diff --git a/Lab06/Old/Exercise6_07/Exercise6_07.ipynb b/Lab06/Old/Exercise6_07/Exercise6_07.ipynb
index 102f6e7..3b95a14 100644
--- a/Lab06/Old/Exercise6_07/Exercise6_07.ipynb
+++ b/Lab06/Old/Exercise6_07/Exercise6_07.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -38,106 +38,7 @@
     "id": "eMkdycnKAC3k",
     "outputId": "98d8e715-c55f-47e3-a26a-2baad7f4139e"
    },
-   "outputs": [
-    {
-     "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>buying</th>\n",
-       "      <th>maint</th>\n",
-       "      <th>doors</th>\n",
-       "      <th>persons</th>\n",
-       "      <th>lug_boot</th>\n",
-       "      <th>safety</th>\n",
-       "      <th>car</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>low</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>med</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>high</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>med</td>\n",
-       "      <td>low</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>med</td>\n",
-       "      <td>med</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  buying  maint doors persons lug_boot safety    car\n",
-       "0  vhigh  vhigh     2       2    small    low  unacc\n",
-       "1  vhigh  vhigh     2       2    small    med  unacc\n",
-       "2  vhigh  vhigh     2       2    small   high  unacc\n",
-       "3  vhigh  vhigh     2       2      med    low  unacc\n",
-       "4  vhigh  vhigh     2       2      med    med  unacc"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# data doesn't have headers, so let's create headers\n",
     "_headers = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'car']\n",
@@ -150,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -160,214 +61,7 @@
     "id": "EQvotJcIAC3o",
     "outputId": "aa12d7dd-dfaf-47c7-8e52-bd019034f4f8"
    },
-   "outputs": [
-    {
-     "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>car</th>\n",
-       "      <th>buying_high</th>\n",
-       "      <th>buying_low</th>\n",
-       "      <th>buying_med</th>\n",
-       "      <th>buying_vhigh</th>\n",
-       "      <th>maint_high</th>\n",
-       "      <th>maint_low</th>\n",
-       "      <th>maint_med</th>\n",
-       "      <th>maint_vhigh</th>\n",
-       "      <th>doors_2</th>\n",
-       "      <th>...</th>\n",
-       "      <th>doors_5more</th>\n",
-       "      <th>persons_2</th>\n",
-       "      <th>persons_4</th>\n",
-       "      <th>persons_more</th>\n",
-       "      <th>lug_boot_big</th>\n",
-       "      <th>lug_boot_med</th>\n",
-       "      <th>lug_boot_small</th>\n",
-       "      <th>safety_high</th>\n",
-       "      <th>safety_low</th>\n",
-       "      <th>safety_med</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</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",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</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",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     car  buying_high  buying_low  buying_med  buying_vhigh  maint_high  \\\n",
-       "0  unacc            0           0           0             1           0   \n",
-       "1  unacc            0           0           0             1           0   \n",
-       "2  unacc            0           0           0             1           0   \n",
-       "3  unacc            0           0           0             1           0   \n",
-       "4  unacc            0           0           0             1           0   \n",
-       "\n",
-       "   maint_low  maint_med  maint_vhigh  doors_2  ...  doors_5more  persons_2  \\\n",
-       "0          0          0            1        1  ...            0          1   \n",
-       "1          0          0            1        1  ...            0          1   \n",
-       "2          0          0            1        1  ...            0          1   \n",
-       "3          0          0            1        1  ...            0          1   \n",
-       "4          0          0            1        1  ...            0          1   \n",
-       "\n",
-       "   persons_4  persons_more  lug_boot_big  lug_boot_med  lug_boot_small  \\\n",
-       "0          0             0             0             0               1   \n",
-       "1          0             0             0             0               1   \n",
-       "2          0             0             0             0               1   \n",
-       "3          0             0             0             1               0   \n",
-       "4          0             0             0             1               0   \n",
-       "\n",
-       "   safety_high  safety_low  safety_med  \n",
-       "0            0           1           0  \n",
-       "1            0           0           1  \n",
-       "2            1           0           0  \n",
-       "3            0           1           0  \n",
-       "4            0           0           1  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# encode categorical variables\n",
     "_df = pd.get_dummies(df, columns=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'])\n",
@@ -376,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -398,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -408,34 +102,7 @@
     "id": "-4nPRPmXAC3s",
     "outputId": "1d259a9b-b588-45e6-e80f-26f58cd6cd0c"
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
