notebooks/Confident Data Chapter 11.ipynb

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    "<img src=\"../images/CONFIDENTDATASCIENCE.png\" alt=\"An image that reads Confident Data Science: The Essential Skills of Data Science, Companion Jupyter Notebook + Coding Examples. Also shows book image.\"/>"
   ]
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   "source": [
    "## Chapter 11 *Business Value + Clients*\n",
    "\n",
    "To give citation to these coding examples give reference to an appropriate chapter and page of:\n",
    "\n",
    "*Confident Data Science: Discover the essential skills of data science* <br>\n",
    "(Confident Series, 15) 1st Edition by Adam Ross Nelson (Author) <br>\n",
    "Publisher : Kogan Page (September 26, 2023).\n",
    "\n",
    "\n",
    "## Wrangle The Data"
   ]
  },
  {
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   "metadata": {},
   "outputs": [
    {
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     "text": [
      "(192, 11)\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>ShareCommentary</th>\n",
       "      <th>Reactions</th>\n",
       "      <th>Comments</th>\n",
       "      <th>User</th>\n",
       "      <th>Sentiment_ggl</th>\n",
       "      <th>Magnitude_ggl</th>\n",
       "      <th>Negative_nltk</th>\n",
       "      <th>Neutral_nltk</th>\n",
       "      <th>Positive_nltk</th>\n",
       "      <th>Compound_nltk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>Taking a moment to give some visibility to the...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1011</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.6259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022-09-14</td>\n",
       "      <td>I had a client tell me yesterday that he bough...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.894</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.9688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2021-08-20</td>\n",
       "      <td>Social responsibility and corporate social res...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1051</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.9831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                                    ShareCommentary  Reactions  \\\n",
       "116 2020-02-05  Taking a moment to give some visibility to the...        2.0   \n",
       "20  2022-09-14  I had a client tell me yesterday that he bough...       27.0   \n",
       "183 2021-08-20  Social responsibility and corporate social res...        7.0   \n",
       "\n",
       "     Comments  User  Sentiment_ggl  Magnitude_ggl  Negative_nltk  \\\n",
       "116       0.0  1011            0.4            2.2          0.038   \n",
       "20       10.0  1021            0.0            3.6          0.000   \n",
       "183       3.0  1051            0.4            2.3          0.014   \n",
       "\n",
       "     Neutral_nltk  Positive_nltk  Compound_nltk  \n",
       "116         0.853          0.109         0.6259  \n",
       "20          0.894          0.106         0.9688  \n",
       "183         0.718          0.268         0.9831  "
      ]
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   "source": [
    "# Import the Pandas, Numpy, & Seaborn libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Set the Seaborn display context to talk\n",
    "sns.set_context('talk')\n",
    "\n",
    "# Load ConfidentDataCh9Social.csv\n",
    "df = pd.read_csv('../data/confident_ch9socialsents.csv', \n",
    "                 parse_dates=['Date'])\n",
    "\n",
    "# Drop outliers (known from previous notebooks & chapters)\n",
    "df.drop([0, 86], inplace=True)\n",
    "\n",
    "# Display the shape of the data\n",
    "print(df.shape)\n",
    "\n",
    "# Show the results\n",
    "df.sample(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Three Added Steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median reactions are: 8.0\n",
      "Median comments are: 1.0\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ShareCommentary</th>\n",
       "      <th>Reactions</th>\n",
       "      <th>Comments</th>\n",
       "      <th>len</th>\n",
       "      <th>group</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>wed_t_sat</th>\n",
       "      <th>Reac_bin</th>\n",
       "      <th>Comm_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>First-time claims for unemployment insurance t...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1269</td>\n",
       "      <td>Economist</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>First-time claims for unemployment insurance t...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>665</td>\n",
       "      <td>Economist</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>I was re-watching Star Trek Voyager season 3 e...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265</td>\n",
