From a46a3d471d3e982cd6dbd0a37488b92b8bd86515 Mon Sep 17 00:00:00 2001
From: nabihanaqvie <nabihanaqvie@gmail.com>
Date: Thu, 9 Dec 2021 18:06:16 -0500
Subject: [PATCH] no punct removal

---
 SANA-P2.ipynb | 278 ++++++++++++++++++++++++--------------------------
 1 file changed, 136 insertions(+), 142 deletions(-)

diff --git a/SANA-P2.ipynb b/SANA-P2.ipynb
index 0a7111f..217424e 100644
--- a/SANA-P2.ipynb
+++ b/SANA-P2.ipynb
@@ -346,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "id": "ec7f25c2",
    "metadata": {},
    "outputs": [],
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "id": "9074d2fb",
    "metadata": {},
    "outputs": [],
@@ -487,7 +487,7 @@
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>Islam, Modernity New Millennium: Themes Critic...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Religious Studies/ Islamic Studies/Philosophy ...</td>\n",
        "      <td>As world becomes increasingly globalised Islam...</td>\n",
        "      <td>[As, world, becomes, increasingly, globalised,...</td>\n",
@@ -500,7 +500,7 @@
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>Analytic Philosophy Perspective Critical Ratio...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Analytical Philosophy/ Critical Rationalism</td>\n",
        "      <td>The present paper attempt explore impact Karl ...</td>\n",
        "      <td>[The, present, paper, attempt, explore, impact...</td>\n",
@@ -513,7 +513,7 @@
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>The Misty Land Ideas Light Dialogue: An Anthol...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Islamic philosophy/ Philosophy/ Comparative</td>\n",
        "      <td>Comparative philosophy, like types philosophy,...</td>\n",
        "      <td>[Comparative, philosophy, ,, like, type, philo...</td>\n",
@@ -526,7 +526,7 @@
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>Socio-Ethical Consequences Convergent Technolo...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Socio- Ethical Consequences/ Converging Scienc...</td>\n",
        "      <td>Critical assessment social changes impact fact...</td>\n",
        "      <td>[Critical, assessment, social, change, impact,...</td>\n",
@@ -539,7 +539,7 @@
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>“Shi‘as Britain: The Earliest Cases (Part 1)”,...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Shi'a Muslim/Counterflows Colonialism/The East...</td>\n",
        "      <td>Shi�a Muslims UK, whether individually groups ...</td>\n",
        "      <td>[Shi�a, Muslims, UK, ,, whether, individually,...</td>\n",
@@ -565,7 +565,7 @@
        "    <tr>\n",
        "      <th>635</th>\n",
        "      <td>Bashir, Shahzad Reading self Persian prose poe...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>Sufism/Persian poetry/historiography/17th cent...</td>\n",
        "      <td>Let us start thought experiment. Five hundred ...</td>\n",
        "      <td>[Let, u, start, thought, experiment, ., Five, ...</td>\n",
@@ -578,7 +578,7 @@
        "    <tr>\n",
        "      <th>636</th>\n",
        "      <td>Everlasting Doubt: Uncertainty Islamic Represe...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>historiography/Islamic history/Persian Arabic ...</td>\n",
        "      <td>Utilizing treatments uncertainty regarding his...</td>\n",
        "      <td>[Utilizing, treatment, uncertainty, regarding,...</td>\n",
@@ -591,7 +591,7 @@
        "    <tr>\n",
        "      <th>637</th>\n",
        "      <td>The Origins Rhetorical Evolution Term Qizilbās...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>historiography/Islamic history/qizilbash/Safav...</td>\n",
        "      <td>This essay traces use term Qizilb?sh select li...</td>\n",
        "      <td>[This, essay, trace, use, term, Qizilb, ?, sh,...</td>\n",
@@ -604,7 +604,7 @@
        "    <tr>\n",
        "      <th>638</th>\n",
        "      <td>Bashir, Shahzad Shah Ismaʿil Qizilbash: Cannib...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>historiography/Islamic history/qizilbash/Safav...</td>\n",
        "      <td>The first decade sixteeth century Common Era s...</td>\n",
        "      <td>[The, first, decade, sixteeth, century, Common...</td>\n",
@@ -617,7 +617,7 @@
        "    <tr>\n",
        "      <th>641</th>\n",
        "      <td>Empirical Spirits: Islam, Spiritism, Virtues S...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>Spiritism Iran/science/religion/morality</td>\n",
