Coding from scratch. To better understand the process of Naive Bayes model training. And in the end, we make another Naive Bayes Classifier through normal workflow using sklearn. In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features. A naive Bayes classifier considers each of these features to be independent to each other. That why it is referred as "Naive".

The basic aim of this post is to predict customer churn for a certain bank i.e. which customer is going to leave this bank service. Fully-connected neural networks with different architectures will be explored and compared. Since this is a binary prediction , logistic regression may also works pretty well. Feel free to give it a shot~

The SAT, or Scholastic Aptitude Test, is a test that high schoolers take in the US before applying to college. Colleges take the test scores into account when making admissions decisions, so it's fairly important to do well on. The test is divided into 3 sections, each of which is scored out of 800 points. The total score is out of 2400 (although this has changed back and forth a few times, the scores in this dataset are out of 2400). High schools are often ranked by their average SAT scores, and high SAT scores are considered a sign of how good a school district is.