       "      <td>Biz Owner</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Industry insights and trends:\\nStaying up-to-d...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>570</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Congratulations to the Data Coalition for thei...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92</td>\n",
       "      <td>C-Suite</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>Being an economist may not be the most glamoro...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>830</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       ShareCommentary  Reactions  Comments  \\\n",
       "104  First-time claims for unemployment insurance t...        3.0       1.0   \n",
       "134  First-time claims for unemployment insurance t...        4.0       0.0   \n",
       "114  I was re-watching Star Trek Voyager season 3 e...        4.0       0.0   \n",
       "180  Industry insights and trends:\\nStaying up-to-d...        4.0       3.0   \n",
       "110  Congratulations to the Data Coalition for thei...        1.0       0.0   \n",
       "192  Being an economist may not be the most glamoro...        3.0       4.0   \n",
       "\n",
       "      len      group  hashtags  wed_t_sat  Reac_bin  Comm_bin  \n",
       "104  1269  Economist         0          1         0         1  \n",
       "134   665  Economist         0          1         0         0  \n",
       "114   265  Biz Owner         1          1         0         0  \n",
       "180   570  Marketing         1          1         0         1  \n",
       "110    92    C-Suite         1          0         0         0  \n",
       "192   830  Marketing         1          1         0         1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Engineer a new feature that will show length of the post\n",
    "df['len'] = df['ShareCommentary'].str.len()\n",
    "\n",
    "# Engineer a new feature that will regroup users\n",
    "df['group'] = df['User'].map({1010:'Biz Owner',1011:'Biz Owner',\n",
    "                              1020:'Biz Owner',1021:'Biz Owner',\n",
    "                              1030:'Economist',1031:'Economist',\n",
    "                              1040:'C-Suite',1041:'C-Suite',\n",
    "                              1050:'Marketing',1051:'Marketing'})\n",
    "\n",
    "# Count the number of hashtags in each post\n",
    "df['hashtags'] = df['ShareCommentary'].str.count('#')\n",
    "\n",
    "# Convert hashtags to a binary predictor\n",
    "# Use list comprehension to set posts with hashtag counts \n",
    "# at or below 4 as a 1 and otherwise as 0\n",
    "df['hashtags'] = [1 if x <=4 \n",
    "                  else 0 for x in df['hashtags']]\n",
    "\n",
    "# Create a binary predictor variable that will be 1 if the post\n",
    "# occurred on a Wednesday, Thursday, Friday, or Saturday\n",
    "df['wed_t_sat'] = [1 if x in ['Wednesday','Thursday',\n",
    "                              'Friday','Saturday'] \n",
    "                   else 0 for x in df['Date'].dt.day_name()]\n",
    "\n",
    "# Create new binary target var (Reac short for Reactions bin short for binary)\n",
    "# Preserve a display of the cut points (print median Reactions)\n",
    "print(f\"Median reactions are: {df['Reactions'].describe()['50%']}\")\n",
    "\n",
    "# Use list comprehension to set posts with reaction counts \n",
    "# at or above the median as 1 and otherwise as 0\n",
    "df['Reac_bin'] = [1 if x >= df['Reactions'].describe()['50%'] \n",
    "                  else 0 for x in df['Reactions']]\n",
    "\n",
    "# Create new binary target var (Comm short for Comments bin short for binary)\n",
    "# Preserve a display of the cut points (print median Reactions)\n",
    "print(f\"Median comments are: {df['Comments'].describe()['50%']}\")\n",
    "\n",
    "# Use list comprehension to set posts with reaction counts \n",
    "# at or above the median as 1 and otherwise as 0\n",
    "df['Comm_bin'] = [1 if x >= df['Comments'].describe()['50%'] \n",
    "                  else 0 for x in df['Comments']]\n",
    "\n",
    "# Show the results\n",
    "df[['ShareCommentary','Reactions','Comments','len','group',\n",
    "    'hashtags','wed_t_sat','Reac_bin','Comm_bin']].sample(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting For Accurate Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Reactions</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Reac_bin</th>\n",
       "      <th>Comm_bin</th>\n",
       "      <th>len</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>wed_t_sat</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022-06-30</td>\n",
       "      <td>21.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1463</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2022-06-12</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>792</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2022-10-20</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1283</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022-10-26</td>\n",
