Model_Creation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"updated_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>train_0</td>\n",
       "      <td>The bitcoin halving is cancelled due to</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>train_1</td>\n",
       "      <td>MercyOfAllah In good times wrapped in its gran...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>train_2</td>\n",
       "      <td>266 Days No Digital India No Murder of e learn...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>train_3</td>\n",
       "      <td>India is likely to run out of the remaining RN...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>train_4</td>\n",
       "      <td>In these tough times the best way to grow is t...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5282</th>\n",
       "      <td>train_6856</td>\n",
       "      <td>The spread of the novel among asylum seekers o...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5283</th>\n",
       "      <td>train_6857</td>\n",
       "      <td>Hundreds of Jewish  patients are being treated...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5284</th>\n",
       "      <td>train_6858</td>\n",
       "      <td>Beats me Honestly most of the people I follow ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5285</th>\n",
       "      <td>train_6859</td>\n",
       "      <td>Help us reach more people by donating and shar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5286</th>\n",
       "      <td>train_6860</td>\n",
       "      <td>Interest Rate Swap Derivative Pricing in Pytho...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5287 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ID                                               text  target\n",
       "0        train_0            The bitcoin halving is cancelled due to       1\n",
       "1        train_1  MercyOfAllah In good times wrapped in its gran...       0\n",
       "2        train_2  266 Days No Digital India No Murder of e learn...       1\n",
       "3        train_3  India is likely to run out of the remaining RN...       1\n",
       "4        train_4  In these tough times the best way to grow is t...       0\n",
       "...          ...                                                ...     ...\n",
       "5282  train_6856  The spread of the novel among asylum seekers o...       1\n",
       "5283  train_6857  Hundreds of Jewish  patients are being treated...       1\n",
       "5284  train_6858  Beats me Honestly most of the people I follow ...       0\n",
       "5285  train_6859  Help us reach more people by donating and shar...       0\n",
       "5286  train_6860  Interest Rate Swap Derivative Pricing in Pytho...       0\n",
       "\n",
       "[5287 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df  = df.drop(\"ID\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The bitcoin halving is cancelled due to</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MercyOfAllah In good times wrapped in its gran...</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>266 Days No Digital India No Murder of e learn...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India is likely to run out of the remaining RN...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>In these tough times the best way to grow is t...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5282</th>\n",
       "      <td>The spread of the novel among asylum seekers o...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5283</th>\n",
       "      <td>Hundreds of Jewish  patients are being treated...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5284</th>\n",
       "      <td>Beats me Honestly most of the people I follow ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5285</th>\n",
       "      <td>Help us reach more people by donating and shar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5286</th>\n",
       "      <td>Interest Rate Swap Derivative Pricing in Pytho...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5287 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  target\n",
       "0               The bitcoin halving is cancelled due to       1\n",
       "1     MercyOfAllah In good times wrapped in its gran...       0\n",
       "2     266 Days No Digital India No Murder of e learn...       1\n",
       "3     India is likely to run out of the remaining RN...       1\n",
       "4     In these tough times the best way to grow is t...       0\n",
       "...                                                 ...     ...\n",
       "5282  The spread of the novel among asylum seekers o...       1\n",
       "5283  Hundreds of Jewish  patients are being treated...       1\n",
       "5284  Beats me Honestly most of the people I follow ...       0\n",
       "5285  Help us reach more people by donating and shar...       0\n",
       "5286  Interest Rate Swap Derivative Pricing in Pytho...       0\n",
       "\n",
       "[5287 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=\"target\", data=df)\n",
    "plt.setp(ax.get_xticklabels(), rotation=90)\n",
    "#axes.set_title('Polarity Score By Dates')\n",
    "plt.xlabel('Covid Msg or Not')\n",
    "plt.ylabel('Tweets')\n",
    "for p in ax.patches:\n",
    "        ax.annotate('{:.0f}'.format(p.get_height()), (p.get_x()+0.3, p.get_height()),\n",
    "                    ha='center', va='bottom',\n",
    "                    color= 'black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "allWords = ' '.join([twts for twts in df['text']])\n",
    "wordCloud = WordCloud(width=1600, height=800, random_state=21, max_font_size=200,colormap='jet',max_words=100).generate(allWords)\n",
