Final_Documented-DimensionReduction_with_RFE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "civil-valentine",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "cooperative-mistake",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "linear-artist",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "elementary-interpretation",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Review_ID</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviewer_Location</th>\n",
       "      <th>Review_Text</th>\n",
       "      <th>Branch</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>is_good_review</th>\n",
       "      <th>review_clean</th>\n",
       "      <th>neg</th>\n",
       "      <th>...</th>\n",
       "      <th>word_yet</th>\n",
       "      <th>word_york</th>\n",
       "      <th>word_you</th>\n",
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       "      <th>word_yr</th>\n",
       "      <th>word_yrs</th>\n",
       "      <th>word_yum</th>\n",
       "      <th>word_yummy</th>\n",
       "      <th>word_zero</th>\n",
       "      <th>word_zone</th>\n",
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       "      <th>0</th>\n",
       "      <td>122944085</td>\n",
       "      <td>5</td>\n",
       "      <td>United States</td>\n",
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       "      <td>Disneyland_California</td>\n",
       "      <td>2011</td>\n",
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       "      <td>5</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Pricey but worth it, unfortunately the big thu...</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>pricey worth unfortunately big thunder mountai...</td>\n",
       "      <td>0.115</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>168087311</td>\n",
       "      <td>4</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Disneyland is a magical place. Everyone, parti...</td>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>disneyland magical place everyone particularly...</td>\n",
       "      <td>0.000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>282417360</td>\n",
       "      <td>5</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Disney land is a must when visiting Paris. The...</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>disney land must visit paris frozen show best ...</td>\n",
       "      <td>0.028</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>160953212</td>\n",
       "      <td>3</td>\n",
       "      <td>India</td>\n",
       "      <td>The HK Disneyland is definitely small, but it'...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>hk disneyland definitely small definitely fun ...</td>\n",
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       "      <td>Australia</td>\n",
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       "      <td>Disneyland_California</td>\n",
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       "      <td>174264188</td>\n",
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       "      <td>United Kingdom</td>\n",
       "      <td>I visited Disneyland Paris with my family (tee...</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>visit disneyland paris family teenage daughter...</td>\n",
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       "      <td>5</td>\n",
       "      <td>United States</td>\n",
       "      <td>What a great day we had we did so much before ...</td>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>great day much great guide grandson amaze well...</td>\n",
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       "      <td>226159775</td>\n",
       "      <td>5</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>It has been a childhood dream of mine to go to...</td>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>childhood dream mine go disneyland whether you...</td>\n",
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       "    <tr>\n",
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       "      <td>143521522</td>\n",
       "      <td>4</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Well my wife and I visted in October 2012 for ...</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
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       "<p>4004 rows × 2150 columns</p>\n",
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      ],
      "text/plain": [
       "      Review_ID  Rating Reviewer_Location  \\\n",
       "0     122944085       5     United States   \n",
       "1     290851203       5    United Kingdom   \n",
       "2     168087311       4            Canada   \n",
       "3     282417360       5          Pakistan   \n",
       "4     160953212       3             India   \n",
       "...         ...     ...               ...   \n",
       "3999  290217727       5         Australia   \n",
       "4000  174264188       1    United Kingdom   \n",
       "4001  638583788       5     United States   \n",
       "4002  226159775       5       Philippines   \n",
       "4003  143521522       4    United Kingdom   \n",
       "\n",
       "                                            Review_Text  \\\n",
       "0     Spotlessly clean and so organized considering ...   \n",
       "1     Pricey but worth it, unfortunately the big thu...   \n",
       "2     Disneyland is a magical place. Everyone, parti...   \n",
       "3     Disney land is a must when visiting Paris. The...   \n",
       "4     The HK Disneyland is definitely small, but it'...   \n",
       "...                                                 ...   \n",
       "3999  There's a reason why they call Disneyland  The...   \n",
       "4000  I visited Disneyland Paris with my family (tee...   \n",
       "4001  What a great day we had we did so much before ...   \n",
       "4002  It has been a childhood dream of mine to go to...   \n",
       "4003  Well my wife and I visted in October 2012 for ...   \n",
       "\n",
       "                     Branch  Year  Month  is_good_review  \\\n",
       "0     Disneyland_California  2011     10               1   \n",
       "1          Disneyland_Paris  2015      7               1   \n",
       "2     Disneyland_California  2013      7               1   \n",
       "3          Disneyland_Paris  2015      6               1   \n",
