diff --git a/4-Classification/3-Classifiers-2/notebook.ipynb b/4-Classification/3-Classifiers-2/notebook.ipynb
index 4659a7b6..20ecc4e5 100644
--- a/4-Classification/3-Classifiers-2/notebook.ipynb
+++ b/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -9,12 +9,179 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>...</th>\n",
+       "      <th>whiskey</th>\n",
+       "      <th>white_bread</th>\n",
+       "      <th>white_wine</th>\n",
+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
+       "      <th>zucchini</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>indian</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 382 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
        "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
        "0           0  indian       0         0      0           0      0   \n",
@@ -38,11 +205,11 @@
        "4                        0     0     0    0      0       1         0  \n",
        "\n",
        "[5 rows x 382 columns]"
-      ],
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+      ]
      },
+     "execution_count": 38,
      "metadata": {},
-     "execution_count": 9
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -53,11 +220,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "0    indian\n",
@@ -68,8 +234,9 @@
        "Name: cuisine, dtype: object"
       ]
      },
+     "execution_count": 39,
      "metadata": {},
-     "execution_count": 10
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -79,12 +246,179 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>almond</th>\n",
+       "      <th>angelica</th>\n",
+       "      <th>anise</th>\n",
+       "      <th>anise_seed</th>\n",
+       "      <th>apple</th>\n",
+       "      <th>apple_brandy</th>\n",
+       "      <th>apricot</th>\n",
+       "      <th>armagnac</th>\n",
+       "      <th>artemisia</th>\n",
+       "      <th>artichoke</th>\n",
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+       "      <th>whiskey</th>\n",
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+       "      <th>whole_grain_wheat_flour</th>\n",
+       "      <th>wine</th>\n",
+       "      <th>wood</th>\n",
+       "      <th>yam</th>\n",
+       "      <th>yeast</th>\n",
+       "      <th>yogurt</th>\n",
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+       "      <td>0</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 380 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
        "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
        "0       0         0      0           0      0             0        0   \n",
@@ -108,17 +442,147 @@
        "4                        0     0     0    0      0       1         0  \n",
        "\n",
        "[5 rows x 380 columns]"
-      ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+      ]
      },
+     "execution_count": 40,
      "metadata": {},
-     "execution_count": 11
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
     "cuisines_feature_df.head()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C = 10\n",
+    "# Create different classifiers.\n",
+    "classifiers = {\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
+    "    'KNN classifier': KNeighborsClassifier(C),\n",
+    "    'SVC': SVC(),\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy (train) for Linear SVC: 78.6% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.66      0.72      0.69       233\n",
+      "      indian       0.88      0.86      0.87       236\n",
+      "    japanese       0.80      0.73      0.76       250\n",
+      "      korean       0.82      0.75      0.78       248\n",
+      "        thai       0.79      0.87      0.82       232\n",
+      "\n",
+      "    accuracy                           0.79      1199\n",
+      "   macro avg       0.79      0.79      0.79      1199\n",
+      "weighted avg       0.79      0.79      0.79      1199\n",
+      "\n",
+      "Accuracy (train) for KNN classifier: 73.0% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.67      0.69      0.68       233\n",
+      "      indian       0.81      0.80      0.80       236\n",
+      "    japanese       0.66      0.84      0.74       250\n",
+      "      korean       0.92      0.53      0.68       248\n",
+      "        thai       0.70      0.80      0.75       232\n",
+      "\n",
+      "    accuracy                           0.73      1199\n",
+      "   macro avg       0.75      0.73      0.73      1199\n",
+      "weighted avg       0.75      0.73      0.73      1199\n",
+      "\n",
+      "Accuracy (train) for SVC: 81.9% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.73      0.75      0.74       233\n",
+      "      indian       0.90      0.88      0.89       236\n",
+      "    japanese       0.84      0.79      0.81       250\n",
+      "      korean       0.87      0.79      0.83       248\n",
+      "        thai       0.78      0.89      0.83       232\n",
+      "\n",
+      "    accuracy                           0.82      1199\n",
+      "   macro avg       0.82      0.82      0.82      1199\n",
+      "weighted avg       0.82      0.82      0.82      1199\n",
+      "\n",
+      "Accuracy (train) for RFST: 84.8% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.77      0.79      0.78       233\n",
+      "      indian       0.90      0.92      0.91       236\n",
+      "    japanese       0.89      0.80      0.84       250\n",
+      "      korean       0.87      0.82      0.84       248\n",
+      "        thai       0.81      0.91      0.86       232\n",
+      "\n",
+      "    accuracy                           0.85      1199\n",
+      "   macro avg       0.85      0.85      0.85      1199\n",
+      "weighted avg       0.85      0.85      0.85      1199\n",
+      "\n",
+      "Accuracy (train) for ADA: 69.9% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.62      0.48      0.54       233\n",
+      "      indian       0.84      0.84      0.84       236\n",
+      "    japanese       0.69      0.57      0.62       250\n",
+      "      korean       0.66      0.81      0.73       248\n",
+      "        thai       0.68      0.79      0.73       232\n",
+      "\n",
+      "    accuracy                           0.70      1199\n",
+      "   macro avg       0.70      0.70      0.69      1199\n",
+      "weighted avg       0.70      0.70      0.69      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "    y_pred = classifier.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
+    "    print(classification_report(y_test,y_pred))"
+   ]
   }
  ],
  "metadata": {
@@ -126,8 +590,8 @@
    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
   },
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -139,7 +603,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.0"
+   "version": "3.12.0"
   },
   "metadata": {
    "interpreter": {
@@ -149,4 +613,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}