Meta-Features_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "pd.set_option('display.max_colwidth', 80)\n",
    "\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "STOP_WORDS = stopwords.words('english')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use('ggplot')\n",
    "\n",
    "import seaborn as sb\n",
    "sb.set_style('whitegrid')\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 10,7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>All residents asked to 'shelter in place' are being notified by officers. No...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13,000 people receive #wildfires evacuation orders in California</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as smoke from #wildfires pours in...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>10869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two giant cranes holding a bridge collapse into nearby homes http://t.co/STf...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>10870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@aria_ahrary @TheTawniest The out of control wild fires in California even i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>10871</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M1.94 [01:04 UTC]?5km S of Volcano Hawaii. http://t.co/zDtoyd8EbJ</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>10872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Police investigating after an e-bike collided with a car in Little Portugal....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>10873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Latest: More Homes Razed by Northern California Wildfire - ABC News http...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id keyword location  \\\n",
       "0         1     NaN      NaN   \n",
       "1         4     NaN      NaN   \n",
       "2         5     NaN      NaN   \n",
       "3         6     NaN      NaN   \n",
       "4         7     NaN      NaN   \n",
       "...     ...     ...      ...   \n",
       "7608  10869     NaN      NaN   \n",
       "7609  10870     NaN      NaN   \n",
       "7610  10871     NaN      NaN   \n",
       "7611  10872     NaN      NaN   \n",
       "7612  10873     NaN      NaN   \n",
       "\n",
       "                                                                                 text  \\\n",
       "0               Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all   \n",
       "1                                              Forest fire near La Ronge Sask. Canada   \n",
       "2     All residents asked to 'shelter in place' are being notified by officers. No...   \n",
       "3                   13,000 people receive #wildfires evacuation orders in California    \n",
       "4     Just got sent this photo from Ruby #Alaska as smoke from #wildfires pours in...   \n",
       "...                                                                               ...   \n",
       "7608  Two giant cranes holding a bridge collapse into nearby homes http://t.co/STf...   \n",
       "7609  @aria_ahrary @TheTawniest The out of control wild fires in California even i...   \n",
       "7610                M1.94 [01:04 UTC]?5km S of Volcano Hawaii. http://t.co/zDtoyd8EbJ   \n",
       "7611  Police investigating after an e-bike collided with a car in Little Portugal....   \n",
       "7612  The Latest: More Homes Razed by Northern California Wildfire - ABC News http...   \n",
       "\n",
       "      target  \n",
       "0          1  \n",
       "1          1  \n",
       "2          1  \n",
       "3          1  \n",
       "4          1  \n",
       "...      ...  \n",
       "7608       1  \n",
       "7609       1  \n",
       "7610       1  \n",
       "7611       1  \n",
       "7612       1  \n",
       "\n",
       "[7613 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7613 entries, 0 to 7612\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   id        7613 non-null   int64 \n",
      " 1   keyword   7552 non-null   object\n",
      " 2   location  5080 non-null   object\n",
      " 3   text      7613 non-null   object\n",
      " 4   target    7613 non-null   int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 297.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns that won't be analyzed\n",
    "train.drop(['id', 'keyword', 'location'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create meta features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "      <th>tweet_len</th>\n",
       "      <th>hashtag_count</th>\n",
       "      <th>hashtag_yn</th>\n",
       "      <th>mention_count</th>\n",
       "      <th>mention_yn</th>\n",
       "      <th>punctuation_count</th>\n",
       "      <th>punctuation_yn</th>\n",
       "      <th>word_count</th>\n",
       "      <th>unique_word_count</th>\n",
       "      <th>stopword_count</th>\n",
       "      <th>url_count</th>\n",
       "      <th>url_yn</th>\n",
       "      <th>avg_word_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>13</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>7</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.57</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All residents asked to 'shelter in place' are being notified by officers. No...</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13,000 people receive #wildfires evacuation orders in California</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as smoke from #wildfires pours in...</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.50</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",
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       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>Two giant cranes holding a bridge collapse into nearby homes http://t.co/STf...</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>@aria_ahrary @TheTawniest The out of control wild fires in California even i...</td>\n",
       "      <td>1</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>M1.94 [01:04 UTC]?5km S of Volcano Hawaii. http://t.co/zDtoyd8EbJ</td>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>Police investigating after an e-bike collided with a car in Little Portugal....</td>\n",
       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>The Latest: More Homes Razed by Northern California Wildfire - ABC News http...</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                 text  \\\n",
       "0               Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all   \n",
       "1                                              Forest fire near La Ronge Sask. Canada   \n",
       "2     All residents asked to 'shelter in place' are being notified by officers. No...   \n",
       "3                   13,000 people receive #wildfires evacuation orders in California    \n",
       "4     Just got sent this photo from Ruby #Alaska as smoke from #wildfires pours in...   \n",
       "...                                                                               ...   \n",
       "7608  Two giant cranes holding a bridge collapse into nearby homes http://t.co/STf...   \n",
       "7609  @aria_ahrary @TheTawniest The out of control wild fires in California even i...   \n",
       "7610                M1.94 [01:04 UTC]?5km S of Volcano Hawaii. http://t.co/zDtoyd8EbJ   \n",
       "7611  Police investigating after an e-bike collided with a car in Little Portugal....   \n",
       "7612  The Latest: More Homes Razed by Northern California Wildfire - ABC News http...   \n",
       "\n",
       "      target  tweet_len  hashtag_count  hashtag_yn  mention_count  mention_yn  \\\n",
       "0          1         57              1           1              0           0   \n",
       "1          1         32              0           0              0           0   \n",
       "2          1        112              0           0              0           0   \n",
       "3          1         57              1           1              0           0   \n",
       "4          1         72              2           1              0           0   \n",
       "...      ...        ...            ...         ...            ...         ...   \n",
       "7608       1         73              0           0              0           0   \n",
       "7609       1        106              0           0              2           1   \n",