-      "  FutureWarning)\n",
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n",
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
-      "  \"this warning.\", FutureWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
-       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
-       "                   warm_start=False)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# train a Logistic Regression model\n",
     "model = LogisticRegression()\n",
@@ -444,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -458,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -472,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -482,15 +149,7 @@
     "id": "phVn4D7cAC3z",
     "outputId": "fde5fd3f-a262-4414-ca2d-d7cf3ff6223f"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.46172597937303816\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "recall_score = recall_score(y_val, y_pred, average='macro')\n",
     "print(recall_score)"
@@ -529,7 +188,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.6"
   },
   "mimetype": "text/x-python",
   "name": "python",
diff --git a/Lab06/Old/Exercise6_08/Exercise6_08.ipynb b/Lab06/Old/Exercise6_08/Exercise6_08.ipynb
index c50b4a5..b3418e4 100644
--- a/Lab06/Old/Exercise6_08/Exercise6_08.ipynb
+++ b/Lab06/Old/Exercise6_08/Exercise6_08.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,108 +21,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "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>buying</th>\n",
-       "      <th>maint</th>\n",
-       "      <th>doors</th>\n",
-       "      <th>persons</th>\n",
-       "      <th>lug_boot</th>\n",
-       "      <th>safety</th>\n",
-       "      <th>car</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>low</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>med</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>high</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>med</td>\n",
-       "      <td>low</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>med</td>\n",
-       "      <td>med</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  buying  maint doors persons lug_boot safety    car\n",
-       "0  vhigh  vhigh     2       2    small    low  unacc\n",
-       "1  vhigh  vhigh     2       2    small    med  unacc\n",
-       "2  vhigh  vhigh     2       2    small   high  unacc\n",
-       "3  vhigh  vhigh     2       2      med    low  unacc\n",
-       "4  vhigh  vhigh     2       2      med    med  unacc"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# data doesn't have headers, so let's create headers\n",
     "_headers = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'car']\n",
@@ -135,216 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "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>car</th>\n",
-       "      <th>buying_high</th>\n",
-       "      <th>buying_low</th>\n",
-       "      <th>buying_med</th>\n",
-       "      <th>buying_vhigh</th>\n",
-       "      <th>maint_high</th>\n",
-       "      <th>maint_low</th>\n",
-       "      <th>maint_med</th>\n",
-       "      <th>maint_vhigh</th>\n",
-       "      <th>doors_2</th>\n",
-       "      <th>...</th>\n",
-       "      <th>doors_5more</th>\n",
-       "      <th>persons_2</th>\n",
-       "      <th>persons_4</th>\n",
-       "      <th>persons_more</th>\n",
-       "      <th>lug_boot_big</th>\n",
-       "      <th>lug_boot_med</th>\n",
-       "      <th>lug_boot_small</th>\n",
-       "      <th>safety_high</th>\n",
-       "      <th>safety_low</th>\n",
-       "      <th>safety_med</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</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",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</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",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     car  buying_high  buying_low  buying_med  buying_vhigh  maint_high  \\\n",
-       "0  unacc            0           0           0             1           0   \n",
-       "1  unacc            0           0           0             1           0   \n",
-       "2  unacc            0           0           0             1           0   \n",
-       "3  unacc            0           0           0             1           0   \n",
-       "4  unacc            0           0           0             1           0   \n",
-       "\n",
-       "   maint_low  maint_med  maint_vhigh  doors_2  ...  doors_5more  persons_2  \\\n",
-       "0          0          0            1        1  ...            0          1   \n",
-       "1          0          0            1        1  ...            0          1   \n",
-       "2          0          0            1        1  ...            0          1   \n",
-       "3          0          0            1        1  ...            0          1   \n",
-       "4          0          0            1        1  ...            0          1   \n",
-       "\n",
-       "   persons_4  persons_more  lug_boot_big  lug_boot_med  lug_boot_small  \\\n",
-       "0          0             0             0             0               1   \n",
-       "1          0             0             0             0               1   \n",
-       "2          0             0             0             0               1   \n",
-       "3          0             0             0             1               0   \n",