        "      <td>This article examines aspects reception French...</td>\n",
        "      <td>[This, article, examines, aspect, reception, F...</td>\n",
@@ -646,18 +646,18 @@
        "638  Bashir, Shahzad Shah Ismaʿil Qizilbash: Cannib...   \n",
        "641  Empirical Spirits: Islam, Spiritism, Virtues S...   \n",
        "\n",
-       "                     Category  \\\n",
-       "8          Islamic Philosophy   \n",
-       "12         Islamic Philosophy   \n",
-       "15         Islamic Philosophy   \n",
-       "16         Islamic Philosophy   \n",
-       "29         Islamic Philosophy   \n",
-       "..                        ...   \n",
-       "635  Anthropology Shi’a Islam   \n",
-       "636  Anthropology Shi’a Islam   \n",
-       "637  Anthropology Shi’a Islam   \n",
-       "638  Anthropology Shi’a Islam   \n",
-       "641  Anthropology Shi’a Islam   \n",
+       "                    Category  \\\n",
+       "8         islamic philosophy   \n",
+       "12        islamic philosophy   \n",
+       "15        islamic philosophy   \n",
+       "16        islamic philosophy   \n",
+       "29        islamic philosophy   \n",
+       "..                       ...   \n",
+       "635  anthropology shia islam   \n",
+       "636  anthropology shia islam   \n",
+       "637  anthropology shia islam   \n",
+       "638  anthropology shia islam   \n",
+       "641  anthropology shia islam   \n",
        "\n",
        "                                          Sub-Category  \\\n",
        "8    Religious Studies/ Islamic Studies/Philosophy ...   \n",
@@ -816,7 +816,7 @@
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>Islam, Modernity New Millennium: Themes Critic...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Religious Studies/ Islamic Studies/Philosophy ...</td>\n",
        "      <td>As world becomes increasingly globalised Islam...</td>\n",
        "      <td>As world becomes increasingly globalised Islam...</td>\n",
@@ -826,7 +826,7 @@
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>Analytic Philosophy Perspective Critical Ratio...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Analytical Philosophy/ Critical Rationalism</td>\n",
        "      <td>The present paper attempt explore impact Karl ...</td>\n",
        "      <td>The present paper attempt explore impact Karl ...</td>\n",
@@ -836,7 +836,7 @@
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>The Misty Land Ideas Light Dialogue: An Anthol...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Islamic philosophy/ Philosophy/ Comparative</td>\n",
        "      <td>Comparative philosophy, like types philosophy,...</td>\n",
        "      <td>Comparative philosophy , like type philosophy ...</td>\n",
@@ -846,7 +846,7 @@
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>Socio-Ethical Consequences Convergent Technolo...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Socio- Ethical Consequences/ Converging Scienc...</td>\n",
        "      <td>Critical assessment social changes impact fact...</td>\n",
        "      <td>Critical assessment social change impact facto...</td>\n",
@@ -856,7 +856,7 @@
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>“Shi‘as Britain: The Earliest Cases (Part 1)”,...</td>\n",
-       "      <td>Islamic Philosophy</td>\n",
+       "      <td>islamic philosophy</td>\n",
        "      <td>Shi'a Muslim/Counterflows Colonialism/The East...</td>\n",
        "      <td>Shi�a Muslims UK, whether individually groups ...</td>\n",
        "      <td>Shi�a Muslims UK , whether individually group ...</td>\n",
@@ -876,7 +876,7 @@
        "    <tr>\n",
        "      <th>635</th>\n",
        "      <td>Bashir, Shahzad Reading self Persian prose poe...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>Sufism/Persian poetry/historiography/17th cent...</td>\n",
        "      <td>Let us start thought experiment. Five hundred ...</td>\n",
        "      <td>Let u start thought experiment . Five hundred ...</td>\n",
@@ -886,7 +886,7 @@
        "    <tr>\n",
        "      <th>636</th>\n",
        "      <td>Everlasting Doubt: Uncertainty Islamic Represe...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>historiography/Islamic history/Persian Arabic ...</td>\n",
        "      <td>Utilizing treatments uncertainty regarding his...</td>\n",