       "7610       1         58              0           0              0           0   \n",
       "7611       1        119              0           0              0           0   \n",
       "7612       1         82              0           0              0           0   \n",
       "\n",
       "      punctuation_count  punctuation_yn  word_count  unique_word_count  \\\n",
       "0                     1               1          13                 13   \n",
       "1                     1               1           7                  7   \n",
       "2                     3               1          22                 20   \n",
       "3                     2               1           8                  8   \n",
       "4                     2               1          16                 15   \n",
       "...                 ...             ...         ...                ...   \n",
       "7608                  5               1          11                 11   \n",
       "7609                  5               1          20                 17   \n",
       "7610                 11               1           8                  8   \n",
       "7611                  5               1          19                 19   \n",
       "7612                  7               1          13                 13   \n",
       "\n",
       "      stopword_count  url_count  url_yn  avg_word_len  \n",
       "0                  6          0       0          4.38  \n",
       "1                  0          0       0          4.57  \n",
       "2                 11          0       0          5.09  \n",
       "3                  1          0       0          7.12  \n",
       "4                  7          0       0          4.50  \n",
       "...              ...        ...     ...           ...  \n",
       "7608               2          1       1          6.64  \n",
       "7609               9          0       0          5.30  \n",
       "7610               2          1       1          7.25  \n",
       "7611               5          0       0          6.26  \n",
       "7612               3          1       1          6.31  \n",
       "\n",
       "[7613 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the number of characters on a tweet excluding spaces\n",
    "train['tweet_len'] = train['text'].apply(lambda x: len(x) - x.count(' '))\n",
    "\n",
    "#Calculate the number of hashtags on a tweet\n",
    "train['hashtag_count'] = train['text'].apply(lambda x: len([c for c in str(x) if c=='#']))\n",
    "\n",
    "#Classify the tweet according to presence of hashtags (1:yes, 0: no)\n",
    "train['hashtag_yn'] = train['hashtag_count'].apply(lambda x: np.where(x>0, 1, 0))\n",
    "\n",
    "#Calculate the number of mentions on a tweet\n",
    "train['mention_count'] = train['text'].apply(lambda x: len([c for c in str(x) if c == '@']))\n",
    "\n",
    "#Classify the tweet according to presence of mentions (1:yes, 0: no)\n",
    "train['mention_yn'] = train['mention_count'].apply(lambda x: np.where(x>0, 1, 0))\n",
    "\n",
    "#Calculate the number of characters that belong to punctuation signs/marks\n",
    "train['punctuation_count'] = train['text'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]))\n",
    "\n",
    "#Classify the tweet according to presence of punctuation (1:yes, 0: no)\n",
    "train['punctuation_yn'] = train['punctuation_count'].apply(lambda x: np.where(x>0, 1, 0))\n",
    "\n",
    "#Calculate the number of words on a tweet\n",
    "train['word_count'] = train['text'].apply(lambda x: len(str(x).split()))\n",
    "\n",
    "#Calculate the number of unique words on a tweet\n",
    "train['unique_word_count'] = train['text'].apply(lambda x: len(set(str(x).split())))\n",
    "\n",
    "#Calculate the number of stop words on a tweet\n",
    "train['stopword_count'] = train['text'].apply(lambda x: len([w for w in str(x).lower().split() if w in STOP_WORDS]))\n",
    "\n",
    "#Calculate the number of urls on a tweet\n",
    "train['url_count'] = train['text'].apply(lambda x: len([w for w in str(x).lower().split() if 'http' in w or 'https' in w]))\n",
    "\n",
    "#Classify the tweet according to presence of urls (1:yes, 0: no)\n",
    "train['url_yn'] = train['url_count'].apply(lambda x: np.where(x>0, 1, 0))\n",
    "\n",
    "#Calculate the average word length on a tweet\n",
    "train['avg_word_len'] = train['text'].apply(lambda x: round(np.mean([len(w) for w in str(x).split()]),2))\n",
    "\n",
    "train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot histograms of the created meta features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hist_plot_function(column):\n",
    "    #Divide the train dataset according to the target variable (0 or 1).\n",
    "    a = train[train['target']==0][column].values\n",
    "    b = train[train['target']==1][column].values\n",
    "    \n",
    "    #Calculate the T-test for the means of both groups.\n",
    "    print(stats.ttest_ind(a, b, equal_var=False))\n",
    "    \n",
    "    #Create histograms for both groups on the same figure\n",
    "    plt.title(column)\n",
    "    plt.hist(a, alpha=0.5, density=True, label='not disaster')\n",
    "    plt.hist(b, alpha=0.5, density=True, label='disaster')\n",
    "    plt.legend(loc='best')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-18.552410449296456, pvalue=3.5791996060605243e-75)\n"
     ]
    },
    {
     "data": {
      "image/png": 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x9oQQrgHqgZsyZxWJMcbTQghfA35L5tuOD8cY7yu24Orq6oo8n78t6Onp8VuQkiQlsNnAFWMcBE7faPGzOeMLgAUbvWfvYea6Gbi5+DLfMHXqVDo6Oujq6tqaabZ5K1asYPLkyUW9p7q6mqlTpyaqSJKk7deYu/FpVVUV06ZNK3cZFa+9vX27vfhdkqRK4/kjSZKkxAxckiRJiRm4JEmSEjNwSZIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYkZuCRJkhIzcEmSJCVm4JIkSUrMwCVJkpSYgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYgYuSZKkxAxckiRJiRm4JEmSEjNwSZIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYkZuCRJkhIzcEmSJCVm4JIkSUrMwCVJkpSYgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYgYuSZKkxAxckiRJiRm4JEmSEjNwSZIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYkZuCRJkhIzcEmSJCVm4JIkSUqsdnMrhBCqgeuAvYFe4OQY49Kc8VOA04B+4JIY470hhLcCN2bnrwJOjTHGEMLHgQuy694YY1xQ6h2SJEmqNIUc4ToGGB9jPAA4F7hq3UAIYSZwBnAgcCRweQhhHHAx8N0Y46HAZdnldcB84MPAIcCp2fdLkiRt0zZ7hAs4CHgAIMa4JISwX87Y+4DFMcZeoDeEsBSYBXwVWJGzjR6gGVgaY+wECCH8DjgYuGOkjbe2ttLT00Nra2vheyV7tgXsWfHsWfHsWfEqsWdta+toa+sodxnD6u/vp62tPe/YskmDDHa8NMoVVbbR+IwVErh24I3wBDAQQqiNMfbnGesCJscY2wFCCAGYR+YoWVO+dTe38ebmZlpbW2lubi6gVK1jz4pnz4pnz4pnz4pXiT2rXtZFU1flXgbd1tZOU9P0vGMzZjQRZkwa5YoqW6k+Yy0tLcOOFfJpWQnk/s5UZ8NWvrFJwOsAIYQPAncDJ8QY40jrSpIkbcsKOcK1GPg4cHsIYX/gqZyxx4FLQwjjgXFkThs+nQ1b3wY+EmP8S3bdVuDtIYSpwCrg78gc/ZIkSdqmFRK4FgJHhBAeI/ONw7khhK+QuR7rFyGEa4FHyRwtOz/G2BNCuAaoB27KnFUkxhhPy77vV9l1b4wxvpxgnyRJkirKZgNXjHEQOH2jxc/mjC8AFmz0nr2Hmese4J7iy5QkSRq7KveKP0mSpG2EgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYgYuSZKkxAxckiRJiRm4JEmSEjNwSZIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYkZuCRJkhIzcEmSJCVm4JIkSUrMwCVJkpSYgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYgYuSZKkxAxckiRJiRm4JEmSEjNwSZIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYkZuCRJkhKrLXcBkiRtSwbXdDO0Zk1Za6jvWc1AR1XesYHlQ/SteqWgeaqnTKN26vRSlrbdMnBJklRCQ2vWsPalF8taw0B3N2sbGvKO9dV0sKbzxYLmmbD/oWDgKglPKUqSJCVm4JIkSUrMwCVJkpSYgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYgYuSZKkxGo3t0IIoRq4Dtgb6AVOjjEuzRk/BTgN6AcuiTHemzN2JjAzxnhu9vVXgJOAtuwqp8UYY4n2RZIkqSJtNnABxwDjY4wHhBD2B64CjgYIIcwEzgD2A8YDvwsh/JrMkbMFwPuBO3Pm2hf4fIyxpXS7IEmSVNkKCVwHAQ8AxBiXhBD2yxl7H7A4xtgL9IYQlgKzgKXAT4GHgD1z1p8NnJcNar+MMV6+uY23trbS09NDa2trQTukDHtWPHtWPHtWPHtWvErsWdvaOtraOvKO1fesZqC7e5Qr2tDg4CDdw9SwelUN7W3tBc1Tv2wZq/oGS1laRRqNz1ghgWsHYEXO64EQQm2MsT/PWBcwOcbYCTwYQjhxo7luBb4HrAQWhhA+lnsKMp/m5mZaW1tpbm4uoFStY8+KZ8+KZ8+KZ8+KV4k9q17WRVNX/sugBzqqWNvQMMoVbai7u5uGYWqY2DiR6U3TC5pnwowZvOVtoZSlVaRSfcZaWoY/gVfIRfMrgUm578mGrXxjk4DX800SQqgCrokxtscY+4BfAu8pYPuSJEljWiFHuBYDHwduz17D9VTO2OPApSGE8cA4oBl4eph5dgCeDiE0A6uBw4Abt7RwSZKksaKQwLUQOCKE8BhQBczNfttwaYzxFyGEa4FHyRwtOz/G2JNvkhjjihDC14Dfkvm248MxxvtKsheSJEkVbLOBK8Y4CJy+0eJnc8YXkPlGYr73/mSj1zcDNxddpSRJ0hjmjU8lSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYgYuSZKkxAxckiRJiRm4JEmSEjNwSZIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYkZuCRJkhIzcEmSJCVm4JIkSUrMwCVJkpSYgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYgYuSZKkxAxckiRJiRm4JEmSEjNwSZIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMRqy12AJEkA/R3tDHYu32R5Y2cbfc9X1vGBgd5aBjo68o4Nru