       "4       Disneyland_HongKong  2012      9               0   \n",
       "...                     ...   ...    ...             ...   \n",
       "3999  Disneyland_California  2015      7               1   \n",
       "4000       Disneyland_Paris  2013      8               0   \n",
       "4001  Disneyland_California  2018     12               1   \n",
       "4002  Disneyland_California  2013     10               1   \n",
       "4003       Disneyland_Paris  2012     10               1   \n",
       "\n",
       "                                           review_clean    neg  ...  word_yet  \\\n",
       "0     spotlessly clean organize consider thousand vi...  0.000  ...       0.0   \n",
       "1     pricey worth unfortunately big thunder mountai...  0.115  ...       0.0   \n",
       "2     disneyland magical place everyone particularly...  0.000  ...       0.0   \n",
       "3     disney land must visit paris frozen show best ...  0.028  ...       0.0   \n",
       "4     hk disneyland definitely small definitely fun ...  0.000  ...       0.0   \n",
       "...                                                 ...    ...  ...       ...   \n",
       "3999  there's reason call disneyland happy place ear...  0.042  ...       0.0   \n",
       "4000  visit disneyland paris family teenage daughter...  0.081  ...       0.0   \n",
       "4001  great day much great guide grandson amaze well...  0.000  ...       0.0   \n",
       "4002  childhood dream mine go disneyland whether you...  0.000  ...       0.0   \n",
       "4003  well wife visted october parcs real complaint ...  0.030  ...       0.0   \n",
       "\n",
       "      word_york  word_you  word_young  word_yr  word_yrs  word_yum  \\\n",
       "0           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "1           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
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       "4           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "...         ...       ...         ...      ...       ...       ...   \n",
       "3999        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "4000        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "4001        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "4002        0.0       0.0    0.432261      0.0       0.0       0.0   \n",
       "4003        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "\n",
       "      word_yummy  word_zero  word_zone  \n",
       "0            0.0        0.0        0.0  \n",
       "1            0.0        0.0        0.0  \n",
       "2            0.0        0.0        0.0  \n",
       "3            0.0        0.0        0.0  \n",
       "4            0.0        0.0        0.0  \n",
       "...          ...        ...        ...  \n",
       "3999         0.0        0.0        0.0  \n",
       "4000         0.0        0.0        0.0  \n",
       "4001         0.0        0.0        0.0  \n",
       "4002         0.0        0.0        0.0  \n",
       "4003         0.0        0.0        0.0  \n",
       "\n",
       "[4004 rows x 2150 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Note that Dataframe has 2150 columns/dimensions\n",
    "word_df = pd.read_csv(\"Resources/reviews_sample_data.csv\")\n",
    "word_cleaned = word_df.dropna(axis='columns', how='all')\n",
    "word_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "forbidden-infection",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Review_ID</th>\n",
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       "      <th>word_yr</th>\n",
       "      <th>word_yrs</th>\n",
       "      <th>word_yum</th>\n",
       "      <th>word_yummy</th>\n",
       "      <th>word_zero</th>\n",
       "      <th>word_zone</th>\n",
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       "  </thead>\n",
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       "      <td>122944085</td>\n",
       "      <td>5</td>\n",
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       "      <td>2011</td>\n",
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       "      <td>290851203</td>\n",
       "      <td>5</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Pricey but worth it, unfortunately the big thu...</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>pricey worth unfortunately big thunder mountai...</td>\n",
       "      <td>0.115</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>168087311</td>\n",
       "      <td>4</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Disneyland is a magical place. Everyone, parti...</td>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>disneyland magical place everyone particularly...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>282417360</td>\n",
       "      <td>5</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Disney land is a must when visiting Paris. The...</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>disney land must visit paris frozen show best ...</td>\n",
       "      <td>0.028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>160953212</td>\n",
       "      <td>3</td>\n",
       "      <td>India</td>\n",
       "      <td>The HK Disneyland is definitely small, but it'...</td>\n",
       "      <td>Disneyland_HongKong</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>hk disneyland definitely small definitely fun ...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3999</th>\n",
       "      <td>290217727</td>\n",
       "      <td>5</td>\n",
       "      <td>Australia</td>\n",
       "      <td>There's a reason why they call Disneyland  The...</td>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>there's reason call disneyland happy place ear...</td>\n",
       "      <td>0.042</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4000</th>\n",
       "      <td>174264188</td>\n",
       "      <td>1</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>I visited Disneyland Paris with my family (tee...</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>visit disneyland paris family teenage daughter...</td>\n",
       "      <td>0.081</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4001</th>\n",
       "      <td>638583788</td>\n",
       "      <td>5</td>\n",
       "      <td>United States</td>\n",