        "      <td>Utilizing treatment uncertainty regarding hist...</td>\n",
@@ -896,7 +896,7 @@
        "    <tr>\n",
        "      <th>637</th>\n",
        "      <td>The Origins Rhetorical Evolution Term Qizilbās...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>historiography/Islamic history/qizilbash/Safav...</td>\n",
        "      <td>This essay traces use term Qizilb?sh select li...</td>\n",
        "      <td>This essay trace use term Qizilb ? sh select l...</td>\n",
@@ -906,7 +906,7 @@
        "    <tr>\n",
        "      <th>638</th>\n",
        "      <td>Bashir, Shahzad Shah Ismaʿil Qizilbash: Cannib...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>historiography/Islamic history/qizilbash/Safav...</td>\n",
        "      <td>The first decade sixteeth century Common Era s...</td>\n",
        "      <td>The first decade sixteeth century Common Era s...</td>\n",
@@ -916,7 +916,7 @@
        "    <tr>\n",
        "      <th>641</th>\n",
        "      <td>Empirical Spirits: Islam, Spiritism, Virtues S...</td>\n",
-       "      <td>Anthropology Shi’a Islam</td>\n",
+       "      <td>anthropology shia islam</td>\n",
        "      <td>Spiritism Iran/science/religion/morality</td>\n",
        "      <td>This article examines aspects reception French...</td>\n",
        "      <td>This article examines aspect reception French ...</td>\n",
@@ -942,18 +942,18 @@
        "638  Bashir, Shahzad Shah Ismaʿil Qizilbash: Cannib...   \n",
        "641  Empirical Spirits: Islam, Spiritism, Virtues S...   \n",
        "\n",
-       "                     Category  \\\n",
-       "8          Islamic Philosophy   \n",
-       "12         Islamic Philosophy   \n",
-       "15         Islamic Philosophy   \n",
-       "16         Islamic Philosophy   \n",
-       "29         Islamic Philosophy   \n",
-       "..                        ...   \n",
-       "635  Anthropology Shi’a Islam   \n",
-       "636  Anthropology Shi’a Islam   \n",
-       "637  Anthropology Shi’a Islam   \n",
-       "638  Anthropology Shi’a Islam   \n",
-       "641  Anthropology Shi’a Islam   \n",
+       "                    Category  \\\n",
+       "8         islamic philosophy   \n",
+       "12        islamic philosophy   \n",
+       "15        islamic philosophy   \n",
+       "16        islamic philosophy   \n",
+       "29        islamic philosophy   \n",
+       "..                       ...   \n",
+       "635  anthropology shia islam   \n",
+       "636  anthropology shia islam   \n",
+       "637  anthropology shia islam   \n",
+       "638  anthropology shia islam   \n",
+       "641  anthropology shia islam   \n",
        "\n",
        "                                          Sub-Category  \\\n",
        "8    Religious Studies/ Islamic Studies/Philosophy ...   \n",
@@ -1048,7 +1048,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 16,
    "id": "6de30185",
    "metadata": {},
    "outputs": [
@@ -1280,7 +1280,7 @@
        "[198 rows x 7 columns]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1295,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 17,
    "id": "3716277c",
    "metadata": {},
    "outputs": [
@@ -1560,7 +1560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 18,
    "id": "af0017c2",
    "metadata": {},
    "outputs": [],
@@ -1588,7 +1588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 19,
    "id": "84f02753",
    "metadata": {},
    "outputs": [
@@ -1620,7 +1620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 20,
    "id": "0884090a",
    "metadata": {},
    "outputs": [
@@ -1968,7 +1968,7 @@
        "[198 rows x 5095 columns]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1981,7 +1981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 21,
    "id": "7c7b006c",
    "metadata": {},
    "outputs": [
@@ -2337,7 +2337,7 @@
        "[198 rows x 5095 columns]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2358,7 +2358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 22,
    "id": "3434bbd9",
    "metadata": {},
    "outputs": [
@@ -2429,7 +2429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 23,
    "id": "5f381aa4",
    "metadata": {},
    "outputs": [
@@ -2837,7 +2837,7 @@
        "[198 rows x 16578 columns]"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2850,7 +2850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 24,
    "id": "3d14eb7e",
    "metadata": {},
    "outputs": [
