Implementing a Naive Bayes Classifier. In this post, we are going to discuss the workings of Naive Bayes classifier implementationally with Python by applying it to a real world dataset.

In the context of Supervised Learning (Classification), Naive Bayes or rather Bayesian Learning acts as a gold standard for evaluating other learning algorithms along with acting as a powerful probabilistic modelling technique.

In this post, we are going to discuss the workings of Naive Bayes classifier implementationally with Python by applying it to a real world dataset.

The post is divided more broadly into the following parts:

- Data Preprocessing
- Training the model
- Predicting the results
- Checking the performance of the model

The above parts can be further divided as follows:

- Importing the libraries
- Importing the dataset
- Splitting the dataset into the training set and testing set
- Feature Scaling

→ Training the model

- Training the Naive Bayes model on the training set

→ Predicting the results

- Predicting the test set results

→ Checking the performance of the model

- Making the Confusion Matrix

→ Visualisation

- Visualising the Confusion Matrix

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Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.