       "      <td>29.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>692</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>669</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Date  Reactions  Comments  Reac_bin  Comm_bin   len  hashtags  \\\n",
       "23  2022-06-30       21.0      45.0         1         1  1463         1   \n",
       "72  2022-06-12        5.0       0.0         0         0   792         1   \n",
       "29  2022-10-20       18.0       0.0         1         0   158         0   \n",
       "56  2021-01-06        9.0       5.0         1         1  1283         1   \n",
       "13  2022-10-26       29.0      20.0         1         1   692         1   \n",
       "177 2021-06-07        8.0       2.0         1         1   669         1   \n",
       "\n",
       "     wed_t_sat  \n",
       "23           1  \n",
       "72           0  \n",
       "29           1  \n",
       "56           1  \n",
       "13           1  \n",
       "177          0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Date','Reactions','Comments','Reac_bin',\n",
    "    'Comm_bin','len','hashtags','wed_t_sat']].sample(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>ShareCommentary</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>17</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>22</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hashtags</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52</td>\n",
       "      <td>16</td>\n",
       "      <td>27</td>\n",
       "      <td>40</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ShareCommentary  0   1   2   3   4   5   6   7   9   10  12  13  14  17  19  \\\n",
       "hashtags                                                                      \n",
       "0                 0   0   0   0   0   7   5   3   1   2   1   2   2   2   1   \n",
       "1                52  16  27  40  18   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "ShareCommentary  20  22  \n",
       "hashtags                 \n",
       "0                12   1  \n",
       "1                 0   0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the hastag work\n",
    "pd.crosstab(\n",
    "    df['ShareCommentary'].str.count('#'), df['hashtags']).transpose()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Date</th>\n",
       "      <th>Friday</th>\n",
       "      <th>Monday</th>\n",
       "      <th>Saturday</th>\n",
       "      <th>Sunday</th>\n",
       "      <th>Thursday</th>\n",
       "      <th>Tuesday</th>\n",
       "      <th>Wednesday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wed_t_sat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date       Friday  Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday\n",
       "wed_t_sat                                                                \n",
       "0               0      30         0       7         0       22          0\n",
       "1              33       0        19       0        57        0         24"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the wed_t_sat variable results\n",
    "pd.crosstab(\n",
    "    df['Date'].dt.day_name(), df['wed_t_sat']).transpose()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Reactions</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>...</th>\n",
       "      <th>32.0</th>\n",
       "      <th>35.0</th>\n",
       "      <th>36.0</th>\n",
       "      <th>37.0</th>\n",
       "      <th>38.0</th>\n",
       "      <th>41.0</th>\n",
       "      <th>45.0</th>\n",
       "      <th>46.0</th>\n",
       "      <th>53.0</th>\n",
       "      <th>55.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reac_bin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Reactions  0.0   1.0   2.0   3.0   4.0   5.0   6.0   7.0   8.0   9.0   ...  \\\n",
       "Reac_bin                                                               ...   \n",
       "0             2     8     8    17    18     9    12    15     0     0  ...   \n",
       "1             0     0     0     0     0     0     0     0    12     7  ...   \n",
       "\n",
       "Reactions  32.0  35.0  36.0  37.0  38.0  41.0  45.0  46.0  53.0  55.0  \n",
       "Reac_bin                                                               \n",
       "0             0     0     0     0     0     0     0     0     0     0  \n",
       "1             1     2     1     1     2     1     1     1     1     1  \n",
       "\n",
       "[2 rows x 39 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for proper coding of the Reac_bin variable\n",
    "pd.crosstab(\n",
    "    df['Reactions'], df['Reac_bin']).transpose()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Comments</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>...</th>\n",
