A Naive Bayes spam classifier implementation in Go, enabling text classification system using the Naive Bayes algorithm with Laplace smoothing to classify messages as spam or not spam.

• Naive Bayes Classification: Uses probabilistic classification based on Bayes' theorem with naive independence assumptions
• Laplace Smoothing: Implements additive smoothing to handle zero probabilities for unseen words
• Training & Classification: Simple API for training on labeled datasets and classifying new messages
• Real Dataset Testing: Includes tests with actual spam/ham email datasets

go get github.com/igomez10/nspammer

package main

import (
    "fmt"
    "github.com/igomez10/nspammer"
)

func main() {
    // Create training dataset (map[string]bool where true = spam, false = not spam)
    trainingData := map[string]bool{
        "buy viagra now":           true,
        "get rich quick":           true,
        "meeting at 3pm":           false,
        "project update report":    false,
    }

    // Create and train classifier
    classifier := nspammer.NewSpamClassifier(trainingData)

    // Classify new messages
    isSpam := classifier.Classify("buy now")
    fmt.Printf("Is spam: %v\n", isSpam)
}

Creates a new spam classifier and trains it on the provided dataset. The dataset is a map where keys are text messages and values indicate whether the message is spam (true) or not spam (false).

Classifies the input text as spam (true) or not spam (false) based on the trained model.

The classifier uses the Naive Bayes algorithm:

1. Training Phase:

   • Calculates prior probabilities: P(spam) and P(not spam)
   • Builds a vocabulary from all training messages
   • Counts word occurrences in spam and non-spam messages
   • Stores word frequencies for likelihood calculations

2. Classification Phase:

   • Calculates log probabilities to avoid numerical underflow
   • Computes: log(P(spam)) + Σ log(P(word|spam))
   • Computes: log(P(not spam)) + Σ log(P(word|not spam))
   • Returns true (spam) if the spam score is higher

3. Laplace Smoothing:

   • Adds a smoothing constant to avoid zero probabilities for unseen words
   • Formula: P(word|class) = (count + α) / (total + α × vocabulary_size)
   • Default α = 1.0

The project includes support for the Kaggle Spam Mails Dataset. To download it:

This script requires the Kaggle CLI to be installed and configured.

Run the test suite:

The tests include:

• Simple classification examples
• Real-world email dataset evaluation
• Accuracy measurements on train/test splits