    "plt.figure(figsize = (10, 8), facecolor = 'k')\n",
    "plt.imshow(wordCloud, interpolation=\"bilinear\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to C:\\Users\\NILESH\n",
      "[nltk_data]     VERMA\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to C:\\Users\\NILESH\n",
      "[nltk_data]     VERMA\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "import ftfy\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cleanTxt(text):\n",
    "    text = ' '.join(re.sub(\"([^0-9A-Za-z \\t])\", \" \", text).split()) #remove punctuation\n",
    "    text = ftfy.fix_text(text) #fix weirdly encoded texts \n",
    "    text = text.lower() # all to lower latter\n",
    "    #stop words\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    word_tokens = nltk.word_tokenize(text) \n",
    "    filtered_sentence = [w for w in word_tokens if not w in stop_words]\n",
    "    #Word Lemmatization\n",
    "    text = WordNetLemmatizer().lemmatize(text,\"v\")\n",
    "    #joining text\n",
    "    text = ' '.join(filtered_sentence)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.33 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Clean the tweets\n",
    "df['clean_text'] = df['text'].apply(cleanTxt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "      <th>clean_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The bitcoin halving is cancelled due to</td>\n",
       "      <td>1</td>\n",
       "      <td>bitcoin halving cancelled due</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MercyOfAllah In good times wrapped in its gran...</td>\n",
       "      <td>0</td>\n",
       "      <td>mercyofallah good times wrapped granular detai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>266 Days No Digital India No Murder of e learn...</td>\n",
       "      <td>1</td>\n",
       "      <td>266 days digital india murder e learning 2g on...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India is likely to run out of the remaining RN...</td>\n",
       "      <td>1</td>\n",
       "      <td>india likely run remaining rna kits essential ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>In these tough times the best way to grow is t...</td>\n",
       "      <td>0</td>\n",
       "      <td>tough times best way grow learn case teach hel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5282</th>\n",
       "      <td>The spread of the novel among asylum seekers o...</td>\n",
       "      <td>1</td>\n",
       "      <td>spread novel among asylum seekers adds pile al...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5283</th>\n",
       "      <td>Hundreds of Jewish  patients are being treated...</td>\n",
       "      <td>1</td>\n",
       "      <td>hundreds jewish patients treated arab practiti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5284</th>\n",
       "      <td>Beats me Honestly most of the people I follow ...</td>\n",
       "      <td>0</td>\n",
       "      <td>beats honestly people follow sports fans l sha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5285</th>\n",
       "      <td>Help us reach more people by donating and shar...</td>\n",
       "      <td>0</td>\n",
       "      <td>help us reach people donating sharing ramadan ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5286</th>\n",
       "      <td>Interest Rate Swap Derivative Pricing in Pytho...</td>\n",
       "      <td>0</td>\n",
       "      <td>interest rate swap derivative pricing python h...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5287 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  target  \\\n",
       "0               The bitcoin halving is cancelled due to       1   \n",
       "1     MercyOfAllah In good times wrapped in its gran...       0   \n",
       "2     266 Days No Digital India No Murder of e learn...       1   \n",
       "3     India is likely to run out of the remaining RN...       1   \n",
       "4     In these tough times the best way to grow is t...       0   \n",
       "...                                                 ...     ...   \n",
       "5282  The spread of the novel among asylum seekers o...       1   \n",
       "5283  Hundreds of Jewish  patients are being treated...       1   \n",
       "5284  Beats me Honestly most of the people I follow ...       0   \n",
       "5285  Help us reach more people by donating and shar...       0   \n",
       "5286  Interest Rate Swap Derivative Pricing in Pytho...       0   \n",
       "\n",
       "                                             clean_text  \n",
       "0                         bitcoin halving cancelled due  \n",
       "1     mercyofallah good times wrapped granular detai...  \n",
       "2     266 days digital india murder e learning 2g on...  \n",
       "3     india likely run remaining rna kits essential ...  \n",
       "4     tough times best way grow learn case teach hel...  \n",
       "...                                                 ...  \n",
       "5282  spread novel among asylum seekers adds pile al...  \n",
       "5283  hundreds jewish patients treated arab practiti...  \n",
       "5284  beats honestly people follow sports fans l sha...  \n",
       "5285  help us reach people donating sharing ramadan ...  \n",
       "5286  interest rate swap derivative pricing python h...  \n",
       "\n",
       "[5287 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%time\n",
    "#Train and evaluate the model\n",
    "vect = TfidfVectorizer(ngram_range=(1,1),stop_words='english').fit(df.clean_text)\n",
