Calculate the probabilities of predicting each class

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Reference no: EM133370674

Naive Bayes Classifier

Here we to develop a PYTHON code to implement your own naive Bayes classifier.
You need to implement a naive Bayes classifier from scratch **using the training data of wine datasets train_x and train_y**. The required functions are listed below. You can add more functions as you need. **No library versions of naive bayes classifier are allowed**.

1. train_val_split

**Randomly** split training data into train and val set. 80% of the training data will be the train set and 20% of the training data will be the val set.

2. normalization (data preprocessing)

You should normalize all data for each attribute first.

3. cross_validation_split

**Randomly** split data into 5 folds.
4. predict

Predict the class label for a given x.

5. accurate

Calculate the accuracy percentage of the predictions. Remember to average the results of k folds

6. gaussian_probability

Calculate the Gaussian probability distribution function for the given x
5. class_probability

Calculate the probabilities of predicting each class for a given x using the naive Bayes algorithm. Be aware that we have multiple input variables. And you may want to use the gaussian_probability function here.

Reference no: EM133370674

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