SVM spam classification model added

projects_Intermediate/AI_ML/Spam_Classification/SVM_spam_classifier_Model.py

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+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+
+# Sample data (replace with your own dataset)
+emails = ["Buy our amazing products now!",
+          "Congratulations! You've won $1000.",
+          "Hello, how are you doing?",
+          "Discounts on luxury watches!",
+          "Meeting at 3 pm today."]
+
+labels = [1, 1, 0, 1, 0]  # 1 for spam, 0 for not spam
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(emails, labels, test_size=0.2, random_state=42)
+
+# Create a TF-IDF Vectorizer to convert text to numerical features
+vectorizer = TfidfVectorizer()
+X_train_tfidf = vectorizer.fit_transform(X_train)
+X_test_tfidf = vectorizer.transform(X_test)
+
+# Create an SVM classifier
+svm_classifier = SVC(kernel='linear', C=1.0)
+
+# Train the SVM classifier
+svm_classifier.fit(X_train_tfidf, y_train)
+
+# Make predictions on the test data
+y_pred = svm_classifier.predict(X_test_tfidf)
+
+# Calculate accuracy and display the results
+accuracy = accuracy_score(y_test, y_pred)
+print("Accuracy:", accuracy)
+
+confusion = confusion_matrix(y_test, y_pred)
+print("Confusion Matrix:")
+print(confusion)
+
+classification_rep = classification_report(y_test, y_pred)
+print("Classification Report:")
+print(classification_rep)