diff --git a/notebooks/live_coding_11_NLP_3_tfifd_and_machine_learning.ipynb b/notebooks/live_coding_11_NLP_3_tfifd_and_machine_learning.ipynb
index d90a212..a3afbfb 100644
--- a/notebooks/live_coding_11_NLP_3_tfifd_and_machine_learning.ipynb
+++ b/notebooks/live_coding_11_NLP_3_tfifd_and_machine_learning.ipynb
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "id": "0953c3ec",
    "metadata": {
     "editable": true,
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "b07bb307",
    "metadata": {
     "editable": true,
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "id": "58260b8c-a93d-438e-878f-c0ecc5e18516",
    "metadata": {
     "editable": true,
@@ -180,7 +180,8 @@
    ],
    "source": [
     "\"\"\"\n",
-    "This code block downloads the data from zenodo and stores it in a local 'datasets' folder.\n",
+    "This code block downloads the data from zenodo and stores it in\n",
+    "a local 'datasets' folder.\n",
     "\"\"\"\n",
     "\n",
     "import requests\n",
@@ -226,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "id": "c6cabdf7",
    "metadata": {},
    "outputs": [
@@ -251,6 +252,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>S.No.</th>\n",
        "      <th>Review</th>\n",
        "      <th>Rating</th>\n",
        "    </tr>\n",
@@ -258,27 +260,32 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>1</td>\n",
        "      <td>nice hotel expensive parking got good deal sta...</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>2</td>\n",
        "      <td>ok nothing special charge diamond member hilto...</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>3</td>\n",
        "      <td>nice rooms not 4* experience hotel monaco seat...</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>unique, great stay, wonderful time hotel monac...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>unique \\tgreat stay \\twonderful time hotel mon...</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>great stay great stay, went seahawk game aweso...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>great stay great stay \\twent seahawk game awes...</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -286,28 +293,28 @@
        "</div>"
       ],
       "text/plain": [
-       "                                              Review  Rating\n",
-       "0  nice hotel expensive parking got good deal sta...       4\n",
-       "1  ok nothing special charge diamond member hilto...       2\n",
-       "2  nice rooms not 4* experience hotel monaco seat...       3\n",
-       "3  unique, great stay, wonderful time hotel monac...       5\n",
-       "4  great stay great stay, went seahawk game aweso...       5"
+       "   S.No.                                             Review  Rating\n",
+       "0      1  nice hotel expensive parking got good deal sta...       4\n",
+       "1      2  ok nothing special charge diamond member hilto...       2\n",
+       "2      3  nice rooms not 4* experience hotel monaco seat...       3\n",
+       "3      4  unique \\tgreat stay \\twonderful time hotel mon...       5\n",
+       "4      5  great stay great stay \\twent seahawk game awes...       5"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "filename = \"../datasets/tripadvisor_hotel_reviews.csv\"\n",
-    "data = pd.read_csv(filename)\n",
+    "data = pd.read_csv(filename, encoding=\"ansi\")\n",
     "data.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "id": "eb8b6172",
    "metadata": {},
    "outputs": [
@@ -317,13 +324,14 @@
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
       "RangeIndex: 20491 entries, 0 to 20490\n",
-      "Data columns (total 2 columns):\n",
+      "Data columns (total 3 columns):\n",
       " #   Column  Non-Null Count  Dtype \n",
       "---  ------  --------------  ----- \n",
-      " 0   Review  20491 non-null  object\n",
-      " 1   Rating  20491 non-null  int64 \n",
-      "dtypes: int64(1), object(1)\n",
-      "memory usage: 320.3+ KB\n"
+      " 0   S.No.   20491 non-null  int64 \n",
+      " 1   Review  20491 non-null  object\n",
+      " 2   Rating  20491 non-null  int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 480.4+ KB\n"
      ]
     }
    ],
@@ -333,23 +341,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "id": "f2bc7712",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\flori\\AppData\\Local\\Temp\\ipykernel_7500\\890204809.py:1: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sb.countplot(data=data,\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "Text(0.5, 1.0, 'Rating Distribution')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -379,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "id": "acd4c215",
    "metadata": {},
    "outputs": [
@@ -404,17 +423,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "id": "6874ac61",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(16392, 46816)"
+       "(16392, 46813)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -435,17 +454,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "id": "b68dc085",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'nice hotel expensive parking got good deal stay hotel anniversary, arrived late evening took advice previous reviews did valet parking, check quick easy, little disappointed non-existent view room room clean nice size, bed comfortable woke stiff neck high pillows, not soundproof like heard music room night morning loud bangs doors opening closing hear people talking hallway, maybe just noisy neighbors, aveda bath products nice, did not goldfish stay nice touch taken advantage staying longer, location great walking distance shopping, overall nice experience having pay 40 parking night,  '"
