python code to email spam filter - Naive Bayes algorithm

 example Python code to implement an email spam filter using the Naive Bayes algorithm:

import os
import numpy as np
import pandas as pd
import nltk
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, confusion_matrix

# Set the path of the dataset directory
data_dir = "data/"

# Read the emails from the dataset directory
emails = []
labels = []
for folder in os.listdir(data_dir):
    if folder == "ham":
        label = 0
    elif folder == "spam":
        label = 1
    else:
        continue
    folder_path = os.path.join(data_dir, folder)
    for file in os.listdir(folder_path):
        file_path = os.path.join(folder_path, file)
        with open(file_path, "r", encoding="utf8", errors="ignore") as f:
            email = f.read()
        emails.append(email)
        labels.append(label)

# Preprocess the emails
nltk.download("punkt")
nltk.download("wordnet")
lemmatizer = WordNetLemmatizer()
tokenizer = CountVectorizer().build_tokenizer()
preprocessed_emails = []
for email in emails:
    tokens = tokenizer(email)
    lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]
    preprocessed_email = " ".join(lemmatized_tokens)
    preprocessed_emails.append(preprocessed_email)

# Split the data into training and testing sets
X = preprocessed_emails
y = np.array(labels)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Vectorize the emails
vectorizer = CountVectorizer()
X_train_vect = vectorizer.fit_transform(X_train)
X_test_vect = vectorizer.transform(X_test)

# Train the Naive Bayes classifier
classifier = MultinomialNB()
classifier.fit(X_train_vect, y_train)

# Evaluate the classifier on the testing set
y_pred = classifier.predict(X_test_vect)
accuracy = accuracy_score(y_test, y_pred)
confusion = confusion_matrix(y_test, y_pred)
print("Accuracy:", accuracy)
print("Confusion matrix:\n", confusion)

This code reads the emails from a directory and preprocesses them using NLTK to tokenize and lemmatize the text. It then splits the data into training and testing sets and vectorizes the emails using the CountVectorizer from scikit-learn. Finally, it trains a Naive Bayes classifier on the training set and evaluates its performance on the testing set using accuracy and confusion matrix.

The requirements.txt file lists the Python packages required to run the email spam filter code. Here is an example requirements.txt file:

makefile
nltk==3.6.3
pandas==1.3.4
scikit-learn==1.0.2

This file specifies the version numbers of the nltk, pandas, and scikit-learn packages that the code requires. You can create this file by running the following command in your command prompt or terminal:

pip freeze > requirements.txt

This command writes all currently installed Python packages and their versions to the requirements.txt file. You can then edit this file to remove any unnecessary packages and specify the exact versions required by your code.