-       "4          0             0             0             1               0   \n",
-       "\n",
-       "   safety_high  safety_low  safety_med  \n",
-       "0            0           1           0  \n",
-       "1            0           0           1  \n",
-       "2            1           0           0  \n",
-       "3            0           1           0  \n",
-       "4            0           0           1  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# encode categorical variables\n",
     "_df = pd.get_dummies(df, columns=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'])\n",
@@ -353,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,36 +65,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
-      "  FutureWarning)\n",
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n",
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
-      "  \"this warning.\", FutureWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
-       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
-       "                   warm_start=False)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# train a Logistic Regression model\n",
     "model = LogisticRegression()\n",
@@ -409,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -419,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,17 +96,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.4649367623880367\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "f1_score = f1_score(y_val, y_pred, average='macro')\n",
     "print(f1_score)"
@@ -470,7 +129,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.6"
   },
   "mimetype": "text/x-python",
   "name": "python",
diff --git a/Lab06/Old/Exercise6_09/Exercise6_09.ipynb b/Lab06/Old/Exercise6_09/Exercise6_09.ipynb
index 06a4a6d..1a62374 100644
--- a/Lab06/Old/Exercise6_09/Exercise6_09.ipynb
+++ b/Lab06/Old/Exercise6_09/Exercise6_09.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,108 +21,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "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>buying</th>\n",
-       "      <th>maint</th>\n",
-       "      <th>doors</th>\n",
-       "      <th>persons</th>\n",
-       "      <th>lug_boot</th>\n",
-       "      <th>safety</th>\n",
-       "      <th>car</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>low</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>med</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>high</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>med</td>\n",
-       "      <td>low</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>med</td>\n",
-       "      <td>med</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  buying  maint doors persons lug_boot safety    car\n",
-       "0  vhigh  vhigh     2       2    small    low  unacc\n",
-       "1  vhigh  vhigh     2       2    small    med  unacc\n",
-       "2  vhigh  vhigh     2       2    small   high  unacc\n",
-       "3  vhigh  vhigh     2       2      med    low  unacc\n",
-       "4  vhigh  vhigh     2       2      med    med  unacc"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# data doesn't have headers, so let's create headers\n",
     "_headers = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'car']\n",
@@ -135,216 +36,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "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>car</th>\n",
-       "      <th>buying_high</th>\n",
-       "      <th>buying_low</th>\n",
-       "      <th>buying_med</th>\n",
-       "      <th>buying_vhigh</th>\n",
-       "      <th>maint_high</th>\n",
-       "      <th>maint_low</th>\n",
-       "      <th>maint_med</th>\n",
-       "      <th>maint_vhigh</th>\n",
-       "      <th>doors_2</th>\n",
-       "      <th>...</th>\n",
-       "      <th>doors_5more</th>\n",
-       "      <th>persons_2</th>\n",
-       "      <th>persons_4</th>\n",
-       "      <th>persons_more</th>\n",
-       "      <th>lug_boot_big</th>\n",
-       "      <th>lug_boot_med</th>\n",
-       "      <th>lug_boot_small</th>\n",
-       "      <th>safety_high</th>\n",
-       "      <th>safety_low</th>\n",
-       "      <th>safety_med</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</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",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</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",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     car  buying_high  buying_low  buying_med  buying_vhigh  maint_high  \\\n",
-       "0  unacc            0           0           0             1           0   \n",
-       "1  unacc            0           0           0             1           0   \n",
-       "2  unacc            0           0           0             1           0   \n",
-       "3  unacc            0           0           0             1           0   \n",
-       "4  unacc            0           0           0             1           0   \n",
-       "\n",
-       "   maint_low  maint_med  maint_vhigh  doors_2  ...  doors_5more  persons_2  \\\n",
-       "0          0          0            1        1  ...            0          1   \n",
-       "1          0          0            1        1  ...            0          1   \n",
-       "2          0          0            1        1  ...            0          1   \n",
-       "3          0          0            1        1  ...            0          1   \n",
