RandomForestClassifier(n_estimators=10, random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier(n_estimators=10, random_state=42)
XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=None, n_jobs=None,\n", + " num_parallel_tree=None, random_state=None, ...)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
XGBClassifier(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=None, n_jobs=None,\n", + " num_parallel_tree=None, random_state=None, ...)
MultiOutputClassifier(estimator=SVC(), n_jobs=-1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
MultiOutputClassifier(estimator=SVC(), n_jobs=-1)
SVC()
SVC()
KNeighborsClassifier(n_neighbors=3)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier(n_neighbors=3)
DecisionTreeClassifier(random_state=42)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(random_state=42)
Model: \"sequential_1\"\n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n", + "┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n", + "│ dense_2 (Dense) │ (None, 10) │ 6,462,880 │\n", + "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n", + "│ dense_3 (Dense) │ (None, 10) │ 110 │\n", + "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n", + "\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n", + "│ dense_2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m6,462,880\u001b[0m │\n", + "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n", + "│ dense_3 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m110\u001b[0m │\n", + "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Total params: 6,462,990 (24.65 MB)\n", + "\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,462,990\u001b[0m (24.65 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Trainable params: 6,462,990 (24.65 MB)\n", + "\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,462,990\u001b[0m (24.65 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
Non-trainable params: 0 (0.00 B)\n", + "\n" + ], + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "input_shape = X_train.shape[1]\n", + "\n", + "model_6 = tf.keras.Sequential([\n", + " tf.keras.layers.Input(shape=(input_shape,)),\n", + " tf.keras.layers.Dense(10, activation='relu'),\n", + " tf.keras.layers.Dense(y_train.shape[1], activation='softmax')\n", + "])\n", + "\n", + "model_6.compile(loss='categorical_crossentropy',\n", + " optimizer=tf.keras.optimizers.SGD(),\n", + " metrics=['accuracy'])\n", + "\n", + "model_6.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m126s\u001b[0m 8ms/step - accuracy: 0.9728 - loss: 0.1719 - val_accuracy: 0.9928 - val_loss: 0.0325\n", + "Epoch 2/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m125s\u001b[0m 8ms/step - accuracy: 0.9947 - loss: 0.0283 - val_accuracy: 0.9971 - val_loss: 0.0202\n", + "Epoch 3/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m133s\u001b[0m 8ms/step - accuracy: 0.9972 - loss: 0.0181 - val_accuracy: 0.9971 - val_loss: 0.0153\n", + "Epoch 4/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m132s\u001b[0m 8ms/step - accuracy: 0.9972 - loss: 0.0144 - val_accuracy: 0.9971 - val_loss: 0.0128\n", + "Epoch 5/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m469s\u001b[0m 30ms/step - accuracy: 0.9972 - loss: 0.0120 - val_accuracy: 0.9971 - val_loss: 0.0113\n", + "Epoch 6/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m138s\u001b[0m 9ms/step - accuracy: 0.9974 - loss: 0.0102 - val_accuracy: 0.9973 - val_loss: 0.0102\n", + "Epoch 7/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m142s\u001b[0m 9ms/step - accuracy: 0.9975 - loss: 0.0092 - val_accuracy: 0.9975 - val_loss: 0.0094\n", + "Epoch 8/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4099s\u001b[0m 256ms/step - accuracy: 0.9976 - loss: 0.0087 - val_accuracy: 0.9977 - val_loss: 0.0088\n", + "Epoch 9/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 8ms/step - accuracy: 0.9980 - loss: 0.0079 - val_accuracy: 0.9979 - val_loss: 0.0083\n", + "Epoch 10/10\n", + "\u001b[1m15788/15788\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1035s\u001b[0m 65ms/step - accuracy: 0.9980 - loss: 0.0077 - val_accuracy: 0.9980 - val_loss: 0.0078\n" + ] + }, + { + "data": { + "text/plain": [ + "
+
+
+### 📈 **Performance of the Models based on the Accuracy Scores**
+
+1. Random Forest Classifier: 99.77%
+2. XGBoost: 99.79%
+3. SVM: 99.99%
+4. KNN: 99.99%
+5. Decision Tree: 100%
+6. Dense Model: 99.80%
+
+
+### 📢 **Conclusion**
+
+Decision Tree is proven to be the best model with the accuracy score of 100%
+
+### ✒️ **Your Signature**
+
+Aditi Kala
diff --git a/ACI IoT Network Traffic Dataset Analysis/requirements.txt b/ACI IoT Network Traffic Dataset Analysis/requirements.txt
new file mode 100644
index 000000000..8493c72c5
--- /dev/null
+++ b/ACI IoT Network Traffic Dataset Analysis/requirements.txt
@@ -0,0 +1,4 @@
+sci-kit learn
+matplotlib
+numpy
+pandas
\ No newline at end of file