0b5Wo0Fhi4JEkVYbBzOWuWPLLJ8r62dtY0TR/9gkbQN2VX1r60Mu9YzbSmUa5GY0Fl/ZNBkiRpG2TgkiRJSszAJUmSlJiBS5IkKTEvmpcklU37ql6Wr858q2+gt5a+Kbtuss7quia6GieOcmUj62mYDOS/aF7Kx8AlSVm5P/xTaVtbR/WyrpLOOW1iPdMbx5V0ztGyfHUfi5a2ATDQ0ZH3m3/d3d00NAyMdmkj2qt5arlL0Bhj4JKkrNwf/qm0tXXQ1FXaqzkO2aNpzAYuaXvhNVySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYgYuSZKkxLzxqSSNcb39A8QS371+tKzu7S93CdKoMHBJ0hi3Ys1aWpd1lLuMLdI8Y1K5S5BGhacUJUmSEjNwSZIkJWbgkiRJSmyz13CFEKqB64C9gV7g5Bjj0pzxU4DTgH7gkhjjvTljZwIzY4znZl9/HLggu+6NMcYFJdwXSZKkilTIRfPHAONjjAeEEPYHrgKOBgghzATOAPYDxgO/CyH8msyRswXA+4E7s+vWAfOB9wKrgcUhhHtijH8r7S5JkiRVlkJOKR4EPAAQY1xCJlyt8z5gcYyxN8a4AlgKzCITvn4KXJqzbjOwNMbYGWPsA34HHLz1uyBJklTZCjnCtQOwIuf1QAihNsbYn2esC5gcY+wEHgwhnDjCPF3A5M1tvLW1lZ6eHlpbWwsoVevYs+LZs+Jtaz1rW1tHW1va2yv09/fT1tZe0jl3mVhV8jlHS27t9T2rGeju3mSdwcFBuvMsL6e+3r5haxrX2EdvmesdqWerV9XQXuDnpX7ZMlb1DZaytIo0Gn+XFRK4VgK5N0qpzoatfGOTgNcLnGekdddrbm6mtbWV5ubmAkrVOvasePaseGO1Z/0d7Qx2Lt9k+dBQFVNrqpJue1VPL43j6ko2X9WECUxqbKSpaahkc46m3NoHOqpY29CwyTrd3d005FleTvXj6oetqWZcPTVlrneknk1snMj0pukFzTNhxgze8rZQytIqUqn+LmtpaRl2rJDAtRj4OHB79hqup3LGHgcuDSGMB8aROW349DDztAJvDyFMBVYBfwfMK2D7klRSg53LWbPkkU2W903ZlbUvrUy67YHu7ryhYkvV7bJryeaSlE4hgWshcEQI4TGgCpgbQvgKmeuxfhFCuBZ4lMz1YOfHGHvyTRJjXJt936+y694YY3y5JHshSZJUwTYbuGKMg8DpGy1+Nmd8AZlvJOZ77082en0PcE/RVUqSJI1h3vhUkiQpMQOXJElSYgYuSZKkxAq5aF6SVKGGBvoZ7F7FQMemt7kYCwYnVa2vfXBtX5mrkdIxcEnSGDbU18tARztrX3qx3KVskYFJrK+9ZlpTeYuREvKUoiRJUmIGLkmSpMQMXJIkSYkZuCRJkhIzcEmSJCVm4JIkSUrMwCVJkpSYgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYrXlLkDStqV9VS/LV/eVu4wRDfTW0jdl102W9zRMBlaOej2Stn0GLkkltXx1H4uWtpW7jBENdHSw9qVNg9VezVPLUI2k7YGBS1JB+jvaGexcvsGyxs42+p7f8MqEgd5aBjo6RrO0og2urewjcJK2PQYuSQUZ7FzOmiWPbLCsr62dNU3TN1w2Zde8R48qSc20pnKXIGk740XzkiRJiRm4JEmSEjNwSZIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYl5p3kpsXyPxBmLBrpXlbsESRqzDFxSYvkeiTMW1b/9neUuQZLGLE8pSpIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYkZuCRJkhLzxqeqSOW4O3tjZxt9z5f+3yDeoV2SZOBSRSrH3dn72tpZ0zS95PN6h3ZJkqcUJUmSEjNwSZIkJWbgkiRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYl541NJkrYjaxt35L/ZtaB163trqVnWlbagAk2bWM/0xnHlLmOLGbgkSdqOdA1W8/RfVha0bt1ABzWdVYkrKswhezSN6cDlKUVJkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKbLPfUgwhVAPXAXsDvcDJMcalOeOnAKcB/cAlMcZ7QwjTgVuACcArwNwYY3cI4VrgQGDdd0yPjjGuKOUOSZIkVZpCjnAdA4yPMR4AnAtctW4ghDATOINMiDoSuDyEMA64ALglxngw8CSZQAawL3BkjPHQ7H+GLUmStM0rJHAdBDwAEGNcAuyXM/Y+YHGMsTcbnpYCs3LfA9wPHJ49UvZ24AchhMUhhH8s0T5IkiRVtEJufLoDkHskaiCEUBtj7M8z1gVM3mj5umUTge8AVwM1wG9DCP8ZY/yvkTbe2tpKT08Pra2theyPssZ6zxo72+hrax/Vbfb399OeYJuTZnbRNcr7kkK+/cjXs9V1TXR3d49maUUb19hHb54a+3r7ktc+ODhY0m2Ma+yjr7eu4ns+nNyeD/f7UuqelcJIn5Xh9mM0jdSzYj7nNatX0TcwVMrSttiySYMMdryUZO7R+JlZSOBaCUzKeV2dDVv5xiYBr+csX5OzrBv4doyxGyCE8Bsy14WNGLiam5tpbW2lubm5gFK1zljvWd/z1axpmj6q22xva2d6gm3WT5rEuFHelxTy7Ue+nnU1TqShYWA0Sytazbh6ahoaNlleP66ehjzLS6m7u7uk26gZVz8qdaeSW/twvy+l7lkpjNTz4fZjNI3Us2I+L3UTG6mZOq2UpW2xGTOaCDMmbX7FLVCqn5ktLS3DjhVySnEx8FGAEML+wFM5Y48DB4cQxocQJgPNwNO57wGOAh4F3gH8LoRQE0KoI3Pa8YnidkWSJGnsKSRwLQR6QgiPAfOBs0IIXwkh/H2M8W/AtWQC1WNrSAQAAAi6SURBVG+A82OMPcAlwKdDCIuBA4DvxhhbgZ8BS4BFwE9jjH8u/S5JkiRVls2eUowxDgKnb7T42ZzxBcCCjd6zDPhInrmuBK7cokolSZLGKG98KkmSlJiBS5IkKTEDlyRJUmKF3BZCUhmsmLozHUN15S5jvdqqSfRP2XWDZavrmuhqnLjBsp6GyWTuDCNJWsfAJVWojqE6/v0vlRNcalaNY2D5hvVk7vWz4T239mqeOpplSdKYYODahvR3tDPYuRzI3qn9+bF7xnige1W5S5AkqWQMXNuQwc7lrFnyCAB9be2jfqf2Uqp/+zvLXYIkSSUzdg+BSJIkjREGLkmSpMQMXJIkSYkZuCRJkhIzcEmSJCVm4JIkSUrMwCVJkpSYgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpsdpyFyCltGLqznQM1RW07uq6JroaJ5a8htqqSfRP2bXo9/U0TAZWlrweSdLoM3Bpm9YxVMe//6Ww0NLd3U1Dw0DJa6hZNY6B5cUHp72ap5a8FklSeXhKUZIkKTEDlyRJUmIGLkmSpMQMXJIkSYkZuCRJkhIzcEmSJCVm4JIkSUrMwCVJkpSYgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJGbgkSZISM3BJkiQlZuCSJElKzMAlSZKUmIFLkiQpMQOXJElSYgYuSZKkxGrLXUC59Xe0M9i5vNxllMRA96pkc6+YujMdQ3XJ5t9YbdUk+qfsutXz9DRMBlZu9TySJG2N7T5wDXYuZ82SR8pdRknUv/2dyebuGKrj3/8yesGlZtU4BpZv/fb2ap5agmokSdo6nlKUJElKzMAlSZKUmIFLkiQpse3+Gi5JkpTf0EA/Ax2V8cWygeVD9K16ZYvfXz1lGrVTp5ewouIYuCRJUl5Dfb30L3+p3GUA0FfTwZrOF7f4/RP2PxTKGLg8pShJkpSYR7hGUep7WeXeu2p1XRNdjRNLNrf3s5IkacsZuEZR6ntZ5d67qru7m4aGgZLN7f2sJEnacp5SlCRJSszAJUmSlJiBS5IkKTEDlyRJUmIGLkmSpMQMXJIkSYkZuCRJkhIzcEmSJCW22RufhhCqgeuAvYFe4OQY49Kc8VOA04B+4JIY470hhOnALcAE4BVgboyxO9+6pd4hSZKkSlPIEa5jgPExxgOAc4Gr1g2EEGYCZwAHAkcCl4cQxgEXALfEGA8GngROG2FdSZKkbVohgesg4AGAGOMSYL+csfcBi2OMvTHGFcBSYFbue4D7gcNHWFeSJGmbVsizFHcAVuS8Hggh1MYY+/OMdQGTN1qeb1nu8hG1tLRs8P8k9txv8+uUyIEzpqfdwNsmJZv6wPftnmzuvEq0L6Nedz5buC8VUXuuAvej4urOZ5h9GRO15zFW64aNak/4d1ipjdjzCt+Poj4vFbIvA8DSrfkZ+voqGCFLJM0ZFBa4VgK53a7Ohq18Y5OA13OWr8mzbON1hzV79uyqAuqTJEmqaIWcUlwMfBQghLA/8FTO2OPAwSGE8SGEyUAz8HTue4CjgEdHWFeSJGmbVjU0NDTiCjnfUpwFVAFzyYSppTHGX2S/eXgqmfB2WYzxzhDCDOAmMkex2oHjY4yr862baL8kSZIqxmYDlyRJkraONz6VJElKzMAlSZKUmIFLkiQpsUJuC1E2m3uskDJCCHXAjcCuwDjgEuAZ4CfAEJlvg34xxjhYphIrVgjhTUALcASZR079BHs2rBDCecDfA/Vk/mwuwp4NK/tn8yYyfzYHgFPwc5ZXCOH9wBUxxkNDCHuQp0chhH8B5pDp4ZkxxsfLVnAF2Khn+wDfIfM56wU+H2Nc5iP1NpTbs5xlxwNfyj5RJ+8jC0ux7Uo/wjXsY4W0gc8By7OPUjoK+C5wNfD17LIq4Ogy1leRsj8MbyBzvziwZyMKIRwKfIDM47kOAd6CPducjwK1McYPABcBl2LPNhFC+N/AD4Hx2UWb9CiEsC+Zz937gU8D3ytHrZUiT8++TSY0HArcBZzjI/U2lKdnZIPqSWQ+ZyM9snCrVXrgGumxQnrDHcA3cl73A7PJHH2ANx6vpA3NA75P5gHrYM8250gy9+FbCNwD3Is925z/C9Rmj9bvAKzFnuXzPHBszut8PToIeDDGOBRj/CuZvjaNbpkVZeOefTrG+Mfsr2uBHnyk3sY26FkIYRrwLeDMnHWS9azSA1fexwqVq5hKFWNcFWPsCiFMAv4N+DpQFWNcd8+Pgh6jtD0JIZwItMUYf5Wz2J6NbDqZf/R8Cjgd+BmZJ0/Ys+GtInM68VlgAXAtfs42kb0n49qcRfl6tEWPh9tWbdyzGOOrACGEDwD/BMzHnm0gt2chhBrgR8BZZPqyTrKeVXrgGumxQsoRQngL8Fvg5hjjLUDuNSGbfYzSdugfgSNCCI8A+wA/Bd6UM27PNrUc+FWMsS/GGMn8Czr3LyJ7tqmzyPTsHWSuRb2JzPVv69iz/PL9/VX04+G2NyGEfyBz1H5OjLENezaS2cDbgeuBW4F3hhCuIWHPKj1wjfRYIWVl7+z/IHBOjPHG7OIns9fcwBuPV1JWjPHvYoyHZK93+CPweeB+ezai3wEfCSFUhRB2AiYCD9uzEXXyxr+WO4A6/LNZiHw9WgwcGUKoDiG8lcw/wNvLVWClCSF8jsyRrUNjjC9kF/tIvWHEGB+PMb4r+zPg08AzMcYzSdizSj89t5DMUYjHeOOxQtrU14ApwDdCCOuu5foycG0IoR5oJXOqUSP7KrDAnuUXY7w3hPB3ZP5Cqga+CPw39mwk84EbQwiPkjmy9TXgP7Fnm7PJn8UY40C2j//BG58/sf702LXAX4G7QggAi2KM/xJCuJZMYK0Gzo8x9pSv0soXY/xbqp75aB9JkqTEKv2UoiRJ0phn4JIkSUrMwCVJkpSYgUuSJCkxA5ckSVJiBi5JkqTEDFySJEmJ/X9pxK4cwGmccgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-4.953220497150671, pvalue=7.489786192994653e-07)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-6.431117161163684, pvalue=1.3543550627782149e-10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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OPfXUvOxlL8u+++6bgYGBB+57ylOekr/6q7/KkiVLMjAwkLe+9a255ZZbctppp+WKK67ITjvtlEWLFmXjxo0ppeSlL31pdt555+yxxx454IADssMOO+Siiy7Kk570pJx22ml5wxvekE2bNiVJzjrrrNx6660z9r3OlAUP3QW2tYaGhsZXrFgxo9t8qB/+y9ey438Od/U1Zsv2zzw02+xXej3GjOiHD4jkF1mT/mRd+k8/rMmaNWt+6cNX57NufMBt62c8NDSUFStWLGg9vr/2awEA9AmRBADQIJIAABpEEgD0gYULF2Z0dLTXY8xZo6OjW/zuOe9uA4A+sMsuu2Tt2rUZGRnp9Sh94c4778zy5ctnbHsLFy7MLrvsskXPEUkA0AcWLFiQXXfdtddj9I3bbrut5+/2c7gNAKBBJAEANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCg4WF5xe17tl2W23d+bK/HmBF7jm+TPXs9BADwSx6WkXTn/Quy6vt39XqMGXHYY8dEEgD0IYfbAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaJjys9tKKa9I8oqJm9slOTDJnrXWO7o3FgBAb00ZSbXWjyT5SJKUUv5PkksEEgAw1y0YHx+f1gNLKU9Lcl6t9dDJHjc0NDS+ZMmSGRht8+5Yd2+u+ddvd/U1Zstv/cYB2XHHbXs9xowYHR3Ndttt1+sxeBBr0p+sS/+xJv1nttZk/fr1WbFixYLWfVPuSXqQNyY5fToPHBwc3ILNbrlVq65Pt0NstixbtiyDg/v1eowZMTw83PW1Z8tYk/5kXfqPNek/s7UmQ0NDm71vWidul1J2SvLEWus/ztRQAAD9bLrvbvvNJF/u5iAAAP1kupFUktzczUEAAPrJtM5JqrW+o9uDAAD0ExeTBABoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADSIJAKBBJAEANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgIbF03lQKeXUJL+TZJskF9ZaP9TVqQAAemzKPUmllEOTPDvJwUmek+TRXZ4JAKDnprMn6YVJvp1kZZIdk/xFVycCAOgD04mk3ZL8WpKXJNknyd+VUp5Yax3f3BOGh4dnaLy2sU1jWb9+fVdfY7aMjIx0/ec1W0ZHR+fM9zJXWJP+ZF36jzXpP/2wJtOJpNuT3FBr3ZikllJGk+ye5NbNPWFwcHCGxmtbter6LFmypKuvMVuWLVuWwcH9ej3GjBgeHu762rNlrEl/si79x5r0n9lak6Ghoc3eN513t/1zkheVUhaUUvZOsjSdcAIAmLOmjKRa65VJrkvyb0muSPLqWuumbg8GANBL07oEQK31L7s9CABAP3ExSQCABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADSIJAKBBJAEANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADYun86BSynVJ7py4+Z+11uO7NxIAQO9NGUmllO2SpNZ6aNenAQDoE9PZk3RAkiWllKsnHv/GWuu1kz1heHh4JmbbrLFNY1m/fn1XX2O2jIyMdP3nNVtGR0fnzPcyV1iT/mRd+o816T/9sCbTiaT1Sc5L8sEkj0/yhVJKqbXev7knDA4OztB4batWXZ8lS5Z09TVmy7JlyzI4uF+vx5gRw8PDXV97tow16U/Wpf9Yk/4zW2syNDS02fumE0nfSXJjrXU8yXdKKbcn2SvJD2dmPACA/jOdd7f9cZJ3JkkpZe8kOyb5cTeHAgDotensSfpQko+UUv45yXiSP57sUBsAwFwwZSTVWjcmOXYWZgEA6BsuJgkA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADSIJAKBBJAEANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANi6fzoFLKI5IMJXl+rfWG7o4EANB7U+5JKqUMJLk4yb3dHwcAoD9M53DbeUnel+RHXZ4FAKBvTHq4rZTyiiRraq1fLKWcOt2NDg8Pb+1ckxrbNJb169d39TVmy8jISNd/XrNldHR0znwvc4U16U/Wpf9Yk/7TD2sy1TlJf5xkvJRyWJIDk3y0lPI7tdafTPakwcHBmZqvadWq67NkyZKuvsZsWbZsWQYH9+v1GDNieHi462vPlrEm/cm69B9r0n9ma02GhoY2e9+kkVRr/c2f/XMp5atJTpoqkAAA5gKXAAAAaJjWJQCSpNZ6aBfnAADoK/YkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADSIJAKBBJAEANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADSIJAKBBJAEANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoWDzVA0opi5J8IElJsinJ8bXWm7o9GABAL01nT9JvJ0mt9eAkf53k/K5OBADQB6bck1Rr/Xwp5cqJm7+W5KdTPWd4eHhr55rU2KaxrF+/vquvMVtGRka6/vOaLaOjo3Pme5krrEl/si79x5r0n35YkykjKUlqrfeXUi5NclSSl071+MHBwa2da1KrVl2fJUuWdPU1ZsuyZcsyOLhfr8eYEcPDw11fe7aMNelP1qX/WJP+M1trMjQ0tNn7pn3idq315UmekOQDpZSlMzAXAEDfmjKSSinHlVJOnbi5PslYOidwAwDMWdM53HZ5kg+XUr6WZCDJ62uto90dCwCgt6Zz4vY9SY6ZhVkAAPqGi0kCADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0TOdjSQCAh4nb7t6Q2+/Z2OsxttqGBdv0egSRBABzye33bMw1N67p9Rhb7YnLxns9gsNtAAAtIgkAoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADSIJAKBBJAEANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoGHxZHeWUgaSXJLksUm2TXJmrfXvZmEuAICemjSSkvxRkttrrceVUnZNcl0SkQQAfWps/T3ZtPb2Xo+x1QaWLuv1CFNG0meSfPZBt+/v4iwAwFYav/ee3Pdf3+v1GFtt4SMe3+sRJo+kWuvdSVJKWZZOLL15OhsdHh7e+skmMbZpLOvXr+/qa8yWkZGRrv+8Zsvo6Oic+V7mCmvSn6xL/5lLazIysmFO/I4c2zTW8zWZak9SSimPTrIyyYW11sums9HBwcGtnWtSq1ZdnyVLlnT1NWbLsmXLMji4X6/HmBHDw8NdX3u2jDXpT9al/8ylNVm9+qY58Tty4aKFs7ImQ0NDm71vqhO390hydZI/q7X+3xmeCwCgb021J+mNSXZO8pZSylsmvnZ4rfXe7o4FANBbU52T9Lokr5ulWQAA+oaLSQIANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADSIJAKBBJAEANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaJhWJJVSDiqlfLXLswAA9I3FUz2glPKXSY5Lck/3xwEA6A9TRlKSm5IcneRj093o8PDwrzzQdIxtGsv69eu7+hqzZWRkpOs/r9kyOjo6Z76XucKa9Cfr0n/m0pqMjGyYE78jxzaN9XxNpoykWuvnSimP3ZKNDg4O/soDTceqVddnyZIlXX2N2bJs2bIMDu7X6zFmxPDwcNfXni1jTfqTdek/c2lNVq++aU78jly4aOGsrMnQ0NDmZ+j6qwMAPAyJJACABpEEANAwnRO3U2v9XpJndncUAID+YU8SAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADSIJAKBBJAEANIgkAIAGkQQA0CCSAAAaRBIAQINIAgBoEEkAAA0iCQCgQSQBADSIJACABpEEANAgkgAAGkQSAECDSAIAaBBJAAANIgkAoEEkAQA0iCQAgAaRBADQIJIAABpEEgBAg0gCAGgQSQAADSIJAKBBJAEANCye6gGllIVJLkxyQJINSf6k1npjtwcDAOil6exJ+t0k29Van5Xkr5K8s7sjAQD03nQi6TeS/EOS1FqvTfK0rk4EANAHFoyPj0/6gFLKB5N8rtb6hYnbP0iyb631/tbjh4aGJt8gAEAfWbFixYLW16c8JynJXUmWPej2ws0F0mQvBADwcDKdw21fT/LiJCmlPDPJt7s6EQBAH5jOnqSVSZ5fSvmXJAuSHN/dkQAAem/Kc5IAAOYjF5MEAGgQSQAADSIJAKBhOidu98xUH4lSSnllklcluT/JmbXWK3sy6DwyjTU5OckfTNy8qtZ6+uxPOf9M5+ODJh7z90n+ttb6vtmfcn6Zxn8rhyf53xM3v5nk1bVWJ4l20TTW5JQkL0syluTsWuvKngw6T5VSDkpyTq310Id8/beT/HU6v+svqbV+YLZm6vc9SZv9SJRSyp5JXpvk4CQvTPK2Usq2PZlyfplsTfZN8odJnp3kWUleUErZvydTzj/T+figM5PsMqtTzW+T/beyLMk7kryk1vrMJN9LslsvhpxnJluTndL5nfKsJC9I8q6eTDhPlVL+MskHk2z3kK8PJLkgnTV5TpITJ37/z4p+j6TJPhLlGUm+XmvdUGu9M8mNSfxC7r7J1uSHSV5Ua91Uax1LMpBkdPZHnJcm/figUspL0/m/4y/M/mjz1mRr8ux0rjn3zlLKPyX5aa11zeyPOO9Mtib3JPl+kqUTf43N+nTz201Jjm58fTDJjbXWdbXWjUn+OckhszVUv0fSjknufNDtTaWUxZu5byTJ8tkabB7b7JrUWu+rtd5WSllQSjkvyXW11u/0ZMr5Z7PrUkp5cpJj09ldzeyZ7M+v3ZL8VpI3JDk8yetLKU+Y5fnmo8nWJOn8j97qdA5/vmc2B5vvaq2fS3Jf466e/q7v90ia7CNRHnrfsiR3zNZg89ikH1NTStkuyScmHvM/Z3m2+WyydfkfSR6Z5CtJXpHkz0spL5rd8ealydbk9iTfqLX+pNZ6d5KvJTlwtgechyZbk8OT7JVknySPSfK7pZRnzPJ8/LKe/q7v90ia7CNR/i3JIaWU7Uopy9PZJffvsz/ivLPZNSmlLEjyt0m+VWt9Va11U29GnJc2uy611r+stR40cTLkR5KcX2v9h14MOc9M9ufXUJInl1J2m9iT8cx09mDQXZOtybok9ybZUGsdTecX8U6zPiEPNZzk8aWUXUop2yT5zST/Olsv3tfvbkvjI1FKKX+ezvHJvyulvCfJP6UTe2+a+Beb7trsmiRZlM6JddtOvHMnSU6ttc7av9Dz2KT/rfR2tHlrqj+/Tk3yxYnHfrrW6n/yum+qNTksybWllLF0zn35Ug9nnddKKccm2aHW+jn0CfkAAAA+SURBVP6JNfpiOr/rL6m13jJbc/hYEgCAhn4/3AYA0BMiCQCgQSQBADSIJACABpEEANAgkgAAGkQSAEDD/wf/auTAQT4f1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=9.329414166899953, pvalue=1.372471240805839e-20)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=10.7379183704853, pvalue=1.0586450183474747e-26)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-11.903912819660958, pvalue=2.2156694628883537e-32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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5Z5PXe+65JwsWLNhivylTpjBlypRNtn3gAx/g4Ycf3vj6hz/8ISNHjtz4eu+99+bwww/v69CTK5y5M0mSpCHOYCVJkpSIwUqSJCkRg5UkSUNYcXExLS0tgz2MHVZLS0uv7jp08bokSUPYuHHjaGpqorm5ebCHsl2sXLmSMWPGbLfzFRcXM27cuLz37zFYhRCKgRuAg4BW4KwY4+LN9qkBngIOjDG2hBBGAj8DdgGagc/GGNM9EVKSJAFQVFREdXX1YA9ju1m2bBk1Nem+TiK1fOa2TgVGxBiPBC4CrsltDCGcDDwC1OZs/hfgpRjj0cAtwKVIkiTt4PIJVh8BFgDEGBcBh27W3gmcCDR1dwzwULZdkiRph5bPGqudgJU5rztCCKUxxnaAGOMvAUIIWzumGcjrYmhDQ0M+u/VZS0vLgJ8jtcb1ZTQ2NvW8Yy8tqeqks+ntPh8/FGtZqKxlOtYyLeuZjrVMp9BrmU+wWgVU5bwu3hCq8jymCng3n8HU1dXls1ufNTQ0DPg5Uite0kxNc/qbN2trawi1VT3vuBVDsZaFylqmYy3Tsp7pWMt0CqWW9fX13W7P5yf2QuDjACGEI4CXenMMcArwRB7HSJIkDWn5zFjNB04KITwFFAHTQggXAItjjPdt5Zi5wM0hhCeBNmBqktFKkiQVsB6DVYyxEzhvs82vdrPfXjl/XgtM2XwfSZKkHZnfvC5JkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkRAxWkiRJiRisJEmSEikd7AFsT5XrW2l7Iw72MHqlo7WUjqamLbYXjRxJ8cjKQRiRJEnammEVrIpXr2LdS08P9jB6pW3sXqx/e9UW28v22AsMVpIkFRQvBUqSJCVisJIkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKZHSnnYIIRQDNwAHAa3AWTHGxTntZwPnAu3AzBjjAyGE9wE/BYqAJmBqjHHtAIxfkiSpYOQzY3UqMCLGeCRwEXDNhoYQwq7A+cBRwMnAlSGECmA6cEeM8Rjgd8CZqQcuSZJUaPIJVh8BFgDEGBcBh+a0HQYsjDG2xhhXAouBicALwNjsPjsB65ONWJIkqUD1eCmQTDBamfO6I4RQGmNs76atGRgDvA18J4QwFagAZuQzmIaGhnx267MR69tZ1rhsQM+R2pqyGtau3fIqasma1bR1dPW53yVVnXQ2vd3n41taWgb872u4sJbpWMu0rGc61jKdQq9lPsFqFVCV87o4G6q6a6sC3gV+AHwuxvhwCGEycAswuacT1dXV5TXovvrTikZ2qhk/oOdIrXn0KCorO7bYXjZqNCXjqvvcb21tDaG2qucdt6KhoWHA/76GC2uZjrVMy3qmYy3TKZRa1tfXd7s9n0uBC4GPA4QQjgBeyml7Bjg6hDAihDAGqANeBlbw3kzWO7x3WVCSJGmHlc+M1XzgpBDCU2Tu8psWQrgAWBxjvC+EcB3wBJmQdkmMsSWE8CXg+hBCSfaYLw7Q+CVJkgpGj8EqxtgJnLfZ5ldz2ucB8zY75hXg+BQDlCRJGir8glBJkqREDFaSJEmJ5LPGSj1YOe7/0NRVNiB9t1SOIXPzpSRJKnQGqwSausr4nz8MTPg5oG7cgPQrSZLS81KgJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSqR0sAegwdHa3kFc0tzn4xvXl1G8leOrR5UzfnRFn/uWJGmoMlgNUyvXradhSVOfj29sbKKmufsJz2P3rTFYSZKGpR6DVQihGLgBOAhoBc6KMS7OaT8bOBdoB2bGGB8IIYwC5gJ7A+XAl2KMzwzA+CVJkgpGPmusTgVGxBiPBC4CrtnQEELYFTgfOAo4GbgyhFABfA14OcZ4NHA2EFIPXJIkqdDkE6w+AiwAiDEuAg7NaTsMWBhjbI0xrgQWAxPJhKy2EMLDwDeBh5OOWpIkqQDls8ZqJ2BlzuuOEEJpjLG9m7ZmYAwwHhgbYzw5hPDPwGzgn3s6UUNDQ94D74sR69tZ1rgseb9rympYu3Zt8n4B2lrbuu27ZM1q2jq6+tzvHqOKaOxHLdrb27d6/JKqTjqb3u5z38NNS0vLgH/2hwtrmZb1TMdaplPotcwnWK0CqnJeF2dDVXdtVcC7wHLgvuy2+8lcQuxRXV1dPrv12Z9WNLJTzfjk/TaPHkVlZUfyfgHKK8qprKzcYnvZqNGUjKvuc79Vo0dTU9P3YNbYuIyardSytraGUFvVbZu21NDQMOCf/eHCWqZlPdOxlukUSi3r6+u73Z7PpcCFwMcBQghHAC/ltD0DHB1CGBFCGAPUAS8DT244BjgG+F3fhi1JkjR05DNjNR84KYTwFFAETAshXAAsjjHeF0K4DniCTEi7JMbYEkKYBdwUQvgNsJ48LgNKkiQNdT0GqxhjJ3DeZptfzWmfB8zb7Jgm4LQUA5QkSRoqfKSNJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpSIwUqSJCmR0sEegPqmq6OdjqblfT6+s6qoX8eXt6yho6mo27a1K0p5ZfnSvPopGjmK4spRee1bPaqc8aMr8h6jJEnbm8FqiOpqa6V9+dt9Pr6jCta//Vbfj1+7lvWVld22raiClxvy67tsj70oGVed177H7ltjsJIkFTQvBUqSJCVisJIkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlUjrYA9ie1lRUsXzsXsn7bakcA6xK3q8kSRpahlWwWtlexNN/SB+ADqgbl7xPSZI09HgpUJIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpRIj18QGkIoBm4ADgJagbNijItz2s8GzgXagZkxxgdy2o4Bbo0x7pl64JIkSYUmnxmrU4ERMcYjgYuAazY0hBB2Bc4HjgJOBq4MIVRk2/YEvgqUpR60JElSIcrnkTYfARYAxBgXhRAOzWk7DFgYY2wFWkMIi4GJIYSXgO8D5wD1+Q6moaEh74H3RWdHJ2vXrk3eb1tr24D0u62+K0a30dqPc/Z3zJ2dW69lb/ouWbOato6uvPZdUtVJZ9PbeY9xqGhpaRnwz/5wYS3Tsp7pWMt0Cr2W+QSrnYCVOa87QgilMcb2btqagTHA9cDsGOOfQwh5D6auri7vffvi6adfoLKyMnm/5RXlA9LvtvouqSinpB/n7O+Y165du9Xje9N32ajRlIyrzmvf2toaQm1V3mMcKhoaGgb8sz9cWMu0rGc61jKdQqllfX3380b5XApcBeT+NCvOhqru2qqANuBo4N9CCL8GxoUQbu/tgCVJkoaafGasFgJ/C9wZQjgCeCmn7RngihDCCKACqAOeiTFunKYKIfxvjPEfE45ZkiSpIOUTrOYDJ4UQngKKgGkhhAuAxTHG+0II1wFPkJn9uiTG2DJww5UkSSpcPQarGGMncN5mm1/NaZ8HzNvG8bv2eXSSJElDiF8QKkmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqRESnvaIYRQDNwAHAS0AmfFGBfntJ8NnAu0AzNjjA+EEN4H/CjbfxFwTowxDsD4JUmSCkY+M1anAiNijEcCFwHXbGgIIewKnA8cBZwMXBlCqAAuB66PMR4HzAKuTDxuSZKkgpNPsPoIsAAgxrgIODSn7TBgYYyxNca4ElgMTAS+Cvwiu08p0JJsxJIkSQWqx0uBwE7AypzXHSGE0hhjezdtzcCYGOMygBBCAGaTmfXqUUNDQ16D7qvOjk7Wrl2bvN+21rYB6XdbfVeMbqO1H+fs75g7O7dey970XbJmNW0dXXntu6Sqk86mt/Me41DR0tIy4J/94cJapmU907GW6RR6LfMJVquAqpzXxdlQ1V1bFfAuQAjhb8iszToj3/VVdXV1+ezWZ08//QKVlZXJ+y2vKB+QfrfVd0lFOSX9OGd/x7x27dqtHt+bvstGjaZkXHVe+9bW1hBqq3recYhpaGgY8M/+cGEt07Ke6VjLdAqllvX19d1uz+dS4ELg4wAhhCOAl3LangGODiGMCCGMAeqAl7Oh6j+Aj8UYf9ufgUuSJA0V+cxYzQdOCiE8ReYOv2khhAuAxTHG+0II1wFPkAlpl8QYW0II3wXKgZszVwOJMcZzB+YtSJIkFYYeg1WMsRM4b7PNr+a0zwPmbXbMQUlGJ0mSNIT4BaGSJEmJ5HMpUCoIre0dxCXNA9J39ahyxo+uGJC+JUnDh8FKQ8bKdetpWNI0IH0fu2+NwUqS1G9eCpQkSUrEYCVJkpSIwUqSJCkRg5UkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkREoHewAa3ro62uloWp