    "text_vectorized = vect.transform(df.clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Vocabulary: 13986\n",
      "Shape of Train Data: (5287, 13986)\n"
     ]
    }
   ],
   "source": [
    "print(\"Length of Vocabulary:\", len(vect.vocabulary_))\n",
    "print(\"Shape of Train Data:\", text_vectorized.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:144: FutureWarning: The sklearn.metrics.classification module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from yellowbrick.text import FreqDistVisualizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a9ae22648>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features   = vect.get_feature_names()\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "visualizer = FreqDistVisualizer(features=features, orient='v')\n",
    "visualizer.fit(text_vectorized)\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Creation Start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTING MODELS\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import RidgeClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "# IMPORTING SCORING PARAMETERS\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import recall_score, precision_score, roc_auc_score, f1_score, accuracy_score,matthews_corrcoef\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vOTING cLASSIFIRE\n",
    "clf1 = LogisticRegression(C = 0.1,random_state=0,solver='saga')\n",
    "clf2 = SGDClassifier(loss='log',max_iter=1, tol=1e-1)\n",
    "eclf1 = VotingClassifier(estimators=[('lr', clf1),('sg',clf2)],voting ='soft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = {\n",
    "\"MultinomialNB\": MultinomialNB(alpha = 0.5),\n",
    "\"SGDClassifier\": SGDClassifier(loss='log',max_iter=10, tol=1e-3),\n",
    "\"LogisticRegression\": LogisticRegression(C = 1.1,random_state=0,solver='saga'),\n",
    "\"BernoulliNB\": BernoulliNB(),\n",
    "\"RidgeClassifier\":RidgeClassifier(alpha=1.1,normalize=True,max_iter=100,class_weight=\"balanced\",solver='auto',random_state=0),\n",
    "\"RandomForestClassifier\":RandomForestClassifier(n_estimators=100, max_depth=3, random_state=0),\n",
    "\"PassiveAggressiveClassifier\":PassiveAggressiveClassifier(),\n",
    "\"Perceptron\":Perceptron(tol=1e-3, random_state=0),\n",
    "\"Ensemble_Voting_Model\":eclf1,\n",
    "\"LinearSVC\":LinearSVC(class_weight='balanced',random_state=0,C=1.1,max_iter=100)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "MultinomialNB\n",
      "----------------------------------\n",
      "test_accuracy              0.876865\n",
      "test_sensitivity/recall    0.845709\n",
      "test_specificity           0.905678\n",
      "test_precision             0.892532\n",
      "test_f1                    0.868276\n",
      "test_roc_auc               0.875693\n",
      "test_Mcc                   0.754105\n",
      "dtype: float64\n",
      "----------------------------------\n",
      "SGDClassifier\n",
      "----------------------------------\n",
      "test_accuracy              0.902215\n",
      "test_sensitivity/recall    0.955526\n",
      "test_specificity           0.852869\n",
      "test_precision             0.857611\n",
      "test_f1                    0.903811\n",
      "test_roc_auc               0.904197\n",
      "test_Mcc                   0.810089\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "LogisticRegression\n",
      "----------------------------------\n",
      "test_accuracy              0.902970\n",
      "test_sensitivity/recall    0.960644\n",
      "test_specificity           0.849590\n",
      "test_precision             0.855507\n",
      "test_f1                    0.904933\n",
      "test_roc_auc               0.905117\n",
      "test_Mcc                   0.812419\n",
      "dtype: float64\n",
      "----------------------------------\n",
      "BernoulliNB\n",
      "----------------------------------\n",
      "test_accuracy              0.891051\n",
      "test_sensitivity/recall    0.865784\n",
      "test_specificity           0.914415\n",
      "test_precision             0.903907\n",
      "test_f1                    0.884131\n",
      "test_roc_auc               0.890099\n",
      "test_Mcc                   0.782508\n",
      "dtype: float64\n",
      "----------------------------------\n",
      "RidgeClassifier\n",
      "----------------------------------\n",
      "test_accuracy              0.847924\n",
      "test_sensitivity/recall    0.846904\n",
      "test_specificity           0.848848\n",
      "test_precision             0.838937\n",
      "test_f1                    0.842604\n",
      "test_roc_auc               0.847876\n",
      "test_Mcc                   0.696000\n",
      "dtype: float64\n",
      "----------------------------------\n",
      "RandomForestClassifier\n",
      "----------------------------------\n",
      "test_accuracy              0.628907\n",
      "test_sensitivity/recall    0.236929\n",
      "test_specificity           0.991627\n",
      "test_precision             0.962787\n",
      "test_f1                    0.379354\n",
      "test_roc_auc               0.614278\n",
      "test_Mcc                   0.353089\n",
      "dtype: float64\n",
      "----------------------------------\n",
      "PassiveAggressiveClassifier\n",
      "----------------------------------\n",
      "test_accuracy              0.893136\n",
      "test_sensitivity/recall    0.913804\n",
      "test_specificity           0.874000\n",
      "test_precision             0.870695\n",
      "test_f1                    0.891553\n",
      "test_roc_auc               0.893902\n",
      "test_Mcc                   0.787567\n",
      "dtype: float64\n",