-       "4          0          0            1        1  ...            0          1   \n",
-       "\n",
-       "   persons_4  persons_more  lug_boot_big  lug_boot_med  lug_boot_small  \\\n",
-       "0          0             0             0             0               1   \n",
-       "1          0             0             0             0               1   \n",
-       "2          0             0             0             0               1   \n",
-       "3          0             0             0             1               0   \n",
-       "4          0             0             0             1               0   \n",
-       "\n",
-       "   safety_high  safety_low  safety_med  \n",
-       "0            0           1           0  \n",
-       "1            0           0           1  \n",
-       "2            1           0           0  \n",
-       "3            0           1           0  \n",
-       "4            0           0           1  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# encode categorical variables\n",
     "_df = pd.get_dummies(df, columns=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'])\n",
@@ -353,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -371,36 +65,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
-      "  FutureWarning)\n",
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n",
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
-      "  \"this warning.\", FutureWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
-       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
-       "                   warm_start=False)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# train a Logistic Regression model\n",
     "model = LogisticRegression()\n",
@@ -409,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -419,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,17 +96,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.8764478764478765\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "_accuracy = accuracy_score(y_val, y_pred)\n",
     "print(_accuracy)"
@@ -463,7 +122,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.6"
   },
   "mimetype": "text/x-python",
   "name": "python",
diff --git a/Lab06/Old/Exercise6_10/Exercise6_10.ipynb b/Lab06/Old/Exercise6_10/Exercise6_10.ipynb
index d5c9880..9b6f917 100644
--- a/Lab06/Old/Exercise6_10/Exercise6_10.ipynb
+++ b/Lab06/Old/Exercise6_10/Exercise6_10.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -38,108 +38,7 @@
     "id": "eMkdycnKAC3k",
     "outputId": "e4f4f1e3-9b87-4cd5-9539-4692a32407b6"
    },
-   "outputs": [
-    {
-     "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>buying</th>\n",
-       "      <th>maint</th>\n",
-       "      <th>doors</th>\n",
-       "      <th>persons</th>\n",
-       "      <th>lug_boot</th>\n",
-       "      <th>safety</th>\n",
-       "      <th>car</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>low</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>med</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>small</td>\n",
-       "      <td>high</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>med</td>\n",
-       "      <td>low</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>vhigh</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>med</td>\n",
-       "      <td>med</td>\n",
-       "      <td>unacc</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  buying  maint doors persons lug_boot safety    car\n",
-       "0  vhigh  vhigh     2       2    small    low  unacc\n",
-       "1  vhigh  vhigh     2       2    small    med  unacc\n",
-       "2  vhigh  vhigh     2       2    small   high  unacc\n",
-       "3  vhigh  vhigh     2       2      med    low  unacc\n",
-       "4  vhigh  vhigh     2       2      med    med  unacc"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# data doesn't have headers, so let's create headers\n",
     "_headers = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'car']\n",
@@ -152,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -162,200 +61,7 @@
     "id": "EQvotJcIAC3o",
     "outputId": "ac75fe26-8ead-45a5-c7e2-7820f589f454"
    },
-   "outputs": [
-    {
-     "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>car</th>\n",
-       "      <th>buying_high</th>\n",
-       "      <th>buying_low</th>\n",
-       "      <th>buying_med</th>\n",
-       "      <th>buying_vhigh</th>\n",
-       "      <th>maint_high</th>\n",
-       "      <th>maint_low</th>\n",
-       "      <th>maint_med</th>\n",
-       "      <th>maint_vhigh</th>\n",
-       "      <th>doors_2</th>\n",
-       "      <th>doors_3</th>\n",
-       "      <th>doors_4</th>\n",
-       "      <th>doors_5more</th>\n",
-       "      <th>persons_2</th>\n",
-       "      <th>persons_4</th>\n",
-       "      <th>persons_more</th>\n",
-       "      <th>lug_boot_big</th>\n",
-       "      <th>lug_boot_med</th>\n",
-       "      <th>lug_boot_small</th>\n",
-       "      <th>safety_high</th>\n",
-       "      <th>safety_low</th>\n",
-       "      <th>safety_med</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</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",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</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",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>unacc</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     car  buying_high  buying_low  ...  safety_high  safety_low  safety_med\n",