@@ -2881,7 +2881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 25,
    "id": "fa264dff",
    "metadata": {},
    "outputs": [
@@ -3229,7 +3229,7 @@
        "[198 rows x 5095 columns]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3250,7 +3250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 26,
    "id": "499fc2a1",
    "metadata": {},
    "outputs": [
@@ -3271,7 +3271,7 @@
        "Name: AbstractL_len, Length: 198, dtype: int64"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3284,7 +3284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 27,
    "id": "93b13343",
    "metadata": {},
    "outputs": [
@@ -3305,7 +3305,7 @@
        "Name: TitleL_len, Length: 198, dtype: int64"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3325,7 +3325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 28,
    "id": "44e204d5",
    "metadata": {},
    "outputs": [],
@@ -3337,7 +3337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 29,
    "id": "216b54ff",
    "metadata": {},
    "outputs": [
@@ -3364,7 +3364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 30,
    "id": "eef942c9",
    "metadata": {},
    "outputs": [
@@ -3390,7 +3390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 31,
    "id": "38e7109e",
    "metadata": {},
    "outputs": [
@@ -3424,7 +3424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 32,
    "id": "b88950da",
    "metadata": {},
    "outputs": [],
@@ -3443,7 +3443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 33,
    "id": "1722d3da",
    "metadata": {},
    "outputs": [
@@ -3453,7 +3453,7 @@
        "array([0.   , 0.425, 1.   , 1.   , 1.   ])"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3475,7 +3475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 37,
    "id": "c04cfb92",
    "metadata": {},
    "outputs": [],
@@ -3486,7 +3486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 38,
    "id": "445f4121",
    "metadata": {},
    "outputs": [
@@ -3500,33 +3500,33 @@
     }
    ],
    "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X_features, sana['Category'], test_size=0.2)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_features, sana['Category'])\n",
     "rf = RandomForestClassifier(n_estimators=50, max_depth=20, n_jobs=-1)\n",
     "rf_model = rf.fit(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 39,
    "id": "e0249091",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[(0.02479082748091723, 4101),\n",
-       " (0.019196875261184954, 3341),\n",
-       " (0.01676309822733517, 1139),\n",
-       " (0.015084168276684951, 3878),\n",
-       " (0.012908928194046774, 4193),\n",
-       " (0.012024349525323243, 2472),\n",
-       " (0.010970728756183592, 2399),\n",
-       " (0.010392433008603456, 2817),\n",
-       " (0.009774754870580714, 3439),\n",
-       " (0.009482045374293568, 2272)]"
+       "[(0.04545696202665166, 3341),\n",
+       " (0.017211924209996978, 4209),\n",
+       " (0.017146944659435386, 2399),\n",
+       " (0.016874001365951637, 4938),\n",
+       " (0.015812739349219385, 4101),\n",
+       " (0.01535053511767938, 1139),\n",
+       " (0.015050707561433922, 4860),\n",
+       " (0.012467425358615183, 3878),\n",
+       " (0.012129006047015087, 3441),\n",
+       " (0.010324250735023039, 2974)]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3537,7 +3537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 40,
    "id": "8d817afe",
    "metadata": {},
    "outputs": [
@@ -3557,7 +3557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 41,
    "id": "94fc7e68",
    "metadata": {},
    "outputs": [
@@ -3565,7 +3565,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Precision:1.0/Recall:0.2/Accuracy:0.8\n"
+      "Precision:1.0/Recall:0.444/Accuracy:0.9\n"
      ]
     }
    ],
@@ -3584,7 +3584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 42,
    "id": "bb4ba980",
    "metadata": {},
    "outputs": [
@@ -3594,7 +3594,7 @@
        "array([0.   , 0.425, 1.   , 1.   , 1.   ])"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3616,7 +3616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 43,
    "id": "a6ebc7d6",
    "metadata": {},
    "outputs": [
@@ -3630,14 +3630,14 @@
     }
    ],
    "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X_features, sana['Category'], test_size=0.2)\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_features, sana['Category'])\n",
     "rf = RandomForestClassifier(n_estimators=50, max_depth=20, n_jobs=-1)\n",
