Introduction:

This report analyzes the performance of four classification models: Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM). The analysis focuses on the task of classifying text documents into different categories, and the models are evaluated based on their precision score.

Methodology:

A dataset of text documents was divided into training and testing sets. The four classification models were trained on the training set. The models were evaluated on the testing set using the precision score metric. The results were compared to identify the model with the highest precision score. Results:

The following table summarizes the precision scores of the four models: Model Precision Score

1. BNB 96%
2. MNB 100%
3. GNB 48%
4. SVM 87%

Email-spam-classifier

In this project, we aim to build a spam email classifier using a macine learning approach. The classifier will be able to differentiate between spam and ham (non-spam) emails based on the content of the emails.
label
'Spam' indicates that the email is classified as spam. 'Ham' denotes that the email is legitimate (ham). text This column contains the actual content of the email messages.