-       "0  unacc            0           0  ...            0           1           0\n",
-       "1  unacc            0           0  ...            0           0           1\n",
-       "2  unacc            0           0  ...            1           0           0\n",
-       "3  unacc            0           0  ...            0           1           0\n",
-       "4  unacc            0           0  ...            0           0           1\n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# encode categorical variables\n",
     "_df = pd.get_dummies(df, columns=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'])\n",
@@ -364,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -386,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -396,32 +102,7 @@
     "id": "-4nPRPmXAC3s",
     "outputId": "429ffe12-5d71-4048-c55d-5cb56162c398"
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
-       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
-       "                   warm_start=False)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {
-      "tags": []
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# train a Logistic Regression model\n",
     "model = LogisticRegression()\n",
@@ -430,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -444,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -458,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -468,15 +149,7 @@
     "id": "phVn4D7cAC3z",
     "outputId": "0f9a4e44-5e96-4ce0-8b78-08c5a91040a7"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.22578836752298448\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "_loss = log_loss(y_val, model.predict_proba(X_val))\n",
     "print(_loss)"
@@ -484,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 0,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -515,7 +188,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.6"
   },
   "mimetype": "text/x-python",
   "name": "python",
diff --git a/Lab06/Old/Exercise6_11/Exercise6_11.ipynb b/Lab06/Old/Exercise6_11/Exercise6_11.ipynb
index 4acdcda..3b457b1 100644
--- a/Lab06/Old/Exercise6_11/Exercise6_11.ipynb
+++ b/Lab06/Old/Exercise6_11/Exercise6_11.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -31,100 +31,7 @@
     "id": "mxGIoM_5ilQF",
     "outputId": "e3bdf790-f9c5-4b87-e72e-9c68bb869517"
    },
-   "outputs": [
-    {
-     "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>Age</th>\n",
-       "      <th>Delivery_Nbr</th>\n",
-       "      <th>Delivery_Time</th>\n",
-       "      <th>Blood_Pressure</th>\n",
-       "      <th>Heart_Problem</th>\n",
-       "      <th>Caesarian</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>22</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>26</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>26</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>28</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>22</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Age  Delivery_Nbr  Delivery_Time  Blood_Pressure  Heart_Problem  Caesarian\n",
-       "0   22             1              0               2              0          0\n",
-       "1   26             2              0               1              0          1\n",
-       "2   26             2              1               1              0          0\n",
-       "3   28             1              0               2              0          0\n",
-       "4   22             2              0               1              0          1"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# data doesn't have headers, so let's create headers \n",
     "\n",
@@ -141,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -170,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -180,32 +87,7 @@
     "id": "Ej6uIbezirCR",
     "outputId": "b0e201d2-d55f-4dbc-c21d-e05c2cf6935b"
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
-      "  FutureWarning)\n",
-      "/Users/robert/anaconda3/envs/tensorflow/lib/python3.7/site-packages/sklearn/utils/validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-      "  y = column_or_1d(y, warn=True)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
-       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
-       "                   warm_start=False)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = LogisticRegression() \n",
     "\n",
@@ -214,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -227,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -241,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -254,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -264,22 +146,14 @@
     "id": "laUL4vKgjSNa",
     "outputId": "dcece431-2930-4cb7-fcbb-627f234cef94"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.  0.  0.  0.5 0.5 1.  1. ]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(_false_positive) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -289,23 +163,14 @@
     "id": "CWVZIRPFjlDN",
     "outputId": "40253e1c-782a-451d-ca2f-a1fe75bed8ea"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.         0.16666667 0.33333333 0.33333333 0.83333333 0.83333333\n",
-      " 1.        ]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(_true_positive) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -315,23 +180,14 @@