      "----------------------------------\n",
      "Perceptron\n",
      "----------------------------------\n",
      "test_accuracy              0.878383\n",
      "test_sensitivity/recall    0.890996\n",
      "test_specificity           0.866713\n",
      "test_precision             0.861051\n",
      "test_f1                    0.875663\n",
      "test_roc_auc               0.878854\n",
      "test_Mcc                   0.757327\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_stochastic_gradient.py:557: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------\n",
      "Ensemble_Voting_Model\n",
      "----------------------------------\n",
      "test_accuracy              0.901647\n",
      "test_sensitivity/recall    0.946864\n",
      "test_specificity           0.859788\n",
      "test_precision             0.862284\n",
      "test_f1                    0.902467\n",
      "test_roc_auc               0.903326\n",
      "test_Mcc                   0.807581\n",
      "dtype: float64\n",
      "----------------------------------\n",
      "LinearSVC\n",
      "----------------------------------\n",
      "test_accuracy              0.902972\n",
      "test_sensitivity/recall    0.945289\n",
      "test_specificity           0.863796\n",
      "test_precision             0.865688\n",
      "test_f1                    0.903572\n",
      "test_roc_auc               0.904543\n",
      "test_Mcc                   0.809828\n",
      "dtype: float64\n",
      "Wall time: 4.25 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>test_sensitivity/recall</th>\n",
       "      <th>test_specificity</th>\n",
       "      <th>test_precision</th>\n",
       "      <th>test_f1</th>\n",
       "      <th>test_roc_auc</th>\n",
       "      <th>test_Mcc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultinomialNB</td>\n",
       "      <td>0.876865</td>\n",
       "      <td>0.845709</td>\n",
       "      <td>0.905678</td>\n",
       "      <td>0.892532</td>\n",
       "      <td>0.868276</td>\n",
       "      <td>0.875693</td>\n",
       "      <td>0.754105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>0.902215</td>\n",
       "      <td>0.955526</td>\n",
       "      <td>0.852869</td>\n",
       "      <td>0.857611</td>\n",
       "      <td>0.903811</td>\n",
       "      <td>0.904197</td>\n",
       "      <td>0.810089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.902970</td>\n",
       "      <td>0.960644</td>\n",
       "      <td>0.849590</td>\n",
       "      <td>0.855507</td>\n",
       "      <td>0.904933</td>\n",
       "      <td>0.905117</td>\n",
       "      <td>0.812419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.891051</td>\n",
       "      <td>0.865784</td>\n",
       "      <td>0.914415</td>\n",
       "      <td>0.903907</td>\n",
       "      <td>0.884131</td>\n",
       "      <td>0.890099</td>\n",
       "      <td>0.782508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RidgeClassifier</td>\n",
       "      <td>0.847924</td>\n",
       "      <td>0.846904</td>\n",
       "      <td>0.848848</td>\n",
       "      <td>0.838937</td>\n",
       "      <td>0.842604</td>\n",
       "      <td>0.847876</td>\n",
       "      <td>0.696000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.628907</td>\n",
       "      <td>0.236929</td>\n",
       "      <td>0.991627</td>\n",
       "      <td>0.962787</td>\n",
       "      <td>0.379354</td>\n",
       "      <td>0.614278</td>\n",
       "      <td>0.353089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PassiveAggressiveClassifier</td>\n",
       "      <td>0.893136</td>\n",
       "      <td>0.913804</td>\n",
       "      <td>0.874000</td>\n",
       "      <td>0.870695</td>\n",
       "      <td>0.891553</td>\n",
       "      <td>0.893902</td>\n",
       "      <td>0.787567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>0.878383</td>\n",
       "      <td>0.890996</td>\n",
       "      <td>0.866713</td>\n",
       "      <td>0.861051</td>\n",
       "      <td>0.875663</td>\n",
       "      <td>0.878854</td>\n",
       "      <td>0.757327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ensemble_Voting_Model</td>\n",
       "      <td>0.901647</td>\n",
       "      <td>0.946864</td>\n",
       "      <td>0.859788</td>\n",
       "      <td>0.862284</td>\n",
       "      <td>0.902467</td>\n",
       "      <td>0.903326</td>\n",
       "      <td>0.807581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.902972</td>\n",
       "      <td>0.945289</td>\n",
       "      <td>0.863796</td>\n",
       "      <td>0.865688</td>\n",
       "      <td>0.903572</td>\n",
       "      <td>0.904543</td>\n",
       "      <td>0.809828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Model  test_accuracy  test_sensitivity/recall  \\\n",
       "0                MultinomialNB       0.876865                 0.845709   \n",
       "1                SGDClassifier       0.902215                 0.955526   \n",
       "2           LogisticRegression       0.902970                 0.960644   \n",
       "3                  BernoulliNB       0.891051                 0.865784   \n",
       "4              RidgeClassifier       0.847924                 0.846904   \n",
       "5       RandomForestClassifier       0.628907                 0.236929   \n",
       "6  PassiveAggressiveClassifier       0.893136                 0.913804   \n",
       "7                   Perceptron       0.878383                 0.890996   \n",
       "8        Ensemble_Voting_Model       0.901647                 0.946864   \n",