       "      <th>19.0</th>\n",
       "      <th>20.0</th>\n",
       "      <th>21.0</th>\n",
       "      <th>22.0</th>\n",
       "      <th>24.0</th>\n",
       "      <th>28.0</th>\n",
       "      <th>31.0</th>\n",
       "      <th>38.0</th>\n",
       "      <th>45.0</th>\n",
       "      <th>58.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comm_bin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Comments  0.0   1.0   2.0   3.0   4.0   5.0   6.0   7.0   8.0   9.0   ...  \\\n",
       "Comm_bin                                                              ...   \n",
       "0           81     0     0     0     0     0     0     0     0     0  ...   \n",
       "1            0    20     9    12    15    10     9     9     1     3  ...   \n",
       "\n",
       "Comments  19.0  20.0  21.0  22.0  24.0  28.0  31.0  38.0  45.0  58.0  \n",
       "Comm_bin                                                              \n",
       "0            0     0     0     0     0     0     0     0     0     0  \n",
       "1            1     1     1     1     1     1     2     1     1     1  \n",
       "\n",
       "[2 rows x 26 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for proper coding of the Comm_bin variable\n",
    "pd.crosstab(\n",
    "    df['Comments'], df['Comm_bin']).transpose()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select + Apply\n",
    "\n",
    "### K-Nearest Neighbors\n",
    "\n",
    "#### How It Works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sentiment Magnitude')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis = pd.DataFrame(\n",
    "    {'Magnitude_ggl':[13,10,12,10,3,5,5,2,9,8,5],\n",
    "     'len':[20,18,25,22,6,4,8,1,11,12,18],\n",
    "     'Engage':['High Engagement','High Engagement',\n",
    "               'High Engagement','High Engagement',\n",
    "               'Low Engagement','Low Engagement',\n",
    "               'Low Engagement','Low Engagement',\n",
    "               'Unk Engagement','Unk Engagement',\n",
    "               'Unk Engagement']})\n",
    "\n",
    "plt.figure(figsize=(9, 6))\n",
    "sns.scatterplot(data=vis, y='Magnitude_ggl',\n",
    "                x='len', hue='Engage',\n",
    "                s=1000, style='Engage',\n",
    "                palette=['red', 'green', 'blue'])\n",
    "\n",
    "plt.annotate('A', (10.75,9.5), fontsize=30)\n",
    "plt.annotate('B', (11.75,8.5), fontsize=30)\n",
    "plt.annotate('C', (17.75,5.5), fontsize=30)\n",
    "\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)\n",
    "plt.title('Posts By Sentiment Magnitude + Length', fontsize=18)\n",
    "plt.xlabel('post length', fontsize=14)\n",
    "plt.ylabel('Sentiment Magnitude', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making It Work (K-Nearest Neighbors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Date</th>\n",
       "      <th>ShareCommentary</th>\n",
       "      <th>Reactions</th>\n",
       "      <th>Comments</th>\n",
       "      <th>User</th>\n",
       "      <th>Sentiment_ggl</th>\n",
       "      <th>Magnitude_ggl</th>\n",
       "      <th>Negative_nltk</th>\n",
       "      <th>Neutral_nltk</th>\n",
       "      <th>Positive_nltk</th>\n",
       "      <th>Compound_nltk</th>\n",
       "      <th>len</th>\n",
       "      <th>group</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>wed_t_sat</th>\n",
       "      <th>Reac_bin</th>\n",
       "      <th>Comm_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-04-10</td>\n",
       "      <td>Ok, I’m just going to say it: I am not a guy. ...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1040</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.069</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.6115</td>\n",
       "      <td>459</td>\n",
       "      <td>C-Suite</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-12-10</td>\n",
       "      <td>U.S. Department of Education has released its ...</td>\n",
       "      <td>53.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1040</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.889</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.3595</td>\n",
       "      <td>150</td>\n",
       "      <td>C-Suite</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-08-19</td>\n",
       "      <td>This week I lost a contract. A big one.\"\\r\\n\"\"...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1011</td>\n",
       "      <td>0.2</td>\n",
       "      <td>11.3</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.9632</td>\n",
       "      <td>1231</td>\n",
       "      <td>Biz Owner</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-04-09</td>\n",
       "      <td>A Saturday morning creep.  On LinkedIn.  Total...</td>\n",
       "      <td>41.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.777</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.8957</td>\n",