+       "1"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -457,18 +476,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "id": "23725d3a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<1x46816 sparse matrix of type '<class 'numpy.float64'>'\n",
-       "\twith 248 stored elements in Compressed Sparse Row format>"
+       "<1x46813 sparse matrix of type '<class 'numpy.float64'>'\n",
+       "\twith 247 stored elements in Compressed Sparse Row format>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -502,31 +521,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 17,
    "id": "4079580b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array(['zoned', 'zones', 'zonethis', 'zoning', 'zoo', 'zoogarten',\n",
-       "       'zooji', 'zoological', 'zoologicher', 'zoologische',\n",
-       "       'zoologischer', 'zoologisher', 'zoom', 'zooming', 'zooms', 'zoomy',\n",
-       "       'zoran', 'zorbas', 'zucca', 'zuid', 'zum', 'zumo', 'zurich',\n",
-       "       'zvago', 'zyrtec', 'zytec', 'zz', 'zzzt', 'zzzzt', 'zzzzzs',\n",
-       "       'zzzzzzzzz', 'µlack', 'ààmake', 'ààneither', 'ààthe', 'ààthere',\n",
-       "       'âge', 'âme', 'ânes', 'âre', 'âs', 'ä__ç', 'ä__ç_é_', 'ä_ëù_ez',\n",
-       "       'äarely', 'äbec', 'äcialy', 'äciate', 'äciated', 'äcor', 'äe',\n",
-       "       'äed', 'äellement', 'äes', 'ägal', 'älanie', 'älie', 'ändo',\n",
-       "       'änial', 'äon', 'äpublique', 'ära', 'ärico', 'äring', 'äro', 'äs',\n",
-       "       'ätence', 'ätro', 'äut', 'åeach', 'åreakfast', 'æn', 'ærom',\n",
-       "       'æsimas', 'èery', 'èou', 'é__', 'é_ç', 'é_ç_a', 'é_çål', 'éenny',\n",
-       "       'êtyle', 'î_', 'î__ere', 'î__here', 'î__hese', 'öreat', 'ù_', 'ùn',\n",
-       "       'ùoom', 'ûan', 'ü_e', 'üefinitely', 'üescribed', 'üifficult',\n",
-       "       'üust', 'üâjili', 'üã', 'üè__', 'üècia'], dtype=object)"
+       "array(['zones', 'zonethis', 'zoning', 'zoo', 'zoogarten', 'zooji',\n",
+       "       'zoological', 'zoologicher', 'zoologische', 'zoologischer',\n",
+       "       'zoologisher', 'zoom', 'zooming', 'zooms', 'zoomy', 'zoran',\n",
+       "       'zorbas', 'zucca', 'zuid', 'zum', 'zumo', 'zurich', 'zvago',\n",
+       "       'zyrtec', 'zytec', 'zz', 'zzzt', 'zzzzt', 'zzzzzs', 'zzzzzzzzz',\n",
+       "       'µlack', 'ààmake', 'ààneither', 'ààthe', 'ààthere', 'âge', 'âme',\n",
+       "       'ânes', 'âre', 'âs', 'ä__ç', 'ä__ç_é_', 'ä_ëù_ez', 'äarely',\n",
+       "       'äbec', 'äcialy', 'äciate', 'äciated', 'äcor', 'äe', 'äed',\n",
+       "       'äellement', 'äes', 'ägal', 'älanie', 'älie', 'ändo', 'änial',\n",
+       "       'äon', 'äpublique', 'ära', 'ärico', 'äring', 'äro', 'äs', 'ätence',\n",
+       "       'ätro', 'äut', 'åeach', 'åreakfast', 'æn', 'ærom', 'æsimas',\n",
+       "       'èery', 'èou', 'é__', 'é_ç_a', 'é_çål', 'é_çœybiscus', 'é_çžadizo',\n",
+       "       'éenny', 'êtyle', 'öreat', 'ù_šäœ', 'ù_šäœî_', 'ù_šäœî__ere',\n",
+       "       'ù_šäœî__here', 'ù_šäœî__hese', 'ùn', 'ùoom', 'ûan', 'ü_e',\n",
+       "       'üefinitely', 'üescribed', 'üifficult', 'üust', 'üâjili', 'üã',\n",
+       "       'üè__', 'üècia'], dtype=object)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -550,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 18,
    "id": "e5732ec6",
    "metadata": {},
    "outputs": [
@@ -560,7 +580,7 @@
        "(16392, 8533)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 19,
    "id": "169d4709",
    "metadata": {},
    "outputs": [
@@ -597,7 +617,7 @@
        "       'zona', 'zone', 'zones', 'zoo', 'äcor', 'äes', 'äs'], dtype=object)"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -617,7 +637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 20,
    "id": "a8ed78c1",
    "metadata": {},
    "outputs": [
@@ -669,7 +689,7 @@
        "       0.03443276, 0.14240732, 0.13344956, 0.06313989, 0.05398083])"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -680,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 21,
    "id": "19b4001c",
    "metadata": {},
    "outputs": [
@@ -709,7 +729,7 @@
        "       2259, 6005, 2046, 8145, 5973, 1503])"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -720,7 +740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 22,
    "id": "aa862adf",
    "metadata": {},
    "outputs": [
@@ -827,7 +847,7 @@
        "[215 rows x 2 columns]"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -842,7 +862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 23,
    "id": "49c3790f",
    "metadata": {},
    "outputs": [
@@ -850,11 +870,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.03345672 0.         0.         0.         0.         0.\n",
+      "[0.03830825 0.         0.         0.11754202 0.         0.\n",
       " 0.         0.         0.         0.         0.         0.\n",
       " 0.         0.         0.         0.         0.         0.\n",
       " 0.         0.        ]\n",
-      "(1, 46816)\n"
+      "(1, 8533)\n"
      ]
     }
    ],
@@ -899,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 24,
