Update README.md

README.md

@@ -13,4 +13,58 @@ tags:
 - Email Spam Detection
 - Naive Bayes
 - TF-IDF
----
+---
+
+# 🧠 DarkNeuronAI - Spam Detection v1.0
+
+A lightweight **Naive Bayes + TF-IDF** based spam detection model developed by **DarkNeuronAI**.  
+It classifies emails as **Spam (1)** or **Ham (0)** with high accuracy and fast performance — ideal for simple text classification tasks.
+
+---
+
+## 🚀 Model Overview
+- **Algorithm:** Naive Bayes (MultinomialNB)  
+- **Vectorization:** TF-IDF (Term Frequency - Inverse Document Frequency)  
+- **Goal:** Classify email/text messages as Spam or Ham  
+- **Performance:** High accuracy on real-world datasets  
+
+---
+
+## 🧩 Files Included
+- `spam_detection_model.pkl` → Trained Naive Bayes model  
+- `spam_detection_vectorizer.pkl` → TF-IDF vectorizer for text preprocessing  
+- `example_usage.py` → Example code to use the model  
+- `requirements.txt` → Dependencies list  
+
+---
+
+## 💡 How to Use
+```python
+import joblib
+import re
+import string
+
+# Load model and vectorizer
+model = joblib.load("spam_detection_model.pkl")
+vectorizer = joblib.load("spam_detection_vectorizer.pkl")
+
+# Text cleaning function
+def clean_text(text):
+    text = text.lower()  # lowercase
+    text = re.sub(r'\d+', '', text)  # remove digits
+    text = text.translate(str.maketrans('', '', string.punctuation))  # remove punctuation
+    return text.strip()  # remove spaces
+
+# Example email
+email = "Congratulations! You have won $1000. Click here to claim now!"
+
+# Clean and transform
+clean_email = clean_text(email)
+email_vec = vectorizer.transform([clean_email])
+
+# Predict
+result = model.predict(email_vec)
+print("Spam" if result[0] == 1 else "Ham")
+```
+
+# Developed With ❤️ By DarkNeuronAI