       "      <td>289</td>\n",
       "      <td>Biz Owner</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-11-11</td>\n",
       "      <td>A week out from Thanksgiving, and I have found...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1021</td>\n",
       "      <td>0.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.776</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>1875</td>\n",
       "      <td>Biz Owner</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>2021-08-26</td>\n",
       "      <td>Recruiting and hiring best practices:\\nRecruit...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1051</td>\n",
       "      <td>0.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.9905</td>\n",
       "      <td>661</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>2021-02-07</td>\n",
       "      <td>Success stories and case studies:\\nReading suc...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1050</td>\n",
       "      <td>0.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.585</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.9943</td>\n",
       "      <td>610</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2021-08-12</td>\n",
       "      <td>Networking and building professional relations...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1051</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.9744</td>\n",
       "      <td>696</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>2021-02-27</td>\n",
       "      <td>Being an economist may not be the most glamoro...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1050</td>\n",
       "      <td>0.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.749</td>\n",
       "      <td>0.251</td>\n",
       "      <td>0.9931</td>\n",
       "      <td>830</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>I have been writing a lot about commuting to w...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1050</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.115</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>942</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                                    ShareCommentary  Reactions  \\\n",
       "1   2021-04-10  Ok, I’m just going to say it: I am not a guy. ...       55.0   \n",
       "2   2020-12-10  U.S. Department of Education has released its ...       53.0   \n",
       "3   2022-08-19  This week I lost a contract. A big one.\"\\r\\n\"\"...       45.0   \n",
       "4   2022-04-09  A Saturday morning creep.  On LinkedIn.  Total...       41.0   \n",
       "5   2021-11-11  A week out from Thanksgiving, and I have found...       36.0   \n",
       "..         ...                                                ...        ...   \n",
       "189 2021-08-26  Recruiting and hiring best practices:\\nRecruit...        3.0   \n",
       "190 2021-02-07  Success stories and case studies:\\nReading suc...        2.0   \n",
       "191 2021-08-12  Networking and building professional relations...        3.0   \n",
       "192 2021-02-27  Being an economist may not be the most glamoro...        3.0   \n",
       "193 2021-04-29  I have been writing a lot about commuting to w...        6.0   \n",
       "\n",
       "     Comments  User  Sentiment_ggl  Magnitude_ggl  Negative_nltk  \\\n",
       "1        18.0  1040           -0.2            4.3          0.069   \n",
       "2         9.0  1040            0.2            0.8          0.000   \n",
       "3         9.0  1011            0.2           11.3          0.045   \n",
       "4        58.0  1020            0.0            5.0          0.223   \n",
       "5        21.0  1021            0.5           14.5          0.014   \n",
       "..        ...   ...            ...            ...            ...   \n",
       "189       1.0  1051            0.6            3.1          0.000   \n",
       "190       1.0  1050            0.7            3.6          0.000   \n",
       "191       1.0  1051            0.5            2.9          0.000   \n",
       "192       4.0  1050            0.7            4.2          0.000   \n",
       "193       3.0  1050            0.2            2.7          0.115   \n",
       "\n",
       "     Neutral_nltk  Positive_nltk  Compound_nltk   len      group  hashtags  \\\n",
       "1           0.816          0.115         0.6115   459    C-Suite         1   \n",
       "2           0.889          0.111         0.3595   150    C-Suite         0   \n",
       "3           0.816          0.139         0.9632  1231  Biz Owner         1   \n",
       "4           0.777          0.000        -0.8957   289  Biz Owner         1   \n",
       "5           0.776          0.210         0.9968  1875  Biz Owner         1   \n",
       "..            ...            ...            ...   ...        ...       ...   \n",
       "189         0.653          0.347         0.9905   661  Marketing         1   \n",