7Xvp1VRXnv21sdy7toW/1Ov/ooHltN6bjxiUYkSRqKDFYaVF1trbQvfzuvfTuqYP3bbw3IONpKmli3on99jzziODBYSdKw5qVASZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYkYrCRJkhIxWEmSJCVisJIkSUrEYCVJkpSIwUqSJCkRgzWCBbQAAAaFSURBVJUkSVIiBitJkqREDFaSJEmJGKwkSZISMVhJkiQlYrCSJElKxGAlSZKUiMFKkiQpEYOVJElSIgYrSZKkRAxWkiRJiRisJEmSEjFYSZIkJWKwkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYmUDvYApB3ZstWtLF/Tts19GteXUbykudd9V48qZ/zoir4OTZI0AAxWErB+9M78nr361Ud5ayklmwWkNa3t/PZPK7Z5XGNjEzXNvZ88PnbfGoOVJBUYg5UENHcW8/IfVvWrj7KOJkpWFG2yra62ql99SpKGFoOVNES1tncQ+3AJsSdeYpSkvjNYSUPUynXraVjSlLxfLzFKUt95V6AkSVIiBitJkqREvBQoJdLV0U5H0/JNtnVWFW2xbXPlLWvoaCra5j7dyafvvuhY3kV7WSul48Yn71uSdnQ9BqsQQjFwA3AQ0AqcFWNcnNN+NnAu0A7MjDE+EEIYD9wGjATeAabFGNcOwPilgtHV1kr78rc32dZRBevffmubx3WsXcv6yspeny+fvvtizZhOXgNK1qddZ+WieEnDQT4zVqcCI2KMR4YQjgCuAT4JEELYFTgfOBQYATwZQvgl8C3gthjjT0IIF5EJXnMG4g1ISqu5s5hnX1tKUUnahfHH7jOOnSrak/a5udErGml7Y9MVDsVjq519k7Td5BOsPgIsAIgxLgohHJrTdhiwMMbYCrSGEBYDE7PHzMru81D2zwYraYjobvatv9pKmli34q2kfW5xjsZlrKvZNESN+OCRdK5If8l0ezMgFqb2pmV5fb66C/2FxM9XOkVdXV3b3CGEcBNwd4zxoezrPwITYoztIYTPAAfGGL+ebbsFuAX4fnb7uhDCBOCWGONHtnWe+vr6bQ9EkiSpgEyaNGmLBbL5zFitAnK/Pro4xti+lbYq4N2c7etytvV6cJIkSUNJPvOSC4GPA2TXWL2U0/YMcHQIYUQIYQxQB7ycewxwCvBEshFLkiQVqHwuBW64K3AiUARMIxOaFscY78veFXgOmZA2K8Z4dwihFriZzGzVMmBqjHHNwL0NSZKkwddjsJIkSVJ+CvcWBUmSpCHGYCVJkpSIwUqSJCmRYfGswJ4ey6P8hBAOB66KMR4XQtgX+AnQReZO0C/GGDsHc3xDQQihDPgRsBdQAcwEXsFa9loIoQSYBwSgg8yNNUVYyz4LIewC1AMnkXlM2U+wln0SQngeWJl9+XvgRuA/yNT1kRjjtwdrbENNCOFi4O+AcjI/yx+ngD+bw2XGauNjeYCLyDyWR70QQvi/wE1kHl0EcC1waYzxaDI/zD45WGMbYj4DLM/W7RTgeqxlX/0tQIzxKDKP0boWa9ln2dB/I5nvHwRr2WchhBEAMcbjsr+mkfni7KlknkxyeAjhkMEc41ARQjgO+DBwFHAssCcF/tkcLsFqk8fykHm2oXrnDeC0nNeTyPyrATKPLTpxu49oaLoL+GbO63asZZ/EGO8l81UvAH8FLMFa9sdsMj/838m+tpZ9dxBQGUJ4JITwqxDCMUBFjPGNGGMX8DBwwuAOccg4mcz3Z84H7gceoMA/m8MlWO3Ee1OyAB0hhGFxGTSVGOPdwPqcTUXZ/0EANANjtv+ohp4Y4+oYY3MIoQr4L+BSrGWfZR+tdTPwPTL1tJZ9EEL4HNAYY3w4Z7O17Lu1ZILqycB5wI+z2zawnvkbT2YyZAqZWt5K5gkwBfvZHC7BaluP5VHf5F7PzuuxRcoIIewJ/Dfw0xjjbVjLfokxfhZ4P5n1ViNzmqxl/j4PnBRC+DVwMJlnvu6S024te+c14Gcxxq4Y42tk/mE/LqfdeuZvOfBwjLEtxhiBFjYNUgVXy+ESrLb1WB71zfPZa9/gY4vyln0qwSPA12OMP8putpZ9EEI4I7uoFTKzAZ3Ab61l78UYj4kxHhtjPA54Afhn4CFr2WefJ7uWN4SwO1AJrAkh7BNCKCIzk2U98/Mk8LEQQlG2lqOAxwr5szlcLofNJ/Ovsad477E86p+vAvNCCOVAA5nLMOrZN4CxwDdDCBvWWn0ZuM5a9to9wI9DCP8DlAFfIVM/P5dp+N943/0Q+EkI4Ukyd659nkzwvxUoIXNX4NODOL4hI8b4QHaN2jNkJoO+SOYuy4L9bPpIG0mSpESGy6VASZKkAWewkiRJSsRgJUmSlIjBSpIkKRGDlSRJUiIGK0mSpEQMVpIkSYn8f5v4Hj/btEOwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-9.76102356201562, pvalue=2.2801369826957385e-22)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-3.5827341440382523, pvalue=0.0003421596979870606)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-4.778767395366659, pvalue=1.797127049027708e-06)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=8.875095987137843, pvalue=8.622747411999423e-19)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-17.539903857541645, pvalue=1.7317786641919075e-67)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-22.411673734734613, pvalue=1.373041252538912e-107)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-15.981940590967973, pvalue=1.4750739635911822e-56)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in train.columns[2:]:\n",
    "    hist_plot_function(column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_text(text):\n",
    "    # Make text lowercase\n",
    "    text = text.lower()\n",
    "    # Remove links\n",
    "    text = re.sub('https?://\\S+|www\\.\\S+', '', text)\n",
    "    text = re.sub('<.*?>+', '', text)\n",
    "    # Remove mentions\n",
    "    text = re.sub('@\\S+', '', text)\n",
    "    # Remove punctuation\n",
    "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
    "    text = re.sub('\\n', '', text) \n",
    "    # Remove words containing numbers\n",
    "    text = re.sub('\\w*\\d\\w*', '', text)\n",
    "    # Tokenization\n",
    "    tokens = re.split('\\W+', text)\n",
    "    # Remove stopwords\n",
    "    tokens = [word for word in tokens if word not in STOP_WORDS]\n",
    "    # Remove short tokens\n",
    "    tokens = [word for word in tokens if len(word)>2]\n",
    "    text = ' '.join(tokens)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rober\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:3118: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "text                           0\n",
       "target                         0\n",
       "tweet_len                      0\n",
       "hashtag_count                  0\n",
       "hashtag_yn                     0\n",
       "mention_count                  0\n",
       "mention_yn                     0\n",
       "punctuation_count              0\n",
       "punctuation_yn                 0\n",
       "word_count                     0\n",
       "unique_word_count              0\n",
       "stopword_count                 0\n",
       "url_count                      0\n",
       "url_yn                         0\n",
       "avg_word_len                   0\n",
       "processed_text                 0\n",
       "processed_tweet_len            0\n",
       "processed_word_count           0\n",
       "processed_unique_word_count    0\n",
       "processed_avg_word_len         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Apply the process_text function on a raw tweet\n",
    "train['processed_text'] = train['text'].apply(lambda x: process_text(x))\n",
    "\n",
    "#Calculate the number of characters on a tweet excluding spaces\n",
    "train['processed_tweet_len'] = train['processed_text'].apply(lambda x: len(x) - x.count(' '))\n",
    "\n",
    "#Calculate the number of words on a tweet\n",
    "train['processed_word_count'] = train['processed_text'].apply(lambda x: len(str(x).split()))\n",
    "\n",
    "#Calculate the number of unique words on a tweet\n",
    "train['processed_unique_word_count'] = train['processed_text'].apply(lambda x: len(set(str(x).split())))\n",
    "\n",
    "#Calculate the average word length on a tweet\n",
    "train['processed_avg_word_len'] = train['processed_text'].apply(lambda x: round(np.mean([len(w) for w in str(x).split()]),2))\n",
    "train['processed_avg_word_len'].fillna(0, inplace=True)\n",
    "\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-18.28331562647583, pvalue=4.6040104909310724e-73)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-11.123116551189279, pvalue=1.6230753874171583e-28)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-10.490958980231994, pvalue=1.4304015429537798e-25)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-10.390149889617302, pvalue=4.299734228703451e-25)\n"
     ]
    },
    {
     "data": {
      "image/png": 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+vEtRkiSpMAOXJElSYQYuSZKkwpzDJUnSRmCwb6yasPkmbLvFpoO2v+HOwCVJ0kZgsG+sesMu2zUduJYvX87BBx/MAQccwPTp09lxxx0HdOw//vGPPPjgg7zpTW8a0H6GkpcUJUnSkDjttNMGHLYA5syZw/333z8ILRo6jnBJkqRB9/TTT3PyySezePFiXvSiFwFw1FFHMWPGDBYtWsS555777MOhzz//fKAKZEuWLOGJJ57gHe94B0ceeSRXX301N954Ix0dHey5556cfPLJfPGLX6S7u5tXvepVvOAFL+CMM84AYKuttuKss85i7ty5nH/++YwePZojjjiCQw89tGXvQx8DlyRJGnSzZs3iZS97GSeeeCL//d//zb333vts2Xe/+10OPPBAjj76aL73ve+xePFinnjiCaZNm8ab3/xmHnnkEY466iiOPPJIbrjhBj7xiU+wxx57cM0119Db28txxx3Hww8/zAEHHMARRxzBWWedxS677MJ1113Hl770Jf72b/+W5cuXc91117XwHfhLBi5JkjToHnroIfbbbz8AJk+ezKhRf44cJ5xwAl/4whd473vfy8SJE9l9993Zdttt+epXv8rtt9/OFltswcqVKwE4++yzueKKKzj//PPZY489WP2RhPPnz+fTn/40AM888wwvfvGLAZ79f7swcEmSpEG3884789Of/pSpU6cyd+7cZwMUwE033cRhhx3Gxz72MS677DK++c1vsmTJEvbYYw+OPPJI5syZw1133QXAN7/5TT796U+z6aabcvTRR/OTn/yEjo4Oenp6gCpYnXvuuey44450dnayYEF1Y0BHR3tNUzdwSZK0EZiw+Sa8YZfBe5j1hM3X/szbd73rXZx66qm8853vZOedd2b06NHPlu22226ccsopjB07ltGjR/OZz3yGP/zhD8yYMYObbrqJrbbaipEjR7JixQoign/4h39g6623ZuLEiUyePJktttiCSy+9lFe84hXMmDGDj33sY6xatQqAM888k0cffXTQznOwjFh9aK6ddHZ29k6ZMqX4cdrlye4DlY8sGUbPUuzh9bvv0upmqMFw+ZwMN/ZL+2mXPlmwYAHbbTd4AWtDt2zZMjbbbLNB219/729nZydTpkwZ0V/99hpvkyRJGoYMXJIkSYUZuCRJkgozcEmSNAx1dHTQ3d3d6mYMS93d3et9F6R3KUqSNAxts802LFy4kCVLlrS6KW3hySefZPz48YOyr46ODrbZZpv12sbAJUnSMDRixAgmTJjQ6ma0jccee6yld216SVGSJKkwA5ckSVJhBi5JkqTCDFySJEmFrXPSfER0ADOBycBy4JjMnLdane2Ae4DdMrM7Ik4B3lIXbwXskJk7RMRJwNFA3/Nnjs/MHJxTkSRJak/N3KV4KDAmM/eJiL2BC4BD+goj4iDgHGBi37rMPKdeR0TcDHysLtoTeE9mdg5O8yVJktpfM5cU9wVuA8jMOcBeq5X3AFOBhatvGBGHA09k5nfqVVOAUyPi7og49Tm3WpIkaQPSzAjXlsCTDcurImJUZq4EyMw7ACKiv21PBd7ZsPwN4P8Ci4FZEfG2zLx5bQfv6upqookD093dPSTHKW3BM6NZsOCvcu8GaZcx44ZFnwwnw+VzMtzYL+3HPmlPre6XZgLXYmBcw3JHX9ham4h4ObCob75XRIwAPpuZT9bL3wZeBaw1cE2aNKmJJg5