    "id": "9ecbed72",
    "metadata": {},
    "outputs": [
@@ -914,7 +934,7 @@
        "Name: Rating, dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -935,20 +955,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 25,
    "id": "c427e117",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "LinearRegression()"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -971,7 +991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
    "id": "9963dae0",
    "metadata": {},
    "outputs": [],
@@ -983,7 +1003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 27,
    "id": "51e79d29",
    "metadata": {},
    "outputs": [
@@ -994,7 +1014,7 @@
        "       4.5, 4.3, 5.3, 5.7, 3. , 4.5, 4.4])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1005,7 +1025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 28,
    "id": "ff9b3a9d",
    "metadata": {},
    "outputs": [
@@ -1016,7 +1036,7 @@
        "      dtype=int64)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1035,7 +1055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 29,
    "id": "b40cad58",
    "metadata": {},
    "outputs": [
@@ -1045,13 +1065,13 @@
        "<Axes: xlabel='Rating', ylabel='Count'>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1066,17 +1086,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 30,
    "id": "98421bdb",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7872191832942967"
+       "0.787206806488315"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1096,17 +1116,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 31,
    "id": "41f7732c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(-1.8635146070281188, 7.274303749464632)"
+       "(-1.8632384891273492, 7.2737304843207395)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1126,7 +1146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 32,
    "id": "733c0fca",
    "metadata": {},
    "outputs": [
@@ -1134,7 +1154,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Mean absolute error after clipping the predictions: 0.6740064461149007\n"
+      "Mean absolute error after clipping the predictions: 0.6740048418378741\n"
      ]
     }
    ],
@@ -1147,7 +1167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 33,
    "id": "45848c1c",
    "metadata": {},
    "outputs": [
@@ -1157,13 +1177,13 @@
        "<Axes: xlabel='Rating', ylabel='Count'>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1189,7 +1209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 34,
    "id": "a70afa1a",
    "metadata": {},
    "outputs": [
@@ -1197,7 +1217,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\flori\\anaconda3\\envs\\ai_smart_health\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "C:\\Users\\flori\\anaconda3\\envs\\data_science\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
       "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
       "\n",
       "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
@@ -1210,13 +1230,13 @@
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression()"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1230,7 +1250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
    "id": "98a753d6",
    "metadata": {},
    "outputs": [],
@@ -1242,7 +1262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 36,
    "id": "835acb48",
    "metadata": {},
    "outputs": [
@@ -1253,7 +1273,7 @@
        "      dtype=int64)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1264,7 +1284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 37,
    "id": "6daf658c",
    "metadata": {},
    "outputs": [
@@ -1275,7 +1295,7 @@
        "      dtype=int64)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1286,7 +1306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 38,
    "id": "57c09f27",
    "metadata": {},
    "outputs": [
@@ -1296,13 +1316,13 @@
        "<Axes: xlabel='Rating', ylabel='Count'>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1317,17 +1337,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 39,
    "id": "73246c8b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.4981702854354721"
+       "0.4996340570870944"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1348,7 +1368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 40,
    "id": "1e27f698",
    "metadata": {},
    "outputs": [
@@ -1357,14 +1377,14 @@
      "output_type": "stream",
      "text": [
       "[[ 167   65    7   30   16]\n",
-      " [  60  121   59   78   37]\n",
-      " [  12   62  108  228   61]\n",
-      " [   5   14   49  614  521]\n",
-      " [   1    6    7  338 1433]]\n",
+      " [  59  119   61   78   38]\n",
+      " [  12   62  107  229   61]\n",
+      " [   5   14   48  612  524]\n",
+      " [   1    6    7  337 1434]]\n",
       "              precision    recall  f1-score   support\n",
       "\n",
       "           1       0.68      0.59      0.63       285\n",
-      "           2       0.45      0.34      0.39       355\n",
+      "           2       0.45      0.34      0.38       355\n",
       "           3       0.47      0.23      0.31       471\n",
       "           4       0.48      0.51      0.49      1203\n",
       "           5       0.69      0.80      0.74      1785\n",
@@ -1394,13 +1414,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 41,
    "id": "7796bcb4",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 900x700 with 2 Axes>"
       ]