       "9                    LinearSVC       0.902972                 0.945289   \n",
       "\n",
       "   test_specificity  test_precision   test_f1  test_roc_auc  test_Mcc  \n",
       "0          0.905678        0.892532  0.868276      0.875693  0.754105  \n",
       "1          0.852869        0.857611  0.903811      0.904197  0.810089  \n",
       "2          0.849590        0.855507  0.904933      0.905117  0.812419  \n",
       "3          0.914415        0.903907  0.884131      0.890099  0.782508  \n",
       "4          0.848848        0.838937  0.842604      0.847876  0.696000  \n",
       "5          0.991627        0.962787  0.379354      0.614278  0.353089  \n",
       "6          0.874000        0.870695  0.891553      0.893902  0.787567  \n",
       "7          0.866713        0.861051  0.875663      0.878854  0.757327  \n",
       "8          0.859788        0.862284  0.902467      0.903326  0.807581  \n",
       "9          0.863796        0.865688  0.903572      0.904543  0.809828  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "result = []\n",
    "m = []\n",
    "for name, model in clf.items():\n",
    "    scoring = {\n",
    "    'accuracy': make_scorer(accuracy_score),\n",
    "    'sensitivity/recall': make_scorer(recall_score),\n",
    "    'specificity': make_scorer(recall_score,pos_label=0),\n",
    "    'precision': make_scorer(precision_score),\n",
    "    'f1':make_scorer(f1_score),\n",
    "    'roc_auc':make_scorer(roc_auc_score),\n",
    "    'Mcc':make_scorer(matthews_corrcoef)\n",
    "    }\n",
    "    cv_results = cross_validate(model,text_vectorized, df.target, cv=10, scoring=scoring)\n",
    "    print(\"----------------------------------\")\n",
    "    print(name)\n",
    "    print(\"----------------------------------\")\n",
    "    cr = pd.DataFrame(cv_results)\n",
    "    cr = cr.append({'Model': name}, ignore_index=True)\n",
    "    testre = cr.drop(['fit_time', 'score_time'], axis=1)\n",
    "    print(testre.mean(axis = 0))\n",
    "    #print(name)\n",
    "    m.append(name)\n",
    "    result.append(testre.mean(axis = 0))\n",
    "result = pd.DataFrame(result)\n",
    "result.insert(0, \"Model\",m, True)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25a9df4b088>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "locs = [\"upper left\", \"lower left\", \"center right\"]\n",
    "ax = result.plot(x=\"Model\", y=[\"test_accuracy\", \"test_sensitivity/recall\", \"test_specificity\"], kind=\"bar\",figsize=(10, 5),fontsize=14,color=['blue', 'red', 'green'])\n",
    "ax.set_xlabel(\"MODEL NAME\",fontsize=16)\n",
    "ax.set_ylabel(\"SCORE(0-1)\",fontsize=16)\n",
    "#ax.set_title('Sentiment Analysis Model Comparison',fontsize=20)\n",
    "#ax.legend(loc=1, bbox_to_anchor=(0.15,0.01))\n",
    "colors = ['blue', 'red', 'green']\n",
    "lines = [Line2D([0], [0], color=c, linewidth=3, linestyle='--') for c in colors]\n",
    "labels = ['Accuracy', 'Sensitivity', 'Specificity']\n",
    "plt.legend(lines, labels,loc='center right', bbox_to_anchor=(0.1, -0.5),fontsize=14)\n",
    "#plt.legend(lines, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def highlight_max(s):\n",
    "    '''\n",
    "    highlight the maximum in a Series yellow.\n",
    "    '''\n",
    "    is_max = s == s.max()\n",
    "    return ['background-color: yellow' if v else '' for v in is_max]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = result.sort_values(by=['test_accuracy'],ascending=False)\n",
    "result = result.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['test_accuracy', 'test_sensitivity/recall', 'test_specificity', 'test_precision','test_f1', 'test_roc_auc','test_Mcc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "result[columns] = ( 100 * result[columns]).round(2).astype(str) + '%'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >test_accuracy</th>        <th class=\"col_heading level0 col2\" >test_sensitivity/recall</th>        <th class=\"col_heading level0 col3\" >test_specificity</th>        <th class=\"col_heading level0 col4\" >test_precision</th>        <th class=\"col_heading level0 col5\" >test_f1</th>        <th class=\"col_heading level0 col6\" >test_roc_auc</th>        <th class=\"col_heading level0 col7\" >test_Mcc</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row0_col0\" class=\"data row0 col0\" >LinearSVC</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row0_col1\" class=\"data row0 col1\" >90.3%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row0_col2\" class=\"data row0 col2\" >94.53%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row0_col3\" class=\"data row0 col3\" >86.38%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row0_col4\" class=\"data row0 col4\" >86.57%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row0_col5\" class=\"data row0 col5\" >90.36%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row0_col6\" class=\"data row0 col6\" >90.45%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row0_col7\" class=\"data row0 col7\" >80.98%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row1_col0\" class=\"data row1 col0\" >LogisticRegression</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row1_col1\" class=\"data row1 col1\" >90.3%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row1_col2\" class=\"data row1 col2\" >96.06%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row1_col3\" class=\"data row1 col3\" >84.96%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row1_col4\" class=\"data row1 col4\" >85.55%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row1_col5\" class=\"data row1 col5\" >90.49%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row1_col6\" class=\"data row1 col6\" >90.51%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row1_col7\" class=\"data row1 col7\" >81.