MV1fXkByntI5HlrDdkuFxH8So0T1MmrRLq5uhBsPlczLc2C/txz5pT0PRL52da54x1cy382zgrQD1HK4HmjzuVODWhuUtgZ9HxBZ1+HoT4FwuSZI07DUTuGYB3RFxD3ARcGJEnBQRf7eO7QJ4uG+hHtn6V+D7wA+AX2TmLc+t2ZIkSRuOdV5SzMwe4ITVVj/YT72dVlv+QD91rgKuWr8mSpIkbdiGx4QfSZKkNmbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmHN/PCpNhA9S59m1cLHW92MQTF683HrriRJ0gbCwDWM9C57mmd+/+tWN2NQdGz/0lY3QZKkQeMlRUmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCRq2rQkR0ADOBycBy4JjMnLdane2Ae4DdMrM7IkYAvwceqqv8MDNPjYi3A58EVgJXZOblg3cqkiRJ7WmdgQs4FBiTmftExN7ABcAhfYURcRBwDjCxYZuXAPdn5tsb6o0GLgJeDTwNzI6ImzLzfwZ+GpIkSe2rmcC1L3AbQGbOiYi9VivvAaYCnQ3rpgDPj4jvA8uAE4FNgXmZ+QRARNwN7AdcN6AzkCRJanPNBK4tgScblldFxKjMXAmQmXcARETjNn8Czs7M6yJiX+BrVKGrcT9LgPHrOnhXV1cTTRyY7u7uITlOaUuWLGfp0qWtbsag6FnVMyz6ZDgZLp+T4cZ+aT/2SXtqdb80E7gWA+Maljv6wtZa/JhqnhaZeXdEPJ8qYDXuZxywaF0HnzRpUhNNHJiurq4hOU5pc+fOZ+zYsa1uxqDoGNkxLPpkOBkun5Phxn5pP/ZJexqKfuns7FxjWTN3Kc4G3gpQz+F6oIltPgV8pN5mMvBbYC7w0ojYJiI2AV4P/LCJfUmSJG3QmhnhmgUcGBH3ACOA6RFxEtV8rG+tYZtzgK9FxDSqka73ZeYz9XbfoQp6V2TmHwZ+CpIkSe1tnYErM3uAE1Zb/WA/9XZqeP0EMK2fOjcBN613KyVJkjZg/vCpJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKmwUeuqEBEdwExgMrAcOCYz561WZzvgHmC3zOyOiPHA14AtgU2AkzLzhxFxOPBvwO/qTT+VmXcN2tlIkiS1oXUGLuBQYExm7hMRewMXAIf0FUbEQcA5wMSGbU4C7szMz0ZEAF8H9qz/+2hmXj9YJyBJktTumglc+wK3AWTmnIjYa7XyHmAq0Nmw7iKq0bC+Y3TXr6cAr4qIjwA/Aj6WmSufY9slSZI2CM0Eri2BJxuWV0XEqL6glJl3AFQDWZXMXFSv24Hq0uJH6qI7gBuBXwFfAE4APr+2g3d1dTVzHgPS3d09JMcpbcmS5SxdurTVzRgUPat6hkWfDCfD5XMy3Ngv7cc+aU+t7pdmAtdiYFzDckczo1IRsRvwDeDkhnlaVzSEsf8H/P269jNp0qQmmjgwXV1dQ3Kc0ubOnc/YsWNb3YxB0TGyY1j0yXAyXD4nw4390n7sk/Y0FP3S2dm5xrJm7lKcDbwVoJ7D9cC6NoiIlwPXAUdm5q31uhHAzyLiBXW1A/jLy5CSJEnDUjMjXLOAAyPiHmAEMD0iTgLmZea31rDN2cAY4HP1pcYnM/OQiDgGuCEilgFzgcsHfAaSJEltbp2BKzN7qOZaNXqwn3o7Nbw+ZPXyev3twO3r10RJkqQNmz98KkmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqbNS6KkREBzATmAwsB47JzHmr1dkOuAfYLTO7I2Iz4GvA9sAS4L2ZuSAi3g58ElgJXJGZlw/q2UiSJLWhZka4DgXGZOY+wCnABY2FEXEQcDswsWH1/wYeyMz9gCuBj0fEaOAi4M3AG4DjImKHgZ+CJElSe2smcO0L3AaQmXOAvVYr7wGmAgv72wa4tS6fBMzLzCcycwVwN7Dfc2+6JEnShmGdlxSBLYEnG5ZXRcSozFwJkJl3AETEmrZZAozvZz9969eqq6uriSYOTHd395Acp7QlS5azdOnSVjdjUPSs6hkWfTKcDJfPyXBjv7Qf+6Q9tbpfmglci4FxDcsdfWGryW3GAYv62U/f+rWaNGlSE00cmK6uriE5Tmlz585n7NixrW7GoOgY2TEs+mQ4GS6fk+HGfmk/9kl7Gop+6ezsXGNZM5cUZwNvBYiIvYEH1mcb4GDgB0AX8NKI2CYiNgFeD/ywiX1JkiRt0JoZ4ZoFHBgR9wAjgOkRcRLVfKxvrWGbS4GvRsTdwArgyMx8pt7uO1RB74rM/MPAT0GSJKm9rTNwZWYPcMJqqx/sp95ODa+XAu/op85NwE3r3UpJkqQNmD98KkmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqbNS6KkREBzATmAwsB47JzHkN5ccCxwMrgTMy8+aI+CywR11lB2BRZu4dERcDrwOW1GWHZOaTg3Y2kiRJbWidgQs4FBiTmftExN7ABcAhABGxA/AhYC9gDHB3RNyRmR+py0cDdwPH1vvaEzgoMx8b3NOQJElqX81cUtwXuA0gM+dQhas+rwFmZ+byeqRqHrB7Q/kHgdsz84F6pOylwBcjYnZEvH9QzkCSJKnNNTPCtSXQeNlvVUSMysyV/ZQtAcYDRMQmVJcaX1OXbQ5cAlwIjAS+HxE/zsyfre3gXV1dzZzHgHR3dw/JcUpbsmQ5S5cubXUzBkXPqp5h0SfDyXD5nAw39kv7sU/aU6v7pZnAtRgY17DcUYet/srGAYvq11OB/2qYo7UU+FxmLgWIiO9RzQtba+CaNGlSE00cmK6uriE5Tmlz585n7NixrW7GoOgY2TEs+mQ4GS6fk+HGfmk/9kl7Gop+6ezsXGNZM5cUZwNvBajncD3QUPYjYL+IGBMR44FJwM/rsqnArQ11X0Y1x2tkPbdrX+D+Zk9CkiRpQ9VM4JoFdEfEPcBFwIkRcVJE/F1m/g9wMfAD4HvAaZnZXW8XwMN9O8nMLuBqYA5wF3BlZv5i8E5FkiSpPa3zkmJm9gAnrLb6wYbyy4HL+9luWj/rzgPOW/9mSpIkbbj84VNJkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMH5UvUsAAAr1SURBVAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmGj1lUhIjqAmcBkYDlwTGbOayg/FjgeWAmckZk3R8Q2wC+Bn9fVZmXm5/qrO6hnI0mS1IbWGbiAQ4ExmblPROwNXAAcAhAROwAfAvYCxgB3R8QdwJ7A1zPzg307WVPdzFw+mCckSZLUbpoJXPsCtwFk5pyI2Kuh7DXA7Do0LY+IecDuwBRgz4i4C3iUKmi9eg117xu0s5EkSWpDzQSuLYEnG5ZXRcSozFzZT9kSYDzwINCZmd+NiHcBlwA3rqHuWnV1dTXRxIHp7u4ekuOUtmTJcpYuXdrqZgyKnlU9w6JPhpPh8jkZbuyX9mOftKdW90szgWsxMK5huaMOW/2VjQMWAfcCfd/8s4DPAFeuoe5aTZo0qYkmDkxXV9eQHKe0uXPnM3bs2FY3Y1B0jOwYFn0ynAyXz8lwY7+0H/ukPQ1Fv3R2dq6xrJm7FGcDbwWo53A90FD2I2C/iBgTEeOBSVQT5b8E/H1d5wCgcy11JUmShrVmRrhmAQdGxD3ACGB6RJwEzMvMb0XExcAPqMLbaZnZHRGnAFdExD8BT1Pd2fg//dUtcVKSJEntZJ2BKzN7gBNWW/1gQ/nlwOWrbfMr4I397Ouv6kqSJA13/vCpJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgob1eoGtIOxzyxnxfxsdTMGrGfFqlY3QZIk9cPABXQ8tZhlD9zb6mYM3Mte2+oWSJKkfnhJUZIkqTADlyRJUmHrvKQYER3ATGAysBw4JjPnNZQfCxwPrATOyMybI+JFwBX1/kcAx2VmRsRJwNHAgnrz4zNzw588JUmStBbNzOE6FBiTmftExN7ABcAhABGxA/AhYC9gDHB3RNwBnA58PjNvjIiDgLOBw4E9gfdkZufgn4okSVJ7auaS4r7AbQCZOYcqXPV5DTA7M5dn5pPAPGB34J+Bb9d1RgHd9espwKkRcXdEnDoI7ZckSWp7zYxwbQk82bC8KiJGZebKfsqWAOMz8zGAiAjgfKpRMoBvAP8XWAzMioi3ZebNazt4V1dXUycyEGOeWcljCx4rfpzSlv/NCpYuXdrqZgyKnlU9Q9L3al53d7d90obsl/Zjn7SnVvdLM4FrMTCuYbmjDlv9lY0DFgFExBup5n4dVc/fGgF8th4JIyK+DbwKWGvgmjRpUjPnMSC/e2IBW263bfHjlLZs000YO3Zsq5sxKDpGdgxJ36t5XV1d9kkbsl/aj33SnoaiXzo71zxjqpnANRt4O/DNeg7XAw1lPwLOjIgxwKbAJODnddj6HPCWzPxNXXfLumwS8DTwJqqJ9ZIkScNaM4FrFnBgRNxDdcfh9Ppuw3mZ+a2IuBj4AdV8sNMyszsiPgtsAny1uqpIZubxEfGvwPep7na8MzNvKXBOkiRJbWWdgSsze4ATVlv9YEP55cDlq20zeQ37ugq4av2bKUmStOHyh08lSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFjWp1A9rB05uO4/Gtd2p1MwZsxcgxrW6CJEnqh4ELeHLlCO79zeJWN2PAJm/X6hZIkqT+eElRkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgob1eoGSP0ZCayYn61uxqDo2HoCo7bZttXNkCS10DoDV0R0ADOBycBy4JjMnNdQfixwPLASOCMzb46IbYFrgM2APwLTM3Npf3UH+4Q0PIx4ZgXL5tzb6mYMis323h8MXJK0UWtmhOtQYExm7hMRewMXAIcARMQOwIeAvYAxwN0RcQfwSeCazPxKRJwCHB8RX++vbmYuH/Sz0gZv5aab86utd2p1MwbFDr2bsEOrGyFJaqlmAte+wG0AmTknIvZqKHsNMLsOTcsjYh6we73NWXWdW+vX89dQ975BORMNK0tW9nLvbxa3uhmDYupOPQYuSRukx55azuNPr2h1MwbF8hGbtPT4zQSuLYEnG5ZXRcSozFzZT9kSYPxq6/tb17h+rTo7O5to4sCMGgWve83OxY9T3tPD5DwAlg+bc1m2bNGQ/DkeCsPlPIYb+6X92CftqZX90kzgWgyMa1juqMNWf2XjgEUN65f1s271ums0ZcqUEU20T5Ikqa0187MQs4G3AtRzuB5oKPsRsF9EjImI8cAk4OeN2wAHAz9YS11JkqRhbURvb+9aKzTcpbg7MAKYThWm5mXmt+o7D4+jCm9nZeb1ETER+CrVKNZjwJGZ+XR/dQudlyRJUttYZ+CSJEnSwPhL85IkSYUZuCRJkgozcEmSJBW20T5LcV2PLNLQi4jXAudm5v4RsQvwFaCX6m7WD2RmTyvbtzGJiNHAFcBOwKbAGcBc7JOWioiRwOVAAKuobmIagf3SchGxPdAJHEj1+LqvYJ+0VET8hD///uevgMuAz1H1z+2Z+emhbM/GPML17COLgFOoHlmkFomIjwJfonrsE8CFwMczcz+qL5RDWtW2jdS7gcfr9/9g4PPYJ+3g7QCZ+TqqR6hdiP3ScvU/UC6j+u1JsE9aLiLGAGTm/vV/04EvAEdSPQ3ntRGx51C2aWMOXH/xyCKqZzyqdeYDhzcsTwHuql/fCkwd8hZt3K4DPtGwvBL7pOUy80aqn9YB+BvgEeyXdnA+1Zf5H+tl+6T1JgNjI+L2iPheRLwe2DQz52dmL/Ad4IChbNDGHLj6fWRRqxqzsat/k+2ZhlUj6g8FNPkYKA2ezHwqM5dExDjgP4CPY5+0hcxcGRFfBS6h6hv7pYUi4n3Agsz8TsNq+6T1llIF4YOAE4Av1+v6DHm/bMyBa22PLFLrNc53WOdjoDT4IuKFwPeBqzLzGuyTtpGZ7wVeRjWfa7OGIvtl6L0fODAi/hPYA7gS2L6h3D5pjV8CX8vM3sz8JdUAyzYN5UPeLxtz4FrbI4vUej+JiP3r132Ph9IQqZ8WcTvwscy8ol5tn7RYRBwVEafWi0upQvCP7ZfWyczXZ+YbMnN/4KfAe4Bb7ZOWez/13OyI2BEYCzwdES+JiBFUI19D2i8b8yW0WVT/KrmHPz+ySO3jn4HLI2IToIvq0omGzr8CWwOfiIi+uVwfBi62T1rqBuDLEfFfwGjgI1R94Welvfj3V+v9O/CViLib6m7R91P9A+VqYCTVXYr3DmWDfLSPJElSYRvzJUVJkqQhYeCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhf1/KEEnJuPQh9sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in train.columns[16:]:\n",
    "    hist_plot_function(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>target</th>\n",
       "      <th>tweet_len</th>\n",
       "      <th>hashtag_count</th>\n",
       "      <th>hashtag_yn</th>\n",
       "      <th>mention_count</th>\n",
       "      <th>mention_yn</th>\n",
       "      <th>punctuation_count</th>\n",
       "      <th>punctuation_yn</th>\n",
       "      <th>word_count</th>\n",
       "      <th>unique_word_count</th>\n",
       "      <th>stopword_count</th>\n",
       "      <th>url_count</th>\n",
       "      <th>url_yn</th>\n",
       "      <th>avg_word_len</th>\n",
       "      <th>processed_tweet_len</th>\n",
       "      <th>processed_word_count</th>\n",
       "      <th>processed_unique_word_count</th>\n",
       "      <th>processed_avg_word_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "      <td>7613.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.43</td>\n",
       "      <td>87.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.27</td>\n",
       "      <td>6.84</td>\n",
       "      <td>0.96</td>\n",
       "      <td>14.90</td>\n",
       "      <td>14.34</td>\n",
       "      <td>4.52</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.52</td>\n",
       "      <td>6.13</td>\n",
       "      <td>51.24</td>\n",
       "      <td>8.34</td>\n",
       "      <td>8.14</td>\n",
       "      <td>6.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.50</td>\n",
       "      <td>29.15</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.44</td>\n",
       "      <td>4.61</td>\n",
       "      <td>0.21</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.28</td>\n",
       "      <td>3.42</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.68</td>\n",
       "      <td>21.06</td>\n",
       "      <td>3.39</td>\n",
       "      <td>3.26</td>\n",
       "      <td>1.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>67.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.88</td>\n",
       "      <td>36.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.00</td>\n",
       "      <td>92.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.93</td>\n",
       "      <td>52.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>6.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.00</td>\n",
       "      <td>114.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.06</td>\n",
       "      <td>67.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>6.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.00</td>\n",
       "      <td>138.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.33</td>\n",
       "      <td>118.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        target  tweet_len  hashtag_count  hashtag_yn  mention_count  \\\n",
       "count  7613.00    7613.00        7613.00     7613.00        7613.00   \n",
       "mean      0.43      87.11           0.45        0.23           0.36   \n",
       "std       0.50      29.15           1.10        0.42           0.72   \n",
       "min       0.00       6.00           0.00        0.00           0.00   \n",
       "25%       0.00      67.00           0.00        0.00           0.00   \n",
       "50%       0.00      92.00           0.00        0.00           0.00   \n",
       "75%       1.00     114.00           0.00        0.00           1.00   \n",
       "max       1.00     138.00          13.00        1.00           8.00   \n",
       "\n",
       "       mention_yn  punctuation_count  punctuation_yn  word_count  \\\n",
       "count     7613.00            7613.00         7613.00     7613.00   \n",
       "mean         0.27               6.84            0.96       14.90   \n",
       "std          0.44               4.61            0.21        5.73   \n",
       "min          0.00               0.00            0.00        1.00   \n",
       "25%          0.00               3.00            1.00       11.00   \n",
       "50%          0.00               6.00            1.00       15.00   \n",
       "75%          1.00              10.00            1.00       19.00   \n",
       "max          1.00              61.00            1.00       31.00   \n",
       "\n",
       "       unique_word_count  stopword_count  url_count   url_yn  avg_word_len  \\\n",
       "count            7613.00         7613.00    7613.00  7613.00       7613.00   \n",
       "mean               14.34            4.52       0.62     0.52          6.13   \n",
       "std                 5.28            3.42       0.66     0.50          1.68   \n",
       "min                 1.00            0.00       0.00     0.00          2.25   \n",
       "25%                11.00            2.00       0.00     0.00          4.88   \n",
       "50%                14.00            4.00       1.00     1.00          5.93   \n",
       "75%                18.00            7.00       1.00     1.00          7.06   \n",
       "max                29.00           19.00       4.00     1.00         19.33   \n",
       "\n",
       "       processed_tweet_len  processed_word_count  processed_unique_word_count  \\\n",
       "count              7613.00               7613.00                      7613.00   \n",
       "mean                 51.24                  8.34                         8.14   \n",
       "std                  21.06                  3.39                         3.26   \n",
       "min                   0.00                  0.00                         0.00   \n",
       "25%                  36.00                  6.00                         6.00   \n",
       "50%                  52.00                  8.00                         8.00   \n",
       "75%                  67.00                 11.00                        11.00   \n",
       "max                 118.00                 21.00                        20.00   \n",
       "\n",
       "       processed_avg_word_len  \n",
       "count                 7613.00  \n",
       "mean                     6.24  \n",
       "std                      1.76  \n",
       "min                      0.00  \n",
       "25%                      5.45  \n",
       "50%                      6.10  \n",
       "75%                      6.80  \n",
       "max                     50.00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(train.describe(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>target</th>\n",
       "      <th>tweet_len</th>\n",
       "      <th>hashtag_count</th>\n",
       "      <th>hashtag_yn</th>\n",
       "      <th>mention_count</th>\n",
       "      <th>mention_yn</th>\n",
       "      <th>punctuation_count</th>\n",
       "      <th>punctuation_yn</th>\n",
       "      <th>word_count</th>\n",
       "      <th>unique_word_count</th>\n",
       "      <th>stopword_count</th>\n",
       "      <th>url_count</th>\n",
       "      <th>url_yn</th>\n",
       "      <th>avg_word_len</th>\n",
       "      <th>processed_tweet_len</th>\n",
       "      <th>processed_word_count</th>\n",
       "      <th>processed_unique_word_count</th>\n",
       "      <th>processed_avg_word_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4342.0</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.0</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "      <td>4342.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>81.98</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.31</td>\n",
       "      <td>6.31</td>\n",
       "      <td>0.94</td>\n",
       "      <td>14.70</td>\n",
       "      <td>14.10</td>\n",
       "      <td>4.82</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.41</td>\n",
       "      <td>5.87</td>\n",
       "      <td>47.54</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.81</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.80</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.46</td>\n",
       "      <td>4.83</td>\n",
       "      <td>0.24</td>\n",
       "      <td>6.16</td>\n",
       "      <td>5.65</td>\n",
       "      <td>3.63</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.74</td>\n",
       "      <td>21.43</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.43</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>58.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.60</td>\n",
       "      <td>31.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>87.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.54</td>\n",
       "      <td>48.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>110.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.82</td>\n",
       "      <td>64.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>6.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>138.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>61.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>29.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>16.86</td>\n",
       "      <td>116.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       target  tweet_len  hashtag_count  hashtag_yn  mention_count  \\\n",
       "count  4342.0    4342.00        4342.00      4342.0        4342.00   \n",
       "mean      0.0      81.98           0.39         0.2           0.43   \n",
       "std       0.0      30.80           1.01         0.4           0.78   \n",
       "min       0.0       6.00           0.00         0.0           0.00   \n",
       "25%       0.0      58.00           0.00         0.0           0.00   \n",
       "50%       0.0      87.00           0.00         0.0           0.00   \n",
       "75%       0.0     110.00           0.00         0.0           1.00   \n",
       "max       0.0     138.00          11.00         1.0           8.00   \n",
       "\n",
       "       mention_yn  punctuation_count  punctuation_yn  word_count  \\\n",
       "count     4342.00            4342.00         4342.00     4342.00   \n",
       "mean         0.31               6.31            0.94       14.70   \n",
       "std          0.46               4.83            0.24        6.16   \n",
       "min          0.00               0.00            0.00        1.00   \n",
       "25%          0.00               3.00            1.00       10.00   \n",
       "50%          0.00               6.00            1.00       15.00   \n",
