Download this code from https://codegive.com Naive Bayes is a simple and effective classification algorithm based on Bayes' theorem. It assumes that the features used to describe an observation are independent, which is a "naive" assumption but often holds true in practice. This tutorial will guide you through implementing a Naive Bayes classifier in Python, using a fictional dataset. Before we begin, make sure you have the necessary libraries installed. You can install them using the following: Now, let's import the required libraries in your Python script or Jupyter notebook: Let's create a fictional dataset for the purpose of this tutorial. You can replace this with your own dataset: Split the dataset into training and testing sets to evaluate the model's performance: Create an instance of the Gaussian Naive Bayes classifier and train it using the training data: Use the trained model to make predictions on the test set: Evaluate the performance of the model using metrics such as accuracy, precision, recall, and F1 score: Congratulations! You've successfully implemented a Naive Bayes classifier in Python. This tutorial covered the basic steps from importing libraries to evaluating the model's performance. Feel free to experiment with different datasets and explore other variations of Naive Bayes classifiers, such as MultinomialNB or BernoulliNB, based on your specific use case. ChatGPT