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row2_col0\" class=\"data row2 col0\" >SGDClassifier</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row2_col1\" class=\"data row2 col1\" >90.22%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row2_col2\" class=\"data row2 col2\" >95.55%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row2_col3\" class=\"data row2 col3\" >85.29%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row2_col4\" class=\"data row2 col4\" >85.76%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row2_col5\" class=\"data row2 col5\" >90.38%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row2_col6\" class=\"data row2 col6\" >90.42%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row2_col7\" class=\"data row2 col7\" >81.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row3_col0\" class=\"data row3 col0\" >Ensemble_Voting_Model</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row3_col1\" class=\"data row3 col1\" >90.16%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row3_col2\" class=\"data row3 col2\" >94.69%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row3_col3\" class=\"data row3 col3\" >85.98%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row3_col4\" class=\"data row3 col4\" >86.23%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row3_col5\" class=\"data row3 col5\" >90.25%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row3_col6\" class=\"data row3 col6\" >90.33%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row3_col7\" class=\"data row3 col7\" >80.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row4_col0\" class=\"data row4 col0\" >PassiveAggressiveClassifier</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row4_col1\" class=\"data row4 col1\" >89.31%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row4_col2\" class=\"data row4 col2\" >91.38%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row4_col3\" class=\"data row4 col3\" >87.4%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row4_col4\" class=\"data row4 col4\" >87.07%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row4_col5\" class=\"data row4 col5\" >89.16%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row4_col6\" class=\"data row4 col6\" >89.39%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row4_col7\" class=\"data row4 col7\" >78.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row5_col0\" class=\"data row5 col0\" >BernoulliNB</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row5_col1\" class=\"data row5 col1\" >89.11%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row5_col2\" class=\"data row5 col2\" >86.58%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row5_col3\" class=\"data row5 col3\" >91.44%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row5_col4\" class=\"data row5 col4\" >90.39%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row5_col5\" class=\"data row5 col5\" >88.41%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row5_col6\" class=\"data row5 col6\" >89.01%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row5_col7\" class=\"data row5 col7\" >78.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row6_col0\" class=\"data row6 col0\" >Perceptron</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row6_col1\" class=\"data row6 col1\" >87.84%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row6_col2\" class=\"data row6 col2\" >89.1%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row6_col3\" class=\"data row6 col3\" >86.67%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row6_col4\" class=\"data row6 col4\" >86.11%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row6_col5\" class=\"data row6 col5\" >87.57%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row6_col6\" class=\"data row6 col6\" >87.89%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row6_col7\" class=\"data row6 col7\" >75.73%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row7_col0\" class=\"data row7 col0\" >MultinomialNB</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row7_col1\" class=\"data row7 col1\" >87.69%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row7_col2\" class=\"data row7 col2\" >84.57%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row7_col3\" class=\"data row7 col3\" >90.57%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row7_col4\" class=\"data row7 col4\" >89.25%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row7_col5\" class=\"data row7 col5\" >86.83%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row7_col6\" class=\"data row7 col6\" >87.57%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row7_col7\" class=\"data row7 col7\" >75.41%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row8_col0\" class=\"data row8 col0\" >RidgeClassifier</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row8_col1\" class=\"data row8 col1\" >84.79%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row8_col2\" class=\"data row8 col2\" >84.69%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row8_col3\" class=\"data row8 col3\" >84.88%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row8_col4\" class=\"data row8 col4\" >83.89%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row8_col5\" class=\"data row8 col5\" >84.26%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row8_col6\" class=\"data row8 col6\" >84.79%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row8_col7\" class=\"data row8 col7\" >69.6%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row9_col0\" class=\"data row9 col0\" >RandomForestClassifier</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row9_col1\" class=\"data row9 col1\" >62.89%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row9_col2\" class=\"data row9 col2\" >23.69%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row9_col3\" class=\"data row9 col3\" >99.16%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row9_col4\" class=\"data row9 col4\" >96.28%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row9_col5\" class=\"data row9 col5\" >37.94%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row9_col6\" class=\"data row9 col6\" >61.43%</td>\n",