     "rf_model = rf.fit(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 44,
    "id": "c44b24ca",
    "metadata": {},
    "outputs": [
@@ -3657,7 +3657,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 45,
    "id": "a8efb7ae",
    "metadata": {},
    "outputs": [
@@ -3665,7 +3665,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Precision:1.0/Recall:0.333/Accuracy:0.7\n"
+      "Precision:1.0/Recall:0.412/Accuracy:0.8\n"
      ]
     }
    ],
@@ -3683,7 +3683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 46,
    "id": "fcf38fa7",
    "metadata": {},
    "outputs": [
@@ -3746,7 +3746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 47,
    "id": "466eda99",
    "metadata": {},
    "outputs": [
@@ -3772,13 +3772,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Binary Logistic Model Score for Doc2Vec: 87.5 % \n",
+      "Binary Logistic Model Score for Doc2Vec: 82.5 % \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x396 with 2 Axes>"
       ]
@@ -3809,7 +3809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 48,
    "id": "87ed7ee3",
    "metadata": {},
    "outputs": [
@@ -3867,7 +3867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 49,
    "id": "4d1ecf56",
    "metadata": {},
    "outputs": [
@@ -3893,13 +3893,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Binary Logistic Model Score for Doc2Vec: 90.0 % \n",
+      "Binary Logistic Model Score for Doc2Vec: 92.5 % \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 576x396 with 2 Axes>"
       ]
@@ -3934,7 +3934,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 50,
    "id": "e9b96c16",
    "metadata": {},
    "outputs": [],
@@ -3945,7 +3945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 51,
    "id": "c8e9fe4d",
    "metadata": {},
    "outputs": [],
@@ -3962,7 +3962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 52,
    "id": "c5cc8bf6",
    "metadata": {},
    "outputs": [],
@@ -3974,7 +3974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 53,
    "id": "b1bba5d6",
    "metadata": {},
    "outputs": [],
@@ -3985,7 +3985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 54,
    "id": "713c4855",
    "metadata": {},
    "outputs": [],
@@ -4009,7 +4009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 55,
    "id": "066882f4",
    "metadata": {},
    "outputs": [
@@ -4021,7 +4021,7 @@
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " embedding (Embedding)       (None, None, 100)         454100    \n",
+      " embedding (Embedding)       (None, None, 100)         442700    \n",
       "                                                                 \n",
       " lstm (LSTM)                 (None, 100)               80400     \n",
       "                                                                 \n",
@@ -4030,8 +4030,8 @@
       " dense_1 (Dense)             (None, 1)                 101       \n",
       "                                                                 \n",
       "=================================================================\n",
-      "Total params: 544,701\n",
-      "Trainable params: 544,701\n",
+      "Total params: 533,301\n",
+      "Trainable params: 533,301\n",
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n"
      ]
@@ -4050,7 +4050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 56,
    "id": "1e96bf86",
    "metadata": {},
    "outputs": [],
@@ -4063,7 +4063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 57,
    "id": "7ec828e2",
    "metadata": {},
    "outputs": [
@@ -4072,25 +4072,25 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/10\n",
-      "2/2 [==============================] - 2s 536ms/step - loss: 0.6933 - accuracy: 0.4730 - precision_m: 0.2708 - recall_m: 0.3638 - val_loss: 0.6887 - val_accuracy: 0.6600 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
+      "2/2 [==============================] - 2s 557ms/step - loss: 0.6916 - accuracy: 0.5811 - precision_m: 0.1364 - recall_m: 0.1452 - val_loss: 0.6878 - val_accuracy: 0.6800 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
       "Epoch 2/10\n",
-      "2/2 [==============================] - 0s 188ms/step - loss: 0.6841 - accuracy: 0.6892 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.6813 - val_accuracy: 0.6600 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