     "id": "87z9brVnjmht",
     "outputId": "7c8b8e45-ad81-411d-e7dc-316bd65c6d9d"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1.555085   0.555085   0.55005002 0.48012821 0.32088069 0.22067501\n",
-      " 0.19652383]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(_thresholds) "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -341,18 +197,7 @@
     "id": "6XgGwPzqjoV-",
     "outputId": "bf98a239-c612-4a66-ddc2-0a019307a4f6"
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"import matplotlib.pyplot as plt \\n\\n%matplotlib inline \\n\\n \\n\\nplt.plot(_false_positive, _true_positive, lw=2, label='Receiver Operating Characteristic') \\n\\nplt.xlim(0.0, 1.2) \\n\\nplt.ylim(0.0, 1.2) \\n\\nplt.xlabel('False Positive Rate') \\n\\nplt.ylabel('True Positive Rate') \\n\\nplt.title('Receiver Operating Characteristic') \\n\\nplt.show() \""
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot the RoC \n",
     "# Uncomment the following block of code to see the plot\n",
@@ -399,7 +244,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.6"
   }
  },
  "nbformat": 4,
diff --git a/Lab06/Old/Exercise6_12/Exercise6_12.ipynb b/Lab06/Old/Exercise6_12/Exercise6_12.ipynb
index 251ddb6..3b8d62c 100644
--- a/Lab06/Old/Exercise6_12/Exercise6_12.ipynb
+++ b/Lab06/Old/Exercise6_12/Exercise6_12.ipynb
@@ -1,286 +1,158 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "file_extension": ".py",
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.7.4"
-    },
-    "mimetype": "text/x-python",
-    "name": "python",
-    "npconvert_exporter": "python",
-    "pygments_lexer": "ipython3",
-    "version": 3,
-    "colab": {
-      "name": "Exercise6.12.ipynb",
-      "provenance": []
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "QWyZQ1u8DuI_"
+   },
+   "source": [
+    "# Computing the ROC AUC for the Caesarian Dataset"
+   ]
   },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "QWyZQ1u8DuI_",
-        "colab_type": "text"
-      },
-      "source": [
-        "# Computing the ROC AUC for the Caesarian Dataset"
-      ]
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "p89bSWY_DuJD"
+   },
+   "outputs": [],
+   "source": [
+    "# import libraries\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
     },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "p89bSWY_DuJD",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# import libraries\n",
-        "import pandas as pd\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "from sklearn.linear_model import LogisticRegression"
-      ],
-      "execution_count": 0,
-      "outputs": []
+    "colab_type": "code",
+    "id": "vW0hhGg3DuJI",
+    "outputId": "a79179c0-dedd-41dc-f13e-93c1e2120617"
+   },
+   "outputs": [],
+   "source": [
+    "# data doesn't have headers, so let's create headers\n",
+    "_headers = ['Age', 'Delivery_Nbr', 'Delivery_Time', 'Blood_Pressure', 'Heart_Problem', 'Caesarian']\n",
+    "# read in cars dataset\n",
+    "df = pd.read_csv('../Dataset/caesarian.csv.arff', names=_headers, index_col=None, skiprows=15)\n",
+    "df.head()\n",
+    "\n",
+    "# target column is 'Caesarian'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "MGk_0UmkDuJN"
+   },
+   "outputs": [],
+   "source": [
+    "# target column is 'Caesarian'\n",
+    "\n",
+    "features = df.drop(['Caesarian'], axis=1).values\n",
+    "labels = df[['Caesarian']].values\n",
+    "\n",
+    "# split 80% for training and 20% into an evaluation set\n",
+    "X_train, X_eval, y_train, y_eval = train_test_split(features, labels, test_size=0.2, random_state=0)\n",
+    "\n",
+    "# further split the evaluation set into validation and test sets of 10% each\n",
+    "X_val, X_test, y_val, y_test = train_test_split(X_eval, y_eval, test_size=0.5, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 136
     },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "vW0hhGg3DuJI",
-        "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 204
-        },
-        "outputId": "a79179c0-dedd-41dc-f13e-93c1e2120617"
-      },
-      "source": [
-        "# data doesn't have headers, so let's create headers\n",
-        "_headers = ['Age', 'Delivery_Nbr', 'Delivery_Time', 'Blood_Pressure', 'Heart_Problem', 'Caesarian']\n",
-        "# read in cars dataset\n",
-        "df = pd.read_csv('../Dataset/caesarian.csv.arff', names=_headers, index_col=None, skiprows=15)\n",
-        "df.head()\n",
-        "\n",
-        "# target column is 'Caesarian'"
-      ],
-      "execution_count": 2,
-      "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>Age</th>\n",
-              "      <th>Delivery_Nbr</th>\n",
-              "      <th>Delivery_Time</th>\n",
-              "      <th>Blood_Pressure</th>\n",
-              "      <th>Heart_Problem</th>\n",
-              "      <th>Caesarian</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>22</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>26</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>26</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>28</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>22</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   Age  Delivery_Nbr  Delivery_Time  Blood_Pressure  Heart_Problem  Caesarian\n",
-              "0   22             1              0               2              0          0\n",
-              "1   26             2              0               1              0          1\n",