       "      <td>What a great day we had we did so much before ...</td>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>great day much great guide grandson amaze well...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4002</th>\n",
       "      <td>226159775</td>\n",
       "      <td>5</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>It has been a childhood dream of mine to go to...</td>\n",
       "      <td>Disneyland_California</td>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>childhood dream mine go disneyland whether you...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.432261</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4003</th>\n",
       "      <td>143521522</td>\n",
       "      <td>4</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>Well my wife and I visted in October 2012 for ...</td>\n",
       "      <td>Disneyland_Paris</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>well wife visted october parcs real complaint ...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4004 rows × 2150 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Review_ID  Rating Reviewer_Location  \\\n",
       "0     122944085       5     United States   \n",
       "1     290851203       5    United Kingdom   \n",
       "2     168087311       4            Canada   \n",
       "3     282417360       5          Pakistan   \n",
       "4     160953212       3             India   \n",
       "...         ...     ...               ...   \n",
       "3999  290217727       5         Australia   \n",
       "4000  174264188       1    United Kingdom   \n",
       "4001  638583788       5     United States   \n",
       "4002  226159775       5       Philippines   \n",
       "4003  143521522       4    United Kingdom   \n",
       "\n",
       "                                            Review_Text  \\\n",
       "0     Spotlessly clean and so organized considering ...   \n",
       "1     Pricey but worth it, unfortunately the big thu...   \n",
       "2     Disneyland is a magical place. Everyone, parti...   \n",
       "3     Disney land is a must when visiting Paris. The...   \n",
       "4     The HK Disneyland is definitely small, but it'...   \n",
       "...                                                 ...   \n",
       "3999  There's a reason why they call Disneyland  The...   \n",
       "4000  I visited Disneyland Paris with my family (tee...   \n",
       "4001  What a great day we had we did so much before ...   \n",
       "4002  It has been a childhood dream of mine to go to...   \n",
       "4003  Well my wife and I visted in October 2012 for ...   \n",
       "\n",
       "                     Branch  Year  Month  is_good_review  \\\n",
       "0     Disneyland_California  2011     10               1   \n",
       "1          Disneyland_Paris  2015      7               1   \n",
       "2     Disneyland_California  2013      7               1   \n",
       "3          Disneyland_Paris  2015      6               1   \n",
       "4       Disneyland_HongKong  2012      9               0   \n",
       "...                     ...   ...    ...             ...   \n",
       "3999  Disneyland_California  2015      7               1   \n",
       "4000       Disneyland_Paris  2013      8               0   \n",
       "4001  Disneyland_California  2018     12               1   \n",
       "4002  Disneyland_California  2013     10               1   \n",
       "4003       Disneyland_Paris  2012     10               1   \n",
       "\n",
       "                                           review_clean    neg  ...  word_yet  \\\n",
       "0     spotlessly clean organize consider thousand vi...  0.000  ...       0.0   \n",
       "1     pricey worth unfortunately big thunder mountai...  0.115  ...       0.0   \n",
       "2     disneyland magical place everyone particularly...  0.000  ...       0.0   \n",
       "3     disney land must visit paris frozen show best ...  0.028  ...       0.0   \n",
       "4     hk disneyland definitely small definitely fun ...  0.000  ...       0.0   \n",
       "...                                                 ...    ...  ...       ...   \n",
       "3999  there's reason call disneyland happy place ear...  0.042  ...       0.0   \n",
       "4000  visit disneyland paris family teenage daughter...  0.081  ...       0.0   \n",
       "4001  great day much great guide grandson amaze well...  0.000  ...       0.0   \n",
       "4002  childhood dream mine go disneyland whether you...  0.000  ...       0.0   \n",
       "4003  well wife visted october parcs real complaint ...  0.030  ...       0.0   \n",
       "\n",
       "      word_york  word_you  word_young  word_yr  word_yrs  word_yum  \\\n",
       "0           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "1           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "2           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "3           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "4           0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "...         ...       ...         ...      ...       ...       ...   \n",
       "3999        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "4000        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "4001        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "4002        0.0       0.0    0.432261      0.0       0.0       0.0   \n",
       "4003        0.0       0.0    0.000000      0.0       0.0       0.0   \n",
       "\n",
       "      word_yummy  word_zero  word_zone  \n",
       "0            0.0        0.0        0.0  \n",
       "1            0.0        0.0        0.0  \n",
       "2            0.0        0.0        0.0  \n",
       "3            0.0        0.0        0.0  \n",
       "4            0.0        0.0        0.0  \n",
       "...          ...        ...        ...  \n",
       "3999         0.0        0.0        0.0  \n",
       "4000         0.0        0.0        0.0  \n",
       "4001         0.0        0.0        0.0  \n",
       "4002         0.0        0.0        0.0  \n",
       "4003         0.0        0.0        0.0  \n",
       "\n",
       "[4004 rows x 2150 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_working = word_cleaned.dropna()\n",
    "word_working"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "another-dublin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['neg',\n",
       " 'neu',\n",
       " 'pos',\n",
       " 'compound',\n",
       " 'characters',\n",
       " 'words',\n",
       " 'doc2vec_vector_0',\n",
       " 'doc2vec_vector_1',\n",
       " 'doc2vec_vector_2',\n",
       " 'doc2vec_vector_3',\n",
       " 'doc2vec_vector_4',\n",
       " 'word_ability',\n",
       " 'word_able',\n",
       " 'word_absolute',\n",
       " 'word_absolutely',\n",
       " 'word_accept',\n",
       " 'word_access',\n",
       " 'word_accessible',\n",
       " 'word_accommodate',\n",
       " 'word_accommodation',\n",
       " 'word_accordingly',\n",
       " 'word_account',\n",
       " 'word_accurate',\n",
       " 'word_across',\n",
       " 'word_act',\n",
       " 'word_action',\n",
       " 'word_activity',\n",
       " 'word_actor',\n",
       " 'word_actual',\n",
       " 'word_actually',\n",
       " 'word_add',\n",
       " 'word_addition',\n",
       " 'word_additional',\n",
       " 'word_admission',\n",
       " 'word_admit',\n",
       " 'word_adorable',\n",
       " 'word_adult',\n",
       " 'word_adults',\n",
       " 'word_advance',\n",
       " 'word_advantage',\n",
       " 'word_adventure',\n",
       " 'word_adventureland',\n",
       " 'word_advertise',\n",
       " 'word_advice',\n",
       " 'word_advise',\n",
       " 'word_aerosmith',\n",
       " 'word_affect',\n",
       " 'word_afford',\n",
       " 'word_affordable',\n",
       " 'word_afraid',\n",
       " 'word_after',\n",
       " 'word_afternoon',\n",
       " 'word_afterwards',\n",
       " 'word_again',\n",
       " 'word_age',\n",
       " 'word_ages',\n",
       " 'word_ago',\n",
       " 'word_agree',\n",
       " 'word_ahead',\n",
       " 'word_aim',\n",
       " 'word_air',\n",
       " 'word_airport',\n",
       " 'word_aladdin',\n",
       " 'word_alice',\n",
       " 'word_alike',\n",
       " 'word_all',\n",
       " 'word_allow',\n",
       " 'word_almost',\n",
       " 'word_alone',\n",
       " 'word_along',\n",
       " 'word_alot',\n",
       " 'word_already',\n",
       " 'word_also',\n",
       " 'word_alternative',\n",
       " 'word_although',\n",
       " 'word_alton',\n",
       " 'word_always',\n",
       " 'word_amaze',\n",
       " 'word_amazing',\n",
       " 'word_ambience',\n",
       " 'word_america',\n",
       " 'word_american',\n",
       " 'word_among',\n",
       " 'word_amount',\n",
       " 'word_amusement',\n",
       " 'word_anaheim',\n",
       " 'word_and',\n",
       " 'word_angeles',\n",
       " 'word_animal',\n",
       " 'word_animation',\n",
       " 'word_anna',\n",
       " 'word_anniversary',\n",
       " 'word_annoy',\n",
       " 'word_annual',\n",
       " 'word_another',\n",
       " 'word_answer',\n",
       " 'word_anymore',\n",
       " 'word_anyone',\n",
       " 'word_anything',\n",
       " 'word_anytime',\n",
       " 'word_anyway',\n",
       " 'word_anywhere',\n",
       " 'word_apart',\n",
       " 'word_app',\n",
       " 'word_appal',\n",
       " 'word_apparently',\n",
       " 'word_appeal',\n",
       " 'word_appear',\n",
       " 'word_apply',\n",
       " 'word_appreciate',\n",
       " 'word_approach',\n",
       " 'word_approx',\n",
       " 'word_apps',\n",
       " 'word_april',\n",
       " 'word_area',\n",
       " 'word_ariel',\n",
       " 'word_arm',\n",
       " 'word_armageddon',\n",
       " 'word_around',\n",
       " 'word_arrival',\n",
       " 'word_arrive',\n",
       " 'word_arrived',\n",
       " 'word_art',\n",
       " 'word_as',\n",
       " 'word_asia',\n",
       " 'word_asian',\n",
       " 'word_aside',\n",
       " 'word_ask',\n",
       " 'word_aspect',\n",
       " 'word_assistance',\n",
       " 'word_assume',\n",
       " 'word_astro',\n",
       " 'word_at',\n",
       " 'word_ate',\n",
       " 'word_atmosphere',\n",
       " 'word_attempt',\n",
       " 'word_attend',\n",
       " 'word_attendance',\n",
       " 'word_attendant',\n",
       " 'word_attention',\n",
       " 'word_attitude',\n",
       " 'word_attraction',\n",
       " 'word_attractions',\n",
       " 'word_attractive',\n",
       " 'word_auberge',\n",
       " 'word_audience',\n",
       " 'word_august',\n",
       " 'word_australia',\n",
       " 'word_autograph',\n",
       " 'word_autographs',\n",
       " 'word_autopia',\n",
       " 'word_available',\n",
       " 'word_average',\n",
       " 'word_avoid',\n",
       " 'word_aware',\n",
       " 'word_away',\n",
       " 'word_awe',\n",
       " 'word_awesome',\n",
       " 'word_awful',\n",
       " 'word_baby',\n",
       " 'word_back',\n",
       " 'word_backpack',\n",
       " 'word_bad',\n",
       " 'word_badge',\n",
       " 'word_badly',\n",
       " 'word_bag',\n",
       " 'word_bakery',\n",
       " 'word_ball',\n",
       " 'word_ban',\n",
       " 'word_band',\n",
       " 'word_bang',\n",
       " 'word_bar',\n",
       " 'word_barely',\n",
       " 'word_base',\n",
       " 'word_basic',\n",
       " 'word_basically',\n",
       " 'word_bathroom',\n",
       " 'word_bay',\n",
       " 'word_bayou',\n",
       " 'word_bbq',\n",
       " 'word_be',\n",
       " 'word_beach',\n",
       " 'word_bear',\n",
       " 'word_bearable',\n",
       " 'word_beat',\n",
       " 'word_beautiful',\n",
       " 'word_beautifully',\n",
       " 'word_beauty',\n",
       " 'word_become',\n",
       " 'word_bed',\n",
       " 'word_beer',\n",
       " 'word_before',\n",
       " 'word_beforehand',\n",
       " 'word_begin',\n",
       " 'word_behind',\n",
       " 'word_believe',\n",
       " 'word_belle',\n",
       " 'word_bench',\n",
       " 'word_benefit',\n",
       " 'word_beside',\n",
       " 'word_besides',\n",
       " 'word_best',\n",
       " 'word_better',\n",
       " 'word_beverage',\n",
       " 'word_beware',\n",
       " 'word_beyond',\n",
       " 'word_big',\n",
       " 'word_bill',\n",
       " 'word_birthday',\n",
       " 'word_bit',\n",
       " 'word_bite',\n",
       " 'word_black',\n",
       " 'word_blast',\n",
       " 'word_blaster',\n",
       " 'word_block',\n",
       " 'word_blow',\n",
       " 'word_blue',\n",
       " 'word_board',\n",
       " 'word_boat',\n",
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       " 'word_bottle',\n",
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       " 'word_bowl',\n",
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       " 'word_boyfriend',\n",
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       " 'word_breakfast',\n",
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       " 'word_breathtaking',\n",
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       " 'word_brilliant',\n",
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       " 'word_brings',\n",
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       " 'word_buffalo',\n",
       " 'word_buffet',\n",
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       " 'word_buggy',\n",
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       " 'word_burger',\n",
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       " 'word_busy',\n",
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       " 'word_buzz',\n",
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       " 'word_cafe',\n",
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       " 'word_california',\n",
       " 'word_call',\n",
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       " 'word_cant',\n",
       " 'word_cantonese',\n",
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       " 'word_captain',\n",
       " 'word_capture',\n",
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       " 'word_card',\n",
       " 'word_care',\n",
       " 'word_careful',\n",
       " 'word_carefully',\n",
       " 'word_caribbean',\n",
       " 'word_carnation',\n",
       " 'word_carousel',\n",
       " 'word_carriage',\n",
       " 'word_carribean',\n",
       " 'word_carry',\n",
       " 'word_cart',\n",
       " 'word_cartoon',\n",
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       " 'word_casey',\n",
       " 'word_cash',\n",
       " 'word_cast',\n",
       " 'word_castle',\n",
       " 'word_catch',\n",
       " 'word_cater',\n",
       " 'word_caught',\n",
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       " 'word_celebrate',\n",
       " 'word_celebration',\n",
       " 'word_center',\n",
       " 'word_central',\n",
       " 'word_centre',\n",
       " 'word_ceremony',\n",
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       " 'word_certainly',\n",
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       " 'word_chance',\n",
       " 'word_change',\n",
       " 'word_chaotic',\n",
       " 'word_character',\n",
       " 'word_charge',\n",
       " 'word_charm',\n",
       " 'word_chase',\n",
       " 'word_chat',\n",
       " 'word_cheap',\n",
       " 'word_cheaper',\n",
       " 'word_check',\n",
       " 'word_cheerful',\n",
       " 'word_cheese',\n",
       " 'word_cheyenne',\n",
       " 'word_chicken',\n",
       " 'word_child',\n",
       " 'word_childhood',\n",
       " 'word_children',\n",
       " 'word_chill',\n",
       " 'word_china',\n",
       " 'word_chinese',\n",
       " 'word_chip',\n",
       " 'word_chocolate',\n",
       " 'word_choice',\n",
       " 'word_choose',\n",
       " 'word_chose',\n",
       " 'word_christmas',\n",
       " 'word_churros',\n",
       " 'word_cigarette',\n",
       " 'word_cinderella',\n",
       " 'word_circle',\n",
       " 'word_city',\n",
       " 'word_claim',\n",
       " 'word_class',\n",
       " 'word_classic',\n",
       " 'word_clean',\n",
       " 'word_cleanliness',\n",
       " 'word_clear',\n",
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       " 'word_climb',\n",
       " 'word_clock',\n",
       " 'word_close',\n",
       " 'word_closed',\n",
       " 'word_closer',\n",
       " 'word_closing',\n",
       " 'word_closure',\n",
       " 'word_clothes',\n",
       " 'word_club',\n",
       " 'word_clue',\n",
       " 'word_co',\n",
       " 'word_coast',\n",
       " 'word_coaster',\n",
       " 'word_coffee',\n",
       " 'word_coke',\n",
       " 'word_cold',\n",
       " 'word_collect',\n",
       " 'word_color',\n",
       " 'word_colour',\n",
       " 'word_com',\n",
       " 'word_come',\n",
       " 'word_comfortable',\n",
       " 'word_comment',\n",
       " 'word_compact',\n",
       " 'word_company',\n",
       " 'word_compare',\n",
       " 'word_comparison',\n",
       " 'word_complain',\n",
       " 'word_complaint',\n",
       " 'word_complete',\n",
       " 'word_completely',\n",
       " 'word_complimentary',\n",
       " 'word_con',\n",
       " 'word_concept',\n",
       " 'word_concern',\n",
       " 'word_concerned',\n",
       " 'word_condition',\n",
       " 'word_consider',\n",
       " 'word_consideration',\n",
       " 'word_constantly',\n",
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       " 'word_convenient',\n",
       " 'word_conversation',\n",
       " 'word_convince',\n",
       " 'word_cook',\n",
       " 'word_cool',\n",
       " 'word_cooler',\n",
       " 'word_corn',\n",
       " 'word_corner',\n",
       " 'word_correct',\n",
       " 'word_cost',\n",
       " 'word_costly',\n",
       " 'word_costume',\n",
       " 'word_could',\n",
       " 'word_count',\n",
       " 'word_counter',\n",
       " 'word_counterpart',\n",
       " 'word_country',\n",
       " 'word_couple',\n",
       " 'word_coupon',\n",
       " 'word_course',\n",
       " 'word_courteous',\n",
       " 'word_cover',\n",
       " 'word_crazy',\n",
       " 'word_cream',\n",
       " 'word_create',\n",
       " 'word_credit',\n",
       " 'word_crew',\n",
       " 'word_crockett',\n",
       " 'word_cross',\n",
       " 'word_crow',\n",
       " 'word_crowd',\n",
       " 'word_crowded',\n",
       " 'word_crowds',\n",
       " 'word_cruise',\n",
       " 'word_crush',\n",
       " 'word_cry',\n",
       " 'word_cue',\n",
       " 'word_cuisine',\n",
       " 'word_culture',\n",
       " 'word_cup',\n",
       " 'word_current',\n",
       " 'word_currently',\n",
       " 'word_customer',\n",
       " 'word_cut',\n",
       " 'word_cute',\n",
       " 'word_dad',\n",
       " 'word_daily',\n",
       " 'word_dance',\n",
       " 'word_dangerous',\n",
       " 'word_dark',\n",
       " 'word_darth',\n",
       " 'word_date',\n",
       " 'word_daughter',\n",
       " 'word_davy',\n",
       " 'word_day',\n",
       " 'word_days',\n",
       " 'word_dca',\n",
       " 'word_de',\n",
       " 'word_dead',\n",
       " 'word_deal',\n",
       " 'word_dear',\n",
       " 'word_dec',\n",
       " 'word_december',\n",
       " 'word_decent',\n",
       " 'word_decide',\n",
       " 'word_decision',\n",
       " 'word_deck',\n",
       " 'word_decor',\n",
       " 'word_decorate',\n",
       " 'word_decoration',\n",
       " 'word_dedicate',\n",
       " 'word_deep',\n",
       " 'word_definately',\n",
       " 'word_definite',\n",
       " 'word_definitely',\n",
       " 'word_degree',\n",
       " 'word_delay',\n",
       " 'word_delicious',\n",
       " 'word_delight',\n",
       " 'word_delightful',\n",
       " 'word_deliver',\n",
       " 'word_demand',\n",
       " 'word_depend',\n",
       " 'word_describe',\n",
       " 'word_design',\n",
       " 'word_designate',\n",
       " 'word_desk',\n",
       " 'word_despite',\n",
       " 'word_destination',\n",
       " 'word_detail',\n",
       " 'word_did',\n",
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       " 'word_die',\n",
       " 'word_difference',\n",
       " 'word_different',\n",
       " 'word_difficult',\n",
       " 'word_difficulty',\n",
       " 'word_din',\n",
       " 'word_diner',\n",
       " 'word_dining',\n",
       " 'word_dinner',\n",
       " 'word_direct',\n",
       " 'word_direction',\n",
       " 'word_directly',\n",
       " 'word_dirty',\n",
       " 'word_disability',\n",
       " 'word_disable',\n",
       " 'word_disabled',\n",
       " 'word_disappoint',\n",
       " 'word_disappointed',\n",
       " 'word_disappointing',\n",
       " 'word_disappointment',\n",
       " 'word_discount',\n",
       " 'word_discover',\n",
       " 'word_discoveryland',\n",
       " 'word_disgust',\n",
       " 'word_dislike',\n",
       " 'word_disney',\n",
       " 'word_disneyland',\n",
       " 'word_disneylands',\n",
       " 'word_disneyworld',\n",
       " 'word_display',\n",
       " 'word_distance',\n",
       " 'word_dl',\n",
       " 'word_dlp',\n",
       " 'word_do',\n",
       " 'word_doable',\n",
       " 'word_dog',\n",
       " 'word_dole',\n",
       " 'word_dollar',\n",
       " 'word_don',\n",
       " 'word_donald',\n",
       " 'word_dont',\n",
       " 'word_door',\n",
       " 'word_double',\n",
       " 'word_doubt',\n",
       " 'word_down',\n",
       " 'word_download',\n",
       " 'word_downside',\n",
       " 'word_downtown',\n",
       " 'word_drag',\n",
       " 'word_dragon',\n",
       " 'word_draw',\n",
       " 'word_dream',\n",
       " 'word_dress',\n",
       " 'word_drink',\n",
       " 'word_drinking',\n",
       " 'word_drive',\n",
       " 'word_driver',\n",
       " 'word_drop',\n",
       " 'word_dry',\n",
       " 'word_du',\n",
       " 'word_duck',\n",
       " 'word_due',\n",
       " 'word_dumbo',\n",
       " 'word_ear',\n",
       " 'word_earlier',\n",
       " 'word_early',\n",
       " 'word_ears',\n",
       " 'word_earth',\n",
       " 'word_easily',\n",
       " 'word_east',\n",
       " 'word_easter',\n",
       " 'word_easy',\n",
       " 'word_eat',\n",
       " 'word_eatery',\n",
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       " 'word_efficient',\n",
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       " 'word_electric',\n",
       " 'word_element',\n",
       " 'word_elevator',\n",
       " 'word_elsa',\n",
       " 'word_else',\n",
       " 'word_elsewhere',\n",
       " 'word_email',\n",
       " 'word_employee',\n",
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       " 'word_enchant',\n",
       " 'word_encounter',\n",
       " 'word_encourage',\n",
       " 'word_end',\n",
       " 'word_endless',\n",
       " 'word_energy',\n",
       " 'word_engage',\n",
       " 'word_england',\n",
       " 'word_english',\n",
       " 'word_enjoy',\n",
       " 'word_enjoyable',\n",
       " 'word_enjoyed',\n",
       " 'word_enjoyment',\n",
       " 'word_enormous',\n",
       " 'word_enough',\n",
       " 'word_ensure',\n",
       " 'word_enter',\n",
       " 'word_entertain',\n",
       " 'word_entertaining',\n",
       " 'word_entertainment',\n",
       " 'word_entire',\n",
       " 'word_entrance',\n",
       " 'word_entry',\n",
       " 'word_environment',\n",
       " 'word_epcot',\n",
       " 'word_escape',\n",
       " 'word_especially',\n",
       " 'word_essential',\n",
       " 'word_establishment',\n",
       " 'word_etc',\n",
       " 'word_euro',\n",
       " 'word_eurodisney',\n",
       " 'word_europe',\n",
       " 'word_european',\n",
       " 'word_euros',\n",
       " 'word_eurostar',\n",
       " 'word_eve',\n",
       " 'word_even',\n",
       " 'word_evening',\n",
       " 'word_event',\n",
       " 'word_eventually',\n",
       " 'word_ever',\n",
       " 'word_every',\n",
       " 'word_everybody',\n",
       " 'word_everyday',\n",
       " 'word_everyone',\n",
       " 'word_everything',\n",
       " 'word_everytime',\n",
       " 'word_everywhere',\n",
       " 'word_exactly',\n",
       " 'word_example',\n",
       " 'word_excellent',\n",
       " 'word_except',\n",
       " 'word_exception',\n",
       " 'word_exceptional',\n",
       " 'word_excessive',\n",
       " 'word_exchange',\n",
       " 'word_excite',\n",
       " 'word_excited',\n",
       " 'word_excitement',\n",
       " 'word_exciting',\n",
       " 'word_excuse',\n",
       " 'word_exhaust',\n",
       " 'word_exhibit',\n",
       " 'word_exit',\n",
       " 'word_expand',\n",
       " 'word_expect',\n",
       " 'word_expectation',\n",
       " 'word_expected',\n",
       " 'word_expense',\n",
       " 'word_expensive',\n",
       " 'word_experience',\n",
       " 'word_experienced',\n",
       " 'word_explain',\n",
       " 'word_explanation',\n",
       " 'word_explore',\n",
       " 'word_explorer',\n",
       " 'word_express',\n",
       " 'word_extend',\n",
       " 'word_extortionate',\n",
       " 'word_extra',\n",
       " 'word_extremely',\n",
       " 'word_eye',\n",
       " 'word_fab',\n",
       " 'word_fabulous',\n",
       " 'word_face',\n",
       " 'word_facility',\n",
       " 'word_fact',\n",
       " 'word_factor',\n",
       " 'word_fail',\n",
       " 'word_fair',\n",
       " 'word_fairly',\n",
       " 'word_fairy',\n",
       " 'word_fake',\n",
       " 'word_fall',\n",
       " 'word_familiar',\n",
       " 'word_family',\n",
       " 'word_famous',\n",
       " 'word_fan',\n",
       " 'word_fanatic',\n",
       " 'word_fantasmic',\n",
       " 'word_fantastic',\n",
       " 'word_fantasy',\n",
       " 'word_fantasyland',\n",
       " 'word_far',\n",
       " 'word_fashion',\n",
       " 'word_fast',\n",
       " 'word_faster',\n",
       " 'word_fastpass',\n",
       " 'word_fastpasses',\n",
       " 'word_fault',\n",
       " 'word_fav',\n",
       " 'word_favorite',\n",
       " 'word_favorites',\n",
       " 'word_favourite',\n",
       " 'word_fe',\n",
       " 'word_feature',\n",
       " 'word_february',\n",
       " 'word_fee',\n",
       " 'word_feed',\n",
       " 'word_feel',\n",
       " 'word_feeling',\n",
       " 'word_fell',\n",
       " 'word_felt',\n",
       " 'word_ferry',\n",
       " 'word_festival',\n",
       " 'word_few',\n",
       " 'word_fight',\n",
       " 'word_figure',\n",
       " 'word_fill',\n",
       " 'word_film',\n",
       " 'word_final',\n",
       " 'word_finale',\n",
       " 'word_finally',\n",
       " 'word_find',\n",
       " 'word_fine',\n",
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       " 'word_fire',\n",
       " 'word_firework',\n",
       " 'word_fireworks',\n",
       " 'word_first',\n",
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       " 'word_flash',\n",
       " 'word_flight',\n",
       " 'word_float',\n",
       " 'word_floor',\n",
       " 'word_florida',\n",
       " 'word_flow',\n",
       " 'word_flower',\n",
       " 'word_fly',\n",
       " 'word_focus',\n",
       " 'word_folk',\n",
       " 'word_follow',\n",
       " 'word_food',\n",
       " 'word_foot',\n",
       " 'word_for',\n",
       " 'word_force',\n",
       " 'word_forest',\n",
       " 'word_forever',\n",
       " 'word_forget',\n",
       " 'word_form',\n",
       " 'word_forth',\n",
       " 'word_fortunately',\n",
       " 'word_fortune',\n",
       " 'word_forward',\n",
       " 'word_found',\n",
       " 'word_fountain',\n",
       " 'word_fountains',\n",
       " 'word_four',\n",
       " 'word_fourth',\n",
       " 'word_france',\n",
       " 'word_frankly',\n",
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       " 'word_french',\n",
       " 'word_frequent',\n",
       " 'word_fresh',\n",
       " 'word_friday',\n",
       " 'word_friend',\n",
       " 'word_friendly',\n",
       " 'word_friends',\n",
       " 'word_from',\n",
       " 'word_front',\n",
       " 'word_frontierland',\n",
       " 'word_frozen',\n",
       " 'word_fruit',\n",
       " 'word_frustrate',\n",
       " 'word_frustrating',\n",
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       " 'word_full',\n",
       " 'word_fully',\n",
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       " 'word_funny',\n",
       " 'word_fuss',\n",
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       " 'word_garden',\n",
       " 'word_gate',\n",
       " 'word_gear',\n",
       " 'word_general',\n",
       " 'word_generally',\n",
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       " 'word_genuinely',\n",
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       " 'word_ghost',\n",
       " 'word_giant',\n",
       " 'word_gift',\n",
       " 'word_girl',\n",
       " 'word_girlfriend',\n",
       " 'word_girls',\n",
       " 'word_give',\n",
       " 'word_glad',\n",
       " 'word_glass',\n",
       " 'word_go',\n",
       " 'word_god',\n",
       " 'word_gold',\n",
       " 'word_golden',\n",
       " 'word_good',\n",
       " 'word_goodness',\n",
       " 'word_goofy',\n",
       " 'word_gorgeous',\n",
       " 'word_got',\n",
       " 'word_grab',\n",
       " 'word_grand',\n",
       " 'word_grandchild',\n",
       " 'word_granddaughter',\n",
       " 'word_grandkids',\n",
       " 'word_grandparent',\n",
       " 'word_grandson',\n",
       " 'word_grant',\n",
       " 'word_great',\n",
       " 'word_greatly',\n",
       " 'word_green',\n",
       " 'word_greet',\n",
       " 'word_greets',\n",
       " 'word_gripe',\n",
       " 'word_grizzly',\n",
       " 'word_grotto',\n",
       " 'word_ground',\n",
       " 'word_group',\n",
       " 'word_grow',\n",
       " 'word_grown',\n",
       " 'word_grumpy',\n",
       " 'word_guarantee',\n",
       " 'word_guess',\n",
       " 'word_guest',\n",
       " 'word_guide',\n",
       " 'word_gulch',\n",
       " 'word_guy',\n",
       " 'word_halal',\n",
       " 'word_half',\n",
       " 'word_hall',\n",
       " 'word_halloween',\n",
       " 'word_hand',\n",
       " 'word_handicap',\n",
       " 'word_handle',\n",
       " 'word_handy',\n",
       " 'word_hang',\n",
       " 'word_happen',\n",
       " 'word_happily',\n",
       " 'word_happiness',\n",
       " 'word_happy',\n",
       " 'word_hard',\n",
       " 'word_hardly',\n",
       " 'word_hassle',\n",
       " 'word_hat',\n",
       " 'word_hate',\n",
       " 'word_hatter',\n",
       " 'word_haunt',\n",
       " 'word_have',\n",
       " 'word_he',\n",
       " 'word_head',\n",
       " 'word_health',\n",
       " 'word_healthy',\n",
       " 'word_hear',\n",
       " 'word_heard',\n",
       " 'word_heart',\n",
       " 'word_heat',\n",
       " 'word_heaven',\n",
       " 'word_heavy',\n",
       " 'word_height',\n",
       " 'word_hell',\n",
       " 'word_hello',\n",
       " 'word_help',\n",
       " 'word_helpful',\n",
       " 'word_hence',\n",
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       " 'word_here',\n",
       " 'word_hey',\n",
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       " 'word_hide',\n",
       " 'word_high',\n",
       " 'word_highlight',\n",
       " 'word_highly',\n",
       " 'word_hill',\n",
       " 'word_hire',\n",
       " 'word_history',\n",
       " 'word_hit',\n",
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       " 'word_hkd',\n",
       " 'word_hold',\n",
       " 'word_holder',\n",
       " 'word_holiday',\n",
       " 'word_hollywood',\n",
       " 'word_home',\n",
       " 'word_honest',\n",
       " 'word_honestly',\n",
       " 'word_honeymoon',\n",
       " 'word_hong',\n",
       " 'word_hongkong',\n",
       " 'word_hop',\n",
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       " 'word_hopefully',\n",
       " 'word_hopper',\n",
       " 'word_horrendous',\n",
       " 'word_horrible',\n",
       " 'word_hot',\n",
       " 'word_hotdog',\n",
       " 'word_hotel',\n",
       " 'word_hour',\n",
       " 'word_hours',\n",
       " 'word_house',\n",
       " 'word_however',\n",
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       " 'word_hrs',\n",
       " 'word_hubby',\n",
       " 'word_huge',\n",
       " 'word_hugely',\n",
       " 'word_humid',\n",
       " 'word_hundred',\n",
       " 'word_hungry',\n",
       " 'word_hurry',\n",
       " 'word_hurt',\n",
       " 'word_husband',\n",
