SpamEmailClassifier.py

import pandas as pd
import re
import nltk
nltk.download('stopwords')
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
#dd here your dataset
data = pd.read_csv("path of your dataset")
def preprocess_text(text):
    text = re.sub(r'\W', ' ', str(text))
    text = re.sub(r'\s+[a-zA-Z]\s+', ' ', text)
    text = re.sub(r'\^[a-zA-Z]\s+', ' ', text)
    text = re.sub(r'\s+', ' ', text, flags=re.I)
    text = text.lower()
    return text
data['text'] = data['text'].apply(preprocess_text)

# Tokenization and stop words removal
stop_words = set(stopwords.words('english'))
data['text'] = data['text'].apply(lambda x: ' '.join([word for word in x.split() if word not in stop_words]))

# Split the data into features and labels
X = data['text']
y = data['label']

# Label Encoding
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Using TF-IDF for feature extraction
tfidf_vectorizer = TfidfVectorizer(max_features=5000)
X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)
X_test_tfidf = tfidf_vectorizer.transform(X_test)
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix

# Model Training (using Naive Bayes as an example)
model = MultinomialNB()
model.fit(X_train_tfidf, y_train)

# Model Evaluation
y_pred = model.predict(X_test_tfidf)

accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1 Score:", f1)
print("Confusion Matrix:\n", conf_matrix)