       "                        <td id=\"T_888ca8da_b60e_11ea_8bf4_08979870d661row9_col7\" class=\"data row9 col7\" >35.31%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25a9df54188>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.style.apply(highlight_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(C = 1.1,random_state=0,solver='saga')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 30 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(text_vectorized, df.target)\n",
    "predict_train = model.predict(text_vectorized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9701153773406469\n"
     ]
    }
   ],
   "source": [
    "#model Training Accuracy\n",
    "print(\"Accuracy:\", metrics.accuracy_score(df.target,predict_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predication function\n",
    "def predcovidmsg(text):\n",
    "    text = cleanTxt(text)\n",
    "    vtext = vect.transform([text])\n",
    "    result = model.predict_proba(vtext)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.90669876, 0.09330124]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = \"Man it s terrible Not even a reason to get on here until sports start back\"\n",
    "prediction = predcovidmsg(data)\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[90.66987645466457, 9.330123545335416]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[prediction[0][0]*100,prediction[0][1]*100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a pickle file for the TfidfVectorizer\n",
    "pickle.dump(vect, open('tf-vect-transform.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a pickle file for the Logistic Regression model\n",
    "filename = 'covid-msg-lr-model.pkl'\n",
    "pickle.dump(model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xAI Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:144: FutureWarning: The sklearn.metrics.scorer module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.metrics. Anything that cannot be imported from sklearn.metrics is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:144: FutureWarning: The sklearn.feature_selection.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.feature_selection. Anything that cannot be imported from sklearn.feature_selection is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import eli5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        \n",
       "\n",
       "    \n",
       "\n",
       "        \n",
       "            \n",
       "                \n",
       "                \n",
       "    \n",
       "        <p style=\"margin-bottom: 0.5em; margin-top: 0em\">\n",
       "            <b>\n",
       "    \n",
       "        y=1\n",
       "    \n",
       "</b>\n",
       "\n",
       "top features\n",
       "        </p>\n",
       "    \n",
       "    <table class=\"eli5-weights\"\n",
       "           style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto; margin-bottom: 2em;\">\n",
       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature weights. Note that weights do not account for feature value scales, so if feature values have different scales, features with highest weights might not be the most important.\">\n",
       "                    Weight<sup>?</sup>\n",
       "                </th>\n",
       "            \n",
       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "            \n",
       "        </tr>\n",
       "        </thead>\n",
       "        <tbody>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 6707 more positive &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 91.02%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 7270 more negative &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 91.02%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -3.689\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        pic\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.46%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -4.022\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        dog\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.41%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -4.052\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        learning\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 90.08%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -4.251\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        ai\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 89.78%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -4.437\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        cat\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 88.86%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -5.014\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        food\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 87.89%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -5.653\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        mercyofallah\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 86.12%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -6.868\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        python\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 81.41%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -10.424\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        sports\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 80.00%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -11.573\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        ramadan\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "\n",
