14. Project

Overview

This project has been broken down in to the following steps:

  • Step 0: Introduction to the Naive Bayes Theorem
  • Step 1.1: Understanding our dataset
  • Step 1.2: Data Preprocessing
  • Step 2.1: Bag of Words(BoW)
  • Step 2.2: Implementing BoW from scratch
  • Step 2.3: Implementing Bag of Words in scikit-learn
  • Step 3.1: Training and testing sets
  • Step 3.2: Applying Bag of Words processing to our dataset.
  • Step 4.1: Bayes Theorem implementation from scratch
  • Step 4.2: Naive Bayes implementation from scratch
  • Step 5: Naive Bayes implementation using scikit-learn
  • Step 6: Evaluating our model
  • Step 7: Conclusion

If you'd like to work through the notebooks on your own machine or otherwise outside the classroom, you can find the code in this GitHub repo.

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