+      "2/2 [==============================] - 0s 185ms/step - loss: 0.6831 - accuracy: 0.6824 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.6808 - val_accuracy: 0.6800 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
       "Epoch 3/10\n",
-      "2/2 [==============================] - 0s 187ms/step - loss: 0.6721 - accuracy: 0.6892 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.6708 - val_accuracy: 0.6600 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
+      "2/2 [==============================] - 0s 180ms/step - loss: 0.6713 - accuracy: 0.6824 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.6695 - val_accuracy: 0.6800 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
       "Epoch 4/10\n",
-      "2/2 [==============================] - 0s 185ms/step - loss: 0.6539 - accuracy: 0.6892 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.6540 - val_accuracy: 0.6600 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
+      "2/2 [==============================] - 0s 183ms/step - loss: 0.6519 - accuracy: 0.6824 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.6490 - val_accuracy: 0.6800 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
       "Epoch 5/10\n",
-      "2/2 [==============================] - 0s 183ms/step - loss: 0.6249 - accuracy: 0.6892 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.6238 - val_accuracy: 0.6600 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
+      "2/2 [==============================] - 0s 181ms/step - loss: 0.6136 - accuracy: 0.6824 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.6104 - val_accuracy: 0.6800 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
       "Epoch 6/10\n",
-      "2/2 [==============================] - 0s 191ms/step - loss: 0.5716 - accuracy: 0.6892 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.6031 - val_accuracy: 0.6600 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
+      "2/2 [==============================] - 0s 176ms/step - loss: 0.5340 - accuracy: 0.6824 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.8168 - val_accuracy: 0.6800 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
       "Epoch 7/10\n",
-      "2/2 [==============================] - 0s 178ms/step - loss: 0.5039 - accuracy: 0.6892 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.5711 - val_accuracy: 0.6600 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
+      "2/2 [==============================] - 0s 187ms/step - loss: 0.6695 - accuracy: 0.6824 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.5860 - val_accuracy: 0.6800 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
       "Epoch 8/10\n",
-      "2/2 [==============================] - 0s 176ms/step - loss: 0.4323 - accuracy: 0.6892 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.5466 - val_accuracy: 0.6600 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
+      "2/2 [==============================] - 0s 176ms/step - loss: 0.4612 - accuracy: 0.6824 - precision_m: 0.0000e+00 - recall_m: 0.0000e+00 - val_loss: 0.5837 - val_accuracy: 0.6800 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
       "Epoch 9/10\n",
-      "2/2 [==============================] - 0s 185ms/step - loss: 0.3998 - accuracy: 0.7365 - precision_m: 1.0000 - recall_m: 0.2299 - val_loss: 0.5195 - val_accuracy: 0.6800 - val_precision_m: 1.0000 - val_recall_m: 0.0588\n",
+      "2/2 [==============================] - 0s 169ms/step - loss: 0.4569 - accuracy: 0.7365 - precision_m: 1.0000 - recall_m: 0.2127 - val_loss: 0.5801 - val_accuracy: 0.6800 - val_precision_m: 0.0000e+00 - val_recall_m: 0.0000e+00\n",
       "Epoch 10/10\n",
-      "2/2 [==============================] - 0s 167ms/step - loss: 0.3445 - accuracy: 0.9392 - precision_m: 1.0000 - recall_m: 0.8091 - val_loss: 0.4776 - val_accuracy: 0.6800 - val_precision_m: 1.0000 - val_recall_m: 0.0588\n"
+      "2/2 [==============================] - 0s 175ms/step - loss: 0.4417 - accuracy: 0.9392 - precision_m: 1.0000 - recall_m: 0.8062 - val_loss: 0.5647 - val_accuracy: 0.7000 - val_precision_m: 1.0000 - val_recall_m: 0.0625\n"
      ]
     }
    ],
@@ -4103,44 +4103,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 58,
    "id": "5a7c3429",
    "metadata": {},
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 576x396 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 576x396 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 576x396 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],