Model_2_(Negativereason_Classification).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arpit Sidana\\Anaconda2\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "C:\\Users\\Arpit Sidana\\Anaconda2\\lib\\site-packages\\sklearn\\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n",
      "C:\\Users\\Arpit Sidana\\Anaconda2\\lib\\site-packages\\sklearn\\learning_curve.py:23: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (14,6)\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import neighbors\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.linear_model import SGDClassifier \n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import sklearn.metrics as  metrics\n",
    "from IPython.display import display\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn import tree\n",
    "from sklearn import naive_bayes\n",
    "from sklearn.learning_curve import learning_curve\n",
    "from sklearn.metrics import  make_scorer\n",
    "from sklearn.svm import SVC\n",
    "from mlxtend.feature_selection import plot_sequential_feature_selection as plot_sfs\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "import os\n",
    "os.getcwd()\n",
    "\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.cross_validation import  cross_val_score\n",
    "from sklearn.model_selection import  cross_val_score\n",
    "from sklearn import cross_validation\n",
    "\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "wordnet_lemmatizer = WordNetLemmatizer()\n",
    "import re\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from nltk.tokenize import TreebankWordTokenizer\n",
    "from nltk.stem.snowball import EnglishStemmer\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "def my_scorer_function(y_test, y_predicted):\n",
    "    return precision_recall_fscore_support(y_test, y_predicted,pos_label=2)[2][2]\n",
    "def two_scorer():\n",
    "    return make_scorer(my_scorer_function, greater_is_better=True)\n",
    "#import emoji\n",
    "import unicodedata as uni\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "import sys\n",
    "nb_stdout = sys.stdout\n",
    "from vaderSentiment.vaderSentiment import sentiment as vaderSentiment\n",
    "sys.stdout = nb_stdout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insert working directory below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#os.chdir(\"<insert working directory path here>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0     2910\n",
       "3.0     1665\n",
       "2.0     1190\n",
       "8.0      847\n",
       "6.0      724\n",
       "1.0      580\n",
       "5.0      529\n",
       "7.0      481\n",
       "10.0     178\n",
       "9.0       74\n",
       "Name: negativereason, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Tweets.csv\")\n",
    "\n",
    "# Discretize target class\n",
    "class_dict={\"Bad Flight\":1,\"Can't Tell\":2,\"Late Flight\":3,\"Customer Service Issue\":4,\"Flight Booking Problems\":5,\n",
    "            \"Lost Luggage\":6,\"Flight Attendant Complaints\":7,\"Cancelled Flight\":8,\"Damaged Luggage\":9,\"longlines\":10}\n",
    "df.negativereason=df.negativereason.map(class_dict)\n",
    "\n",
    "# Observe class balance in given dataset\n",
    "df.negativereason.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep only tweets which have a negative reason"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.dropna(subset=[\"negativereason\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing values (as % of total dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                         0.000000\n",
       "airline_sentiment                0.000000\n",
       "airline_sentiment_confidence     0.000000\n",
       "negativereason                   0.000000\n",
       "negativereason_confidence        0.000000\n",
       "airline                          0.000000\n",
       "airline_sentiment_gold          99.651340\n",
       "name                             0.000000\n",
       "negativereason_gold             99.651340\n",
       "retweet_count                    0.000000\n",
       "text                             0.000000\n",
       "tweet_coord                     92.776204\n",
       "tweet_created                    0.000000\n",
       "tweet_location                  34.234038\n",
       "user_timezone                   34.539115\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis=0)/len(df)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Transpose class types and map to airlines\n",
    "pd.crosstab(df.airline,df.negativereason)\n",
    "\n",
    "# Append trasposition to dataframe\n",
    "df1=pd.concat([df, pd.get_dummies(df.airline, prefix=None, prefix_sep='_')], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Select relevant columns for analysis\n",
    "df=df1.ix[:,[\"negativereason\",\"negativereason_confidence\",\"American\",\"Delta\",\"Southwest\",\"US Airways\",\"United\",\"Virgin America\",\"retweet_count\",\"text\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>American</th>\n",
       "      <th>Delta</th>\n",
       "      <th>Southwest</th>\n",
       "      <th>US Airways</th>\n",
       "      <th>United</th>\n",
       "      <th>Virgin America</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica seriously would pay $30 a fligh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.3684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica SFO-PDX schedule is still MIA.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica  I flew from NYC to SFO last we...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    negativereason  negativereason_confidence  American  Delta  Southwest  \\\n",
       "3              1.0                     0.7033       0.0    0.0        0.0   \n",
       "4              2.0                     1.0000       0.0    0.0        0.0   \n",
       "5              2.0                     0.6842       0.0    0.0        0.0   \n",
       "15             3.0                     0.3684       0.0    0.0        0.0   \n",
       "17             1.0                     1.0000       0.0    0.0        0.0   \n",
       "\n",
       "    US Airways  United  Virgin America  retweet_count  \\\n",
       "3          0.0     0.0             1.0              0   \n",
       "4          0.0     0.0             1.0              0   \n",
       "5          0.0     0.0             1.0              0   \n",
       "15         0.0     0.0             1.0              0   \n",
       "17         0.0     0.0             1.0              0   \n",
       "\n",
       "                                                 text  \n",
       "3   @VirginAmerica it's really aggressive to blast...  \n",
       "4   @VirginAmerica and it's a really big bad thing...  \n",
       "5   @VirginAmerica seriously would pay $30 a fligh...  \n",
       "15      @VirginAmerica SFO-PDX schedule is still MIA.  \n",
       "17  @VirginAmerica  I flew from NYC to SFO last we...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Engineering - Dictionary Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load dictionaries for positive and negative words (for sentiments)\n",
    "pos=pd.read_csv(\"Positive_Dictionary_Combined.txt\",header=None,names=['Positive_Word'],squeeze=False)\n",
    "neg=pd.read_csv(\"Negative_Dictionary_Combined.txt\",header=None,names=['Negative_Word'],squeeze=False)\n",
    "\n",
    "# Convert all words in dictionary to lower case\n",
    "pos[\"Positive_Word\"] = pos[\"Positive_Word\"].apply(lambda x: x.lower())\n",
    "neg[\"Negative_Word\"] = neg[\"Negative_Word\"].apply(lambda x: x.lower())\n",
    "\n",
    "# Retain only unique words in dictionaries\n",
    "pos.drop_duplicates(inplace=True)\n",
    "neg.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create features to capture positive and negative word count (based on dictionaries)\n",
    "df[\"Positive_Word_Flag\"]=df[\"text\"].apply(lambda x: len([i for i in x.split(' ') if i.lower() in list(pos[\"Positive_Word\"])]))\n",
    "df[\"Negative_Word_Flag\"]=df[\"text\"].apply(lambda x: len([i for i in x.split(' ') if i.lower() in list(neg[\"Negative_Word\"])]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Engineering - Sarcasm (Mix of common positive words with any negative words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sarcasm=['great','congrats','thanks','thank']\n",
    "df[\"Sarcasm_Dummy\"]=df[\"text\"].apply(lambda x: len([i for i in x.split(' ') if i.lower() in sarcasm]))\n",
    "df[\"Sarcasm_Flag\"]=np.where(((df[\"Sarcasm_Dummy\"]>0) & (df[\"Negative_Word_Flag\"]>0)),1,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Engineering - Word Sentiment (VaderSentiment Library)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"Sentiment\"] = df[\"text\"].apply(lambda line: vaderSentiment(line))\n",
    "df[[\"Vader_compound\",\"Vader_neg\",\"Vader_neu\",\"Vader_pos\"]] = df[\"Sentiment\"].apply(pd.Series)\n",
    "df[\"Vader_compound\"] = df[\"Vader_compound\"].apply(lambda x: abs(x))\n",
    "df = df.drop([\"Sentiment\",\"Sarcasm_Dummy\"],1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Engineering - Uppercase (Assumed proxy for extreme tone in text. Example: shouting.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"Uppercase\"] = np.nan\n",
    "df[\"Uppercase\"] = df[\"text\"].apply(lambda line: [np.sum([1 if re.search('[A-Z]{2,}',word) is not None else 0]) for word in line.split(' ')])\n",
    "df[\"Uppercase\"] = df[\"Uppercase\"].apply(lambda x: np.sum(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Feature Engineering - Special Characters in continuation (Assumed proxy for negativity. Example: !!, ???)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"Special_Characters\"] = np.nan\n",
    "df[\"Special_Characters\"] = df[\"text\"].apply(lambda line: [np.sum([1 if re.search('[\\!\\?\\.]{2,}',word.replace(\"https://\",\"\").replace(\"http://\",\"\")) is not None else 0]) for word in line.split(' ')])\n",
    "df[\"Special_Characters\"] = df[\"Special_Characters\"].apply(lambda x: np.sum(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature Engineering - Emojis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "emojis=\"😀_❤️_😁_😂_😃_😄_😅_😆_😇_😈_😉_😊_😋_😌_😍_😎_😏_😐_😑_😒_😓_😔_😕_😖_😗_😘_😙_😚_😛_😜_😝_😞_😟_😠_😡_😢_😣_😤_😥_😦_😧_😨_😩_😪_😫_😬_😭_😮_😯_😰_😱_😲_😳_😴_😵_😶_😷_😸_😹_😺_😻_😼_😽_😾_😿_🙀_🙁_🙂_🙃_🙄_🙅_🙆_🙇_🙈_🙉_🙊_🙋_🙌_🙍_🙎_🙏_🚀_🚁_🚂_🚃_🚄_🚅_🚆_🚇_🚈_🚉_🚊_🚋_🚌_🚍_🚎_🚏_🚐_🚑_🚒_🚓_🚔_🚕_🚖_🚗_🚘_🚙_🚚_🚛_🚜_🚝_🚞_🚟_🚠_🚡_🚢_🚣_🚤_🚥_🚦_🚧_🚨_🚩_🚪_🚫_🚬_🚭_🚮_🚯_🚰_🚱_🚲_🚳_🚴_🚵_🚶_🚷_🚸_🚹_🚺_🚻_🚼_🚽_🚾_🚿_🛀_🛁_🛂_🛃_🛄_🛅_🛋_🛌_🛍_🛎_🛏_🛐_🛠_🛡_🛢_🛣_🛤_🛥_🛩_🛫_🛬_🛰_🛳_🤐_🤑_🤒_🤓_🤔_🤕_🤖_🤗_🤘_🦀_🦁_🦂_🦃_🦄_🧀\"\n",
    "\n",
