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README.md

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+# Malicious URL Detection Models
+
+This directory contains trained machine learning models for detecting malicious URLs. The models are trained to classify URLs into four categories:
+- **benign**
+- **defacement**
+- **malware**
+- **phishing**
+
+## Model Performance Summary
+
+The following table summarizes the accuracy of each model on the test dataset:
+
+| Model | Accuracy |
+|-------|----------|
+| **Extra Trees Classifier** | **97%** |
+| **Random Forest** | **97%** |
+| **Decision Tree** | **96%** |
+| **MLP Classifier** | **96%** |
+| **XGBoost** | **96%** |
+| **Gradient Boosting Classifier** | **94%** |
+| **Logistic Regression** | **87%** |
+| **SGD Classifier** | **87%** |
+| **Adaboost** | **85%** |
+| **Gaussian Naive Bayes** | **80%** |
+
+## Detailed Performance Reports
+
+### Adaboost
+- **Accuracy:** 0.85
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.90      0.97      0.93     85778
+  defacement       0.82      0.76      0.79     19104
+     malware       0.55      0.74      0.63      6521
+    phishing       0.68      0.42      0.52     18836
+
+    accuracy                           0.85    130239
+   macro avg       0.74      0.72      0.72    130239
+weighted avg       0.84      0.85      0.84    130239
+```
+
+### Decision Tree
+- **Accuracy:** 0.96
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.97      0.98      0.98     85778
+  defacement       0.98      0.99      0.98     19104
+     malware       0.95      0.94      0.95      6521
+    phishing       0.87      0.85      0.86     18836
+
+    accuracy                           0.96    130239
+   macro avg       0.95      0.94      0.94    130239
+weighted avg       0.96      0.96      0.96    130239
+```
+
+### Extra Trees Classifier
+- **Accuracy:** 0.97
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.97      0.98      0.98     85778
+  defacement       0.98      0.99      0.99     19104
+     malware       0.98      0.94      0.96      6521
+    phishing       0.91      0.86      0.88     18836
+
+    accuracy                           0.97    130239
+   macro avg       0.96      0.95      0.95    130239
+weighted avg       0.97      0.97      0.97    130239
+```
+
+### Gaussian Naive Bayes
+- **Accuracy:** 0.80
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.86      0.90      0.88     85778
+  defacement       0.67      0.99      0.80     19104
+     malware       0.63      0.69      0.66      6521
+    phishing       0.68      0.19      0.29     18836
+
+    accuracy                           0.80    130239
+   macro avg       0.71      0.69      0.66    130239
+weighted avg       0.80      0.80      0.77    130239
+```
+
+### Gradient Boosting Classifier
+- **Accuracy:** 0.94
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.96      0.99      0.97     85778
+  defacement       0.92      0.97      0.94     19104
+     malware       0.94      0.80      0.87      6521
+    phishing       0.89      0.78      0.83     18836
+
+    accuracy                           0.94    130239
+   macro avg       0.93      0.88      0.90    130239
+weighted avg       0.94      0.94      0.94    130239
+```
+
+### Logistic Regression
+- **Accuracy:** 0.87
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.89      0.97      0.93     85778
+  defacement       0.85      0.95      0.90     19104
+     malware       0.81      0.69      0.74      6521
+    phishing       0.77      0.42      0.55     18836
+
+    accuracy                           0.87    130239
+   macro avg       0.83      0.76      0.78    130239
+weighted avg       0.87      0.87      0.86    130239
+```
+
+### MLP Classifier
+- **Accuracy:** 0.96
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.97      0.98      0.98     85778
+  defacement       0.97      0.97      0.97     19104
+     malware       0.95      0.90      0.92      6521
+    phishing       0.88      0.83      0.86     18836
+
+    accuracy                           0.96    130239
+   macro avg       0.94      0.92      0.93    130239
+weighted avg       0.96      0.96      0.96    130239
+```
+
+### Random Forest
+- **Accuracy:** 0.97
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.98      0.98      0.98     85778
+  defacement       0.98      0.99      0.99     19104
+     malware       0.98      0.94      0.96      6521
+    phishing       0.91      0.87      0.89     18836
+
+    accuracy                           0.97    130239
+   macro avg       0.96      0.95      0.95    130239
+weighted avg       0.97      0.97      0.97    130239
+```
+
+### SGD Classifier
+- **Accuracy:** 0.87
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.89      0.96      0.93     85778
+  defacement       0.83      0.95      0.89     19104
+     malware       0.79      0.71      0.75      6521
+    phishing       0.74      0.40      0.52     18836
+
+    accuracy                           0.87    130239
+   macro avg       0.81      0.76      0.77    130239
+weighted avg       0.86      0.87      0.85    130239
+```
+
+### XGBoost
+- **Accuracy:** 0.96
+- **Report:**
+```
+              precision    recall  f1-score   support
+
+      benign       0.97      0.99      0.98     85778
+  defacement       0.97      0.99      0.98     19104
+     malware       0.98      0.92      0.95      6521
+    phishing       0.91      0.84      0.88     18836
+
+    accuracy                           0.96    130239
+   macro avg       0.96      0.93      0.95    130239
+weighted avg       0.96      0.96      0.96    130239
+```
+
+## Usage
+
+To load a model in Python, you can use `joblib` or `pickle`.
+
+### Using joblib
+
+```python
+import joblib
+
+# Load the model
+model = joblib.load('models/random_forest.pkl')
+
+# Make predictions
+prediction = model.predict(X_test)
+```
+
+### Using pickle
+
+```python
+import pickle
+
+# Load the model
+with open('models/random_forest.pkl', 'rb') as f:
+    model = pickle.load(f)
+
+# Make predictions
+prediction = model.predict(X_test)
+```

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decision_tree.pkl

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label_encoder.pkl

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