       "75%          1.00               9.00            1.00       19.00   \n",
       "max          1.00              61.00            1.00       31.00   \n",
       "\n",
       "       unique_word_count  stopword_count  url_count   url_yn  avg_word_len  \\\n",
       "count            4342.00         4342.00    4342.00  4342.00       4342.00   \n",
       "mean               14.10            4.82       0.51     0.41          5.87   \n",
       "std                 5.65            3.63       0.67     0.49          1.74   \n",
       "min                 1.00            0.00       0.00     0.00          2.25   \n",
       "25%                10.00            2.00       0.00     0.00          4.60   \n",
       "50%                14.00            4.00       0.00     0.00          5.54   \n",
       "75%                18.00            7.00       1.00     1.00          6.82   \n",
       "max                29.00           19.00       4.00     1.00         16.86   \n",
       "\n",
       "       processed_tweet_len  processed_word_count  processed_unique_word_count  \\\n",
       "count              4342.00               4342.00                      4342.00   \n",
       "mean                 47.54                  7.98                         7.81   \n",
       "std                  21.43                  3.55                         3.43   \n",
       "min                   0.00                  0.00                         0.00   \n",
       "25%                  31.00                  5.00                         5.00   \n",
       "50%                  48.00                  8.00                         8.00   \n",
       "75%                  64.00                 11.00                        10.00   \n",
       "max                 116.00                 19.00                        19.00   \n",
       "\n",
       "       processed_avg_word_len  \n",
       "count                 4342.00  \n",
       "mean                     6.05  \n",
       "std                      1.65  \n",
       "min                      0.00  \n",
       "25%                      5.25  \n",
       "50%                      5.90  \n",
       "75%                      6.60  \n",
       "max                     50.00  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(train[train['target']==0].describe(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>tweet_len</th>\n",
       "      <th>hashtag_count</th>\n",
       "      <th>hashtag_yn</th>\n",
       "      <th>mention_count</th>\n",
       "      <th>mention_yn</th>\n",
       "      <th>punctuation_count</th>\n",
       "      <th>punctuation_yn</th>\n",
       "      <th>word_count</th>\n",
       "      <th>unique_word_count</th>\n",
       "      <th>stopword_count</th>\n",
       "      <th>url_count</th>\n",
       "      <th>url_yn</th>\n",
       "      <th>avg_word_len</th>\n",
       "      <th>processed_tweet_len</th>\n",
       "      <th>processed_word_count</th>\n",
       "      <th>processed_unique_word_count</th>\n",
       "      <th>processed_avg_word_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3271.0</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "      <td>3271.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>93.91</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.21</td>\n",
       "      <td>7.54</td>\n",
       "      <td>0.98</td>\n",
       "      <td>15.17</td>\n",
       "      <td>14.66</td>\n",
       "      <td>4.13</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.66</td>\n",
       "      <td>6.47</td>\n",
       "      <td>56.15</td>\n",
       "      <td>8.83</td>\n",
       "      <td>8.58</td>\n",
       "      <td>6.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.26</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.40</td>\n",
       "      <td>4.20</td>\n",
       "      <td>0.14</td>\n",
       "      <td>5.10</td>\n",
       "      <td>4.71</td>\n",
       "      <td>3.07</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.52</td>\n",
       "      <td>19.49</td>\n",
       "      <td>3.10</td>\n",
       "      <td>2.96</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>77.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.48</td>\n",
       "      <td>42.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.35</td>\n",
       "      <td>56.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>6.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>116.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>7.31</td>\n",
       "      <td>70.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>134.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>19.33</td>\n",
       "      <td>118.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       target  tweet_len  hashtag_count  hashtag_yn  mention_count  \\\n",
       "count  3271.0    3271.00        3271.00     3271.00        3271.00   \n",
       "mean      1.0      93.91           0.52        0.27           0.28   \n",
       "std       0.0      25.26           1.21        0.44           0.63   \n",
       "min       1.0      13.00           0.00        0.00           0.00   \n",
       "25%       1.0      77.00           0.00        0.00           0.00   \n",
       "50%       1.0     100.00           0.00        0.00           0.00   \n",
       "75%       1.0     116.50           1.00        1.00           0.00   \n",
       "max       1.0     134.00          13.00        1.00           5.00   \n",
       "\n",
       "       mention_yn  punctuation_count  punctuation_yn  word_count  \\\n",
       "count     3271.00            3271.00         3271.00     3271.00   \n",
       "mean         0.21               7.54            0.98       15.17   \n",
       "std          0.40               4.20            0.14        5.10   \n",
       "min          0.00               0.00            0.00        2.00   \n",
       "25%          0.00               5.00            1.00       11.00   \n",
       "50%          0.00               7.00            1.00       15.00   \n",
       "75%          0.00              10.00            1.00       19.00   \n",
       "max          1.00              27.00            1.00       30.00   \n",
       "\n",
       "       unique_word_count  stopword_count  url_count   url_yn  avg_word_len  \\\n",
       "count            3271.00         3271.00    3271.00  3271.00       3271.00   \n",
       "mean               14.66            4.13       0.77     0.66          6.47   \n",
       "std                 4.71            3.07       0.63     0.47          1.52   \n",
       "min                 2.00            0.00       0.00     0.00          3.00   \n",
       "25%                11.00            2.00       0.00     0.00          5.48   \n",
       "50%                15.00            3.00       1.00     1.00          6.35   \n",
       "75%                18.00            6.00       1.00     1.00          7.31   \n",
       "max                28.00           17.00       3.00     1.00         19.33   \n",
       "\n",
       "       processed_tweet_len  processed_word_count  processed_unique_word_count  \\\n",
       "count              3271.00               3271.00                      3271.00   \n",
       "mean                 56.15                  8.83                         8.58   \n",
       "std                  19.49                  3.10                         2.96   \n",
       "min                   0.00                  0.00                         0.00   \n",
       "25%                  42.00                  7.00                         6.00   \n",
       "50%                  56.00                  9.00                         9.00   \n",
       "75%                  70.00                 11.00                        11.00   \n",
       "max                 118.00                 21.00                        20.00   \n",
       "\n",
       "       processed_avg_word_len  \n",
       "count                 3271.00  \n",
       "mean                     6.48  \n",
       "std                      1.86  \n",
       "min                      0.00  \n",
       "25%                      5.75  \n",
       "50%                      6.36  \n",
       "75%                      7.00  \n",
       "max                     50.00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(train[train['target']==1].describe(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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