       "190         0.585          0.415         0.9943   610  Marketing         1   \n",
       "191         0.774          0.226         0.9744   696  Marketing         1   \n",
       "192         0.749          0.251         0.9931   830  Marketing         1   \n",
       "193         0.728          0.158         0.7869   942  Marketing         1   \n",
       "\n",
       "     wed_t_sat  Reac_bin  Comm_bin  \n",
       "1            1         1         1  \n",
       "2            1         1         1  \n",
       "3            1         1         1  \n",
       "4            1         1         1  \n",
       "5            1         1         1  \n",
       "..         ...       ...       ...  \n",
       "189          1         0         1  \n",
       "190          0         0         1  \n",
       "191          1         0         1  \n",
       "192          1         0         1  \n",
       "193          1         0         1  \n",
       "\n",
       "[192 rows x 17 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(18, 0.32, 'Best Error-Rate With Fewest K')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing sklearn libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Splitting the data into training and testing sets\n",
    "X = df[['Sentiment_ggl', 'Magnitude_ggl', \n",
    "        'len', 'wed_t_sat', 'hashtags']]\n",
    "y = df['Reac_bin']\n",
    "\n",
    "# Split the data for training and testing purposes\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Scaling the data\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Search for optimal k by starting an empty list of error rates\n",
    "error_rates = []\n",
    "\n",
    "# Begin a loop that will loop through all odd numbers from 1 to 41\n",
    "for k in range(1, 41, 2):\n",
    "    # Creating the k-NN classification model\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "    # Generate predictions on the testing data\n",
    "    y_pred_kn = knn.predict(X_test_scaled)\n",
    "    # Calculate and record proprtion of correct classifications\n",
    "    error_rates.append(np.mean(y_pred_kn != y_test))\n",
    "\n",
    "# Plot the error rates (which are inverse of AUC).\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(1, 41, 2), error_rates, color='blue',\n",
    "         linestyle=':', marker='x', markersize=5)\n",
    "plt.title('Error Rates vs. K Values')\n",
    "plt.xlabel('K Value')\n",
    "plt.ylabel('Error Rate')\n",
    "\n",
    "plt.annotate(text='Best Error-Rate With Fewest K', \n",
    "             xy=(15, .29), xytext=(18, .32), fontsize=15,\n",
    "             arrowprops={'arrowstyle':'->', 'connectionstyle':'arc3, rad=-.2'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.65      0.64        23\n",
      "           1       0.76      0.74      0.75        35\n",
      "\n",
      "    accuracy                           0.71        58\n",
      "   macro avg       0.69      0.70      0.70        58\n",
      "weighted avg       0.71      0.71      0.71        58\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/adamrossnelson/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:237: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "# Retrain the model with k = 15 (the optimal)\n",
    "knn = KNeighborsClassifier(n_neighbors=15)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Generate predictions on testing data for later reference\n",
    "y_pred_kn = knn.predict(X_test_scaled)\n",
    "\n",
    "# Save performance results for later review\n",
    "accuracy_kn = accuracy_score(y_test, y_pred_kn)\n",
    "class_report_kn = classification_report(y_test, y_pred_kn)\n",
    "\n",
    "# Display the accuracy score + classification report\n",
    "print(class_report_kn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple Linear Regression\n",
    "\n",
    "#### How It Works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(6, 17, 'Posts With Magnitude 11\\nExpects 14 Reactions')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis = pd.DataFrame(\n",
    "    {'Magnitude_ggl':[13,10,12,10,3,5,5,2,9,8,5],\n",
    "     'Reactions':[20,12,16,9,2,3,6,8,12,13,6]})\n",
    "\n",
    "\n",
    "sns.lmplot(data=vis, x='Magnitude_ggl', y='Reactions', \n",
    "           height=6, aspect=1.5)\n",
    "plt.title('Number Of Reactions By Sentiment Magnitude')\n",
    "plt.xlabel('Sentiment Magnitude Score')\n",
    "plt.ylabel('Count of Post Reactions')\n",
    "\n",
    "plt.annotate(text='', xy=(11, 14), xytext=(11,0),\n",
    "            arrowprops={'arrowstyle':'<->'})\n",
    "plt.annotate(text='', xy=(10.75, 14.5), xytext=(2,14.5),\n",
    "            arrowprops={'arrowstyle':'<->'})\n",
    "plt.annotate(text='Posts With Magnitude 11\\nExpects 14 Reactions',\n",
    "            xy=(10.95, 15), xytext=(6,17), fontsize=15,\n",
    "            arrowprops={'arrowstyle':'->', \n",