@@ -1437,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 42,
    "id": "09e7396c",
    "metadata": {},
    "outputs": [
@@ -1462,6 +1482,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>S.No.</th>\n",
        "      <th>Review</th>\n",
        "      <th>Rating</th>\n",
        "      <th>rating_simplified</th>\n",
@@ -1470,31 +1491,36 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>1</td>\n",
        "      <td>nice hotel expensive parking got good deal sta...</td>\n",
        "      <td>4</td>\n",
        "      <td>good</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>2</td>\n",
        "      <td>ok nothing special charge diamond member hilto...</td>\n",
        "      <td>2</td>\n",
        "      <td>bad</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>3</td>\n",
        "      <td>nice rooms not 4* experience hotel monaco seat...</td>\n",
        "      <td>3</td>\n",
        "      <td>neutral</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>unique, great stay, wonderful time hotel monac...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>unique \\tgreat stay \\twonderful time hotel mon...</td>\n",
        "      <td>5</td>\n",
        "      <td>good</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>great stay great stay, went seahawk game aweso...</td>\n",
+       "      <td>5</td>\n",
+       "      <td>great stay great stay \\twent seahawk game awes...</td>\n",
        "      <td>5</td>\n",
        "      <td>good</td>\n",
        "    </tr>\n",
@@ -1503,15 +1529,22 @@
        "</div>"
       ],
       "text/plain": [
-       "                                              Review  Rating rating_simplified\n",
-       "0  nice hotel expensive parking got good deal sta...       4              good\n",
-       "1  ok nothing special charge diamond member hilto...       2               bad\n",
-       "2  nice rooms not 4* experience hotel monaco seat...       3           neutral\n",
-       "3  unique, great stay, wonderful time hotel monac...       5              good\n",
-       "4  great stay great stay, went seahawk game aweso...       5              good"
+       "   S.No.                                             Review  Rating  \\\n",
+       "0      1  nice hotel expensive parking got good deal sta...       4   \n",
+       "1      2  ok nothing special charge diamond member hilto...       2   \n",
+       "2      3  nice rooms not 4* experience hotel monaco seat...       3   \n",
+       "3      4  unique \\tgreat stay \\twonderful time hotel mon...       5   \n",
+       "4      5  great stay great stay \\twent seahawk game awes...       5   \n",
+       "\n",
+       "  rating_simplified  \n",
+       "0              good  \n",
+       "1               bad  \n",
+       "2           neutral  \n",
+       "3              good  \n",
+       "4              good  "
       ]
      },
-     "execution_count": 45,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1531,7 +1564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 43,
    "id": "0e9e4970",
    "metadata": {},
    "outputs": [
@@ -1559,17 +1592,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 44,
    "id": "b2a2496c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(16392, 46816)"
+       "(16392, 46813)"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1582,7 +1615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 45,
    "id": "dff004d9",
    "metadata": {},
    "outputs": [
@@ -1590,7 +1623,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\flori\\anaconda3\\envs\\ai_smart_health\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "C:\\Users\\flori\\anaconda3\\envs\\data_science\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
       "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
       "\n",
       "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
@@ -1603,13 +1636,13 @@
     {
      "data": {
       "text/html": [
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+       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
       ],
       "text/plain": [
        "LogisticRegression()"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1621,7 +1654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 46,
    "id": "a0ef2a75",
    "metadata": {},
    "outputs": [],
@@ -1633,7 +1666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 47,
    "id": "cd868ef4",
    "metadata": {},
    "outputs": [
@@ -1645,7 +1678,7 @@
        "       'good', 'good', 'good', 'good'], dtype=object)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1656,7 +1689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 48,
    "id": "4b067e49",
    "metadata": {},
    "outputs": [
@@ -1668,7 +1701,7 @@
        "       'good', 'good', 'good', 'good'], dtype=object)"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1679,7 +1712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 49,
    "id": "56187db9",
    "metadata": {},
    "outputs": [
@@ -1687,7 +1720,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[ 505  107   28]\n",
+      "[[ 504  108   28]\n",
       " [  20 2926   42]\n",
       " [  99  296   76]]\n",
       "              precision    recall  f1-score   support\n",
@@ -1712,13 +1745,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 50,
    "id": "1a4ceb03",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 900x700 with 2 Axes>"
       ]