       " 'word_hyperspace',\n",
       " 'word_ice',\n",
       " 'word_iconic',\n",
       " 'word_idea',\n",
       " 'word_ideal',\n",
       " 'word_if',\n",
       " 'word_ignore',\n",
       " 'word_ill',\n",
       " 'word_illumination',\n",
       " 'word_im',\n",
       " 'word_image',\n",
       " 'word_imagination',\n",
       " 'word_imagine',\n",
       " 'word_immaculate',\n",
       " 'word_immediately',\n",
       " 'word_important',\n",
       " 'word_importantly',\n",
       " 'word_impossible',\n",
       " 'word_impress',\n",
       " 'word_impressed',\n",
       " 'word_impression',\n",
       " 'word_impressive',\n",
       " 'word_improve',\n",
       " 'word_improvement',\n",
       " 'word_in',\n",
       " 'word_incident',\n",
       " 'word_include',\n",
       " 'word_increase',\n",
       " 'word_incredible',\n",
       " 'word_incredibly',\n",
       " 'word_indeed',\n",
       " 'word_indian',\n",
       " 'word_indiana',\n",
       " 'word_individual',\n",
       " 'word_indoor',\n",
       " 'word_infant',\n",
       " 'word_info',\n",
       " 'word_inform',\n",
       " 'word_information',\n",
       " 'word_initially',\n",
       " 'word_inn',\n",
       " 'word_inner',\n",
       " 'word_insane',\n",
       " 'word_inside',\n",
       " 'word_instead',\n",
       " 'word_instruction',\n",
       " 'word_interact',\n",
       " 'word_interaction',\n",
       " 'word_interactive',\n",
       " 'word_interest',\n",
       " 'word_interested',\n",
       " 'word_interesting',\n",
       " 'word_international',\n",
       " 'word_invention',\n",
       " 'word_involve',\n",
       " 'word_iron',\n",
       " 'word_ironman',\n",
       " 'word_island',\n",
       " 'word_issue',\n",
       " 'word_it',\n",
       " 'word_item',\n",
       " 'word_its',\n",
       " 'word_jack',\n",
       " 'word_jacket',\n",
       " 'word_jam',\n",
       " 'word_jan',\n",
       " 'word_january',\n",
       " 'word_japan',\n",
       " 'word_jedi',\n",
       " 'word_job',\n",
       " 'word_join',\n",
       " 'word_joke',\n",
       " 'word_jones',\n",
       " ...]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label = \"is_good_review\"\n",
    "#exclude columns not relevant as predictors\n",
    "ignore_cols = [label, \"Rating\", \"Review_ID\", \"Review_Text\", \"review_clean\", \"Reviewer_Location\",\"Branch\",\"Rating\", \"Year\", \"Month\", \"\"]\n",
    "#select all other columns as predictors for backward RFE\n",
    "all_predictors = [c for c in word_working.columns if c not in ignore_cols]\n",
    "all_predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "gothic-report",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       0\n",
       "       ..\n",
       "3999    1\n",
       "4000    0\n",
       "4001    1\n",
       "4002    1\n",
       "4003    1\n",
       "Name: is_good_review, Length: 4004, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sets predictor (X) and criterion(y) variables\n",
    "X = word_working[all_predictors]\n",
    "names=pd.DataFrame(X.columns)\n",
    "y = word_working[label]\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "critical-group",
   "metadata": {},
   "source": [
    "## Recursive Feature Elimination with Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "communist-carter",
   "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>Feature</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>neu</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pos</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>compound</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>characters</td>\n",
       "      <td>2125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>words</td>\n",
       "      <td>2120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>doc2vec_vector_0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>doc2vec_vector_1</td>\n",
       "      <td>1253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>doc2vec_vector_2</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>doc2vec_vector_3</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Feature  Rank\n",
       "0               neg     1\n",
       "1               neu   225\n",
       "2               pos     1\n",
       "3          compound    71\n",
       "4        characters  2125\n",
       "5             words  2120\n",
       "6  doc2vec_vector_0     1\n",
       "7  doc2vec_vector_1  1253\n",
       "8  doc2vec_vector_2   297\n",
       "9  doc2vec_vector_3   144"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "#select Logistic Regression as method for dimension reduction\n",
    "logreg = LogisticRegression()\n",
    "# Import RFE and select 15 variables\n",
    "from sklearn.feature_selection import RFE\n",
    "rfe_mod = RFE(logreg, 15)             \n",
    "myvalues=rfe_mod.fit(X,y)\n",
    "rankings = pd.DataFrame(myvalues.ranking_)\n",
    "ranked = pd.concat([names,rankings], axis=1)\n",
    "# Merges rankings dataframe with feature names dataframe\n",
    "ranked.columns = [\"Feature\", \"Rank\"]\n",
    "ranked.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "boring-wisconsin",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Feature  Rank\n",
      "0                  neg     1\n",
      "2                  pos     1\n",
      "6     doc2vec_vector_0     1\n",
      "54            word_age     1\n",
      "141    word_attraction     1\n",
      "245         word_bring     1\n",
      "364         word_close     1\n",
      "522        word_disney     2\n",
      "823         word_great     1\n",
      "912          word_hour     1\n",
      "1096         word_love     1\n",
      "1195        word_money     1\n",
      "1216         word_must     4\n",
      "1269        word_ocean     1\n",
      "1331       word_parade     3\n",
      "1401       word_plenty     1\n",
      "1411         word_poor     1\n",
      "1719        word_snack     5\n",
      "1994    word_universal     1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#filters dataframe to display only the mostim significant features\n",
    "most_important = ranked.loc[ranked['Rank'] <= 5] \n",
    "print(most_important)\n",
    "\n",
    "most_important['Rank'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pregnant-chick",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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