    "                        'connectionstyle':'arc3, rad=-.9'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making It Work (Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing sklearn libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Date</th>\n",
       "      <th>ShareCommentary</th>\n",
       "      <th>Reactions</th>\n",
       "      <th>Comments</th>\n",
       "      <th>User</th>\n",
       "      <th>Sentiment_ggl</th>\n",
       "      <th>Magnitude_ggl</th>\n",
       "      <th>Negative_nltk</th>\n",
       "      <th>Neutral_nltk</th>\n",
       "      <th>Positive_nltk</th>\n",
       "      <th>Compound_nltk</th>\n",
       "      <th>len</th>\n",
       "      <th>group</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>wed_t_sat</th>\n",
       "      <th>Reac_bin</th>\n",
       "      <th>Comm_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>Navigating the healthcare system can be a tota...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1051</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.886</td>\n",
       "      <td>0.114</td>\n",
       "      <td>0.7096</td>\n",
       "      <td>313</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2020-09-20</td>\n",
       "      <td>Industry events and conferences:\\nAttending in...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1050</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.861</td>\n",
       "      <td>0.139</td>\n",
       "      <td>0.9200</td>\n",
       "      <td>585</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date                                    ShareCommentary  Reactions  \\\n",
       "147 2021-04-29  Navigating the healthcare system can be a tota...       14.0   \n",
       "181 2020-09-20  Industry events and conferences:\\nAttending in...        7.0   \n",
       "\n",
       "     Comments  User  Sentiment_ggl  Magnitude_ggl  Negative_nltk  \\\n",
       "147       4.0  1051           -0.1            1.7            0.0   \n",
       "181       2.0  1050            0.5            2.0            0.0   \n",
       "\n",
       "     Neutral_nltk  Positive_nltk  Compound_nltk  len      group  hashtags  \\\n",
       "147         0.886          0.114         0.7096  313  Marketing         1   \n",
       "181         0.861          0.139         0.9200  585  Marketing         1   \n",
       "\n",
       "     wed_t_sat  Reac_bin  Comm_bin  \n",
       "147          1         1         1  \n",
       "181          0         0         1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              Reactions   R-squared:                       0.297\n",
      "Model:                            OLS   Adj. R-squared:                  0.271\n",
      "Method:                 Least Squares   F-statistic:                     11.66\n",
      "Date:                Mon, 10 Apr 2023   Prob (F-statistic):           2.09e-09\n",
      "Time:                        19:42:22   Log-Likelihood:                -516.71\n",
      "No. Observations:                 144   AIC:                             1045.\n",
      "Df Residuals:                     138   BIC:                             1063.\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept        11.4306      0.745     15.342      0.000       9.957      12.904\n",
      "Sentiment_ggl    -2.1228      0.820     -2.589      0.011      -3.744      -0.502\n",
      "Magnitude_ggl     8.0829      1.486      5.441      0.000       5.146      11.020\n",
      "len              -4.8891      1.471     -3.324      0.001      -7.797      -1.981\n",
      "wed_t_sat         1.9720      0.767      2.571      0.011       0.455       3.489\n",
      "hashtags          0.1435      0.929      0.154      0.878      -1.694       1.981\n",
      "==============================================================================\n",
      "Omnibus:                       57.718   Durbin-Watson:                   1.976\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              173.941\n",
      "Skew:                           1.562   Prob(JB):                     1.70e-38\n",
      "Kurtosis:                       7.386   Cond. No.                         3.83\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Splitting the data into training and testing sets\n",
    "X = df[['Sentiment_ggl', 'Magnitude_ggl', \n",
    "        'len', 'wed_t_sat', 'hashtags']]\n",
    "y = df['Reactions']\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.25, random_state=42)\n",
    "\n",
    "# Scaling the data\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Adding an intercept to the model\n",
    "X_train_scaled = sm.add_constant(X_train_scaled)\n",
    "X_test_scaled = sm.add_constant(X_test_scaled)\n",
    "\n",
    "# Training the OLS regression model\n",
    "ols = sm.OLS(y_train, X_train_scaled)\n",
    "ols_results = ols.fit()\n",
    "\n",