       "        </tbody>\n",
       "    </table>\n",
       "\n",
       "            \n",
       "        \n",
       "\n",
       "        \n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
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       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
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   ],
   "source": [
    "eli5.show_weights(model, vec=vect, top=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'In these tough times the best way to grow is to learn or in my case teach to help people learn to connect Sports and Anal'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.text[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <p style=\"margin-bottom: 0.5em; margin-top: 0em\">\n",
       "            <b>\n",
       "    \n",
       "        y=not covid-19-related\n",
       "    \n",
       "</b>\n",
       "\n",
       "    \n",
       "    (probability <b>0.806</b>, score <b>-1.426</b>)\n",
       "\n",
       "top features\n",
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       "        <thead>\n",
       "        <tr style=\"border: none;\">\n",
       "            \n",
       "                <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\" title=\"Feature contribution already accounts for the feature value (for linear models, contribution = weight * feature value), and the sum of feature contributions is equal to the score or, for some classifiers, to the probability. Feature values are shown if &quot;show_feature_values&quot; is True.\">\n",
       "                    Contribution<sup>?</sup>\n",
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       "            <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
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       "        +0.174\n",
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       "</tr>\n",
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       "            <tr style=\"background-color: hsl(120, 100.00%, 96.47%); border: none;\">\n",
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       "        +0.127\n",
       "    </td>\n",
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       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 98.33%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.044\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        best\n",
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       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 98.37%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.042\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        connect\n",
       "    </td>\n",
       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 98.80%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        +0.027\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        anal\n",
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       "    \n",
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       "        \n",
       "        \n",
       "\n",
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       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 99.05%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.019\n",
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       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        teach\n",
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       "    \n",
       "</tr>\n",
       "        \n",
       "            <tr style=\"background-color: hsl(0, 100.00%, 98.43%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.040\n",
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       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.104\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
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       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.118\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        case\n",
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       "            <tr style=\"background-color: hsl(0, 100.00%, 95.50%); border: none;\">\n",
       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
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       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        people\n",
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       "    <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "        -0.393\n",
       "    </td>\n",
       "    <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "        &lt;BIAS&gt;\n",
       "    </td>\n",
       "    \n",
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       "\n",
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     "execution_count": 41,
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    "eli5.show_prediction(model,df.text[4],vec=vect,target_names=['not covid-19-related', 'covid-19-related'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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