This is part 1 of naive bayes classifier algorithm machine learning tutorial. Naive bayes theorm uses bayes theorm for conditional probability with a naive assumption that the features are not correlated to each other and tries to find conditional probability of target variable given the probabilities of features. We will use titanic survival dataset here and using naive bayes classifier find out the survival probability of titanic travellers. We use sklearn library and python for this beginners machine learning tutorial. GaussianNB is the classifier we use to train our model. There are other classifiers such as MultinomialNB but we will use that in part 2 of the tutorial.
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Code: https://github.com/codebasics/py/blob/master/ML/14_naive_bayes/14_naive_bayes_1_titanic_survival_prediction.ipynb
Naive bayes theory video: https://www.youtube.com/watch?v=Q8l0Vip5YUw
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Exercise solution: https://github.com/codebasics/py/blob/master/ML/14_naive_bayes/Exercise/14_naive_bayes_exercise.ipynb
Topics that are covered in this Video:
00:00 introduction
00:19 Basics of probability
00:52 Conditional probability
01:52 Bayes theorm
04:37 Coding: titanic crash survival
10:00 GaussianNB classifier
Next Video:
Machine Learning Tutorial Python – 15: Naive Bayes Part 2: https://www.youtube.com/watch?v=nHIUYwN-5rM&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=16
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