    "# Renaming the columns in the OLS regression results summary\n",
    "column_names = ['Intercept'] + list(X.columns)\n",
    "ols_results_summary = ols_results.summary(xname=column_names)\n",
    "# Show the results of the regression estimations\n",
    "print(ols_results_summary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean squared error: 8.91\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model\n",
    "y_pred_lm = ols_results.predict(X_test_scaled)\n",
    "mse = mean_squared_error(y_test, y_pred_lm)\n",
    "rmse = np.sqrt(mse)\n",
    "print('Root mean squared error: {:.2f}'.format(rmse))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.473958333333334"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcualte the standard deviation on the number of Reactions\n",
    "df['Reactions'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE in terms of Reactions S.D. : 0.78\n"
     ]
    }
   ],
   "source": [
    "# Calculate RMSE in terms of standard deviations on Reactions\n",
    "print('RMSE in terms of Reactions S.D. : {:.2f}'.format(\n",
    "      rmse / df['Reactions'].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check + Recheck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.71\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.65      0.64        23\n",
      "           1       0.76      0.74      0.75        35\n",
      "\n",
      "    accuracy                           0.71        58\n",
      "   macro avg       0.69      0.70      0.70        58\n",
      "weighted avg       0.71      0.71      0.71        58\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Display knn model (k=15) classification report and other metrics\n",
    "print(\"Accuracy: {:.2f}\".format(accuracy_kn))\n",
    "print(\"Classification Report:\")\n",
    "print(class_report_kn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(28, 23, 'Received 27 Reactions')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare regression test with regression predictions\n",
    "sns.lmplot(data=pd.DataFrame({'y_test':y_test,\n",
    "                              'y_pred_lm':y_pred_lm}),\n",
    "           x='y_test', y='y_pred_lm', \n",
    "           height=6, aspect=1.5)\n",
    "plt.title('Compare Predictions With Test Data')\n",
    "plt.xlabel('Test Data (Actual Reactions)')\n",
    "plt.ylabel('Predicted Data (Predicted Reactions)')\n",
    "\n",
    "\n",
    "plt.annotate(text='', xy=(27, 17), xytext=(27,2),\n",
    "            arrowprops={'arrowstyle':'<->'})\n",
    "\n",
    "plt.annotate(text='', xy=(26.5, 17.75), xytext=(0,17.75),\n",
    "            arrowprops={'arrowstyle':'<->'})\n",
    "\n",
    "plt.annotate(text='Predicted 18 Reactions',\n",
    "            xy=(25, 18), xytext=(18,25), fontsize=15,\n",
    "            arrowprops={'arrowstyle':'->', \n",
    "                        'connectionstyle':'arc3, rad=.3'})\n",
    "\n",
    "plt.annotate(text='Received 27 Reactions',\n",
    "            xy=(27.75, 16), xytext=(28,23), fontsize=15,\n",
    "            arrowprops={'arrowstyle':'->', \n",
    "                        'connectionstyle':'arc3, rad=-.5'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AUC + ROC Box  Out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11ffde220>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "# Generate labels for a binary classification\n",
    "y_true = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1] * 100)\n",
    "\n",
    "# Generate predictions closer to true values\n",
    "y_score_good = y_true + np.random.normal(\n",
    "     loc=0.5, scale=0.5, size=1000)\n",
    "\n",
    "# Generate predictions farther from true values\n",
    "y_score_bad = y_true + np.random.normal(\n",
    "     loc=0.5, scale=1.0, size=1000)\n",
    "\n",
    "# Calculate the fpr and tpr for the good model\n",
    "fpr_good, tpr_good, thresh = metrics.roc_curve(\n",
    "     y_true, y_score_good)\n",
    "roc_auc_good = metrics.auc(fpr_good, tpr_good)\n",
    "\n",
    "# Calculate the fpr and tpr for the bad model\n",
    "fpr_bad, tpr_bad, thresholds = metrics.roc_curve(\n",
    "     y_true, y_score_bad)\n",
    "roc_auc_bad = metrics.auc(fpr_bad, tpr_bad)\n",
    "\n",
    "# Specify the plot size\n",
    "plt.subplots(figsize=(6, 6))\n",
    "\n",
    "# Plot the ROC curves\n",
    "plt.plot(fpr_good, tpr_good, color='green', \n",
    "         label='(AUC = %0.2f)' % roc_auc_good)\n",
    "plt.plot(fpr_bad, tpr_bad, color='red', linestyle=':', \n",
    "         label='(AUC = %0.2f)' % roc_auc_bad)\n",
    "\n",
    "# Plot the reference line\n",
    "plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
    "\n",
    "# Add labels and legend\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Update + Change Log\n",
    "\n",
    "March, 2023 - Created notebook.<br>\n",
    "April, 2023 - Prepared for GitHub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}