data into training set and testing set, and implement K Nearest Neighbor on this data and calculate the accuracy. try several values of K and pick the one with the best accuracy

4. Split the data into training and testing and implement the K-Means Clustering on this data and calculate the accuracy. Try several values of K and pick the one with the best accuracy.

5. Split the data into training and testing and implement the Decision Tree on this data and calculate the accuracy.

6. Which algorithm gives better accuracy?

Reference no: EM133220470

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