Classification in Depth with Scikit-Learn

This project focuses on learning about Bayes' theorem and its application in the Naive Bayes Classifier, which is a popular machine learning model. The project will likely involve understanding the basic principles of the theorem, as well as implementing and training a Naive Bayes Classifier on a simple dataset to gain practical experience.

All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.

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codevalidated

Suppose we have a box of 20 marbles, 5 of which are red and 15 of which are blue. We randomly pick a marble from the box without looking, and we want to calculate the probability that the marble is red, given that it is round. Let's say we know that 3 out of the 5 red marbles are round, and 8 out of the 15 blue marbles are round.

```
# Prior probability of the marble being red
p_red = 0.25
# Likelihood probability of the marble being round given it is red
p_round_given_red = 0.6
# Likelihood probability of the marble being round given it is blue
p_round_given_blue = 8 / 15
```

Let's use Bayes' theorem to calculate the probability of the marble being red given that it is round. Store the result in `p_red_given_round`

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codevalidated

Let's evaluate the performance of the classifier by comparing its predictions to the true labels of the testing data.

Store the accuracy in `accuracy_test`

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