    "emojis_list=emojis.split(\"_\")\n",
    "\n",
    "for i in emojis_list:\n",
    "    df[i]=0\n",
    "    \n",
    "def emoji_f():\n",
    "    for i in emojis_list:\n",
    "        #print i\n",
    "        for j in range(len(df)):\n",
    "            text=df.ix[j,'text']\n",
    "            df.ix[j,i]=str(text).count(str(i))\n",
    "\n",
    "# code to call emojis\n",
    "%time emoji_f()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train and Test set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X=df.ix[:,1:]\n",
    "y=df.ix[:,[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Preprocessing\n",
    "    Includes - \n",
    "    -- tokenization\n",
    "    -- stop words removal\n",
    "    -- punctuation removal\n",
    "    -- lower case conversion\n",
    "    -- removing most frequent and less frequent words\n",
    "    -- removal of alphanumeric and numeric words\n",
    "    -- keeping words of length > 1\n",
    "    -- removal of alphanumeric and numeric strings\n",
    "    -- n-gram (bigram) generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stemmer = EnglishStemmer()\n",
    "analyzer = CountVectorizer().build_analyzer()\n",
    "\n",
    "def lemmatized_words(doc):\n",
    "#   Lemmatization\n",
    "   return (wordnet_lemmatizer.lemmatize(w) for w in analyzer(doc) \n",
    "           if ((len(re.findall(pattern=r'[0-9]',string=w))==0) and (len(w)>1)))\n",
    "\n",
    "# N-grams features\n",
    "v1=CountVectorizer(stop_words ='english',max_df=.5,min_df=2,encoding =\"utf-8\",lowercase=True, ngram_range=(2,2))\n",
    "\n",
    "# Lemmatization features\n",
    "v2=CountVectorizer(stop_words ='english',max_df=.5,min_df=2,encoding =\"utf-8\",lowercase =True,analyzer=lemmatized_words,token_pattern =r'[a-zA-Z]{3,}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer=<function lemmatized_words at 0x00000000118DF828>,\n",
       "        binary=False, decode_error=u'strict', dtype=<type 'numpy.int64'>,\n",
       "        encoding='utf-8', input=u'content', lowercase=True, max_df=0.5,\n",
       "        max_features=None, min_df=2, ngram_range=(1, 1), preprocessor=None,\n",
       "        stop_words='english', strip_accents=None,\n",
       "        token_pattern='[a-zA-Z]{3,}', tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1.fit(X_train.text)\n",
    "v2.fit(X_train.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train_text_TF1=v1.transform(X_train.text)\n",
    "X_train_text_TF2=v2.transform(X_train.text)\n",
    "\n",
    "X_test_text_TF1=v1.transform(X_test.text)\n",
    "X_test_text_TF2=v2.transform(X_test.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_train_newX1=pd.DataFrame(X_train_text_TF1.toarray(), columns=v1.get_feature_names(),index=X_train.index)\n",
    "df_train_newX2=pd.DataFrame(X_train_text_TF2.toarray(), columns=v2.get_feature_names(),index=X_train.index)\n",
    "\n",
    "df_train_newX=df_train_newX1\n",
    "df_train_newX = pd.concat([df_train_newX1, df_train_newX2], axis=1, join='inner')\n",
    "df_train_newX = df_train_newX1.merge(df_train_newX2, left_index=True,right_index=True)\n",
    "\n",
    "df_test_newX1=pd.DataFrame(X_test_text_TF1.toarray(), columns=v1.get_feature_names(),index=X_test.index)\n",
    "df_test_newX2=pd.DataFrame(X_test_text_TF2.toarray(), columns=v2.get_feature_names(),index=X_test.index)\n",
    "\n",
    "df_test_newX=df_test_newX1\n",
    "df_test_newX = pd.concat([df_test_newX1, df_test_newX2], axis=1, join='inner')\n",
    "df_test_newX = df_test_newX1.merge(df_test_newX2, left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge Text Features (Word Count, N-Grams) with original dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train_new = pd.concat([X_train, df_train_newX], axis=1, join='inner')\n",
    "X_test_new = pd.concat([X_test, df_test_newX], axis=1, join='inner')\n",
    "\n",
    "# Drop the \"text\" (tweets) feature\n",
    "X_train_new=X_train_new.drop(\"text\",axis=1)\n",
    "X_test_new=X_test_new.drop(\"text\",axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the model and predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algorithm comparision by cross validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.linear_model import SGDClassifier \n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "\n",
    "nb = MultinomialNB()\n",
    "lr = linear_model.LogisticRegression()\n",
    "etc = ExtraTreesClassifier()\n",
    "sgd = SGDClassifier()\n",
    "dt = tree.DecisionTreeClassifier()\n",
    "bc = BaggingClassifier()\n",
    "abc = AdaBoostClassifier()\n",
    "\n",
    "X_cross=X_train_new.append(X_test_new)\n",
    "y_cross=y_train.append( y_test)\n",
    "labels= np.asarray(y_cross)\n",
    "c, r = labels.shape\n",
    "labels = labels.reshape(c,)\n",
    "clf_l=[nb,lr,etc,sgd,dt,bc,abc]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_cross.shape\n",
    "labels.shape\n",
    "len(y_cross)\n",
    "def my_scorer_function(y_test, y_pred_class):\n",
    "    return metrics.accuracy_score(y_test, y_pred_class)\n",
    "def two_scorer():\n",
    "    return make_scorer(my_scorer_function, greater_is_better=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)\n",
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
      "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
      "           verbose=0, warm_start=False)\n",
      "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n",
      "       eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n",
      "       learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1,\n",
      "       penalty='l2', power_t=0.5, random_state=None, shuffle=True,\n",
      "       verbose=0, warm_start=False)\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best')\n",
      "BaggingClassifier(base_estimator=None, bootstrap=True,\n",
      "         bootstrap_features=False, max_features=1.0, max_samples=1.0,\n",
      "         n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
      "         verbose=0, warm_start=False)\n",
      "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
      "          learning_rate=1.0, n_estimators=50, random_state=None)\n"
     ]
    }
   ],
   "source": [
    "p=[]\n",
    "for i in clf_l:\n",
    "    print i\n",
    "    clf = cross_val_score(estimator=i,cv=10,n_jobs=1,X=np.array(X_cross),y=labels,scoring=two_scorer())\n",
    "    clf\n",
    "    clf.mean()\n",
    "    p.append(clf.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x4d5baf60>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIiImpAAXERExIQW4iIiICSnARURETEgBLiIiYkIKcBERERNSgIuIiJiQAlxERMSEFOAi\nIiImpAAXERExIQW4iIiICSnARURETEgBLiIiYkIKcBERERNSgIuIiJiQAlxERMSEFOAiIiImpAAX\nERExIQW4iIiICSnARURETEgBLiIiYkIKcBERERNSgIuIiJiQAlxERMSEcg3w9PT0/KhDRERE7kGu\nAd62bVtmzpzJ0aNH86MeERERyYNcA3zLli0EBASwcOFCunTpwgcffEBsbGx+1CYiIiI5yDXAS5Qo\nQbdu3VixYgWjRo3iX//6FyEhIQwbNozo6Oj8qFFERET+h0tuC0RHR7N+/Xq++uorKlWqxIQJE2jb\nti179+5l0KBBbNu2LT/qFBERkWxyDfBnn32WHj16sGzZMry9vW3TW7ZsSXh4uF2LExERkbvL9RT6\n1q1bqV27Nt7e3sTHx/PZZ59hGAYAU6ZMsXuBIiIi8nu5BviMGTPuOE2+b98+ZsyYYdeiRERE5I/l\nego9MjKSjRs3AuDp6cmCBQvo0qWL3QsTERGRnOV6BG61Wrly5YrtdVxcHE5OGsBNRETEkXI9Ah86\ndCjdu3enUaNGGIbB0aNH1fctIiLiYLkGeJcuXWjSpAkRERG4uLjw8ssvU6FChfyoTURERHKQa4DH\nxcWxZcsWUlJSMAyDqKgoLly4wPz583NduWEYhIaGcvLkSVxdXZkzZw5VqlT53XLTp0+nTJkyjBs3\n7s+1QkREpIjJtTN7xIgR/Pjjj2zYsIHU1FR27tyZ5z7w7du3k56ezpo1axg/fjxhYWG/W2bNmjX8\n9NNP9165iIhIEZZrEickJDBv3jxat25N27ZtWblyJadOncrTyg8ePEhwcDAAAQEBREZG3jH/8OHD\nHDt2jD59+vyJ0kVERIquXAO8dOnSAPj4+HDixAk8PDzIyMjI08qTk5Px8PCwvXZxccFqtQIQGxvL\n4sWLmT59um1gGBEREcmbXPvAg4KCGDVqFJMmTeK5554jKioKNze3PK3c3d2dlJQU22ur1Wo7/b51\n61auXbvGoEGDiI2NJS0tDV9fX7p16/Ynm2Iunp7ueHl55L7gfyUkuNuxmvyn9qv9eW1/UW47qP1F\nvf1/JNcAHzBgAMnJyXh7e7Nw4UJ++OEHhg8fnqeVBwYGsmvXLtq3b09ERAT+/v62ef3796d///4A\nfPnll5w9e7bIhDdAfHwysbFJ97R8YaL2q/15bX9RbnvW8oWJ2n9v7QdyDPxcA/zJJ59ky5YtANSt\nW5e6devm+ZeGhIQQHh5u6+MOCwtj06ZNpKam0rt37zyvR0RERO6Ua4DXqlWLdevWUb9+fYoXL26b\nXqlSpVxXbrFYmDlz5h3TfHx8frdc9+7d81KriIiI/FeuAX7kyBGOHDlyxzSLxcKOHTvsVpSIiIj8\nsVwDfOfOnflRh4iIiNyDXAP8pZdeuuv0uw3KIiIiIvkj1wBv0qSJ7f8ZGRns2LEDX19fuxYlIiIi\nfyzXAP/fC8x69epF37597VaQiIiI5O6eH+x9+vTpO54PLiIiIvkvT7eRWSwW4PbTxTw9PfXUMBER\nEQfLNcBPnDhh+79hGLYwFxEREcfJ9RT6vn37bCOpnT17lkcffZRDhw7ZvTARERHJWa4BPnfuXF55\n5RUAfH19ee+995gzZ47dCxMREZGc5RrgaWlpdzyExM/PL8+PExURERH7yLUP3NfXlwULFvDYY48B\n8NVXX1G9enV71yUiIiJ/INcj8Dlz5pCamsr48eOZPHkyqampzJ49Oz9qExERkRzkegTu7u5OixYt\nmD59OvHx8ezcuRN398L1gHURERGzyfUIfNq0aWzbts32et++fcyYMcOuRYmIiMgfy/UIPDIyko0b\nNwLg6enJggUL6NKli90LExERkZzlegRutVrvGDo1Li4OJ6d7HoFVRERE/kK5HoEPHTqU7t2706hR\nIwzD4OjRo0yZMiU/ahMREZEc5BrgXbp0oUmTJkRERODi4sLLL79MhQoV8qM2ERERyUGuAR4XF8eW\nLVtISUnBMAyioqK4cOEC8+fPz4/6RERE5C5y7cweMWIEP/74Ixs2bCA1NZWdO3eqD1xERMTBck3i\nhIQE5s2bR+vWrWnbti0rV67k1KlT+VGbiIiI5CDXAC9dujQAPj4+nDhxAg8PD42FLiIi4mC59oEH\nBQUxatQoJk2axHPPPUdUVBRubm75UZuIiIjkINcAHzt2LL/88gve3t4sXLiQH374geHDh+dHbSIi\nIpKDXAMcoGrVqgDUrVuXunXr2rUgERERyZ0uJxcRETEhBbiIiIgJ5XgKfd26dX/4g926dfvLixER\nEZG8yTHA9+3b94c/qAAXERFxnBwDPCws7I7XiYmJtnvCRURExLFy7QM/ceIE7du357HHHiMmJoaQ\nkBCioqLyozYRERHJQa4BPmvWLJYsWUKZMmWoWLEioaGhzJgxIz9qExERkRzkGuCpqan4+fnZXrdo\n0YL09HS7FiUiIiJ/LNcAL1OmDCdOnMBisQCwYcMG9YWLiIg4WK4jsYWGhjJp0iROnTpF48aNqVat\nGgsWLMiP2kRERCQHuQZ41apVWb16NTdu3MBqteLu7p7nlRuGQWhoKCdPnsTV1ZU5c+ZQpUoV2/x/\n//vfvP/++zg5OdG5c2eefvrpP9cKERGRIibHAO/fv7/ttPnd/Otf/8p15du3byc9PZ01a9Zw5MgR\nwsLCWLp0KQBWq5WFCxfyxRdfUKJECTp27EjXrl0pU6bMn2iGiIhI0ZJjgI8cORKATz75hOLFi9Ot\nWzdcXFzYtGkTaWlpeVr5wYMHCQ4OBiAgIIDIyEjbPCcnJ7Zs2YKTkxNxcXEYhkGxYsX+P20REREp\nMnIM8CZNmgAwb948Pv/8c9v0Bg0a0KNHjzytPDk5GQ8Pj99+mYsLVqsVJ6fb1845OTnx9ddfM3Pm\nTB555BHuu+++P9UIERGRoibXPvC0tDTOnj2Lj48PACdPniQjIyNPK3d3dyclJcX2Ont4ZwkJCSEk\nJIRJkyaxbt06unfvfi/1m5anpzteXh65L/hfCQl5v/bADNR+tT+v7S/KbQe1v6i3/4/kGuCTJ0+m\nf//+VKxYEavVSnx8PK+//nqeVh4YGMiuXbto3749ERER+Pv72+YlJyfzwgsv8MEHH+Dq6kqJEiX+\nsM+9sImPTyY2Numeli9M1H61P6/tL8ptz1q+MFH77639QI6Bn2uAP/TQQ+zcuZOffvoJJycn/P39\ncXHJ9ceA20fX4eHh9OnTB7g9vvqmTZtITU2ld+/edO3alaeeeopixYrxwAMP8Nhjj91Dk0RERIqu\nXJM4Pj6eV155hT179pCZmUlQUBChoaGUL18+15VbLBZmzpx5x7SsU/EAvXv3pnfv3n+ibBERkaIt\n15HYpk+fTr169dixYwe7du0iICCAqVOn5kdtIiIikoNcA/z8+fMMHDgQd3d3PDw8GDRoEBcvXsyP\n2kRERCQHuQa4xWLh0qVLttcXL17Mcx+4iIiI2EeuSTx69GieeOIJAgICMAyDI0eOMGvWrPyoTURE\nRHKQa4A/8sgjBAQEcPToUaxWKzNnzqRcuXL5UZuIiIjkIMcAX7du3V2n/+c//wGgW7du9qlIRERE\ncpVjgE+ePJly5crRrFmzu45RrgAXERFxnBwD/Msvv2Tz5s2Eh4dTq1YtOnbsSPPmzX83FKqIiIjk\nvxwDvHbt2tSuXZvx48dz7NgxNm/ezMKFC3nwwQfp1KkTTZs2zc86RUREJJs83Q9Wr1496tWrx4ED\nB3jttdfYuHEjhw8ftndtIiIikoM/DHDDMPjhhx/YunUr3377LbVr16Z///488sgj+VWfiIiI3EWO\nAT5jxgz+85//UKdOHTp06MCECRP0vG4REZECIscAX7t2LWXKlOH48eMcP36chQsX3jF/x44ddi9O\nRERE7i7HAFdAi4iIFFw5Bri3t3d+1iEiIiL3QDd1i4iImJACXERExIQU4CIiIiakABcRETEhBbiI\niIgJKcBFRERMSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIiIiakABcRETEhBbiIiIgJKcBF\nRERMSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIiIiakABcRETEhBbiIiIgJudhz5YZhEBoa\nysmTJ3F1dWXOnDlUqVLFNn/Tpk3861//wsXFBX9/f0JDQ+1ZjoiISKFh1yPw7du3k56ezpo1axg/\nfjxhYWG2eWlpabz11lt89NFHrFq1iqSkJHbt2mXPckRERAoNuwb4wYMHCQ4OBiAgIIDIyEjbPFdX\nV9asWYOrqysAGRkZuLm52bMcERGRQsOuAZ6cnIyHh4fttYuLC1arFQCLxYKnpycAK1euJDU1lebN\nm9uzHBERkULDrn3g7u7upKSk2F5brVacnH7bZzAMg/nz5xMdHc3ixYvtWUqB4+npjpeXR+4L/ldC\ngrsdq8l/ar/an9f2F+W2g9pf1Nv/R+wa4IGBgezatYv27dsTERGBv7//HfNffvllihcvztKlS+1Z\nRoEUH59MbGzSPS1fmKj9an9e21+U2561fGGi9t9b+4EcA9+uAR4SEkJ4eDh9+vQBICwsjE2bNpGa\nmkrdunX54osvaNSoEf3798disfD000/Tpk0be5YkIiJSKNg1wC0WCzNnzrxjmo+Pj+3/x48ft+ev\nFxERKbQ0kIuIiIgJKcBFRERMSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIiIiakABcRETEh\nBbiIiIgJKcBFRERMSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIiIiakABcRETEhBbiIiIgJ\nKcBFRERMSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIiIiakABcRETEhBbiIiIgJKcBFRERM\nSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIiIiakABcRETEhBbiIiIgJKcBFRERMSAEuIiJi\nQgpwERERE7JrgBuGwYwZM+jTpw9PP/0058+f/90yqamp9O3bl7Nnz9qzFBERkULFrgG+fft20tPT\nWbNmDePHjycsLOyO+ZGRkTz11FN3DXYRERHJmV0D/ODBgwQHBwMQEBBAZGTkHfNv3brF0qVL8fX1\ntWcZIiIihY6LPVeenJyMh4fHb7/MxQWr1YqT0+39hoYNGwK3T7WLiIhI3tk1wN3d3UlJSbG9zh7e\nRZ2npzteXh65L/hfCQnudqwm/6n9an9e21+U2w5qf1Fv/x+xa4AHBgaya9cu2rdvT0REBP7+/vb8\ndaYSH59MbGzSPS1fmKj9an9e21+U2561fGGi9t9b+4EcA9+uAR4SEkJ4eDh9+vQBICwsjE2bNpGa\nmkrv3r1ty1ksFnuWISIiUujYNcAtFgszZ868Y5qPj8/vlvvXv/5lzzJEREQKHXVIi4iImJACXERE\nxIQU4CIiIiakABcRETEhBbiIiIgJKcBFRERMSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIi\nIiakABcRETEhBbiIiIgJKcBFRERMSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIiIiakABcR\nETEhBbiIiIgJKcBFRERMSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIiIiakABcRETEhBbiI\niIgJKcBFRERMSAEuIiJiQgpwERERE1KAi4iImJACXERExIQU4CIiIiakABcRETEhuwa4YRjMmDGD\nPn368PTTT3P+/Pk75u/cuZNevXrRp08fPv30U3uWIiIiUqjYNcC3b99Oeno6a9asYfz48YSFhdnm\nZWRkMHfuXFasWMHKlStZu3Yt8fHx9ixHRESk0HCx58oPHjxIcHAwAAEBAURGRtrmnT59mmrVquHu\n7g5Ao0aN+OGHH2jXrl2O67uReMWe5eabP9sOtV/tLwz+TDuKctv/Pz9X0Kj9f2077BrgycnJeHh4\n/PbLXFywWq04OTn9bl7JkiVJSkr6w/Xt+zzUXqUWeF5egez7PNDRZTiM2l9021+U2w5qf1Fv/x+x\n6yl0d3d3UlJSbK+zwjtrXnJysm1eSkoKpUqVsmc5IiIihYZdAzwwMJDdu3cDEBERgb+/v22en58f\n0dHRXL92yuFJAAAgAElEQVR+nfT0dH744QcaNGhgz3JEREQKDYthGIa9Vm4YBqGhoZw8eRKAsLAw\noqKiSE1NpXfv3nzzzTcsXrwYwzDo1asXffv2tVcpIiIihYpdA1xERETsQwO5iIiImJACXERExIQU\n4CIiIiakABcRKcKsVqujSyhQzHRZWJEI8MzMTEeXICaV9eV28+ZNB1fy19EX9p+T9XdLTU11cCV/\nnczMTJycnLBarZw4ccLR5ThcZmYmFouFa9eucfHiRUeXk6tCH+CZmZk4OztjtVrZvn07ly5dcnRJ\nBZp2dn5jGAZOTk7ExcUxePBgzp8/b/rwy/rCjouL0wOE7kHWthAfH8+IESM4c+aM6bcFAGdnZwzD\nYPbs2Wzfvt3R5Tics7MzV65cYdasWZw6dcrR5eSq0Ad4VniPGTOG/fv388svv3D9+nVHl1UgWa1W\n299r0aJFLFu2zFSnk/5qFouF5ORkFixYgJ+fH1WqVLGNJGhWzs7OxMbGMnnyZDw9PR1djmlYLBaS\nkpJ4+eWXadCgAb6+vqbeFrJ/rt966y2OHz9Oz549HViR41mtVqxWK9OnT+fy5cu0bNnSNr2gMu8W\nmAdZG+k777xDsWLFGDt2LB999BFvvvkmixcvdnB1BY+Tk5PtEbBJSUl8/fXXzJkzp1CdPs6L7B9Y\nV1dX7rvvPpydnTlx4kSB/jD/kex1Hzp0iP/85z+2kRFv3brlqLIKvOxnpNzd3SlVqhQXL14kLi7O\ngVX9/2RkZGCxWIDb35H9+/enQoUKrFu3rlB1D+RV1mcj6+zUtGnTOH36NCtWrAB++14siJxDQ0ND\nHV3EXy3rjcjaSNPT0zl79izffPMNnTp1omrVqiQmJtKoUSMHV1rwzJs3jytXrjB37ly6devGl19+\nyYEDB2jWrBkuLnZ99k2BkNXlEhMTw4oVK4iJiaF58+b8+OOPXL58mVKlSuHl5eXoMu9J1jMIEhMT\nSUpK4sEHH6RixYpMmTKFRx99lLJly9o+M/KbrG3h8uXLrFq1ilOnTtGvXz/Cw8OJioqiTp063Hff\nfY4u855kP8s2duxYwsPDOXXqFD169OCrr74iOjqaBg0aFInPOvz22YiJiWHKlCkcOXKEGzduMGXK\nFGbPnk1qaiqNGjWyZUlBU+g+sdk30Hnz5rFx40aio6MJDQ2lc+fOODs7s3z5curUqePoUguE/+3z\nbtKkCUeOHOHf//43Li4uhIWFERMTw6+//uqgCvOXs7Mz8fHxvPDCC/j7+7N582bWrVtHt27diImJ\nYceOHaSnpzu6zHuS9QU1dOhQPvnkEzp37kzr1q0ZMWIE3bt359dff8XZ2dnRZRY4WdvCmDFj8PPz\nY/v27bz99tuMHz+e2NhY/vnPf5rqjMzKlStt1wCFhoYSFBTEyJEj+eyzzzh37hwzZ84kKiqKmJgY\nB1eaP7Kua0hISGDq1Kk8/vjjNGvWjIULFxIdHc0//vEPNm7cSGJiYoE9Ai90AZ51umPYsGGULl2a\npKQktm7dyq5duzAMg40bNzJo0CAeeughR5fqcNkv8Hvttdf4+OOPSU1NZfHixaxevZrNmzdz3333\nsXTpUvz8/Bxdrl2tX7/edtXpsWPHaNWqFW3btuXmzZuUK1eOxMREpkyZQt++fXF1dXVwtXkTHx+P\nYRikp6cze/ZsBg4cyOOPP45hGCxdupRevXoxduxYMjIyHF1qgbJjxw5biO3bt4+mTZvSokULDMPA\ny8uLyMhIFi5cyLPPPmuasxabN28mPDwcb29v4Pb3ZIUKFZg/fz7Tp0/n0qVLJCUlsWTJEqpVq+bg\nau3LMAzef/99rly5/WzulJQUatWqhbu7Ox999BELFy4kMjKS6tWr8/nnn1O6dGkdgdtb9j2kCxcu\n8MADDzB06FB27dpFmzZtKFasGA899BCvvvoqDz/8sAMrLTiyrkAdM2YMN2/epGLFiixbtoxjx44x\nePBgli1bxtWrVwvs3udfwWq1Ehoaire3N+XLl+fw4cPUrFmTb775hnbt2vHGG2/QsGFDPv30UwzD\noFy5co4uOVeGYfD6669z9uxZLBYLrq6u1KtXj9jYWMaPH8/SpUt54IEH2LNnD08++WSh/8LOK6vV\nytSpUylZsiRly5blp59+wt/fn5MnT9KjRw/mzJlD8+bN2bJlC2lpaVSoUMHRJefJtWvX8PPz4+zZ\nswwYMIBDhw5RuXJlXnvtNWrWrMlDDz3E119/zc2bNylWrJijy7Urq9XKtGnTqFatGqVLl+bAgQNk\nZmZy/vx55syZw/PPP0/x4sXZunUrCQkJBf7vUWgCPGsPadOmTVy8eJHt27fz1FNP8dxzz9GkSRM+\n+OADrly5YpqjJ3v6xz/+YXsW+4EDByhZsiTTpk2jTZs2TJ8+nZ9++onmzZvzzjvvUL58+UJ9enXl\nypXEx8fTuHFjvv32Wz799FOOHz/OE088QdmyZVm3bh0LFixg5MiRlChRwtHl5skLL7xA5cqVadSo\nEW+88QbR0dGkpaWxevVqunTpQmxsLKtWrbIdjclt77//PpcvXyYoKIhPP/2UdevWcfr0aerXr0+N\nGjXYuHEjCxYsYPDgwRQvXtzR5eZJeHg4ERERPPDAA9SvX5/IyEi8vLzo0aMHrVu35tKlS4waNYph\nw4ZRt25dR5drd/v37+fMmTPUrFmTjz76iDlz5hAXF8cjjzzC/fffT0REBG+88QazZs2ibNmyji43\nV6Z/GlnWaWCAX375hdDQUF599VX27NnD/PnzmTt3LkuXLmXYsGG22wKKuu+//57mzZuTkJBAUlIS\nL774ImvWrAFg9+7drF69mkWLFuHq6lpgTx39Fa5du0ZaWhpdu3bF29ubUaNGceXKFc6cOUP9+vWp\nU6cOUVFR1K9fnypVqji63DybPXs23t7elC1blp07d+Lp6cmAAQP47LPPKF26NPv37+ell14q9N0i\n9yI9PZ3k5GSef/55SpUqxZNPPsnZs2exWq1UrVqV6tWrExUVRVBQkKm2hStXruDl5cW6detwdXWl\nePHizJ8/n0WLFlGrVi0uX77MzZs3qV69uqNLtSvDMIiLi6NcuXLMmzePXbt2MXXqVNzc3HjnnXcY\nPXo0JUqUwNnZmRIlSphm59b0V6Fn9XnfunULT09PSpQoweeff86wYcPw9vbG2dmZli1b0qJFC0eX\n6nAZGRk4OTlRpUoVtmzZwoABAxgxYgQXL17kjTfeoFixYixfvpznn38eX1/fQh3emzZtAsDPz49j\nx47x448/0rFjRzp06MClS5fYt28fvr6+tGzZktKlSzu42twZhsGqVauoX78+8fHxvPrqq3h5eTF3\n7lx++OEHDh48yJNPPsmjjz5K69atuf/++x1dcoGxatUqXF1dqVSpEps3byYmJoaHHnqIbt26ceTI\nEc6dO0ft2rVNsy3Ab5/1kiVLEhsby3/+8x9iY2Pp1asXFStWZMKECTRq1IgaNWpQpkwZR5drdzt2\n7OCXX37B39+fU6dO2c7GhoSEULFiRebPn0+DBg1o0KABpUqVcnS5eWbaALdarbaA+fjjj5k2bRol\nSpTAxcUFFxcX/Pz8ePDBB/Hx8THN3pQ9ZWZm4uLigmEY/PTTTwQFBWGxWAgLC2PevHlUqFCBmzdv\n0rZt2yKxs1O+fHkqVarExo0b+fvf/87gwYMZNmwYf/vb33jssce4fv069erVo2TJko4uNU8sFgtX\nrlzB19eXUqVK4eHhgZOTE5cuXeL555/n22+/Zd++fbRq1Qo3N7dCvXN2r7y9vbn//vv59ttvadGi\nBU8++SSvv/46xYsXp3fv3pw9e5aGDRua5paxrM+61WrlzTff5IEHHsDb25uLFy+yZ88eevToQfXq\n1XF1daVy5cqOLjdf+Pr6Ur16dYYPH06/fv3o0aMHR44c4YcffqBz587UrFmTatWqmSq8waQBnnXv\nnmEYHDt2jObNm9O4cWMuXbrE6tWr2bdvH7GxsQQHBzu61AIja7zjwYMHk5iYiJOTE4899pjtftAR\nI0bw97//vdB/oLPud3Z1dSUuLo7NmzeTkpJC06ZNCQ4OZsiQIfj7+9OpUyfThPetW7dwdnbG19eX\nqVOnsnPnTqZPnw7c7vO7cOECI0aMoGHDhpQsWVLh/V/Zt4XY2FjeffddSpQoQZ06dahfvz6vvfYa\n5cuXp1evXqYJb/jtrOTo0aMpW7YsLi4u+Pj4kJmZSUpKCt9++y39+/enSpUqGIZRqLeHrPc4LS0N\nV1dXzp8/zz/+8Q/atWtHnTp1OHz4MMePH6dHjx6mC28wYYBnhbfVamXQoEFERUXxzjvv0KtXL1q0\naEFQUBDlypUjLi6OunXrmuqDZ2+zZ8/G19eXZ555htWrV7NhwwYGDBhAsWLFKF68eKEP76wxAmJi\nYpg3bx4NGjSgVq1aHDt2jFOnTtGsWTM6duyIl5eXaU4rWq1WXFxcSExMJDY2lu7du7N+/Xr27t3L\ngAEDMAyDEydOUL9+fdO0KT9k3xbeeust6tatS3BwMFu2bCE5OZl69erRokULqlatapov9uxhvHv3\nbo4ePcro0aN5++23+fXXX7l+/ToPP/wwgYGBtgu0CnN4Z73Hly5d4pVXXuHGjRv079+fmzdv8vrr\nr9OhQwcaNWpEgwYNTJsTpgtwi8WC1WplxowZNG7cmEmTJrF9+3a++OILGjRogJ+fH/Xq1WPr1q1U\nqlSJSpUqObpkh/nf0bUiIiI4c+YM3377LR07duTmzZukpKTQt29fKleuXOj3xi0WC3FxcYwaNYqQ\nkBAuXbpku4Xq9OnTnD9/njZt2pji6lP4bSCKmJgYhg0bhmEYlCxZkmeeeYb169ezdetWhg4dSmBg\noGnOJuSXrG1h2LBhhISEcO7cOXx9ffHy8uLf//43GRkZpurzzv5ZT09PJzU1lZiYGA4dOkTXrl35\n29/+Rnh4OH369CkyY+BbLBYSExOZPHky3bt3p0yZMkRFRREcHEyJEiVYsmQJTz75pKk/G6YZL++r\nr77CarXSuXNnnJycqFWrFtWrV2fGjBk8//zzfPLJJ0ycOJFVq1Zx48YNrl69SsWKFR1dtsNkH6Tl\n3XffpVWrVvTt25f4+HjOnDlj6/PL3s1QWMM7+47J/v37qVmzJvXq1WPmzJkcOXKEWrVq0bdvX0qV\nKmWqv4HFYuHmzZvMnTuXQYMG4ePjw5o1a8jIyODNN99kzJgxXLlyxTT3K+eH7NtCeHg49erV4+9/\n/zuTJ09mz549NG/enIkTJ+Lq6mqa2ycNw7B91idOnIiPjw/PPPMML730Evv37ycpKYnly5czdOjQ\nAn9f818h6ywtwJkzZ4iOjqZcuXLMnz+f1q1bs3TpUjZu3EinTp1MP2SsKY7AZ82axbFjxzh48CDh\n4eG0a9eOsmXL8vPPP1OyZEkCAgLYv38/L7zwAn5+fri7u/PII4+Ybszqv1JWP9jEiRMpUaIEFSpU\n4L777sNisZCQkMBbb73FsGHDaNKkiaNLtausI5OkpCRSUlKwWq2cPn2an3/+mQkTJpCZmcmePXvo\n1auXafbEsx9tOTs7s3btWtLS0li/fj0jR45k/fr11KhRgwEDBpimTfkh6++WnJxMcnIyTk5OfP/9\n90RHRzNlyhSKFy/Onj176NatGx4eHo4uN8+ydkjGjRvHgw8+SLNmzdi2bRtJSUnExsaSmJhI69at\ni8Q1QVnhHR8fT3R0NPXq1cPJyYlr167Rt29f/P39+fHHH2nVqpWp3uOcFPgADwsLIzMzk7lz59K9\ne3dWrlyJu7u77fF+x44dY8mSJQwZMoQWLVrYPqRubm6OLt0hsh9hbNmyhe+++46pU6cyd+5crl69\nytatWxk5ciTBwcHUrl3bwdXaV9Yp5suXLzNlyhQuX75MSEgIjz76KFevXuXcuXOsXr2aKVOmmGKE\nNbiz7/bTTz/F09OTnj17cv/991O1alVKlCjBjh076NmzJ+7u7o4ut8DI2hYuXbrElClTuHr1Kq1a\ntaJr166cO3eOs2fPsnLlSqZMmWKaU8zZ78S5fv06O3bsoHnz5ixfvpxy5cqxb98+evbsSfPmzYvE\naHtZ7/GVK1d44YUXbBdstm3bFjc3Nz788EO+/PJLJk6cyN/+9jdHl/uXKNADuXz//fcMHDiQ7777\njnLlyvHxxx/zwQcf0KRJE27cuEHPnj1tp8lr1arl4GodL/ugNgBnz55l06ZNXL9+nRYtWlChQgVW\nrlzJrFmzTH/qKK9u3LjB6NGjbafI9+7dS506dShWrBgJCQnUq1cPHx8fR5d5T65evcrYsWPp2LEj\nlSpVws/Pj9KlS/Phhx9y9OhRJk6cSM2aNR1dZoGTkpLC+PHjbdvCvn37qFGjBpUrVyYyMpLGjRub\nZkCT7HfiHDp0iIYNG7Jp0yaKFy+Op6cnNWvWZNiwYYSGhhapbSEtLY0JEybQtm1bateuzapVqzh/\n/jwvvfQS8fHxhe66qAL9Ld68eXOGDBnC8OHDadOmDUeOHGH16tVUrFiRadOmce3aNY2u9l/Z+8Gm\nTp2Kr68vycnJDBw4kF9++YXjx4+zfPlynn322UIf3tn7wJKTk0lKSsJqtbJo0SIee+wxli9fzvz5\n8027F75mzRoqVKhAy5YtmTJlCmXLlqVixYpMmjSJ69evm+bCq/yQfVtITEzkzJkz3Lx5k/fff5++\nffuyaNEi3nvvPXr16uXgSu9N9jtxrFYr169fZ9WqVVy9epVPPvmEsLAwRo0aVSTCO/uBi5ubGxUr\nVmTv3r1s3ryZyZMns3LlSmJjY2natKmDK/3rFdhT6Fmnh4KCgoiLi+ODDz4gNDQUPz8/IiMj2bhx\nI+3atStUe1N/VvbT5i+88AKBgYFUr16dDRs2YLVaKV++PNHR0bRr167QP8glex/YwYMHqVKlCj4+\nPjg5OdGpUye8vb3Zs2cP7dq1M82tI1mfhfT0dJydnSlVqhRRUVEcPXqUcePGUbFiRRISEmjatKlp\nxujOD9m3hcjISCpXrkyDBg2wWq2EhIRQrVo1Dh48SPv27U0zzv2pU6ds3T0zZ86kadOmTJs2jWXL\nlrFhwwb69u2Lr68vISEhNGjQwMHV2l/2LqWPPvqItLQ0HnnkEQIDA/Hy8iIjI4Mvv/ySPn36FIo+\n7/9VYAPcYrHY+rMbN27MzZs3Wbt2LaVLl+af//wngwcPLpR7VPcq+wVN169f59dff+Wpp57ijTfe\noFOnTpQtW5a6deva7mkt7LJGJBs8eDAJCQm8+eab9O3bl7Jly7Js2TI+//xzpk6daprxrLOOLmJi\nYhgzZgwXLlzAzc2NQYMGkZmZycGDB/n0008ZMmSIafpu80vWtvDCCy8QHx/PihUraNOmDd7e3rzz\nzjusX7+eyZMnm2Zb+O6779iwYQPOzs5Uq1aNCxcuUKlSJT766COGDBlCeHg43377LT179iwyF/Ba\nLBZiYmKYMmUKFouFs2fPkpSURPXq1Tl9+jRr1qzhlVdeKbTffQWqD/x/+3Dh9pi+Wad833jjDT76\n6CMWLVqk53lnY7VamT9/Po0bN2bJkiUkJiYya9YsqlatysiRI1mwYEGhP5V2+vRpqlWrhtVqZdy4\ncfTo0YOHH36YZ599lqSkJBYvXkypUqXIyMgwXdDFxsYyYcIEOnToQEJCAtHR0XTo0IHKlSuzadMm\nunTpgq+vr6PLLBAMw+Dq1at4eXlx8+ZNxo4dS8+ePalXrx4zZszg1q1bTJgwgSpVqpCWlmaaixfh\n9vPdN2zYQFxcHM2bN8fX15dvvvmGpKQkunXrxrRp0xgwYADNmjVzdKl2lTXu/5NPPgnAvHnzCAwM\nJCAggOnTp+Pq6oqfnx9DhgwhIyOjUF/MWWCOwLNOhVitVj766CMOHTpEpUqVKFWqlO0UcbNmzejc\nuTMPPvigo8t1uOxXoL744oskJiYyfPhwmjRpwoYNG/D29mbJkiWMHj2awMBAB1drX1988QUbNmzg\nb3/7G/fff7/tQQ7vv/8+o0eP5vDhw2zbto1evXoV+A9z1rY+YsQI3N3dqVatGgcOHKBSpUq0a9eO\nDRs2kJGRweHDh/H29radXZDbVq1axbvvvkvjxo3x9PS0fYEvW7aMfv368dNPP7Fp0yZ69+5tmlOq\n8+bNY926dRw9epSnnnqK6OhoTp8+jbu7OyVLlmTfvn0sWrSIUaNG0aJFi0I/INONGzcYN24cly9f\nJjg4mKSkJJycnPjnP//JxIkTOXnyJFeuXCEoKKjQjz5YYALcYrFgGAbDhw/H29ubM2fO8PnnnxMY\nGEiZMmVs4z17eHjYli3MG+kfyX4F6pkzZ3B2dmbjxo0EBQVRs2ZNHn30USpVqkTr1q1p1KiRo8u1\nu+rVq/Prr79y8OBBSpcuja+vL2fPnsXHxwcXFxfOnDnD5MmTTXG0lbVNOzk58corr/Dggw9Sv359\nTp8+zapVq5g9eza3bt3iypUrdO3a1TQhlF/q16/PlStX2LhxI3Xq1MHLy4tTp07RsGFD27YwdepU\n05yFCQsLIzExkYkTJ1K2bFn8/f2pVq0aZ86c4ddff8UwDNq3b28bFhQK94BM58+fJzMzkyFDhrBo\n0SIuX75M//79bY8GrlKlCuvWrWP69OlFYiAvh59Cz36V6NGjR/nkk0+YPXs2o0ePpnLlylSqVIl+\n/foV2o3yXmV1MxiGwVtvvUVUVBTjx4/n5MmTfPbZZ7z44otF4gxF1kNYMjIycHNzY/78+SxdupQb\nN27QsWNHfvzxRyIiIoiIiGDx4sWmePa11Wpl7dq1WK1WWrZsyYkTJ5g5cyaLFy8mICCA/v3706FD\nBz777DMWLlxomlue7C2r2wTg5s2bvPPOO8ydO5f4+HhGjhzJzp07OXToEKdOneKtt96iRo0aDq44\nbw4fPmy7ojxLZmYmsbGxXLt2jd27d3P9+nUGDRpU6I80rVYrEyZMsJ1dmz9/Punp6fTr14+HHnqI\n4cOH88QTT1CuXDleeumlInNbscMDHG7vWS1ZsoQnnniCV199ldOnTzNp0iQeeOABQkNDmT59uoaD\nzCYrvMqXL4/VaiU1NZV+/frx888/s2bNGpYtW1aonzplGAZjx46levXqjBkzxna0nZ6eztKlSzEM\ng8DAQLy9vSldurQpLujJCqESJUpQtWpV2rZti5+fH9u2beOVV15h+fLlREdHs2fPHvr162eKHZL8\nkH1beOGFF7hy5YrtorT58+dz/fp1nn32WW7dukW5cuVMsS1k2bNnD2vXruXNN9+0TTMMg7lz51Ky\nZEmee+45EhISTHMR3p9lGAaTJk2ievXqDB06lJs3b9ruILl58ybPPPMMQUFBjBkzhuTk5ALfTfZX\ncsp9EfuzWCycOXOGEydO8OCDD1KxYkVOnTrFyJEj6datm8L7f2zdupWSJUvy8ssvM2jQIKpWrcrK\nlSupU6cOH3zwgW2kusJq9+7dVKhQgTFjxgC3T6Gnp6fz888/069fPwAOHTpElSpVTPOFPXPmTPz8\n/AgLC7MNCXz9+nVatWrFa6+9Ro8ePahUqRIvv/yywjubf//735QvX54xY8bg5uZmuzjtwIEDDBs2\nDA8PDz788ENq1Khhmm0hi7e3N2XKlCEmJsY2zWKx0KhRI9zc3HB3dy/04Q3w888/4+7uzrBhw3By\ncrLd8rd7926++uorli9fzt69e4mPjy9S4Q0OCvDo6Gjb/y9fvgxA7dq1uXTpEgMHDmTEiBEUL16c\niRMn0qZNGwrASQKHyszMvOP1fffdR0pKCgCVKlXCy8uLhIQEPvnkEzIyMgr93yvrIR5w+y4Fi8WC\nq6sra9euZdWqVYwdO5YBAwaYajhdDw8P+vfvD9x+vnfWv+PHjycwMJDXX3/dNPet56cSJUrYrgNI\nS0sDbg/msX37dt555x0mTZrEqFGjTDl4UcWKFbl58yarVq3i3LlzAJw4cYJly5YViW6yLFkPp8qS\nkZEBgKurK4cPH6ZEiRKsXr3aNNc1/JXyfav+/vvvycjIoFq1aixbtozo6Ghq1KhBhw4dmDBhAo0b\nNyYgIICAgADbzxTmo8ncZL86f+3atTg7O9OmTRvee+89xo0bR/v27Vm/fj2PP/44+/fvx8nJqdD/\nvUqWLMmNGzdIT0/H1dXVNt3f39+2s2OWD3NWF8ihQ4fYv38/bdu2tY2yVbx4cdzc3Lh58yZt27Z1\ndKkFktVq5cCBAxiGgZubm+220/vvv9/2OShfvryDq8yb7NcDwe0dkalTp/LWW2/xwQcfEB8fT2Ji\nIsOGDaNFixYOrDR/1a9fn5SUFN577z0GDx5se6LauXPnuHXrlq1fvLB/791Nvh+B169fn4cffpiv\nv/4aNzc3nn/+efbs2cO6detITEzkxIkT+V1Sgebk5ERmZibDhg0jLS2Nffv28eqrr7JgwQLq1KnD\n2bNnefHFF6levTrR0dG2o7fCrHHjxrZ+sYsXL5Kens6uXbvYunWr6b7YnJycKFmyJAMHDmTt2rUc\nPHgQZ2dnnJyc2Lt3L1euXLEdccjvPfLII1SpUoW+ffvahsz95ptv2LFjh6mevpU9vH/88UfbdA8P\nDyZNmsSkSZOYMGECCxYsKPSjKWaXdYZt3LhxnDx5kgULFnD16lXWr1/Ppk2bGDx4MC4uLnfs+BQl\n+XYRW/ZBWtLT03n77bdxcXGhXbt21KpViwsXLrBq1SqaN2+uQVq43bXg5eWFs7Mze/bsYdu2bcyY\nMYNRo0ZRoUIF6tevT9euXTl48CBHjhxh/fr1vPbaa4V+wJZbt27Z9sDnzp3LjRs3SExM5MaNG0ya\nNMk0VxhnybqfPy0tja1bt/Lxxx/TqVMnWxDNmDFDfd45yL4tvPrqq1y9epVixYoRFxfH5MmTTbMt\nZL+zZP78+bZbowr7+A05OXHiBCVLlryjf99qtRIbG0tYWBgVKlTg8uXLjBkzpsgPYJQvAZ61gVqt\nVqnO/6wAACAASURBVN544w1q1qxJ06ZNWb9+PSkpKTRr1oygoCDb8kX5Hm+4/fzz06dPExAQwBNP\nPAHA7NmzuXjxIpMmTcLX15c5c+Ywa9YsSpcuzc8//0ypUqUK5cV+WUcmqamptotXsu8MJiUlkZaW\nhqurK6VKlXJkqXliGAbffvstZcqUwd3dHT8/vzu298OHD3PgwAGKFStGq1atdKtYNlnv+91GbASI\niYmxHY2ZbXAbwzAYOnQoPj4+xMTEULlyZVq2bEnjxo1t84vCd2J6ejrh4eHs2bMHFxcXhg0bhru7\n++/an5aWZqprXOwlXwZyyToNPHLkSMqWLctXX31FRkYG3bp148SJE/zyyy888MD/tXfnAVGV++PH\n32wDMsMmIKLIpmxqgGgkXhQXNEwzMy2tyJZ79V5XuC4RKOKGdBPF9GJYueaSS7lvZe64gqLmhoqi\nyKIiIiAMzJzfH/5mwr63m3rFcYbn9VdBwecw55zPOc/zeT6Pj/YGXR9O1D+SnJzM3bt3iYuLY+3a\ntdjY2ODv709eXh4lJSXY2toyZ84cBg0aRMuWLYGH871yuVzHkT97muRdWFhITEwMNTU1eHl5PXLz\nNjc3x9LSUi8uZrVarV32dv36debPn4+bm5u2BawkSTRp0oS2bdsSGBho8Gt7n4SmFqSgoICvv/6a\njIwMrKyssLKy0haoKRQKLC0t9WZjktqOHDlCTk4OkydP5i9/+Qvp6emcOHECe3t7nJ2d68U9UZIk\nTE1NqaqqIiUlBbVaTVBQEA0bNtQ274KH+cHExKRe/E3+TJ0m8EOHDqFSqbC1tWXNmjXI5XKioqI4\ne/YsmzZtorS0lC5dutC2bVsaN25cV2Hojc8++4zq6moSExOxsrKiuLgYpVJJYGAgQUFBNGvWjNLS\nUnr16kVoaKjBP5UbGRlRWlrK8OHDiYyMxNjYmAsXLlBSUoKTk9N/fAt7UWm2eXV1dWXixIl07NgR\nV1dXYmNj8fPzw9XV1aA/y/+VkZERJSUlDBs2jL59+3LixAn27t2Ln58fDg4Oenct1N6ECODBgwcs\nXryYdu3a0bRpU2pqati5cyclJSW88sorellF/yQ0f4+ysjLMzMy08/zZ2dk4ODggk8mQyWTaz1if\nPuu6VGcJ/OrVq6xcuZIHDx7g4uJC48aNuX79OqtWreLjjz+mTZs2LF++nEGDBtWLtYx/5urVq6xY\nsYKQkBACAwNZvnw5X331FZcuXWL//v2cPn2a6upqBg0apN1C1VBP4to345ycHM6dO0dQUBALFy6k\nQYMGnDx5kq5du+rV8Y8cORKVSoXmclMqlXh6euLi4sKGDRvo2LEjZmZmenVMz4vmfDh16hSVlZX0\n79+f1atXExoayp07d2jVqpVe/d1qryyZPn06165dw8HBAW9vb2bNmkVlZSXLly9n7Nix7N69Gz8/\nP71bw/4kJEnC2NiY/Px8PvvsM+7evUuHDh1o0aKFdh+Dw4cPExoaavAPMk+qThK4JEnY2dmhUCg4\nefIk+fn5+Pj40LJlS06cOIG5uTlr1qxh9OjR9Wo9438jl8vx9vbm6NGjrF69mmvXrvHNN98QGhqK\nn58fZWVlBAYG4uzsrOtQ61TtJ/HCwkJsbGy4efMmJiYmDB48GHt7e3bv3k2XLl30Ythcw9jYmM2b\nN/PSSy/RuHFjbfGaTCbj3Llz9OjRQ6+S0POg+Rtp9kGwsLDgu+++IzU1lfnz52NpacnatWvp1KmT\nXp0LmuHgxMREysrKsLW15ZdffqFdu3Z0796du3fvEhERgZOTEz///DN9+/Y1yCkyDSMjIyoqKoiL\ni+Pdd9/Fy8uLrVu3olKpiIiIoGHDhoSHh+vFXgbP2zNP4MePH8fU1BSFQoGLiwvOzs4cO3aM4uJi\nrK2tKSsrY9WqVfztb3/Tq2Uedc3ExAQHBwdsbGzYt28fL730El27dsXBwQEXFxeCgoIMPnnDw0R3\n8+ZNxo0bx9mzZzlz5gw9evTg/v37HDt2jEWLFjFlyhS9+FtIksSuXbsoLCykS5cu2Nvbk5CQgI+P\nD25ubhgZGXHs2DGysrLo2LHjI2va67va9Q9xcXFcu3aNo0eP0rlzZwoKCrh9+zbLly9nwoQJ2hGp\nF92SJUto2bIlJiYmzJkzh7KyMqZPn46Pjw9VVVVs2rSJNm3a0KpVK9avX8+aNWuIi4vDzc1N16HX\nido7KhYVFbFo0SLatWvHV199RceOHZk3bx49evQgICBA1IP8gWeawNPT0xk8eDA//vgjFy9eZNOm\nTSgUCs6fP8/Vq1epqKigX79+9O/fH29v72f1a/XW7+ftTExMaNSoEe7u7mRlZXHhwgVtFWp9eTtT\nKpXMmDGDwYMH06FDB+bPn4+HhwedOnWiYcOG9O/fH1dXV12H+afUajXjxo3j0qVLlJaW8vLLL+Pj\n44O3tzcTJkwgJCSECxcusGjRIsaPHy9qQH5HM+cdHR3NwIEDsbGxYcOGDfj4+DB48GBMTU2JjIzU\nqyr927dv4+XlhVKpJDc3l0uXLmFnZ4erqytNmzZFqVTSpEkTPDw8aN68Ob1799aLc/1paB7Q7t69\ny9WrV7G1tSU4OBiAbt264ebmRkZGBq+99ppeFiU+L880gTs5OdG0aVNUKhWtWrXi1VdfpaCggIKC\nAnJzczl06BCvv/66Qc/nPK7aRSwFBQXaHr7GxsY4ODhgbW3NwYMH8fPz04vlUf+L2k/iSqWS/fv3\nI0kSq1evJj4+nlOnTmFubk5QUJBebJ8pSRKjRo3Cw8ODSZMmERISwrJly/j888/p2bMnoaGhDB48\nmPPnz5OYmFjv17LWpjkXlEollZWV3Lt3jzZt2pCamsrw4cOpqKjA398fLy8vvTgXAO0UgKenJ1u2\nbCE6OpqEhASqqqo4dOgQcrmc5s2b4+vri5OTE5IkYW1tbdCtc42MjCgqKmLEiBHcuXOHqVOnEhgY\niL+/P/Pnz2fTpk18+umnoj7qz0jPWEVFhbR582Zp5MiRUlZW1iPfKygoeNa/Tq+pVCpp+PDhUkxM\njLRz585HvldVVSWVlJToKLLnR6VSSZIkSXfu3JEuXbokXbp0Sfrpp5+k7t27S4sWLZIuXrwoDRgw\nQLp+/bqOI318p0+fluLj47X/npaWJg0dOlTasmWL1KdPH+n+/fvSwYMHpStXrugwyhfXrVu3pNjY\nWGn79u3SO++8I7355pvS6dOnpcOHD0sDBgyQiouLdR3iY6upqZEkSZLUarU0depUqby8XEpNTZXe\nf/99qaKiQvruu++kmJiYenGta6hUKqmqqkoaPny4tHTpUkmSJOnYsWPSX/7yFyknJ0e6fv26dOfO\nHR1HqR+e+Ry4mZkZrq6u1NTUsGnTJho0aKCdw7G0tKw3Q8F/5MqVK9omE9OmTaNx48ZMmDABDw8P\n0tPTtUNmmqIdQ2dkZERhYaF2r98VK1agUCjo1q0bJ0+e5MCBA9rmNfrixo0b7N27l1dffZWqqipM\nTEz45JNP8PHx4dSpU7z00ku0bt1a75qN1BVNNXaLFi0wMjLigw8+ICwsjH79+mFubs6pU6ewsrJi\n+fLlTJs2jaZNm+o65Mci/f/qarVazfz587W7Z8XHx3Pv3j1mzpxJXFwcAQEBetOv/X+hGV3RrOO+\nePEigYGBNGnShKZNm1JUVETjxo3x9fUVw+aPqU5q8i0sLOjZsyc1NTWsXbsWf39/bG1t622/Wo3M\nzEyKi4u1yaisrExbyGdsbMySJUtwcHDAx8dHl2E+F5o5MLVazeeff87AgQPx9vZm7969PHjwgMDA\nQLp164ZardabLQKVSqV2eZhCoaCqqgorKytefvllALZs2UJ+fr5BVxQ/KU2tgKenp7YYzc3NjTVr\n1hAZGcnrr7+Om5sbcrmc8PBwvUne8FvdSnR0NJ6enqxdu5bFixcTGRnJihUrqKqq4uzZs7Rt21bH\nkdY9Tfe8kpISsrKy8PT0pKysjPT0dMzMzMjNzeXIkSO8//77ug5Vr9RpK1XNHJaTk1Nd/Qq9UVpa\nirW1NZIksW7dOho1aoRSqeTQoUP06tULZ2dnoqOjmTlzJi4uLroOt05pdowqLi6mqKiIQ4cOYW1t\nzZYtWxg7diznzp3DxsaG8PBwXYf6WDQd1szMzCgvL+err74iMTGRixcvEhsbi7m5OdnZ2axYsYLY\n2Fi96dFd19RqNaNHj2bfvn1s2rTpkYKtsWPHUlhYyLJly3QY4dP5favXGTNm0KdPH1q1agXA3//+\nd8rKypg9ezaOjo5614TmSWke1gsKChg3bhyOjo5YW1vTpEkTbty4gZ2dHRcuXGD8+PF6NdL2Inhu\nm5nUZ6WlpSQnJxMcHKwt3sjPz8fDwwO5XM7GjRsxNjbm7bffplu3broOt85IkkRJSQl2dnYUFRWR\nmJhI//79uXjxIosWLWLMmDE4Ozszc+ZM/vWvf+Hh4aHrkP+UJEn885//xNXVlejoaCorK7VTH8uW\nLePKlSvcu3dPu/Oe2JjkNzExMTg5OdG8eXNWr15NTEzMI30hhg0bRk1NDQsWLNBhlE+m9sYkq1at\nwt/fnx9//BEnJyf69OlDYWEhK1euRJIkXFxcGDFihK5Dfi7UajUJCQm0adOGN998k71797Jnzx7t\nuu+KigqDLtqrK6KtzXNgbW3Na6+9xscff0y/fv2YOnUqK1eu5Nq1a4SEhJCamkpJSYlBP42r1WpG\njBhB586defvtt/n3v//NiRMnSElJITQ0lPv375Obm8uOHTtISkrSi+QNsG/fPhwdHYmOjgZg+vTp\n3Lx5k1u3bjF79mwiIyOprKzE2NhYrPOuJS8vj/DwcO0oS3l5OdOnTyc+Ph4/Pz8AUlNTKSws1GWY\nT0SSJG2Htfj4eNLT0wkNDaVbt25s3LiR4uJijhw5QlJSEr/++itXr17Vdch1Rq1WM2/ePDw9PfHx\n8cHLy4vq6mqUSiUAYWFhbNy4kcuXL+Pl5SXmvJ/Sc9nMpL7SLBXTbFRhaWnJ9u3b8fT0pHfv3uTk\n5HD27FkCAwNp2LAhYJjrvdVqNUOGDKF58+YMGTIEgNDQUNatW8eZM2cIDw+nffv2BAcH06NHD71a\nZlhVVcWiRYs4e/Ys27Zt4+rVq8ycOZPKykrS09Pp1KkTMplMr/q2Pw/W1tba4VK1Wo2/vz8qlYpv\nv/0WLy8v7bSbvtQ/wG/Xbnx8PK6uriQnJ5Oenk5hYSEDBgygZ8+eNGvWjBs3brBu3TqGDBmive4N\niVqtZuzYsVRVVZGbm0tRURHBwcHIZDKysrIoKCjg1q1b/Pzzz3z44YdYWVkZ5H3veRAJvI7U3kI1\nMTERNzc33njjDQICAhg3bhySJFFWVsZ7771n0C0CNRdzbm4uJiYmdO3aFVNTU0xMTHj77bdZvHgx\n+/fvJyIiQludqk8cHBxwcnJCqVTSrl07Pv30U+RyuXZ+PzQ0tN4Xb/4Zzc27devWlJWVsXr1aiIi\nIvTmXKjd06Gqqopt27YRFhaGu7s7YWFhzJo1i+LiYgIDA6moqCAzM5OhQ4fi5eWl48ifPUmSiIqK\nomnTpkycOBF/f3/mzJmDlZUVOTk5vPLKK+zZs4crV64wduxYvWrE8yISCbwOVFZWIpPJkCSJ6Oho\nmjZtqm0j26xZM7p168by5cvp27evwVecf/vttzRo0IAvv/yS8+fPs2zZMrp06aJ9K+3Tpw9r166l\nQ4cOelud7eHhwcsvv4xMJqO0tJT09HRWrFjBsGHD6sXyoMdVu2HPHwkICKBz5856Mx9ae847Ozsb\nS0tLZDIZBw4c0Lb/PHz4MDk5OahUKnr37k1ISIjBPrTv2LGDgwcPMmrUKOzs7Fi4cCHXr1/H1taW\nrKws2rZtS2RkJKGhoaK4+RkQRWzP2MaNG/H29sbX15fTp0+TlJTE5MmTSUpKIiAggLy8PJKSknjw\n4EG9mPe5du2atg/AgwcPmDdvHpcvX2bmzJl6NTz6Z6qqqli3bh2//PILJiYmjB071iDfsJ6WphK5\nuLiY3NxcbG1tcXd31+uaD03sarWakSNHUlNTg6enJ76+vqhUKrZt20ZVVRWzZs0iKyuLrKwsRo0a\nZfA7as2bN4+KigqKi4uprKwkJSUFeNj3okOHDnTt2lXHERoOkcCfsezsbLy8vDh+/DhmZmYcOXKE\n8vJygoKCcHBwYM6cOcyaNQu5XK63N66nobnZKZVK5s2bR2ZmJl999ZVBJXGlUolKpUKlUhnUcf2v\nNMn7zp07DBkyBHd3d/Lz84mKiiI4OFivk7hKpSI2NhZ/f3/atm3L3LlzcXNzo3v37rRu3ZotW7ag\nUqlYtmwZycnJBr0KQfM5A3zzzTfs3buX0aNH065dO3bv3s3ChQuZNm2awW7OogtiCP0ZqampwdjY\nGHt7e06ePMmCBQtwd3cnIiICOzs7cnNzSUtL45NPPsHLy0tvb1hPS3O8JiYmtG3bloKCAlq0aKE3\n/awfh4mJCWZmZqLa/HeMjIy4f/8+qamphIWFER0djampKcuXL6dRo0Z61+96+/bt5OfnY2VlhVwu\nJy8vjy5durBkyRICAgI4ffo0WVlZdOjQgQYNGpCenk50dLRBJ294+Dlr7oNBQUHk5+eTk5PDkSNH\n2LZtG1OmTBFz3s+YeAN/BmrPg02ZMoUePXqgVCrZunUr3bp1Q6FQ8ODBAywtLQkJCdF1uHWqdvEe\noK3CF4Vc9U/tz3337t2sWbOGNm3a8MEHH2Bubs6aNWvYtWsXKSkpmJub68VD7eeff86FCxfw9vbm\n1q1bJCcnc/z4cbKzs2nSpAnBwcGMHDmSqKgo7Zr23zd2MSSaY6uursbMzAx49HOfM2cOJ0+eZMKE\nCQb/AKMLIoE/I2q1mokTJ5KXl8elS5dISUmhvLyc9evX0717d3r27KkXN6j/Re2OS2lpadjY2DBo\n0CCcnJxEEq9nNDf2u3fvcvHiRZydndm/fz/FxcV4e3sTFhaGhYUFZWVlejPdMGnSJGQyGXFxcZSU\nlJCUlMS7775Lo0aNtD0N5HI5w4cPp1u3bno9NfA4NJ9xYWEhs2fPxsXFhSFDhiCTyR55aLl37x42\nNjY6jtYwGXY1xXOguUhTUlKwsrJi8eLF/Pzzz0ycOJH4+Hj69OmDk5OTQV/IGsbGxhQVFZGQkECH\nDh3Iy8tj2rRpxMfH4+joKJJ4PWJiYqJtneng4IBCodD2ND98+DAmJiaEh4frTfI+duwYP/30E7Nn\nzwYe9rXftWsXRkZGbNq0iSNHjiCXy2nUqBG+vr6AYfZ0qM3ExIRbt24xduxY+vbty9q1aykpKSEm\nJuaRQj2RvOuOmAN/Spq1n5qLNDs7G6VSSfv27WnevDk3btxg8eLFvPrqqwQGBhr007hmeZAkSezc\nuZPGjRsTERHB4cOHqampYdWqVXTq1Mmg5ruF/ywzMxNnZ2dqamqYOXMmXbt2JSoqClNTU06cOEGv\nXr2Qy+UEBQXpzVIx+G29/+7duzl48CDZ2dnMnTuXfv36cfPmTRo0aEBYWFi9WDa4cuVK7ZLYdevW\n4eXlxcCBAzlx4gRHjx4lMzMTPz8/7TI6oe6I16GnUHvOe8eOHZw5cwY7OzvkcjlLlizh8OHD3Lx5\nk4iICNLS0qisrDTY5K15kLl9+zYpKSk0b96cRo0akZCQwEcffURYWBgNGjSgurpa16EKdSwjIwNJ\nklCr1ZiammJkZERlZSUAnTp1ory8nPz8fCIiIvQu0ZmbmxMREUFQUBB79uwhPDwcFxcXTp48ya+/\n/qp3x/O08vPzKSsr4+DBg1y6dIlXX32VmpoaRo8eTVxcHMOGDaOgoEA8rD8n4g38KWgKs8aPH092\ndja3bt3i4MGDeHh4UFlZyU8//cQ//vEP3N3duXDhAt27dzfIIpb79+9jYWHBnTt3mDJlCkFBQXTu\n3Bm5XM7t27e5d+8e69evZ8aMGQa/w5rw8C21SZMmzJ8/n/z8fAICAjhx4sQjrTPfe+89rK2tdR3q\nUzE1NaVFixbY2dmRmZlJTk4OGzZsYNiwYfVmS1Bra2vMzMz45ZdfuHz5Mp6enrRv356MjAxycnLY\ntWsXkydP1m4NK9QtMQf+lBYtWoSjoyOffvopxcXFrFy5EktLSz788EPS09PZv38/e/bsYfLkyQa5\nrOj69etcunQJd3d3Ll++THp6On369AF+mxu7du0acXFxerWHs/Dkfr/ywMnJiezsbO7evUtYWBhb\ntmxBkiQSExP1/lyQyWT07NmT6upq5s2bx9SpU+nQoYOuw6pzarUaExMTioqKSE5Opl27dlRUVLB5\n82batWtHUFAQW7duJTY2VmwJ+hyJBP6UHjx4oB0matiwoXZ/awAfHx/u3LlDUlKSwa57VKlU7Ny5\nk2PHjpGamsrs2bNJSEjA2tqa4OBgYmNjMTIyMsiHF+E3mh24CgsLiY2N5cMPP6RTp04cPXqUnJwc\nbt++TWJiIkql0mDOBQsLC/r27UvHjh1xdnY26PoWDU3x6fHjxwkICGDkyJFcv36d9evX88svvxAZ\nGUnv3r0Nvsvci0bMgT+Gy5cva/9Zs+quV69e/PzzzyxdupQzZ86wd+9eAgICALC3t+f11183yOSt\nOX53d3dat26Nvb09p06dIjQ0lISEBIYPH86JEycwNzc3mBu28J9VV1djZGTEvXv3iIqKYuDAgVRU\nVHD+/Hlqampwdnbm4sWLlJeXG9y5IJPJcHZ2Bgy72nzo0KFMmzaNtWvXUlxcjKWlJRkZGQA0a9YM\nCwsLqqqqtC8xwvMl5sD/RGZmJsnJyTg5OeHi4oKRkRHV1dXY29sTFBTE6tWruXbtGm+99RYdO3Y0\n+KdxSZKoqKhgyJAhDB06lJdeeomMjAwKCgrw8PDAzc0NHx8fUYFq4NLS0lAoFDg6OpKbm8vZs2dp\n3bo1ixcvRi6Xk5+fz1tvvUX79u31ZqmY8KiysjJ27dqFubk5FhYWpKWl4efnx08//cShQ4fIy8tj\n7969xMbGah9mhOdLJPA/YWtri6mpKVu3bsXGxkabxI2MjDAzM6NTp05ERETg6elp8MkbHiZwc3Nz\nzp8/T0hICB4eHshkMjIyMli6dCljxowRF3M94Ovri6OjI+vWraNp06ZUV1dTUVHBe++9h0KhYOvW\nrbzxxhsieesxmUyGQqFg3759TJ48mdzcXH744QfMzc25cuUKfn5+DB061CBHGvWFSOB/wszMDHd3\nd6qrq9m8eTM2Nja4urqSmZnJkCFDCAsLo1GjRoBhDqWp1WpSU1Np3bo15eXljB8/nt27d7Njxw6q\nq6uxtbVFoVAQEhLCW2+9hZ2dna5DFupQ7b2vy8vLSUxMxN7engEDBlBSUkJmZiZLlixh+vTpYrtI\nA+Dg4EBeXh6FhYVkZGQQFRXFX//6VwDef/998bCuY2LS4jFYWFgQEREBPNwu9Ny5c+zevZu4uDj8\n/Px0HF3dUavVxMTE4OjoiKWlJZaWlkRGRmJhYYFCoeDgwYMAHDlyhAULFujt8iDh8WgqkQsLC5k0\naRK9e/cmNjaWtLQ0JEkiODiYxo0b07NnT72vNhce0oygJCQk8M0339CmTRsAhgwZYpBLY/WNSOCP\nSZPElUolc+bMYerUqYSFhek6rDqjVquJi4vD2dmZ6Oho4GE7SX9/fywsLFAqlRgbGxMdHa1X/ayF\np2dsbExpaSkjRoygb9++tGjRAl9fX+7evcuCBQuorKxk8ODBug5TeEY0U4KjRo3i1q1byOVy7ddF\n8n4xiAT+BOrT8pERI0agVquZMWMGAMnJydp9zQEqKiq4cuUKgF61xBSeXO3zvLy8HD8/P9577z0A\nTp06RWFhIWlpaVRVVekyTOEZ07RHVqvV1NTUcPjwYfz8/Az2nqePxDKyJ1Qflo9UV1fToUMHbG1t\nOX/+PF9//TU3btwgJiYGExMT9u3bR2BgIElJSQBigxIDplKpMDIyoqqqitLSUhQKBenp6WzYsAF4\n2NDnwIEDyOVyMR9qgDTFukOHDqVr1666Dkf4HbGdqPAfVVZWsmPHDhYuXIiFhQXff/898HAXpq+/\n/pq5c+fSrFkzHUcp1CXNm3d+fj6TJk3CxsaG1q1b4+LiwvTp0wkPDycjI4MvvvhCdN8SBB0QCVz4\nQ0qlkg0bNnDgwAHGjRvHlStXWLRoEfHx8Xh4eOg6POE50Kw8+OijjwAYM2YMM2bMwNfXV9ugRVSb\nC4JuiDlw4Q/JZDJef/11zM3NGTZsGJIk8eWXX4rkbcAkSeLGjRvIZDIaNmyImZkZNjY23Llzhx9+\n+IHk5GQOHDiAg4MD3t7eug5XEOo1kcCF/8rCwoIePXqgVqvx9/cXyduAqdVqhg8fjq2tLVeuXKF9\n+/YEBQXRokULZs2axahRo7C2tubQoUMMGjRI1+EKQr0nhtCFx2LIFffCw+Q9YcIEGjVqRFRUFOfP\nn+f8+fOcOnUKT09Pbt++TXFxMQUFBYwZMwYfHx9dhywI9Z5I4IIgMGzYMABSU1O1XyssLGTTpk24\nuroSHh5OUVERZmZm2Nvb6ypMQRBqEet/BKGe0ywbVCgU2p2mJEnCyckJR0dHdu7ciSRJNG7cWCRv\nQXiBiDlwQajnzMzM6N+/PwqFgqVLl6JUKgkJCQGgQYMGODg46DhCQRD+E5HABUF4pN//999/T8OG\nDSkuLub7778nNjZWtM4UhBeQmAMXBEGrsrKSnTt38u2336JWq5k7d67YLlIQXlDiDVwQBK3fLxsU\nyVsQXlziDVwQhP9DLBsUhBefqEIXBOH/EMlbEF58IoELgiAIgh4SCVwQBEEQ9JBI4IIgCIKgh0QC\nFwRBEAQ9JBK4INQjeXl5+Pr6MmnSpEe+fu7cOXx9fVm/fv1j/ZyjR48SGRn5X/+bzz777LF/niAI\nT04kcEGoZ2xtbdm/fz+1V5Bu3br1ifuci0p1QdAt0chFEOoZS0tLWrZsybFjxwgODgbg4MGD3n6O\nJQAAAi9JREFU2v7ne/bsISUlBUmSaNasGVOmTKFhw4YcOHCApKQkzM3NH9kXPjc3l4SEBEpKSmjQ\noAETJ07E19dXJ8cmCPWJeAMXhHqoZ8+ebN++HYDTp0/j6+uLmZkZt2/fJj4+nvnz57NhwwbatGnD\nlClTUCqVxMTEMHfuXNatW4eFhYX2Z3366aeMHz+eH374gSlTphAVFaWrwxKEekUkcEGoZ4yMjOjS\npQv79u0DHg6fv/baa8DD3ccCAgJwdnYG4J133uHQoUNcvHgRJycn7Zt33759AaioqOD06dN89tln\n9O3blzFjxlBZWcm9e/d0cGSCUL+IIXRBqIcsLS3x8/Pj+PHjHDlyhHHjxrFlyxbUavUjc+OSJKFS\nqTA2NkatVmu/bmr68NahVquxsLDgxx9/1H6vsLAQGxub53cwglBPiTdwQainIiIimDlzJq1bt8bY\n+OGtoLKykqysLG7evAnAqlWraN++Pd7e3hQXF3PhwgUANm/eDIBCocDNzY2NGzcCD+fS33//fR0c\njSDUP+INXBDqqS5dujBhwgSio6O1X3N0dGTq1KkMHz6cmpoamjRpwvTp0zE1NSU5OZlx48ZhampK\nq1attP/PF198waRJk/jmm2+QyWSkpKTo4nAEod4Ru5EJgiAIgh4SQ+iCIAiCoIdEAhcEQRAEPSQS\nuCAIgiDoIZHABUEQBEEPiQQuCIIgCHpIJHBBEARB0EMigQuCIAiCHhIJXBAEQRD00P8DY3KdH/LG\n7LcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4d289710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Index = [1,2,3,4,5,6,7]\n",
    "plt.bar(Index,p)\n",
    "plt.xticks(Index, ['Naive Bayes','Logistic Regression','Extra Trees Classifier','SGD Classifier','Decision Tree Classifier',\n",
    "                   'Bagging Classifer','Ada Boost Classifier'],rotation=45)\n",
    "plt.ylabel('Model accuracy')\n",
    "plt.xlabel('Model')\n",
    "#plt.ylim(0,0.9)\n",
    "plt.title('Model Accuracies')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model accuracy is: 0.583877995643\n"
     ]
    }
   ],
   "source": [
    "# calculate accuracy of class predictions\n",
    "print(\"The model accuracy is: {}\".format(metrics.accuracy_score(y_test, y_pred_class)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PREDICTED</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACTUAL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>81</td>\n",
       "      <td>44</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>265</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>34</td>\n",
       "      <td>524</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PREDICTED   0   1    2    3   4   5  6    7  8  9\n",
       "ACTUAL                                           \n",
       "0          11  11   39   37   1   1  3    0  0  0\n",
       "1           4  81   44  108   0   2  2    3  0  0\n",
       "2           1   6  265   44   1   2  0    6  0  0\n",
       "3           0  28   34  524   3   3  2   13  0  0\n",
       "4           1   5    8   71  11   0  0    5  0  0\n",
       "5           0  10   27   52   0  54  1    0  0  0\n",
       "6           1   8   20   50   0   2  5    1  0  0\n",
       "7           1   8   18   27   0   2  0  121  0  0\n",
       "8           0   3    1    4   0   3  0    0  0  0\n",
       "9           1   1   17   16   0   1  0    1  0  0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "Accuracy of model is 0.5838779956\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4.0    933\n",
       "3.0    473\n",
       "2.0    161\n",
       "8.0    150\n",
       "6.0     70\n",
       "1.0     20\n",
       "5.0     16\n",
       "7.0     13\n",
       "Name: predicted_sentiment, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function for confusion matrix\n",
    "def confusion_matrix_func(y_test,y_predicted):\n",
    "    \n",
    "    confusionMatrix=confusion_matrix(y_test,y_predicted)\n",
    "    Confu_matrix_df=pd.DataFrame(confusionMatrix)\n",
    "    Confu_matrix_df.columns.name='PREDICTED'\n",
    "    Confu_matrix_df.index.name='ACTUAL'\n",
    "    Confu_matrix_df.name='CONFUSION MATRIX'\n",
    "    \n",
    "    print \"Confusion Matrix:\"\n",
    "    print display(Confu_matrix_df)\n",
    "    \n",
    "    confusionmatrix=Confu_matrix_df\n",
    "    print \"Accuracy of model is {:.10f}\".format(metrics.accuracy_score(y_test,y_predicted))\n",
    "    \n",
    "    return Confu_matrix_df\n",
    "\n",
    "# Calculate evaluation metrics with confusion matrix function\n",
    "df_confusion=confusion_matrix_func(y_test,y_pred_class)\n",
    "precision_recall_fscore_support(y_test,y_pred_class,pos_label=2)[2]\n",
    "\n",
    "# Concat predicted and actual sentiment classes for comparison\n",
    "df_analyze_prediction=pd.concat([X_test.text, y_test,pd.DataFrame(y_pred_class,index=X_test.index)], axis=1, join='inner')\n",
    "df_analyze_prediction.columns=[\"text\",\"actual_sentiment\",\"predicted_sentiment\"]\n",
    "\n",
    "df_analyze_prediction.predicted_sentiment.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.56825264434826428, 0.54629629629629628, 0.54554009486799426, None)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "#print(precision_recall_fscore_support(y_test,y_pred_class, average='macro'))\n",
    "#print(precision_recall_fscore_support(y_test,y_pred_class, average='micro'))\n",
    "print(precision_recall_fscore_support(y_test,y_pred_class, average='weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud,STOPWORDS\n",
    "x=pd.read_csv(\"Tweets.csv\")\n",
    "x=x.dropna(subset=[\"negativereason\"])\n",
    "words=(' '.join(list(x['negativereason'].values)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pwDxdXtqP2+9hsbCpB0+a+9J+7GSxf74cb+1TCiWP4m/x4GGBs7Z04AeBUvEy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WC7X957QJ43VpdDCFiqo9IyMBYo8qWSlsq3BK776HGCvDNPv7wSOGk+ZW73JDtSt2xMlt3dfGg8\n9GjbjK99dRHKfxoKV/0aCLxLdAlcWIIAgXeD99ajYe89neKwry5CzW/TKZkmETGYU3w2i8SNYk/F\n6o94kdUzcPhzj1jxUkklavfLdwnfmBdbVq/LZpA/WLEPS1SeGFCOeUFmJf78Qz5eOSV4eU+8vHJK\nNa7/sfsJzW2/FmJ+UeyLTElifXRdHf7wr+7lQ/Ns1qR8HKc9mYn/3hW4DCoYTguYrEkwexCE4GtA\n7baLg/6eVeeB0/UCq84Fq8oBwxnBsGqAUQO8B4LgAQQ3eG8DeG89eE89eFcZeK8yy0yIMnBqSqhJ\nbBQ77fbC+KqQsx5/WV2AO3eG3+Y0HLWI9T7RruDXGBncuT29digLtjgxb6AqaLIdjQk3RdejvGav\nN+jzap7NGrDUKFqz38/BtKcC9y0n0tj1eWvIxxESfGbesrkAg86ObgHT4uGBr64dd5kBuQOjO5GW\n60qOVHhPNTyNm+Cq/RrOynfRYn8NzWUvobl0CZptL6HF/hpaypehtfpTuOtXwdu8lZJpQkjSUmxC\nDQALikPXteoyWcyzWXHHjkJYj49slXHP8Rpc+lYO5tmsmGez4u694T+0Nrwe+FLuTesKIv7gnrY4\nE3P2pFcyDQDvX1UX8sRons2K81+IvCUipwFOf9DU9vid+kD0l9MX9Aj+vLp7nwX3HLTAkBv5Syer\nN4cLXsxqi6n3ZFpFngihHsd5ZVbcvKEg4DblwRQMU+Gyd9rfG0yF0c8OO+sEtNYHXj9w/Q/5AWus\nuyoao5bkZJMQQoh0lF1bIPh7Pc8tDf3hos9icfXn7YuUBAEQeH8Sx7DiOwB8fb8DJ14TeCZ0XpkV\n7iYeiwZ13+DFWMDihjX50Bi7J2dvzqjGlZ/I2zs3URb0CF76AQDDztdj2Pn+toi8V4DXJUDgATBH\nFzGqAVYl7WW6BT3smFsWOCaVhsHtW/1XPwRegNcF8D4BEPzPJ1bFgNPEt7NETj8OPSdo0GuiBj0n\naKDPYsFyDNggr+jhM3UYOsMC3uf/G1bv9uLwOjdK17lxZJ0bLoc0Na65Azj0nKBFr4lq9Jyghc7M\nhIxr4i1GjL/BCN7r/xtW/d45rmBbdkfq8f7luGdf4BNVcxGH+w75H2PeJ8B37HFk/IuCWbX/uSX1\n4/jUsIqgz/exfzZi7J+NcDfz+HxOA3Z+2gqNgcHwmXqc9ldTwPeKr+5vwFn/pCsehBAiJ2Un1PB3\n/Zjfw465pZaIP/gYBmA4aT8k35lVg8veDtwaSJPBRjWj9NOSRpRt8MDVxEOboeiLCBGbX2THvLLw\nl+JZFQONxMlzIEKEJ2sMy0CtBySpIQjhrt2Fop4LDONP8v1zqgx6nKDxL9K7qfttP7i6Fnu+iqzN\nytxSCxgR2/Uy7NG4NP64isdqUDxWAwRoFvHKlCpU7w7chz4Yr1PAk0PKA3Zv6YjlGLAGfwyJ8NSw\ncty5I3hMGiOLC17MxgUvhh6nZq8XG19vwRkPm8FK/J5GCCEkcopPqAH4Z6p7lOPab/NQMFSeBvIH\nfnBj+3+cGC5yK+GP/lyLXf/1JzPvzKrFVZ+mxyw14D8xunt/IdS65DiJEPijiT5dXlc0l0NIusex\ntV7AooHlosq8avd78fLJ/oW71bu8KBimnM0zCOlo6a11+OXDwOtpCFGK5MhcJPLq6dWYX2RH3cHo\nZrFCcUexGcMnN9XjzfOrY76vx3rb25JpALBtDLLtWwp7ol8FFg0q9196l8CPCxtFjzG/yI7FI7qX\n7MRC4AV8eG0d/ntndB0diHjzi+x4saRSkrG8LgGvn12N3V/G3jDd3exP9KN5jznmw2vr8FJJexec\nbx5yxBwHIXIrGkIng0T5UmOGuosXJ/k/aAqGqXDNyryoL4XW7PXgnVl1cJRF33av7BcP5hfZMehs\nLWa+lhPRMc+OqUBTufyb0SQLd5OAx3qWg2GB0/9mxtjro+vUsXpRI9Y+0wSfhOcjzloe84vsYFXA\n1AVmHD878pjczTyWTquJulyBSK9uvw/zi+xQ6YCZr2Wj3xRdxMc2Vfqw7IIa1B2Uth3nokEVUBsZ\n3LqpIOxW9/+5sR47Vzi7/fzQarekMRGSSCPO0GLFP8VPfhAiJ0aIe8f96CXDNsGEEBIQAxScGbhT\ni6uKR8Pm9LuyFEzBWcE72lRGWKdPIhdut8FbZOrBHskuiHLFlkyS9fGTSjL/+6RIhVNyhpoQQuKF\n4YAT3gh89anym1ZsvLIuwRElr2B/JwD4QsSHJ5fB4Kzd/vrzfUuasPtRmt1MZi0OHgZzSlWYEtIN\nPcMJSTHWc2xt/xHpCT5g2331KP/UCcc2D1rL5duRNdltua0OZe+3oGGLG84YSuiCKb6sfcet/rdk\nSDYuiY/FF9bIHQIhcUcz1CRqOScub/u6dsNsGSMhRAYCcOTfThz5d3st8zlJ1EEkmdg+aIXtg/bu\nDVL9nSr+68Swh/2b4BxeFnhjLZI8bDvDrx9hWH9nJUKUihJqEhVD72uhzZ/S9j2rLQTvir0Dhjpz\nDADA07BJbGhg1FlQGfrB07AZQNItDSAxYnU9wGkLJXmOkNTQaudFlYyQ5HP7B7lY/AeaySbKRSUf\nJCrmoX/v9H3BlHUiRmOQN+kz5E36TFxQR+WO/9A/VjT7SZOkV3DyqqPPEVqsTIhS2X4PvVh3wARN\ngiIhJD4ooSZRYRj/U0Y4em2OYWN/E8yd9LkkMR2jNg2VdDySHBgu8tZ2hJDk9PTM8LPPxcPpojlR\nLnr2kogZ+1zX9nXV9xNQcOp6AACrs4Jvjf7yqyZzlGSx0exlihJxwqY0A+ZkoMdFeuiLObjreFR8\n3ortc8Nv2DLiiUwAQPN+Lw48768nZjhgzOvZyBmvAatl0LTXi72PN6FiZXS70R3/QhZySzRQ57Dw\n1PKoWeuGt7F7OdW2u+O7UZE6h8Hg/zMH/J19hRM1q8L34e55uR6Zo/3Pp47xHvdMJgrO0EGVwaDl\niA+HXm3GoddaoorP0JvDyCczYT5ODYZj0LjTg51/c6A+ys25GA4Y+WQmcku00Baw4N1A8z4vKla2\nYu+ipqjGSjbNdeHL8OZ+na/41nAkfVFCTSJmHvpw29e+1tK2r3PHf4yqHybIEVIbbW6JrPdP4sM8\naJ7cIcRdyXd5MA3uXKakzePQ60ojel1phLuWx7chdursOdvf8cLbyOPA8804+4gVDNf5NuZhaox5\nPRsA8GVPO4QwDTfOKbN2O0fV5HKwTtcHvH28E2pVBtv27+yqcZc3ooQ6Z7IWRRf44992dwPGf5SL\nnC5lBsa+Kgx7JBPDHsnED5Mr0XIg9B+K1QJTD3RfaJk1WoOJn+YBAL4aWA5fc5hkkjn6N++C0wPm\nEWqYR6gxcI4JgLh2g3Jbeks9rlqSFfI2t72Xg2cuqU1QRIRIhxJqEjVfa+cPd5WhV8TH6gqnwdD7\namhzJ3f6ebAWb60VK1G36ZqAv2NYPQx9/wJD8WWdYrBOPRj0/u1fFEUUp9p8HHLGvgVWk9v2M0EQ\n0LjrETQfeCHs8cf+PR3vL2vU89AXXdDpdp7G3aj5eToEb+R9dE1DHkRG3790+ImAxj2L0LT3yYjH\nOEZbeA7MQ/8Olb64088FwYemPYvQtG9x2DEC/Vuzx7wKXeE5nW7ncWxH9U/TAT70LCmjMsPY7yYY\nii8Fpy3scD9lQY+J9HFNNlMPWsBq2jPXpt0e1K7zwDREheyx/mRPk8PiHJs1bCKlMrGY8ktBWzLd\nctiL6h/dyBig6pQ4nn0k9Fhn7i1sS6Ybd3qw/rJauKt45EzWYPz77a+H2nVuHFnWguof4r9BS2uZ\nD+surkFGfxVMw1Uwj1Qj6/jYr16MeNzc9jfxOHhU/LcVmgIWBae3lxedsqYAX/ayQwjSoEJrYXHa\npvbnp8ALsK9oBe8WYL1AD07r/yOetceCVadVoen34J0uuibTtevdaNrlha6IRf5pOhzb60zglb3Y\n+pePnGET6kElWvQfp8G+9bT7J1EWSqhJRAy9/9T2dd1m/9eNexfDNOAOAACrKwLfGrzvcebIJ2Eo\nvkyyeOLZYznY2AzDwDzkrzAP+SvsK/uFTQyP4Yz9UHDy6oC/U5sGwXLmLvjctaj8dkTogRgVrGcf\nDvQLmAbeDdPAu2H/okdEMVnO2h+yNplhOJgG3QvToHtR/s1wCJ7INitRmYYjv+TrgL9Tm4fDOnU/\n3A1bUbN2asDbpFPv7NGvZrcl065qH/53XGW32wz7hxm9r/Vvc3/ymnz8OLkq5Jj6Hhw8Dh7fDOk+\no312qQUM67+/gfdmYM/C7iUEnJGByuBfJ1H5dSs2XtX+uNeuceOLIjvOLrOAYRjkjNdgXYL6Cws+\n//3XrmlPssS04Ot5uRGCIODLHuXdfnfCm9koOMP/2jhrnwUre3e/DYC2ZDrQOFvvbOgU40n/yw96\nEpM9rv3qxN7FjQEfl2OyTlD+gusnplfj7qOz98Hc+Z9cLJpRjQMbErPrqNbIwBXuKgIhYVBCTSJi\nHvqPtq899ZsBAE17Hm9LqPNP/hEVXw0Iery7bgOYLteh9T0ubvvaWfZ+wOM8DVsD/rzr7TuP9SGA\n2Bqadk3onGUfwN2wBZy+BzL63tB+u6n7YV/ZF+BDz85pck9G7rh32r5vKX0HHscOqAx9YOzTfpLC\naXJg6H0tWg69Gjy2Lsm0s+xDeBp+hco0FIaes4/GH3wWtyN33Tpo807p8P16uKr+B4H3QGeZDk3W\n8W2/s5yxPaIZYE3OROSO/7Dt+5Yjy+Fp3AmVcQCMva9qv13mSGgLpsJVubLbGKEf1w+QSu0QLee0\nn9AESqYBYMdfHSg8RwddEQdjXxUYDmHLNQIl0wDwZXF5W4I34A5TwMRt6D/a65Q7JtMdHXihGf1u\nUv5mKoGSaQDYeGVd29+JVQdemzH+o/aZ+mDjAMC3Iytw+lZ/4j3kIRN+f6j7lajB97f/zUMl0wCi\nrslORgc3euB2CtDoQ697mfNJHg5tduPxaXE6aWOAP7+ajVFn+1+HVLtNxKKEOo56qzhM0WoxRa9F\nMcchj2NhYlk08TwqfDx2e73Y0OrCdy43DnuTe7c1hgn05tee3LBc4PrGY5yly+EsXd7pZx2Tpfrf\nbo8qnq637zTWtjkAH/3lQsvZ7clo7cY/wVX5ZaffN/7+MBh1NixnbAcAWKceCJtoHkumq1adBm/T\n751+59j5ADL63w7ToPsAAP/f3p3HuVHX/wN/zeRONrvZe5PdlF5AW0o5CpRylqsUEARBgR+eoOJR\nblAQEERABVGOgvoFQeQUBBXRVloBKVfLXVrO3tlNsveZO5n5/ZHudtvdzc595f18PHi4diczn00m\nM+/5fN6f96dqzs8nDKgbT9g48nMh242O/+67y+/7118JZ91RqD34yd1fOq6et85F9fw/ofedb475\n3XBKy+iHi4oZl2Bo010l9zkcTHf8byEKyW27/G7go2tQOecW+PYopu/UzH9o3Peu5Oe67hJYJaCu\nOXxnusLac0oHDC8d1DES4C1a24CX5o8ffAPAR9fJy2UOnjp5RZVtD5k/oM71lX7gLqR52NwTB3y7\n515PJNu98zjTvlsxbkDd9XJmJL0nMN9hiaB5MpfPiGOZgBGGPQ5wYlk0iDeeSOKxK+Tn6dscwG0f\nNcLlowJnRHkUUMs03+nAD/0VWOIVXtrLa7OhwWbDvk4HzvSOP8kHALoLHP5vKIGHBxMY4PULJHzT\nfrCzTW+escvvcv3r4KiaBwBgHNWCUwOMhnHWjjw05AY/HhNMD+Nzvcj2vgVn9cEAAFf9sch0vlhy\n30Ob7xsTTI/8btNdIwH1jpZgvKCRte98YNk9mB6W7XoF+VTrmHzoiYwXTI/W8coRI6kqvuk/nDSg\nBoD+j64fE0wPG/jo2pGAutwd8mTNyM/drwh/+HMHbSV/H3lMXHWK3RWSPOy+0ts4qswfjLx6fOnU\nGS5bIqAe9c/bH5t8lUae5yfokCja+Nsh7HlVccLhwn/WofvVDNaWwaS8i8Mx3DPfa4sAACAASURB\nVB0Rlraz8FwvFp5bvAau/WsSqx9OYkuJB4/qkA0zD3Vin+NcmH+6u+T7TzRSBh8BBdQSvBtsQJO9\n9I1NCbU2FtdU+XFNVfFiy/M8NuTyWNzepfqxR6ucde3Iz9neXRdy6Xp9yUhPZt1h/0bn/xZq2jal\n1B/x0sjPXa8eV3Lb7je/OPI3B/a7D+2rZpXcfvDTm0v+PtP92s5JmqxjTO+6b+p3R35OtS8vua/O\nlxcieFKk5DZCFRKbR35mJhmBGFYqZQUA8kOfw16xp6x2WQFjU+fuMkkG0qQ23jmEfW4pluGzeRkU\nkmMf7va5tUreQQwgHZW+xrVvxs5r/5TzfJhy3iRPIAK8dV4PDn6s+JBVe4QLJ0WD4DI8Vs1rR2Gc\nMoVWwBWAi6fEcPd2cbnwh5zlxSFnCbseac3pYTBjgQMzFrgw81AHmvZ0wO4AbA4GNgfAyvzeT9ar\nz3E8CjmgkCv+b1+8gM1rs9i0NodNa7PobZM3Eu6rYTFzgRMzFjgx81AnasM22Eb/fazKf19h1N+X\nBzq35LFpx9+3eW0GQz36flcooBbowbpqLPHou8AEwzCY63QgGi6edNF8Aae0d6Gdk35zEHhkQVvZ\nvXuo3A712FylJ8lMhHWMXxt3GD9ZwiuA/OAno6qejO39q5z105GfBzZcO+b3u1IndWh4QZ9SeG7y\noepCOkYBtQQ8x49MKFTT9oeSIwH14o1NYybSOWtZVB9STE/gstYM9CbjrFa+h77rpQyWh2I4/qNG\nOALF/bMuBos/bQIAfHLLALbcO3lvuNlweeCSPWK4a5v0CaZG8qsNjXCUSBVSG8syYF2AY0eFGV81\ni+bZDhy5YwqL3DzxX61vnHwjFbE2BqwNI++xr9qJqQc6cdyO6U1658FTQD2JzS1NcBt0uChkt+G9\n5uIJ/nQiiUt6lK8F652yc4h+oiH/fGIL7L5pAACbuxmFtLCJcUalZKWJQmKrkK1K/3pUMMtlJp4A\nJZfN3QJn7eGw+6bD5p0q+gEp2/OGgK3KMwgzk7XndOOQJ4uT7k6KBlHI8OBSPOxVzC5D5/+Zqt65\naGSjz+CNdw1i6POJy+GJtWpOO8AAC/5ag5qFrpF/n3VtJWZdW2nqGtQTKeSKgZCQnGpCjIwC6nFU\nsww2NDfp3QxRvuzz4ss+Lz7J5nCsgikhVfvcMvJzxYxLUDGj9OTBuiNWoX0VLQE+jMtPvtKdXpzV\nh6L20GcV219BwmqZRBgteqeHdb+Sxap94jh+Q/EaaHMxIzWVAaD7tQzWftn6Ob4TyXbuHBFMbS8g\n9qy41ScnxQNrziy+v5XzHDh8ed3IIKGQeuRmtTQUw6Jve3HWTeZPKSLliQLq3awPNaDGpn5+tFpm\n7UgJCUX0ueiyDvNfDPMpZXKQi9ROx5GmafEmMLaxE2IzPWuQ63sH+cQm5Ic2om7hP4TvVEB6ixH5\nGAazHRNfCt/OKl91oe/97M6FScafh6qr43YM7X5+2yA23mnuJa+Vlty68zyfdV0lWp9IqXasgXU5\nLG+OYfHmppFJkhWz7CUXiTGzlx9I4uUHklj6ZA1mHeWa/AUycRyP6w/qQH/cmNdpYi4UUO/wBY8b\n/1dXrXczFJFUKKd6dG80lx8qGTAx9oqROtMM6wHPqXeTUVvnywv0boKqGo55bySY5nkO8RV7QK3c\nazN4rL4Gh7gmLoOmxsPpu9/qxbHvDdcnrsQnN0w8klGx187L9PaH1c+jPWp1/UhqBwXTpTlUyKce\nz8q94liyY/Je/SKXZQPqYcvOKfbQH/b/PDj3tgAETOEQrKc1jz98sxdtH1n7PSTao4AawGfNjahg\nzV8KatiebeMv7CDW6HJu7Sv3KrktY69E0wnF0nB1R76Ezv8dqkgbyK4Yu1/UMuXjsbl3TiyJrxBW\nYs/KSgXTasm0j6pP/B1fyYD6iJfqR37ecI36KUTe6cUHYx0rdRrehmv6sc8viqNxh6+qw2vHq1t5\nyTZqEZShjRMHgnpPylLa64+n8Prjxc6Zhuk2nHKVH/O/OHGp2fG88nACrzyYRFzBXPeJXDbd2vMK\nrHZ+Ka2sA2ongK1ha02EiOTzuowe86Nyhe3eKTq0YDTx+aY8lwfDGvPrkIw8Bm/4PACAp/lsJLc9\noNGRrfOQaUT/mR7HiZuLeconRYN4Ya84CkOjvr0McFLbzuvTtge1qfKQ+DyPir0cYJjxl/fOdBaw\n5Q8JbL0/Ad76a5CMa/vDScy5pRIMy6ByjgMnbmvCyllx7D4wx7qA8Ne8mHNT1YS5zydFg+h+LYN3\nL+hFfmDs1dtRzYzkswNA5yqZtRFNqmNzAQ99vw8Pfb9P76YQMi5jRhAa2Nthx0tN9ZNvaDILYqUX\nLBBqdHWPxLaHhL2I50YqUmhd7SM3tBGOiuLS546KvZEbWCfq9b3vfAM1Bz8GoJhfHH9hhuJtlGrg\n4xtHAuqqOTeVDKgr9/mlYsetP+oVxfYlVSHdAZu7AQDA2HzgC8qmIOzndIjavmKWHfP/VA1nDQt7\nxdgHjobj3SNBKF/gke3hkO3h8OoxY3swuTSPd77eg/l/LtYfXvzZxBOhMx0FfHSdNhNcVy/qwjHv\nNEy4iIyr3oZZ11Vi1nWVSMcKE67ceNRr9XDWsBMuBDPyPvE8cr08sj0FrD5q7PvUsNiFOT+vhKOW\nhd07dl9zflaJOT8rlq/kcsX3PNfD4dXj1O01XtESx5K2IBimuET5iZukd87UHu7CCZ9MPhF+/Y8p\nmCTEqMqyC+pIl1OzYDrD8+gsFLAll8fnuTw+zuXweS6P1nwefQUOvILjqlkF9zW6usfAR5PVPi7q\nfP2kkZ9rD/u3Ym0Ron/dRSM/1xzylOjXZ7p2LuzC2DyoOeiJSV9j92tTzYQvJMDzO1MEKvf5xfgb\nMjb4pnxdkWM6axbC7puuyL7kSLY+NvKzGgH+U/U1k280is3LwDvFPm4wvTvGxsBVb4N/74mD9o5V\nGSxviZVMr3j3gl68uP/Ey40rydXA4qRocCSY5gs8Cumd/+1ee9odtOGAB8afe+KbZhe0qiLDMHDW\nsKiYOf775Khm4Qnbxw2md8c6GLgbbfDPFvegJNWK5hi23j/5Q97gxxN35Xe/NnmPM8/zeGl+OyKP\nmHduCiFWx/BKRnQKUXOZ0G9WeHFrtTqVKPo5Dpd09+GFtLQhOQbAMW4XfhaoxIwSVQcmouTkqZ21\nmHnEljdLeB0QWx4SvC0A9H2wFKlosYybzTsVrrqjwdorMbT5HtHHLmS60f3GKSiktgOMHY6qA+Bu\nPAGDn9468Q4YO4JLtu/yT5muV5BsfQJ8rh823wy46o+Fq27RyDk60d843JZs3zvofuPUku2unH3j\nyGqIsf9MB7hxynCxHgRP3DTyf7lsL7rXfgX5wQ1gXU0IzLsbrrojxrxsvPY1Ld4CxlacQc/zBbSv\nmgs+X6xhzjprUX/ky2CdtYL2NfpvTUYeR//6K0v+rTUHPwFX3dEl9zfR/oFizfOuN08Fn+0BWBec\ngflwNyzGwCc3CtrX7qICUr60rJhTvcAJd5BFrpdD92tZ8BrOm7JXMiO9pBt+0o/tfyq9jPnodBCr\nlnIThQFqDnXC1ciCywCJLXnRkwd9M+3wTrXB7mfAZ4v7GKTJc4SoTolQuKxSPg50OhQPpvs4Dvu2\ntStSI4EH8GI6gxfjO9M2lnhc+F1tNVyTPGTECspVaajY86qRn9OdL4t6LV9I7lymmrGVrAwSWx7a\nJVgK7LcMgf2W7bJNuv0/go/dt/7HCMz9FQDA5qpFw6I3x2xTMqDm82Pa5Ko7Cq66owS3QTVcCj1v\nfx01B/0ZAMA6q1F/xMoxm7WvmouqeXfC3XD8hLuKvzBt5G9kGBuaTvh4zDY8zyO+ohlNJ27XPbc8\nGX0W3tCXAAB23zQ0Hbd+zDZSA2qj6V2TnXwjlRz/8c7JqpMF0wCw5fdDmPa9CjWbZC480POGvM8v\nsTGPRIlJh4QQ4yqblI8alsHzjdKWlx7Pc8kUQpEY5igUTE9kRSqDaa1xhCIxXN4zcf7c/KhyQ8L+\nmZeN/Nz79nmiXtv7/vdHfq4/evKV82LLQ8gNfjTh77lcr+BjpyKPoP2/+xZzuWWILQ+h550LSm4z\ntOkewT2sSsl0rkJsxRTwhbE92IV0DLHlIXC5HvStu2ycV+8qtjyEfGLzuL9LbP8z4iuKoxJdb5ws\nr9EK6P9gKTpXL1K87MSFfp+i+zO74VGX/nXCgkKO4j5CCBlRFikfDIA2hap59BQ4zI0qU5ZOjtFL\noqd5HtNbrV2uhxClCUn3ALRN+dDTzsmUwIrw5H8zpXwQQqyCUj4EUiqY3r+tHR0KLZoi13AAvbKx\nDicouNQ4IaQ8cTkerIMBYwMcNSxyPRNf6455p2Hk5+2PTJ4eQgghVmf5Hup3gg0I2uUtJc7zPJqp\nB5gQS6Ee6rF2rztdSPHoeSMLVx2LynljK2fwBR4rwnRtJISYmxKhsKVzqGc77LKD6VfTGQqmCbGY\n5xrGVjEhwMrZcYxeGcrmYVB/rGvcYPrz2wcpmCaEkB0s3UMttAdqIs8nU/huNxXSJ8Rq2lqaBF9n\nyqmHehhjA+qPd2Pm5T745ziQH+DR/WoWG+8YLC59bYzMN0IIUYQSobBlA+o3gvXYwy49RfxviRR+\nWKKqBiHEvMQ8bJdjQE0IIeWEUj4m4ABkBdNJjqNgmhCLmidh0SRCCCGkFEsG1FtlpHrkeR4z2/Qv\ni0cIUcdtNQG9m0AIIcRiLBlQy0kYmUITEAmxtHnOsRPsCCGEEDksF1Bva2mS/NqDDLBgCyGEEEII\nMRdLJRO6GMAhcUJjd4FDtEBT14n5uBkG36rw4iSPGzUsiyqWhYdh4GQAG8OA43kUAKR4HgmOxwDP\nYUuugBWpNJ5JpsqqYMPB1DutqSUeF871eTHDbkcVy8DHshj+BHIAhjgOfRyP97JZPDCYwIe58l7P\n3AHg0foazHbYEWBZcAAGOR6thTwu6+nHJyq8PwyAB2ursb/LgWqWhQ1AkufRUeDw92QKdwwMKX5M\no5jrsOObFT4c7HKiimXgZ1g4mGJPYw5Acsf1MpIv4NGhJJ5LpfVusuWcX+HFV3xe1LAsAiwDJ8PA\nwTDI8TwyPI8+jkMPx+HvyTQeHkogbbgyGjtZqsrH+6EGNNik1Z2mmfxFTgDzXU5Rr3kjk1WnMTIs\ntMDfMJ55Dgcerq9Go8TzXIgnE0nc1T+EbYWCasfQUtDG4jSvG9dXVYKVcG05s6NbhVaVZpbzcbR9\nHXb8vbEOHoXKnj4ylMDP+gaRNN4tSvD1Rejn+JMqP5ZWVgg+/ty2OHo4ee/LbdWV+GqFT/D2H2Sz\nOKld+++CUmpYFr+srsQXvB5F9tddKOCsjh58mjf3Q6DS53IpM+02/L2hDjU2ZZIj3khncFlPP7Yr\ncK+isnm7kVp3+i+JJC7r6Zf0WqsJ2Vi8HWoU9xoDPoyIPReM+DcMu7DCi58GKhVdQVSMddkcvtLR\njQHjXSrGONjpwDWBShzkdMCu0/ulBCOfj6NNt9uwsrEeHlbd99poawIotcqml2GwUWKaYkehgP2j\nHaJfF7bZsCbUMPmGE3gukcL3TFQF67mGWhwksoNFrI5CAYdEO2C+x2BtVoxd0ViLeU51PwOO53Hn\nwBB+LXFEhQLqUc7zeXF7TZWk45nl5qUFCqiN4+1gA0IyV/pUGs/zODbepXuvTIBlsMjtwp01AThN\nHDhPxIjn42gXVHjx82pp11s5+jgOcwxQhUmJIGS2w47/NtXLbktzJAahN3GxvdITKfA8wgaewO9j\nGHza3ChpREquw2Id2Jo3z+iemgG1nKwBOTbm8jgq3inqNUqEwpbJob6tulLS6+7oH1S4JYTIs7yx\nDvsZNNeXYRi8FCwGAZ/l8lgk8qIl1YO1ARztcSuWTkCkWex24U/1NbodP8CyiIaDeCeTxak6pOKI\ntb/DgfdzuTH/PsdhxyoFgmkAaAsHBQU7v68N4DSF0h1sDIOowONq7aNQIwIKpRRI8XqwARzPo8XA\nDxxqO8Prwb21+pUnvbC7V5fjWqbKh9ThcCtPuCDmsofdhmg4aNhgend7OeyIhoNY0Vir+rGWeD0U\nTOuIQTFw0zOYHm2+y1kM6HQMnIT4fuX4vcFKBdPDJutlPNPrUSyYHu3doPTUEaWd7HEjGg7qGkwP\nY3c8cNxfW613UzT3cXOjrsE0AHys0+Rm/c88BXyrwivpdVETDcsQa3usrgZvGOjmJMbJJp6oRCa3\nyO1CWzgoq76/Wt4ONeK5BvUf6KQ6dZwgtlVGaddSfjXBKK2bAe5RKcBpstuwjwFWHv2ouREP1Bkv\neD3F60ZEpc/biKLhIKpYfcPKvI5ZzJYIqG+RmMt3UEz8hA5ClLYh1IhjPC69myFZOZXdKzeP11fj\ncYP0Sk/koB291WbwPb9Ptbzer02QG725Rd33ZqXCve1iMCgGcQGdg7hShtNjjPhAKsZkmdBG+Q5e\n16tfgQnjnoWElIEPQw2oNsAQpVSnU++0Zb0fasAit1vvZghmlBt6KT8NSJvrI9TrwV2D2xsCflWP\nN0xsmVIl2FBMQzKLtnAQPhOnrS31T1zW0UjfvT8nUrod27x3cpleTGX0bgIpczcFKlGrwwxoJa3N\nmrFQFJnM9pagLrPz5TLSjX1YxY4g6nENUhKm2ndNv7iwRBCkpL9qnHbDAIgY8LOezOcmTv+4tGr8\nc8mI3zm96J/8JNMdEtM9LurRZxYoIUBxRbRv++WXrxqN53l8nMtjcz6P9gKHxI5cMi/DIGRjMcvh\nwHQF8x03ZMdWLyDm91awHnaFO9IGOQ5vZ7LYnC8gwfNwAKizsdjL4VB8Eq7Rqk+8HmzAvGg7Fnkm\n7u1/M53BO9kcfAyD8yq8klf8BYDTPG48l0rjdgH3xrWZDN7O5OBmGJxb4ZU88VfLftfhCbJKKvA8\n3snmsCmXRw/HoYDig1DYbsN8p1OxhUgA452fQrnGOTc2NhvrAeEFnVeyNH1AfY5P2szlXpmrTBEi\nxzYFbgjf6+rFP1NpwTVox+NlGKxqqhvTsyXECe1dMo4sTlihG9D2libRFYGUOrYZ3BSoRLOEc2F3\n/0tncF5nj+j8ejkLnYzW1tKEZoOULauzsVjiHn+OREskNuY9+knfAADpPX/31gbwXGsc55WYrD9e\n7errdhy3TcJ3BADO9XnwhAbD7UoE0+uyOZzS3gWxZQkYADcGKvEdmZ0hRjo/pXq+oRZeiQs6PT6U\nxDW9/Shg4jk4LAAPw+CemgCWeIWlnl2i8+JPpg+opXzxs8Zby4aUkQYZE2jezmRxmoL1d5M8j8Ni\nO2tJV7EMltUEcFyJ3jRAmSL4YuhZj6dcagE1sqysURMewEHRdsQK0qepJnl+pPfu2io/fihiOe7R\nGIbBe6EGHCBhJUE1PLjbxM7OQgH7TdK2UCQmKai2MQy+M8EExY+yORw/yYNwc2sc21uaRK80ekdN\nQPWAeoPIRcd2d3C0HW0yzk8ewA19A7ihbwCVDIP1zY2SVmRlGAb/bKg1RS318Uyz23CgiLz5lak0\nvtXVK+oBmwOQ4Hmc390LjHqbLq2swFWVfoz3tvfrHNuVZQ71KsqfJjp6U2J5vD0iMUWD6fH0czy+\n1tWLUCSG5kgMd/ePX6f93M4eVdtBtPdes/Rg5ZT2LjRHYrKC6d3d0j+IUCQ2krokVqPNJrmkqtom\nC6aHNUscHfnZOCX00hw/aTA9bA8D9p6e5/NInsC9INqOUCQmK5je3QDPY0prHMdLXNxqvssJ5SuD\na+M1gfew09u7EIrE8A2RwXQpdw4Mobk1hlAkhu917UzdTXD615sydUAttXtdr1V0CAEAt4RhslAk\nBq0zlnkAvxwoBjWhSAz/Su7sfXolQ5MRreQTicE0v6NH+T0V8+n3bI3jDIkPkrdUVxnuJje9VXiQ\nzKM4KqXIcduEB8k8gIzBRnJvrxFfSzuz4/yMKBhI7+6jXF5yTvQmE07o2ywgJevZRAqhSAxrVZ5n\n81wqPXJ/Ot0Avf1Gu9aIInXlp3IZwiXGIyXdY45Beou+092HUCSGfdva9W4KUZCXASolpiFplQe6\nJpPFVIlByzYDVVbI8zzSIuNUJUal/pJIin7NXhI+23qV6kFvkfgZTtPw2ik1qD5TYH6wEXza3Aj3\nJCkuM1rjWNqjfS7zBp1WRxzN1AH1Irf2tS8JkUPKIkR9Busp6jbA0BpRzkYJC3/wo3KdtZIFsJ+E\nhzkbw2B/hSuJSHWihhN5R7usR/xiF1L6Fm+vkVZ1q5Spdtu4FSZKSetwfgLSgup7TLQ8uX+SB6ZQ\nJIaUwe5XWjJ1QH3UBDOnCTGqU0T2Rlypw5M+KR9f9UnLMdarQkEnx+HrEvL3/91Yp0JrxPtYYi/a\n6xnp8360TN04QIUHl9clzDmZruOo3vld4s9Po+b6i2HGUoBKM3VAbcaFBwgR4/mkvnU1ibXdJqFH\n8btd+s5BWZXOYEjCKMkZJhpa390FndLf85/3DUp+bUFkMF6rcMrHbAl182fpnCK3IpUBJ/J9kzJy\naSQUTBeZOqCW4n9pqvBBzMNoE4OIdUyRUDGB43k8r/PiCQCwl4TUj3t1Hlr/WMYELTnlwB4cSkh+\n7XKRnzWr8NLaK0WOLPRzHAYMcM08UEK5Rq9JlyU/MmaM0pRGUHYB9X+ox4+YiNOkF1lifG9IGEo3\nUjm1dRIC1BqJC1Eo4WwTlpr8X1q/aj4eRnyAPtsgE6Y7JIygvCuxnKqeOgsFbMpTmYdhZRdQb8jR\ncsnEPIwymYpYj5RFsYx061wiYYLfezIXBpGjz4STeT/W8X75ZlC/z0oJjw+Jq6xSqeDy5loRWk+9\nXJjvE5Spx4QXNVK+/tJQq3cTiAXdLiFn82SdKlSU0lUQF+I7dBzxkVvUS0oig9j3Z3fbdOx9rBcZ\nYEotq6iWa3rFV1Yxk3aZ55YVlV1APcjpn19FytfrlMNPDOA8CVUF3ld5kQYpjomLD/K/LrGyid6i\nefEh+Z0D4690KpReJTJ9Eh58jLbUlJRvy3VVfsXboRYpeeJWV3YB9ZABJiyQ8nWxhDJ4UROupkWs\nxai9UVICvl+qUCtZC60SVvt7RcccaDmebxQ3Mtdr0JFnsVVSvmaS8nk8z0saMbG6sguo8xRQEx1F\nJS6B+7nEpaEJ2d1Mu/hyo+frXCqvlG6DBvtKk/J3xk363uztEDd35GoJC9do4WWRI5KTLZxiFNf1\nDejdBEMyx6enIP0XpyTljpfwUOdjWUTDwfL7whLFvSKhmsB7Bkz3GKbE0txqE9tTOR4p5eDKZUT2\nnwYo5TieZ5MpvZugiodETrgsF2V3f6alYIjevtQhvXxWaziI52iiIiEjtkiYOPdUfY0KLZmYEpP7\nyqUm/RwJi7kY1QoJgT7VdTKv8guoqa4v0dmarLy8xoNcTkTDQbxjwrqlxHysGMctcDk1PV6rAqkX\nBQt+DuP5kciJeVJG/LSSktC0OhOWzyNFZffJuSmeJgZwiAIzpIN2G6LhIDaEGum8JqpZNiivUoQR\naV0+r8Okucx6WOwRt0S8ccNpaWbajd1D/xile0yo7ALqCuqhJgbQWigottBDtY3F5pYg2lqasFDj\nnjdiLovcLtGv+WvC+DfQTw2c4w0AXRInI5PJbZZQTtDIFnvEf0e1dCNNSJyQsR+FVOBnWYAubsQA\n5rS1K1oSj2EYPLMjv/qzXA4nxLsk1UIl1iXlZq3n4h5CPZVM4XoDryqqVz3ncjDT4bBUadH9nMbu\nFCmXXH4pyq6HutYkZWlIeQiptLrXXg4HtoWDiIaDuDlQqcoxiPkcIuFmbYZKxm9lxLdyf5Gl2eQw\n47LjRB97SChrqSVrjQcoq+yiywMN3ItBypNaQfWw8/0+RHcE14dSSkhZ28tCFRRGk1LpY7ZTu/di\nkHr1iEBV1OlnWmX3yZ3k8ejdBELGCEVi+EiDPNBnG2oRDQexuqle9WMR47FbdA6JlJSKAzQcWk9x\nFFAL0UDBJJwW/Y6Wg7I7e/dzUQ81Mabj27swo1Xd3uphMxz2kV7rfSzaa0lIKdM0HFqnvFNhDpcw\naZYQozB1QP15jqZcEWtJ8cXe6t/0D2p2zJVN9YiGg/h2hVezYxKitykaliejvFNhZtPDPTExUwfU\nayRMRCHEDH49MIRQJIZ3NTzHb6quQjQcxDcpsCZloIrVbmidt1y1ZHXMpICamJipA+qX0hm9m0CI\nqr7Q0Y1QJIbVGp7rt+4IrE8RucACIWbipVxVw/HTZ0JMzNQB9YoUBdSkPJzd2YNQJIYnhhKaHfP+\numpEw0EKPIglGb+6dvmpoEmJxMRMffZKHUS7rsqvaDsI0coVvQMIRWI4tb1Ls2NubGnCE/U1mh2P\nEC1Q5Q3jGaJ63cTEyjJh6RyfFzdrOOmLEKW9k82N1K9+M9iAKSpXLDja7UI0HFS9ZjYhWumh4M1w\nBiR8JltzNOWTGIPpA2qe58GIHJKusZm6Y56QXRwa6wAALHA68GxjHdRM0IiGg5jRGkeKyoARk4uY\nYEn1cvN5voCTRL7msHinKm0hRCzTR5Yb6aJICABgTTaH5kgMoUgMfxpUL9d6U0uTqkE7IVrYXKCe\nTaP5MEuVu4h5mT6gXiTx6fQcH62YSKzrJ33FXOvZbXH0qTC03RYOKr5Por7ugjXTHIISRh3XabAy\nKRHn5TQF1MS8TB9QSx14/k1NQNF2EONotmm3AprR9XM85rS1IxSJ4TGFK4RQUG0+6y26GNY0CYu0\nfEy5t4aTkJBKdoqHVlckxmD6gJqQ3e3rNP3UAFVctaNCyDc6exTZHwPgQKdDkX0RbfxPQj3zChOU\nTTzcJT6ooh5qa7iskqp2EWOwRED9ksR61F+ntA9LOtDp1LsJhrYynUEoEsO+be2y9/V8Y50CLSJa\n+UcyJfo1U1WuIKOEc+laXrZm0eqKxCAsEVBf3tsn6XW/oLQPSzrMRQG1a/9L4wAAIABJREFUEN0c\nh1Akht8MDMnaz6m0oqJpxCTkUH/X71OhJcpqMkHQT9TBmmAEhZQHSwTU7RIn2jAAqlj6MlrNgRRQ\ni/Lr/kE0y6gv/ftaejC1srN8Xr2boLgClX00LKrbRczKEgE1AKQlXiA/DDUq3BJzo9tMeeIByYu2\niK0DT4jeNuYobDOqy7vFjzjTtERiBJYJqGe2xiW9zk7BwC4ooC5vUoNqP32PTOP5ZFrvJijqUAkj\nUie202IgRvWchDz/6wOVKrSEEHEsE1DLqa66taVJsXaYXcHkETUle8h31I6VF8V4vL5GhZYQNfyg\nu1f0a870GnfS34N11aJfQ9WOjUtKiYHzTZDnT6zPMgE1ADwkcXU4J8PgACr/BQDImjy3cDlVnZBN\nyuqj8ylv3TSkVF/+VbVxewADrKVuYwRAvwqLURGiNktdia7tG5D82n9RIAZAWi66kQb7Z9ODkSIe\nGJRX+cOozP24qByxKyZ6DRq0tkhYxOlBiR0vRDvnd4mvlU+To4nejHmVlEHOk+1nzTRBUcpw21J/\nheLtIPr6g0WDjj6OQmoAOKOjS/RrTjZgecT/NonvCPmpjI4Xoo03MuIX3TnNwGlJpDxYLqCeLWOx\nigqWxQ8oF0u0b1YYo6zWHdVVejfBMvotGnh+nqfV8QBpaT0PSMhVVptfZM85z/Oy5tsQ7WQkjJae\nb5B7ESlPlguoAWBrXkqWYNF1gUrUGnR406iCBllU4Vy6mCrGadGqHavTNB1tmJSlt5ttxrk2viSh\nd/rGvkEVWkLUcFBU/OTom6lThejIOFdHBR0Wk1cS6cPmxrIOqnMSegYCOgdgC12UO60kKbmpZvDo\nUFLvJhjGknbxaR9vGahu/94O8d/5+4esmcpkRd0cB17CvWiDgc5RUl4sGzU+IfPG+WFzI4IG6o3R\n0l8T4uuAvh1qUKElwj3TQJNKlfSXBnFl8DZI6O3UQ4eEORZHWLiCyTYJo3lXVuo/Z0LKfJeXU9aq\nv10OjomLf+irtrFYaOHvLDEuy0aMV/T2y97HO6FGLC3DnOqrJbx3elYBeIEqtCiuSuTneU6n+Fn5\nZiGlzrFZLJQwmnd5lR96jl+c4HahQsL15v91ia+/TfT1mcT0zWcaamlNAqI5ywbUALC3xNUTR/tJ\noBKflln1D6l9jXq8T4vcLsylUnmKukHCqmPdFq4bKyV4M5PPcuKDlkg4qEJLJlfHsnhYwiJCr6al\n1C8iRjBb4n18azgIlzWnghCDsvSdYpDn8W8Jy5juzs+yiIaDmO2wK9AqdRgh49XPsjhLw9JFLTbW\nVCv0RcNBLG+oNVTd7t05AFxo8VGZgoS8zI8t/FC9KC5tzklU46CaBbBO4ufwFQuPoFhdP89ji4SH\nPgDY0hLEVIPOB/EyDKLhIJ4y0T2MlGbpgBoAvt3dJ6n8znj+21SPaDiIfSVMhlHDhX4vtrc0IRoO\nYqnCeY0HRKWVH7y7NoCDNegx/qrPi7UmnHyyn8uJtnAQrS1NONbt0rs5u2ABbJMQJEldoVQvV/eK\nr0NcxbI4zMJ5mYtlBNVadDPMczjQKjGAP17i30aM43AZn+HroQZJFWHUYAfwZH0NouEgNrY06d0c\nojDLB9QAMK01Lmm28ET+01SHaDiI6wN+xfYpxAluF6Lh4Mh/NwSqYFepuka7yJXURvtHYx2+rWIJ\nu60tTbitxtzlkViGwaM7LqzRcBAOnbutz/N5JAcsclYo1cNjCWkTlv/aUIspBu3tkmt9Li95Uazt\n4SC+puL3/aPmRqyQGBC1Fwr4SGLvJjGWPWWkcO7tcCAaDuJiHUbfGBTTIaPhILaHgzjKYB0pRDll\nEVADQLMC+dS7+76/YiQg+nV1FSoVDG6PdbvwcF31LgG0lNxBOf4ko+fxpuoqbGtpUnRiyL01AUTD\nQUvWSN7WsvNzvlTDKgqnedyIhoO4vUbasr2tEhYIMQIpaR8A8GaoAfdJfK+MTs6iWL+qrkI0HMR+\nCo5OXVDhRTQcREBGDvsBEmoZE2NK8Dx+JbOO+NWBSkTDQfy7sRYVKt1H9rbb8bvawMj1vC0cFL0A\nETEnhley61YhjEonOgtI7oUTK8/zyPI8/p1K43/pDN7MZNFV4JAB4GMY7O2w42i3C2d43Wix2eFk\nir2WUv2qfxB3DQwp9wfsoESeZIHncXS8E5slBF9HuJx4vL5GVE/8plwe9TYWlSIuYqFITHTbxJLy\nXhZ4Hu9nc/hSR7fkyaK7O97twgN11Yo8mGjxvqnBBvkT6/6eTOEH3X3KNAjANyq8uKqyAhUsi6kq\ndAAIpcR3fmMuh6MklDwDgI3NjYpUDWqOxKDWzU3se3RmR5ek5bRHuzlQifNF9rAq8f0U+7eqfU14\nN9SAJgVHivI8j9czWXyrqxcpEeFQ2Mbi4ko/vuT1wCXj/v1qOqNrjr/RPl+9KBEKG3eWnQo4FC+y\nbRoE1XaGgZ1hcJbPi7N85l3B78KuXvxBZtkwG8Pg1eDOOtU8z+OVTBbrszl0cRxYAAGWxXS7DQtd\nLtTIrP99ZLwTe9hseEPn2thKsDEM5ruc4+Y2p3keH2Zz2JjLo61QQJLnkecBDwPU2lhMsdmxr9OB\nZpVWsjyzo1uV/WpBiX71070enL5jEi7HA69nMng/m0N3gQMHoIJlUMuyCNltmGq3YU+7HTYTjK60\nRGKyOx5m7hhiH5bheLyczuCTfDG1hAVQb2Mxx+HAkSoMgR/Q1q5aME30dWC0A2uDDWhR6LpmZxgc\n5XZhE+U0E5nKKqAGAB7aBdVW8M9UGn9QeJ8Mw+BotwtHq3AjDe94et5WMGcqghhuhsHBLicO1mGy\n3PZcHm9kzL2M96zWOD5R6CbKMsARbheOsEB+JAdg37Z2fKhgZRMXy+BErxsnKrbHiR0V60C7hcs4\nEuCQWAe2tDTBZYIHVFI+yjKxh0dx2CLJUR+GEGYZ4rmou0+RnkdSWjxfwKEWqJwwwPP41CQrPGqt\nm+MwQ8e0E6mOiXVio0nz+ok401rj+A+tfkkMpCwD6mEz2+K4T4W8YyuaYvCg+pGhBJ5RoOY4KW2Q\n43BgzDoTvY5pl5bnWw5SPG/47/1oM1vj+FTiynrEnL7V1YslEnP1CVFaWQfUAHBz/yBaIjHJs/7L\nRR7A3DZj9lgtGxjCj8epLdwjo/QfGev+wQT2llEJwqhCkRg4+v6PK4/i+5M28PszMuJo4DYS9azL\n5RCKxBQtjUuIFGUfUAPFnMFwaxxfp9W0SurheLQYrMfq2Hgnbu0fv5TSH4do9EEpe7XGcYPJ6k2L\n0WLC9AYtTW+N4+cG/PzvHxxCs8GuSUQfza1x3D0gr6weIXJQQD3KqnQGoUgMjw5JW/ihHHAo9gZt\n13loNcfzCEVi+KTEog2PJ4yVArJA4uqTenpiKIlQJIahMuj9CUVieCOd0bsZhvW7wUTxXDDAhL8E\nxyEUieEGmXWJibX8sn8IoUgMEROk/ryZySIUielaMo8oq+yqfAjxo95+/Ki3H4c6HXi20RhLlk7k\nV/0DuGtA+6WfD411ooZlsV7BSgBCndXRjdcFVJiQs9qjGiKFYhBQzTJ4L9Ro6AVq7h9MWLpHeiJn\ndvbAxzD4nEpoTWivtnbUsqyiVUDE2KetHb0GCOqJcS2IFSdNv9pUj+kO44Q5aZ7HcfFObKGJs5Zk\nnDPNgN7M5kYqXNxZE8CXvW7VFp0RaoDj8Mu+QfxJ4vLJSurZ0UvkYoAPQo2iFlIRi+N5nNPZg1dN\nXqoNAHo5fmThjlqWwYrGetVqRYvRXSjg6Hgnesq8+k1ix+iHh2GwLtQAn8arnA2YIFjs3vHdB4A1\nwXqE7ereSj7J5XAsTT4jIh2xoxrRKR43flcbELVAmFI25vJY0t5FOf5loKxWSlTKPIcdN1VX4RAN\n6v+uyWTx6/5BvGaSQPJwlxOP1tcoVh/0roFB3N4/BKkhxskeN4S05F8GKb803+nA5ZUVOMbj1uR4\nDw0m8NuBIXSZIIjT0xK3Cw/W16iy71WpNO4cGMK7Ji/ht6fdjj/WVWOmQj2CzyVTuLZ3AN0GPDdP\nEfn9fCOTkf2guo/DjqkiH1yUuK6J/VuNci2dyHynA3fVBFTruV6RSuGO/iFsKJGOaCRW+3ylUiIU\npoBaQce4nPi634e5Dgc8DAMPw8DOFJc5ZhgGPM+jACDPA1nwSPM8Ulxx2dPnU2m8bNH8zXN8HnzP\n70M1y8LLMHAxzEjyfgFAhueR4Hmsz+Zx18Ag3jZ5YKGWA50OnO714Ei3E5UMCzfDwMnsWJUTAINd\nz7MsXzzHkjyP1zJZPDqUNH3QZhQMgIv9FTi7wgP/ju/68HnNozjXIMcXh3hTPI/N+Tz+lkzhCYPl\n9avty14PLvT70GArfvedDAMbw4DbcY4meR5JjsfqdAZ3DQ5hKw2FEx002lj80F+BxR4XfAwLz45r\nK4vid50DkEPxmprkefRwHP6ZSOHBoSQGjRdCEQkooC5TwegyQdu1z7sGXBdN2iHEakLrGmCr0z9N\naDKREFXgIIQYnxKhMFX5IIQQQgghRAYKqAkhhBBCCJGBqnwQUq5YBrCN80ydozxWQgghRAwKqAkp\nUw0f3ApbrX/Mv8dCS3VoDSGEEGJelPJBSJkaL5gmhBBCiHjUQ00IISbT/6shOOfaYd/DDvtUG2xT\nbGBY/asjcf0c8lsLyG/LI7fFHHV4CSFECRRQE1KGPF89XO8mEBkSjyWRELoxC8ABME4GrI+Be5EL\nNb8JCHrpwO+HkH4pg9yHOfBZgM/xxYK8hBBCdkEpH4SUocBt5+rdBKIVDkAG4Ad5FOIcEk8KX1ym\n/6ZBZFZnwfXx4JMUTBNCyEQooCaEEEIIIUQGCqgJIYQQQgiRgQJqQspMzVMX690EQgghxFIooCak\nzDgP21PvJhBCCCGWQgE1IWXGCOXVCCGEECuhgJqQMmKfHdK7CYQQQojlUEBNSBmponJ5hBBCiOIo\noCakjDjnT9O7CYQQQojlUEBNCCGEEEKIDLT0OCk/DMAEvLDPaITnCwfAsWAG7NMawFa4wCUyKMT6\nkP80hsxrnyGzcj34wRT4oYzerZbNMW+K3k0wBabGB+f+U+H+4oFwHbon2Ho/+EIBXLQP2Tc3IvnE\n68hv7gQ/IHzFQWJcTBUDWwML72keuA5zwj7DDlug2NfEDXDIbykg+24Wqf9mkPskD26QA8x/ORiD\n8TJgKxl4FrvgOtINx2w77E02MG4G3BCHfGsBufU5pF/LIrM6A26QB5/g9W42UZMTsNWw8J7hgXuR\nC47ZdrA1NnC9BeQ+ySP9cgbJZ1Mo9FjzOyEWw/O84b4RDENVCEoJRpcJ2q593jXgugZVbo3xuU6Y\nC/+PT4VjTrMi+0st/wD9F/8ZfML4VxDG70bFRYvhOeMg2JprBL0m8fBqVds0cM1fVN2/WBWXLUHF\n5SeDsUkbsCv0DKH/skeRWble4ZapJxwNCtouEoqp3BJtMRUMan8XgOc4t2L7TL+WwcAdQ8i8mVVs\nn1qousYP/4U+ME5l7rf5bXkMPpDA0B+TiuxPjOpfVE6+EQ/0/mRA/cbsRlDbAPReo33bdse4gdo/\nVsNzjPTvR/qVDDq/0WO6AFuJUJgCahOigHoSdha1f7tMk3zhQns/Og68DtD7a8QyYGorUPv0xXDs\nJSxY0ksstFTvJoDxu9H06a8V3y9f4BCfdimQ5xTft5LKKqC2Ac2fNIH1aXNfKXQVEDukA3xak8OJ\nUnmpD1U/EhbgKSF6UAcK0YLqxxF0PheASFj789kM37Wa+wLwne5RfL+DDybQd53+DwpCKBEKU8oH\nsQz/DV9CxYXHanpMW2MVgm33oNAxgI79f6LJMRm/B77vLIL3nIWwtQjrdSZF9r2CqH/5WtX2z9hY\nBLffDW4ojfa9rwQM111RXpo3NYL1aDtVyFZnQ8vmYhCVWZNFxxndmh5/PLW/C8D7ReUDpsmE3m4A\nAPB5Hu0ndSG3Ia95G8jE1Aqkh/nP98F/vg+Df0yg73pzBNZyUEBNTE9oj72abA2VCEaXofvse5Bd\n/amqx2r69HZV929VWp4nbIUbwbZlSD69Bv2XPKLZcUlR3SPViqZ1SOVa4BzpodSlB9ILhDfqP2LF\n2Bk0rawHYJFRD7PzAOFN2p0X/gt88F/gs/xnT1U+iGk1td5jiGB6tNq/XITA3V/XuxlkFFu4Rrfz\nxPvlBQi23aPLsctVy7YmQwTTo/XfqX3qXc1dVYYIpomx1D9ao2kwPVo4GoT/4gpdjq0FCqiJaRl1\nCW3PWYeg6jfn6d0MAsC9ZD80rLlJ30YwDJooqNZEOBoE4zDedWHgtiFNjxd8sx6+L3s1PaYQ8eM6\n9W5CWWvZ1gT3sS5d2xC42o/gO/W6tkEtFFAT0+pYeKPeTZiQ95yFYBur9G5GWau44mRUP/gdvZsB\noDjR2mijKVbTEmnSuwnj0ziPPnBTJexTjJnNmfuYcqj1YqSHTXvQjsb/1OndDMUZ81tHiACFbV3K\n7KdnCLk3N6IQ6wNbUwH7rBAcs0Oy99v43i2GqGhRjlxL5sF/xcmy9sEXOGRXf4r81k7AxsKxdxDO\nQ2bI2mdT2z2IN18kax9krLqHq8HYZAYLPJD5MIv8ZwVwPRyYKgbOuQ4493HI2m1kirZ5o/5v+2Tv\nIx/JI/teDoUeDgzLwD7DBtdCp6xRwcx75ioraCUt2+U/bObbCsi8mQXXzYGtYeBa4II9bJO8P+e+\nDjT8oxYdX9R/0q5SKKAmptb9pTtR++ylgrbNrt2E/mufRn5Dq6hjsHV+1K++HmyV+CHUiqtOwdDt\n/xL9ulKkBumNn/0abMXkuaVmfwhggwHUPPhd0a/rv/YpJB96RdRrqh/8LtxL5gnenmEYNEXuRjx8\nsdjmkRI8J4jLmc5vz6PjS90oRKWVN6y8sgL+7/jA+gUM8qpfNW5ES6u4wInngJ4r+5B8UtoiRZ7F\nLlT+yA/nnMkfOjq+YJ3AyUyaXq8HYxf3IJTfmkf7KV3geoUPr9hn2tH4XC3YgPDEB9fBTjgXOJBd\nkxPVPqOigJqYWvbNjSV/33vhg0j/811Zx+C6BtE++0eAjUUwcreo1/ovO0nxgJqU1vjOzaK2j+99\nJfhBaUWDe8//PwDFxWH8V31B0GsYGwv/tadh8JbnJB2T7Cr0boPgbbkkj7aZcdnHHPj1EAZ+XcyL\nZrwMau6ugvfkseXH+m/XbjKiLcSK6kFu3SsOfkhePkrqhQxSL+xcwcN9vAv1fx5bypPneSohqQPf\nuV44pgoP8zLvZiU/+OQ35tE2px1ggHCb8EmPjX+rs0z1D8qhJqaX+OPLu/z/1DNrEQstRSy0VHYw\nvYsCh1hoKfIRcRccyqXWjpg85cQDLyMWWio5mB5t6LcrRPXsV/xwMWCMdEbTszUJG3Ye/GNCkWB6\nd3ySR/e3+xAJxRAJxZB+cWeAOfBb7SYjNq4UlpPK53hEQjHZwfR40qsyI+9Dz1V9I//ecSr1TmuO\nAWruEH7viYRiyowi8MV99d4kvO600MVvjI4CamJ6A9f/FQDQcdiNiIWWou+iP6t6vM4FN4AbFD5E\nWvP4D1RsDRnmPGJvwdt2HPVzDPz0r4q3QUxQ3fj5HYofv9yw1cKeSrghTrOFJTq/2oNIKIboAe2a\nHG+YrVrYg0XrHso/VIwn8ViqGFy3xJB91xpD+mbSsk14+o8aPcRDv0+g/VTh85xszeYPR83/FxCC\nYiBT2KrMJEUh2ve+SvC2jtnNKraEDKt9Sthkv97vPIDCRvWCHaFBNevVt3yVFXi/ImxeQ9te2ga3\nAFBoN97y85m1OkwMNN7bYHlsLSM4b1rNdIvsOzkknk4K2jb0VqNq7dAKBdSESDRwK+XAGoXve8cJ\n2o7rTyL9r/dVbg3Q/7NnBW3X8P6tKrfE2nxfNNYCLkbXc0Xf5BsR0wutExacts5Sf7Si55J+4Rub\nvI+BAmpCJEose0HvJpAdKn96hqDt2mf/SOWWFCX/8KKg7WwNlSq3xNrse9O8ejHy2zQsOUJ04Zht\nB8NM3judXZ8DP6DNTNG2/YWNEIW3mDuXmgJqQjRgaxk7850ow7H/HoK2G7j57yq3ZFdDAoNq10n7\nqdwSC6PKEaIwPpoJa3WNLwibnNq+WLsUSa6jPPJ+KKAmRIbBO1cI2s596gEqt6R81T53uaDtEvet\nUrkluxoUmPZR80djrOZoRvkI9biK4Tne5GPqZFJCFjgqdGsf4ArNpXbsK28hJT1RQE2IDKln3hK0\nndBeVCIeY5+8ugGfpioDVpT6t7CSh4FbKbUGAGrvqda7CURFgZ8JO89jh3ao3JKxhOZS1/zWvGVm\nKaAmRIbCJmG5YbZgQOWWlCfnwj0FbReffpnKLRlfbt12XY5bLhICV/jzf9NHdb938HyBJnJalf87\nwpad5xPGzZUSsuqmUVFATYgGGLdT7yZYUu3Txl7Ce+geYRNXvd84UuWWWFNBRMqHmNXbzIgvCAuS\n6v6vGq6FdD0qV5QmpR4KqAkh5iViqWU9ZN/ZImi7iosWq9wS6+KGhOeDhqNBuE+0Zh5x55nCV7lr\neKYWzZ+av+4v2ck+XeCKob/XbvVOyUzaSU0BNSHE0gqtPbodm4sLyxtk6/wqt8S62maJW7Sl/qEa\nhKNBuI60Vi9tZq24eQKsn0U4GjR1zirZqfKSCkHbDT0kbHKgGvKtwnrH2YA5Q1NztpoQUvaYCmE9\njcmn16jcEvkYJ9VTloyDpPJ5DX+pRTgahP/7wvJOzaD9ZPGl0HxnexGOBtGySfhS1cR4vGd59G7C\npDJrhK3U6T7anA+7FFATQkyp8qazBG2XfXOjyi0heos0S18+OXB9JcLRIJpW1wPCRs0NK/t+DlxC\nWkk0xsMgHA0We+8PNWdAU86ELOait0JUWA+1Z7E5J85Stwgho7kdsM8KwT6jAY5pDbBNr4dtWj3s\nLbWAnQVYFrAxYGwsMPwf0YXQCh+1f7lI5ZYQI2jdM4aWz6VPPHTMsCMcCYLnefTfNojBuxIKtk47\nbXu2IxyVNwGz4dlaAMUh+tgh2pdYI+qRe25owXmgOR/oKKAmZYMJeOH58gJU/OB42Bopb9DsbCGq\nqUt24hNA7PAOBF9rkLUfhmEQ+HElAj8u1vRtP60L2bfNVcc8EoopEjjZW2wj++n/5QAG7jbnQwYx\nF1u1OTuqKKAmllbz1EVwHr6XKYbDiDiMw+Tj80Rx+S0FRJpjipbIa3yuuJRzbkse8cM7Fduv2iKh\nGIJr6mEPK3Obr7q6ElVXV4JL8Yju1w5+yLi1jMuOxS6FjMec92tzPgYQUoLvhycgGF2GYHQZXEfs\nTcE0IeWELwaTg/cr25vqmGYv5hi3BeGYY46+qNiCTrTNjSu6T9bDoOWzpuKEzousM6HT1MxxOloe\nBdTEMmr+egmC0WWovPaLejeFEKKzvhsGEAnFUIgrvJAFAzStqke4LQjPycavac318IiEYuhe2qf4\nvgPXFCd0Ni6vVXzfRDjGRp1GRkABNTE9x8HTi73RhwmbpEYIKR/RAzsQCcWQ25JXdscMUPfAjprW\nhxt/ElXy2VSx5/4Pyi/s4dzPiXA0iLpHaV6DLiieNgQKqImp1a/5Ger+cbnezSCEGFz88E5EQjGk\nXkorvu+Gp2vR0tpkilzWvp8NIhKKoeNs4SsrCuU51o1wNAjv2caviWwpFFAbAmXeENNq2n4XGLsy\ndzA+X0ChtQeF7d3I/Hc9sm9tRm5DG5CbfLg4GF2mSBsIIerrOq8XAOA53Y26+5TrUWVYBuFIEMnl\naXRf0KvYftWSWZ1FJBQDbEBoXaOilRVqfxtA7W8Dxf0T9dH8UEOggJqYUtPWO2UF0/3XPoXU02vA\nD2UUbBUxolhoqd5NIAaU+nsakb/HwLiBxhfq4ZipzO3Qe5Ibnq1NaJ2q7GRA1RSA6D7F5dv93/ch\ncH2lYrsOR4Nom9cOrkvaYjNEIBEBNT3kqIcCamI61Y98T9JSzT3f+D0yK9er0CJCiFnxaSB+VLEc\nni3Ioml1A1ivvDF0xllcddBswcvg7xIY/F2xOkrw9XrYp8oPEZrXNaJtbhxcD3WjqoUv0HtrBJRD\nTUyF8TrhPm6uqNd0f+VuxEJLKZgmhJRUiHFomxlHJBTDwL3yJ++1bG9SoFX6iB1WzDnv+JL8XOvm\n9U0UbajJXOsOWRad4sRUmjb+RvC2PM8jFlqK7Kufqdgiohc+mRW0nX2W8ZfaJcbTf0tx8l7b3HbJ\nPYCMnUHD32oUbpm2Mm8Wc60joRgKXdJLEIZb6XuoGsqoMQQKqIllxZsv0rsJREX5iLCeM+ehVE6R\nSMf1cGgNx9G6Zxx8Vnxg7Vpg/FrVQkXnFUsQpl6QViml8vIKhVtkbIzM1CFiLhRQE9Oo/ecVgrfV\naiIaU+HW5DhkrNSTbwjazvv/FqrcElIO+ASP1qlxRPaIgefFBdZNq+tVapU+ur7Zi0goBi4l7n2o\nutKvUouMyXMK3R/KCQXUxDSc86cJ2o7rU3bJ4VJsYXMP55pZ4g8vCtrOMTescktIWckBrc1xpF8T\nXiHIMcOa8//bZsTRfkqXqNc45ztUao3xeE/TLqDOx4Sl43jPpCBfLRRQE8vpOOwmzY7l2H8PzY5F\nCDGOzi/3IL1aeFDN+Kw5/J99L4fWPYWXCKy7X6fVFHVYdMd9lHbpPsPVWSZTdUV5jRJoiQJqYgqM\nV/jSvryGPdTebx6p2bGIDNaMZYjOOs/uEbxtw9PWHc3iEzz6bh0QtK2tyQTLSSqEcWh34Rl6QNh9\nT4lSiGR8FFATU7CFa/Vuwric+07RuwllLfW3twVt17Tptyq3hJSr1r2F9c469xfeKWBGg8u068gg\n8jAV1MOgBgqoiSmwAa+g7fi89LJOlid0IpWJrrV9VzwmaDvGXT55m0Rb/CAtqjEst5Wuv8M8J2mf\nqyx0kmjwNWtNkjUKCqiJOQhdZlzk7Hs5vOcdrtmxlMD1JgVtx3j05Z8yAAAMAklEQVRM1JOWFr6i\nQcXFJ6rYEEJI33X9ejfBMOr+qH2ueNtMYaMltvrySbvREgXUxBT4jMDAidGue7Xq9nM1O5YScu9u\nFbSdrcVcuZ6JR14VtJ3/6lNVbgkh5Y3rN/YKI/Y9LB5IiuhPanqxTr12lCkKqIkpcN0ClwG2aXNK\nu07cV5PjKCn17FuCtvOceYjKLVHWwI+fFLxt48e3qdgSQsqb7zxhqXlKy34krMOl4R/azMVpekW/\nlIqeq4WNEjhmOcD4TZTfZwIUUBNTKESEzaZnNOqhrnnoQk2Oo6TMqvWCtqu4aLHKLVFe+n8fC9qO\nrfLCuZBWTiTaK/RaP7+44hx9AmqhJeNsDer3UDMuwDFTv0oaiT8LS+0DgJZPm1RsSfmhgJqYg4jJ\nhkytusvbNrXeo+r+iXi9594reNvaZy4BaJIiUUjllcKuNx0ndavcEnPgC8rPc0k+kxK8beMKdVMd\nWrYEVd2/EB1nCj/XWrZTUK0UCqiJ5TS8caN6+/7gF2BY6w+T1b34E72bIFq/iNSP4ObfwnnwdBVb\nUxrbWIWm7XfpdnwrCK1vhGMf/WvqVl0ubKGM/Hb1eqgrf6RuJ4IQLVuFBWb9vxhUuSWlOec5wFSp\ncw1v/qhRlf2KlXkjC54T9uDC2BmEo/o+BHhPd2u6CI5aKKAmppH9YJug7dgKN+BS/kbbtP0u2OrN\nvcoUNyCsJ8cxKwQ2pNOKZhIlBU5OHFb7j8tRv/p6lVozvupHv49gdBka37sFjNDKNWRCTSvrEY4G\nNcuN3Z3egciwqkv9CEeDCG1oBNuo/W09+HYDGKewIHXwPnXqVWfWZgVv2/JxE6DwLaJ5YyPYgHFC\nqtY9hK9eCRTPZdfh2lV4YhtZhD5oQDgaRO195rrXTMQ4nz4hk+g+6XbB2wa33KnYsL5tegOC0WWW\nCIB6zhaertL49s/BmuwBIhZaKmp7+4xGBKPLUHnjmaq0hw1Vo+7laxGMLkMwugzuY/dR5TjlznWw\nE+FosHhz/l0AjFfdUSTPF9yigun207pUbM1OtmoWze81IhwNovnzJtimqHvNYvzF3k17SNhxhPaa\nStFxuriUmvD2IOzT5b8/jr3tCEeDYL0GC6cKQNf3ekW9pOHp2uLnqVI1FNfCnd/T5vcaLVe+j+F5\nDQv3CqTVxDKzCkaX6d0E0cQGOhMR+7cP3PoPJJatlHQsNhhAw5qbwNjlXyhz61vRtfiXsvejBLHv\nYe6TKLqOvVWl1iiP8bnQ9Pkdkl7LZ/IYvP15JO5bJf7FNgYVlyyB58yDYZ/WIOglSn0vxBIaDEZC\nMZVbIl1ofSNsNaW/m4UeDoO/S2DwXoFVgiZR+4cAvKd6RL9O7fdx0s+TB5LLU+i5uB98Uv4t33um\nBzV3VoGxibtXq/0+hD5oEB2kcQkO0X07wKfFvS++/+dF9e2VisUrar039U/VwH2EtHSK7Ec59Czt\nQ+6TvOjXOubaUXlpBTzHuMF4Jn+POs/pQfqVjJRmKkKJUJgCahMq54DaNq0eDa/dIPp16eUfoO/S\nR8APpifeiGFgm1qH+peuBeMUNh7YefwvwPo9qP3bpSW3M1JAXXnTWfB9e5Gk12bf24r+Hz8JrmMA\nfK5Q/K7aWTAuO9iGKriO2BuuY+dMmJ/c89X7kHnxIxmtF4apq0DTOmO836UoGVAzXmbkP+c+DriO\ndMJ9uAuOPZUd2+ZzPDKvZ5F+NYP0a1lwHQXwCR5ckgeEr7Mjm5CAeiLZT3JIPJZE+sUM+BQPPgeA\n4wGWAeMovpeeE93wf98HW528XrRIc0xUfWAppKaecGkOiUeSSP49jUJ7AXwWQIEvrpZqZ8C4GLgO\ncMD3dR/ch8lLBxh6IoneK9Rf+EVuGk7/7YNIPJMCn+KBPF98HzzF8yFwnR+MQ3h8El/cCe+XPKj8\n3uQ57mo+bIQ+bISt1mA96LuhgFolFFCXVs4BNQA0fn4HWJ/+Exj6rnocqcdeBzD5Z2KkgBrQ7xzS\nKqAGAPveTah/6TpNjiWV1O9FaF2D7EBPC6oGCTICaq30XN0vqoyZVEbJ5Z4Iz/FobRGX0ytV5RUV\nqLpC/1S13Gc5xBd1ga1m0Lxh8gmbavfeN71eD8dU/SfxTsQKAbWxr0aEjKN9zyv0bgKG7l05Ekyb\nUXzWVXo3QXX5T+OITblY72aQMpV6Ia1JMG10fEG7YBoABu4YKvYu64jP84gvKubNc73G6LOMH9aJ\n9Kv6BazlgAJqYkqxGZfpduy+Hz2BwVv+scu/FTrUH8pUEj+QQt/Ff9a7GerLc7rlKZPy1fuTfnR9\nU9yEMCviUhxaw9oF08NaZ8TB5/QJZPksj9Yp2v/NQnR+pQfxJdpMkC1HFFATc0rldAmU4vtejdSj\nr435976LzBecpv66Fj3ffUDvZmgiFlqK/mv+oncziNXxxaH7oT9Rz3TvTwfQNqNdt+O37hEHl+Y0\nPSaX5NA6dZxg2hid1ACA3LocIqGYbg8cVkYBNTG1WGgp+LT6M6H4dDGA57vHrxaQXf2p6m1QQ+b5\n98si/QMAkg+vRiy0FNm3N+vaDp7j0HncrdRzLlN0bjvy28VXH1DLwL1DxQmIehx7mTJVTJRQaC8U\nHyoeUKfetBht09vRc3mfJsfqOr8HbTPHf4BIv2a8VIvWPeJom6vfA8+w1MtpREIxXfOnlWLcDHVC\nBIpPvwxsUwCN796syv5j0y4FMsa5cSuNH0ghFlqKyp+fBd8Fi/Rujuq6T/sNAKD6gW/DffL+mh23\nd+mfkH72bc2OVw5ih3YCAPxLfQj8pFKXNvT+tB9DD+jbI91/6yD6bx0E42XQslGfpaS5Pg5tc/QP\n0HaXeDKFxJMpBNfUwx5WPuTJfZ5D/OjSaRSDDyUkl65TE9fDFSdDskC4VbuJrTzHo3VaXNOqQFqg\nKh8mVO5VPkqxz2hE7b+uBFspvlbsaNm3NqH7rLuBnPDlghvevRm2psC4vzNalY9SnMfMQc2fLgTj\nUKiKBM8j9a/3MXjrP1DYarz8PcbrROD358N9/FxF95t+cQMGfvoMCps7FN0vQFU+SnEf40TV1ZVw\n7qvMwk7jGXw4gb5rBlTbvxLYahaBmyvhO0PetbCUzNtZdH+/F4U2bVMr5PB9xYOa3wRkj8/3/KgP\niUeFrTwLTF6JxSg1393Hu1B7d0DRVR/5LI+B+xIYuE3fZedLobJ5hJTitMN9zGxU3fk1sFXeCTfj\n8wUM3bkCiftfKl2nuhw5bHDs04KqO86DY3ao9LY8j9TTazF070rkI92ABqk4qnA54DhgD1Td8pXJ\n/2YAuQ2tGLpvFTIvfAg+Yf5hSytivAwcs+3wf8sLz2keMHZh9xg+w2PwD0MY/H0C3AAPmCduHJ8L\nYCtY+C/wwnu2F/ag8Iey1Mo0+m4ZRH5rHhC+yrexOQHXPAcCt1XBOWviBzA+B/Td0I/EX5LghcfQ\nlsD4GfjP96Hy8orJa3BzQOKZFAbuHkS+tQCY6HJIATUhhBBCCCEyUB1qQgghhBBCdEYBNSGEEEII\nITJQQE0IIYQQQogMFFATQgghhBAiAwXUhBBCCCGEyEABNSGEEEIIITJQQE0IIYQQQogMFFATQggh\nhBAiAwXUhBBCCCGEyEABNSGEEEIIITJQQE0IIYQQQogMFFATQgghhBAiAwXUhBBCCCGEyEABNSGE\nEEIIITJQQE0IIYQQQogMFFATQgghhBAiAwXUhBBCCCGEyEABNSGEEEIIITJQQE0IIYQQQogMFFAT\nQgghhBAiAwXUhBBCCCGEyEABNSGEEEIIITJQQE0IIYQQQogMdr0bMB6e5/VuAiGEEEIIIYJQDzUh\nhBBCCCEyUEBNCCGEEEKIDBRQE0IIIYQQIgMF1IQQQgghhMhAATUhhBBCCCEyUEBNCCGEEEKIDBRQ\nE0IIIYQQIgMF1IQQQgghhMhAATUhhBBCCCEyUEBNCCGEEEKIDBRQE0IIIYQQIgMF1IQQQgghhMhA\nATUhhBBCCCEyUEBNCCGEEEKIDBRQE0IIIYQQIgMF1IQQQgghhMhAATUhhBBCCCEyUEBNCCGEEEKI\nDBRQE0IIIYQQIgMF1IQQQgghhMhAATUhhBBCCCEyUEBNCCGEEEKIDP8fzfVnTMymNOAAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd8da2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cleaned_word = \" \".join([word for word in words.split()\n",
    "                            if 'http' not in word\n",
    "                                and not word.startswith('@')\n",
    "                                and word != 'RT'\n",
    "                            ])\n",
    "wordcloud = WordCloud(stopwords=STOPWORDS,\n",
    "                      background_color='black',\n",
    "                      width=3000,\n",
    "                      height=2500\n",
    "                     ).generate(cleaned_word)\n",
    "\n",
    "plt.figure(1,figsize=(12, 12))\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}