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:

→ Data Preprocessing

1. Importing the libraries
2. Importing the dataset
3. Splitting the dataset into the training set and testing set
4. Feature Scaling

→ Training the model

1. Training the Naive Bayes model on the training set

→ Predicting the results

1. Predicting the test set results

→ Checking the performance of the model

1. Making the Confusion Matrix

→ Visualisation

1. Visualising the Confusion Matrix

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