-              "2   26             2              1               1              0          0\n",
-              "3   28             1              0               2              0          0\n",
-              "4   22             2              0               1              0          1"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 2
-        }
-      ]
+    "colab_type": "code",
+    "id": "3qwJP9sEDuJQ",
+    "outputId": "de02ebb1-fb71-4283-f90c-1623e14af1f4"
+   },
+   "outputs": [],
+   "source": [
+    "# train a Logistic Regression model\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "v3vFYHI7DuJU"
+   },
+   "outputs": [],
+   "source": [
+    "# make predictions for the validation dataset\n",
+    "y_proba = model.predict_proba(X_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
     },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "MGk_0UmkDuJN",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# target column is 'Caesarian'\n",
-        "\n",
-        "features = df.drop(['Caesarian'], axis=1).values\n",
-        "labels = df[['Caesarian']].values\n",
-        "\n",
-        "# split 80% for training and 20% into an evaluation set\n",
-        "X_train, X_eval, y_train, y_eval = train_test_split(features, labels, test_size=0.2, random_state=0)\n",
-        "\n",
-        "# further split the evaluation set into validation and test sets of 10% each\n",
-        "X_val, X_test, y_val, y_test = train_test_split(X_eval, y_eval, test_size=0.5, random_state=0)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "3qwJP9sEDuJQ",
-        "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 136
-        },
-        "outputId": "de02ebb1-fb71-4283-f90c-1623e14af1f4"
-      },
-      "source": [
-        "# train a Logistic Regression model\n",
-        "model = LogisticRegression()\n",
-        "model.fit(X_train, y_train)"
-      ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
-            "  y = column_or_1d(y, warn=True)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
-              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
-              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
-              "                   warm_start=False)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "v3vFYHI7DuJU",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "# make predictions for the validation dataset\n",
-        "y_proba = model.predict_proba(X_val)"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "s2mk5XKxDuJY",
-        "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 34
-        },
-        "outputId": "28e3e3db-3e8f-42ea-da67-5febd5cae99b"
-      },
-      "source": [
-        "from sklearn.metrics import roc_auc_score\n",
-        "_auc = roc_auc_score(y_val, y_proba[:, 0])\n",
-        "print(_auc)"
-      ],
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "0.5833333333333334\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file
+    "colab_type": "code",
+    "id": "s2mk5XKxDuJY",
+    "outputId": "28e3e3db-3e8f-42ea-da67-5febd5cae99b"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import roc_auc_score\n",
+    "_auc = roc_auc_score(y_val, y_proba[:, 0])\n",
+    "print(_auc)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "Exercise6.12.ipynb",
+   "provenance": []
+  },
+  "file_extension": ".py",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "mimetype": "text/x-python",
+  "name": "python",
+  "npconvert_exporter": "python",
+  "pygments_lexer": "ipython3",
+  "version": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/Lab13/Chapter_13_Unbalanced_Data_sets_v1.0.ipynb b/Lab13/Chapter_13_Unbalanced_Data_sets_v1.0.ipynb
index da5c17f..c80560f 100644
--- a/Lab13/Chapter_13_Unbalanced_Data_sets_v1.0.ipynb
+++ b/Lab13/Chapter_13_Unbalanced_Data_sets_v1.0.ipynb
@@ -1,23 +1,5 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 122
-    },
-    "colab_type": "code",
-    "id": "yswSlRdlXiWW",
-    "outputId": "5f83a1c3-9e2c-48ea-9adf-5506b8b27da4"
-   },
-   "outputs": [],
-   "source": [
-    "from google.colab import drive\n",
-    "drive.mount('/content/drive')"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -29,7 +11,10 @@
    "outputs": [],
    "source": [
     "# Loading the necessary library files\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "\n",
+    "import warnings\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)"
    ]
   },
   {
@@ -46,10 +31,8 @@
    },
    "outputs": [],
    "source": [
-    "# Loading data from the drive\n",
-    "\n",
     "# Please change the filename as per the location where the file is stored\n",
-    "filename = '/content/drive/My Drive/Packt_Colab/bank-full.csv'\n",
+    "filename = './Dataset/bank-full.csv'\n",
     "# Loading the data u'sing pandas\n",
     "\n",
     "bankData = pd.read_csv(filename,sep=\";\")\n",
@@ -461,19 +444,6 @@
     "Let us now try the over sampling method and find what effect it has on the results"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4NrPQWkA9Eyf"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install smote-variants"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -667,6 +637,34 @@
     "from sklearn.metrics import classification_report\n",
     "print(classification_report(y_test, pred))"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {